Uncategorized - Relatix Health

June 1, 2026

Top 10 U.S. Biopharma Clusters 2026

Some of the forces that shape biopharma cluster development are constants year after year, such as the emergence of startups from university and research institute labs to develop new treatments, thanks to ideas backed by the brains of researchers and executives, and the bucks of serial entrepreneurs and other investors.

But in recent years, several additional unique circumstances have come to reshape how much and especially where biopharmas choose to grow, Matthew Gardner, CBRE Americas Life Sciences Leader, shared with GEN recently.

One is increased acquisition of lab and manufacturing properties by “mid-cap” biopharmas ranging between $2 billion and $10 billion in market capitalization (share price times the number of outstanding shares), as they seek to better control their supply chains by maintaining their own infrastructure in evolving from research- to commercialization-focused drug developers.

“They might have been more likely to lease in a different circumstance. They’ve definitely caught an opportunity to jump in and take ownership. That has been an ongoing trend, and that has been true coast-to-coast in most of the major centers,” Gardner said.

Among investor-owners, Gardner said, another transition has begun from pure-play biopharma real estate landlords to investors with broader portfolios encompassing healthcare—a reflection of how the two fields are increasingly converging. During December 2025 and January 2026, for example, the public real estate investment trust (REIT) Healthpeak shelled out $600 million to close on the acquisition of a 1.4-million square foot, 29-acre campus on Gateway Boulevard in South San Francisco, CA, from the nation’s largest biopharma REIT, Alexandria Real Estate Equities and BXP (formerly Boston Properties).

Those and other investors aim to cash in on the improving climate for biopharmas seeking to raise capital, from a recovering venture capital market to increased merger-and-acquisition (M&A) activity, and, in recent weeks, a revived market for initial public offerings (IPO).

Another key factor in recent cluster-building cited by Gardner is the “reshoring” of manufacturing in the U.S. by global biopharma giants, whether to satisfy growing demand for treatments—especially obesity drugs—or avoid tariffs, or both. While many of those new facilities are in manufacturing-heavy clusters like North Carolina and Greater Philadelphia, others have spread into Maryland and Virginia (the BioHealth Capital Region), and several new biomanufacturing sites have been built or are under construction in emerging clusters outside the Top 10—a trend GEN plans to explore in the coming weeks.

Speaking of top 10 clusters, GEN presents its latest edition of its nationally- and regionally-cited annual A-List of its top 10 U.S. biopharma cluster rankings, designed to show which regions are most competitive in attracting life sciences leaders, companies, and institutions. Over more than a decade, GEN has based its rankings on five criteria:

Patents: Figures from the Patent Public Search database of the U.S. Patent and Trademark Office, showing the number of patent families containing the word “biotechnology” and towns and cities within a given region or state.
NIH funding: Figures for NIH funding were taken from the publicly available NIH Research Portfolio Online Reporting Tools (RePORT) database for the current federal fiscal year through May 4, plus all of fiscal year 2025 (October 1, 2024, through September 30, 2025).
Venture capital funding: Figures for all of 2025 and the first quarter of 2026 as compiled by regional life sciences groups and PitchBook, which joins with the National Venture Capital Association to publish the quarterly Venture Monitor reports.
Laboratory space: The total-size-of-market figure, in millions of square feet, as furnished by regional life sciences groups. In regions that did not compile such information, the figure cited is the highest by any of several commercial real estate companies, including CBRE Group, Colliers, Cushman & Wakefield, JLL, and Newmark.
Number of jobs: The preferred sources for job figures were regional life sciences groups. Alternative sources included commercial real estate firms.

1. Boston/Cambridge, MA

Genentech in CT — Genentech has agreed to more than triple its space, growing from 30,000 to 100,000 square feet, within 1 Milestone Street at the Harvard University-owned, Tishman Speyer-developed Enterprise Research Campus in Boston’s Allston section [Breakthrough Properties, Studio Gang & Henning Larsen]

Years of growing into the nation’s top biopharma cluster have taken a toll on Boston and adjacent Cambridge, MA: The Wall Street Journal in December highlighted the inability of Boston-area PhDs to find work, while the region faces a glut of life sciences space as biopharmas and real estate developers scale back earlier plans—a 32.7% availability rate according to CBRE, up 70 basis points from Q1 2025. Takeda Pharmaceutical in March eliminated 247 jobs in Massachusetts, where the company has facilities in Lexington, MA, and Cambridge, part of a $1.3 billion restructuring that cut 634 jobs nationwide. Replimune in April chopped 223 jobs at its Woburn, MA, HQ, and Framingham, MA, manufacturing site after the FDA rejected its BLA seeking approval for RP1 [plus Bristol Myers Squibb’s Opdivo^® (nivolumab)] for advanced melanoma. In February, Takeda placed 449,140 square feet within three Cambridge buildings on the sublease market, a week after Alexandria Real Estate Equities scrapped plans to convert 401 Park Drive in Boston’s Fenway section into lab space, with CEO and chief investment officer Peter M. Moglia saying the real estate investment trust was pivoting to meet growing demand for office space.

Among the region’s growing life-science companies: Genentech agreed to more than triple its space, growing from 30,000 to 100,000 square feet within One Milestone Street at the Harvard University-owned, Tishman Speyer-developed Enterprise Research Campus in Boston’s Allston section. Hemab Therapeutics (based in Cambridge and Copenhagen) and Seaport Therapeutics (Boston) both priced IPOs on April 30, raising $301.5 million and $254.88 million, respectively—a day after Avalyn Pharma (Boston) garnered $300 million in its IPO. In March, Terrestrial Bio became the first life-science tenant at Allston Labworks (250 Western Avenue) by leasing 42,000 square feet at the mixed-use building within Boston’s Allston neighborhood, while AI Proteins in January inked a 40,000-square-foot lease at 660 Commonwealth Avenue, within Related Beal’s One Kenmore Square in Boston. Regional companies finding buyers in April include Boston-based Kelonia Therapeutics and Cambridge-based Ajax Therapeutics, both to be acquired by Eli Lilly (for up to $7 billion and up to $2.3 billion, respectively) and Framingham-based KalVista Pharmaceuticals, to be acquired by Italy’s Chiesi Group for about $1.9 billion.

Boston/Cambridge enjoys the nation’s largest portfolio of lab space (63.2 million square feet according to industry group MassBio), but was bested by the San Francisco Bay Area in NIH funding (7,037 awards totaling $4.339 billion) following a year of government funding cuts. The region also placed second in VC ($6.85 billion in 2025, says MassBio; $1.59 billion in Q1 2026, according to PitchBook data cited by MassBio), but landed third in patents (29,621 families) and just fifth in jobs (117,108, according to MassBio).

2. San Francisco Bay Area

David A. Ricks and Jensen Huang shake hands — Eli Lilly Chair and CEO David A. Ricks and Nvidia Founder, president, and CEO Jensen Huang announce the companies’ five-year, $1 billion partnership to create a “Co-Innovation AI Lab” designed to address key challenges in AI drug discovery, announced on January 12 during the J.P. Morgan 44th Annual Healthcare Conference in San Francisco. The lab will be located within the Bay Area. [Nvidia]

Santa Clara, CA-based Nvidia and Eli Lilly electrified the annual J.P. Morgan Healthcare Conference, held in downtown San Francisco each January, by announcing a five-year, $1-billion collaboration to create a “Co-Innovation AI Lab” in the region to address key challenges in artificial intelligence (AI) drug discovery, powered by a supercomputer that went live in February. That welcome news aside, more than one-third of the region’s life-science space is available for lease (33.7% as of Q1, according to CBRE). And more space has entered the market: Pfizer confirmed plans in April to shut down its 164,000-square-foot research facility at 181 Oyster Point Blvd. in South San Francisco, CA, shifting employees to remote jobs. Cushman & Wakefield is marketing the space for sublease. Also, on the market in “South City” is a 21,552-square-foot lab building and surrounding 3.65 acres previously occupied by the U.S. Department of Agriculture, which is selling the building for just under $48 million. In May, Foster City, CA-based Gilead Sciences disclosed plans to lay off 108 employees based in Redwood City, CA, (and 84 in Rockville, MD) following its $7.8-billion acquisition of Arcellx.

Not all the recent news is bad: Gladstone Institutes plans early next year to open approximately 20 new labs employing about 300 scientists within the 105,000 square feet it agreed to lease in March at 1450 Owens Street, within Alexandria Real Estate Equities’ Alexandria Center^® for Science and Technology–Mission Bay Megacampus. Natera inked a 62,969-square-foot lease at Brittan West in San Carlos, CA, in February. And last fall, Elon Musk’s Neuralink leased the entire approximately 144,000-square-foot 499 Forbes Boulevard in South San Francisco. On the financing side, SF-based Breakout Ventures in March closed its $114-million Fund III, which aims to invest in founder-led companies applying AI in biopharma, while Palo Alto, CA-based Surf Bio, whose lead investor for its only institutional round was Breakout, was acquired by San Diego-based Halozyme Therapeutics for up to $400 million, in a deal announced in January.

San Francisco and its suburbs topped Boston/Cambridge in VC ($7.8 billion in 2025, $1.5 billion in Q1 2026, both according to PitchBook). The Bay Area is second in three criteria: patents (35,166 families), lab space (54.3 million square feet according to Colliers), and jobs (150,491 according to BIOCOM California, but “more than 147,000” according to CBRE, both from last year). In NIH funding, the region is fourth (5,180 awards totaling $3.13 billion).

3. BioHealth Capital Region (Maryland, Virginia, and Washington, D.C.)

AstraZeneca new manufacturing facility in Rivanna Futures, near Charlottesville — AstraZeneca has expanded the scope of its new manufacturing facility in Rivanna Futures, near Charlottesville, VA, into a $4.5 billion project designed to support manufacturing for weight management, metabolic, and cancer technologies, including antibody-drug conjugates. The project is expected to create 600 permanent jobs. [AstraZeneca]

The BHCR takes in Virginia and Maryland, both of which benefited over the past year from the domestic “reshoring” of biomanufacturing by pharma giants. AstraZeneca in November announced $2 billion in plans for Maryland that include a major expansion of its biologics manufacturing facility in Frederick, MD, and a new clinical manufacturing facility in Gaithersburg, MD. A month earlier, AstraZeneca expanded the scope of its new manufacturing facility in Rivanna Futures, near Charlottesville, VA, into a $4.5-billion project designed to support manufacturing for weight management, metabolic, and cancer technologies, including antibody-drug conjugates. The project is expected to create 600 permanent jobs. Also last fall, Merck & Co. broke ground on a $3 billion, 400,000-square-foot Center of Excellence for Pharmaceutical Manufacturing at its longstanding site in Elkton, VA, while Eli Lilly announced plans for a $5-billion manufacturing facility just west of Richmond, VA, in Goochland County that will be the company’s first-ever dedicated, fully integrated active pharmaceutical ingredient (API) and drug product facility for its bioconjugate platform and monoclonal antibody portfolio. However, a longtime strength of the region—the headquarters presence of the NIH and FDA—is now among its most serious challenges as government funding cuts chopped the workforces of both agencies last year by 3,500 and 1,200 jobs, respectively, though the FDA in recent months has worked to hire 1,000+ new staffers to fill reviewer, inspector, and investigator roles. And in May, Gilead Sciences disclosed plans to lay off 84 employees in Rockville, MD (and 108 in Redwood City, CA) following its $7.8-billion acquisition of Arcellx.

The BioHealth Capital Region fulfills its top-three cluster ambitions by continuing to lead the nation in patents (80,808 families) while placing third in NIH funding (4,665 awards totaling $3.474 billion) and lab space (37.208 million square feet according to JLL data cited by BHCR, including 9.2 million square feet of NIH labs in Bethesda, MD). The region is fourth in jobs (135,298, according to JLL and state data cited by BHCR), but seventh in venture capital ($1.117 billion in 2025, zero in Q1 2026, according to BHCR data).

4. New York/New Jersey

rendering of HELIX downtown campus — In New Jersey, New Brunswick’s Planning Board in February approved the $468 million H-3, the third phase of the HELIX downtown campus, a 40-story 554,000 square foot tower, for which the city council approved a 30-year PILOT agreement that will generate $1.8 million a year in annual payments in lieu of taxes [DEVCO New Brunswick Development Corp.]

The Big Apple will soon see a big biotech campus emerge, the $1.6 billion, 2-million-plus-square-foot Science Park and Research Campus (SPARC) Kips Bay, projected to create more than 15,000 jobs by combining life-science space with academic and public health facilities. Exterior demolition is scheduled for the third quarter, followed by construction next year. However, Johnson & Johnson has shifted operations of its JLABS@NYC incubator to site owner New York Genome Center, part of a corporate cutback of its incubator network. The 17-member Emerging Technology Advisory Board appointed by New York Gov. Kathy Hochul (D), who is seeking re-election this year, proposed numerous efforts in December to expand life sciences activity statewide, including a $65-million “Excellence” fund and a $40-million pre-commercialization fund. At deadline, the fate of those efforts was unknown despite a tentative agreement on May 7 of a $268-billion state budget.

In New Jersey, New Brunswick’s Planning Board in February approved the $468-million H-3, the third phase of the HELIX downtown campus, a 40-story, 554,000-square-foot tower, for which the city council approved a 30-year PILOT agreement that will generate $1.8 million a year in annual payments in lieu of taxes. In suburban Westchester County, Regeneron Pharmaceuticals is completing a $1.8-billion HQ expansion in Tarrytown but has scuttled earlier plans to expand across the Hudson River into the Rockland County village of Suffern, where the company spent $39 million to buy an old Avon Cosmetics warehouse for conversion into an infectious disease lab and a cold storage facility. In February, Regeneron hired JLL to market the site for sublease.

New York and its northern New Jersey suburbs lead the nation in NIH funding (7,033 awards totaling $4.396 billion) and are third in jobs (147,900, according to Cushman & Wakefield). From there, the region falls to the middle of the pack, placing fifth in VC ($1 billion in 2025 and about $400 million in Q1 2026, both according to PitchBook), and sixth in both lab space (25.5 million square feet, according to Colliers) and patents (12,523 families).

5. Greater Philadelphia

Eli Lilly Pennsylvania rendering — Eli Lilly made history in January by announcing Pennsylvania’s largest-ever biotech project, a $3.5 billion biomanufacturing site planned for Upper Macungie Township, an hour’s drive northwest of Philadelphia. Lilly plans to base 850 jobs at the plant, which will produce retatrutide and other weight loss drugs when it becomes operational in 2031. Lilly also has plans for Philadelphia, namely a 44,000-square-foot Lilly Gateway Labs innovation hub in Center City West at 2300 Market set to open later this year. [Eli Lilly]

Eli Lilly made history in January by announcing Pennsylvania’s largest-ever biotech project, a $3.5-billion biomanufacturing site planned for Upper Macungie Township, an hour’s drive northwest of Philadelphia. Lilly plans to base 850 jobs at the plant, which will produce retatrutide and other weight loss drugs when it becomes operational in 2031. Lilly also has plans for the City of Brotherly Love, namely a 44,000-square-foot Lilly Gateway Labs innovation hub in Center City West at 2300 Market set to open later this year. And, in Philadelphia’s Old City, Thermo Fisher Scientific last November opened its East Coast Advanced Therapies Collaboration Center (ATxCC) within the BioLabs for Advanced Therapeutics incubator.

Thermo Fisher Scientific executives celebrated the opening of the East Coast Advanced Therapies Collaboration Center (ATxCC) in Philadelphia’s Old City — Thermo Fisher Scientific executives last November celebrated the opening of the biotech tools giant’s East Coast Advanced Therapies Collaboration Center (ATxCC) in Philadelphia’s Old City, within the BioLabs for Advanced Therapeutics incubator. [Thermo Fisher Scientific]

The region’s rich biotech history includes the first gene therapy Luxturna^® marketed by Roche-owned Spark Therapeutics—which is completing its $575 million Gene Therapy Innovation Center in University City despite laying off more than half of its Philly staff last year. In March, TerraPower Isotopes announced plans for a $450-million radioisotope manufacturing facility designed to produce actinium-225 for cancer treatments. The project will employ 225, receive $10 million in state grants, and rise within The Bellwether District, the 1,300-acre former Philadelphia Energy Solutions refinery site. Greater Philadelphia has long benefited from innovations from its institutions, two of which won more than $100 million in NIH funding during the 2025 federal fiscal year, the Perelman School of Medicine at the University of Pennsylvania to Children’s Hospital of Philadelphia (CHOP)—which last year treated KJ Muldoon (“Baby KJ”), the world’s first patient to receive a personalized CRISPR gene-editing therapy (for CPS1 deficiency). The region’s needs for more C-suite talent and venture capital remain persistent challenges to cluster growth, stakeholders told The Philadelphia Inquirer in December, though Audrey Greenberg, chair of corporate development and “Mayo Venture Partner” at Mayo Clinic and founder of AG Capital Advisors, told the Inquirer: “I’m going to be starting my companies all here in Philadelphia, because that’s where I am.”

Greater Philadelphia improved the most this year, climbing two positions in this year’s A-List after remaining fifth in patents (17,090 families) and rising to fifth in lab space (25.9 million square feet, according to Colliers’ data cited by Pennsylvania’s Department of Economic Development or DECD) and NIH funding (3,201 awards totaling $1.94 billion). The region jumped four spots to fifth in VC ($1.31 billion in 2025, $616 million in Q1 2026, says Colliers), but dipped to seventh in jobs (88,000, also according to DECD), including nearly 10,000 with cell and gene therapy expertise.

6. San Diego

Novartis' global Biomedical Research center in San Diego — Novartis broke ground in February on a $1.1 billion, 466,000-square-foot global Biomedical Research center in San Diego, expected to house 1,000 employees when operational in 2029, three months after opening a radioligand therapy manufacturing facility for cancer treatments in Carlsbad, CA. [Novartis]

The Biotechnology Innovation Organization (BIO) expects to draw 20,000 to its BIO International Convention when it returns this month to the San Diego Convention Center. The region remains a vibrant life-sciences cluster: Novartis broke ground in February on a $1.1-billion, 466,000-square-foot global Biomedical Research center in San Diego, expected to house 1,000 employees when operational in 2029, three months after opening a radioligand therapy manufacturing facility for cancer treatments in Carlsbad, CA. Eli Lilly in March completed its $1.2-billion acquisition of home-grown Ventyx Biosciences—months after the pharma opened a Lilly Gateway Labs innovation hub with Alexandria Real Estate Equities in Torrey Pines. The J. Craig Venter Institute—whose founder died April 29 at age 79—plans this summer to move its West Coast headquarters from the University of California San Diego campus in La Jolla to the downtown Research and Development District (RaDD), a $1.6-billion, 1.7-million-square-foot campus on the city’s Pacific coastline completed last year by San Diego-based developer IQHQ—which is fighting an investor’s fraud allegations related to a $50-million investment in 2020. Home-grown F5 Therapeutics (up to 10 employees) folded in March, while two other San Diego biotechs laid off employees this year: Gossamer Bio (65 employees, nearly half its workforce, as of May 15, following a Phase III trial failure) and BioAlta (70% of its staff, which was 41 as of December 31, 2025). In February, San Diego drug developer Iambic Therapeutics inked an up-to-$1.7-billion collaboration with Takeda Pharmaceutical, which will use Iambic’s AI technologies and wet lab capabilities to design and develop small molecule drugs. And global contract development and manufacturing organization (CDMO) Bora Biologics, in January, opened a $30-million expanded manufacturing facility with two to four 2,000-liter bioreactors, corresponding seed trains, and advanced downstream processing equipment.

“America’s Finest City” and vicinity stayed third in VC ($1.9 billion in 2025, says PitchBook, $743 million in Q1 2026 according to a GEN spot-check of recent deals) and fourth in patents (18,314 families) but dipped to fifth in lab space (28.685 million square feet, according to CBRE). While the San Diego region last year rose to ninth in NIH funding (2,001 awards totaling $1.357 billion), it slid to ninth in jobs (71,448, according to year-old BIOCOM California data).

7. North Carolina

Roche’s Genentech subsidiary East Coast manufacturing facility in Holly Springs, NC — Roche’s Genentech subsidiary in January expanded to $2 billion its planned investment in its first East Coast manufacturing facility in Holly Springs, NC, which broke ground last year and is set to support 500+ manufacturing jobs when operational by 2029. [Genentech]

Always strong on drug manufacturing, North Carolina is among the biggest beneficiaries of biopharma’s reshoring push. In April, AbbVie announced a $1.4-billion, 185-acre drug production facility in Durham County near Research Triangle Park (RTP), expected to employ 734. Roche’s Genentech subsidiary in January expanded to $2 billion its planned investment in its first East Coast manufacturing facility in Holly Springs, NC, which broke ground last year and is set to support 500+ manufacturing jobs when operational by 2029. And in November 2025, Novartis said it will expand Tar Heel State operations into a flagship manufacturing hub by adding capabilities for sterile filling of biologics into syringes and vials at its current Durham site, constructing two new Durham facilities for manufacturing biologics and sterile packaging, and building a new Morrisville, NC, site to produce solid dosage tablets and capsules, including packaging. Morrisville is where Novartis also plans to build a 56,200-square-foot facility focused on API manufacturing for solid dosage tablets, capsules, and RNA therapeutics, a project announced April 30. Manufacturing sites account for most of the combined $24.5 billion in new or expanded facilities with a potential 15,000+ new jobs that life sciences companies have announced statewide since 2021, according to the state-funded North Carolina Biotechnology Center. As for startups, Raleigh-based Slate Medicines launched in February with $130 million in Series A financing to fund development of therapies led by its migraine candidate, the anti-PACAP monoclonal antibody SLTE-1009 licensed from Zhongshan, China-based DartsBio Pharmaceuticals, and set to start Phase I trials in mid-2026.

The Tar Heel State climbed to fourth in VC ($1.6 billion in 2025, $276.8 million in Q1 2026, both according to the state-funded North Carolina Biotechnology Center). But North Carolina showed consistency on the other criteria, ranking seventh in NIH funding (2,248 awards totaling $1.589 billion) and lab space (18.6 million square feet, according to JLL), and eighth in jobs (76,000, says the Center) and patents (5,992 families).

8. Los Angeles / Orange County, CA

Amgen, Thousand Oaks, CA — Amgen executives mark the groundbreaking for the biotech giant’s $600 million center for science and innovation being built within its Thousand Oaks, CA, headquarters campus, set to integrate Research & Development and Process Development teams to smoothen the transition from drug discovery to commercial manufacturing. [Amgen]

The region’s biopharma anchor Amgen broke ground last fall on a $600-million center for science and innovation being built within its Thousand Oaks, CA, headquarters campus, set to integrate research & development and process development teams to smooth the transition from drug discovery to commercial manufacturing. “With the first shovel in the ground, we’re reaffirming something essential: We discover here, we manufacture here, we deliver for patients from Thousand Oaks to all around the world,” Amgen chairman and CEO Robert A. Bradway said. Regional industry group BioscienceLA CEO Stephanie Hsieh recently acknowledged the region’s fragmentation as a challenge—from 88 cities in LA County alone, to the numerous county, city, and private agencies focused on growing the bioindustry— while citing strengths such as corporate anchors Amgen, Takeda Pharmaceutical, and Gilead Sciences-owned Kite Pharma, plus institutions like USC, UCLA, Cedars-Sinai, and City of Hope.

California signaled interest in growing the region’s biopharma industry last August when the state-funded California Jobs First Regional Investment Initiative awarded $23.92 million to a coalition led by Los Angeles County’s Department of Economic Opportunity (DEO) toward four programs intended to create 10,000 jobs by 2030. Most of the money ($19 million) was approved for a DEO revolving loan fund to support startups, especially those looking to graduate from the Larta Institute’s commercialization and capital access accelerator into lab space within Los Angeles County. Larta was awarded $3.3 million to expand its Heal.LA Bioscience & Healthcare Accelerator and assist small startups via its Larta Impact Fund, a revolving loan fund.

Los Angeles/ Orange County would still lead the nation in jobs, based on a year-old BIOCOM California tally of 155,571, which also includes San Bernardino and Ventura counties; figures run as low as 116,000, compiled last year for the four counties plus Riverside and Santa Barbara counties (regional industry group SoCalBio). The region finished seventh in patents (7,211 families), eighth in lab space (11.7 million square feet, according to JLL), and 10th in both NIH funding (1,911 awards totaling $1.243 billion) and VC ($500 million in 2025, zero in Q1 2026, according to PitchBook).

9. Chicagoland

AbbVie, North Chicago — AbbVie plans to build two new active pharmaceutical ingredient (API) manufacturing facilities totaling $380 million at its campus in North Chicago, IL, where the biopharma giant is headquartered. [AbbVie]

At least one developer has pivoted to a large non-biotech tenant to help fill a Chicago campus once envisioned as a life-sciences mecca: Trammell Crow in March inked a $100-million, 169,860-square-foot lease with candy/chocolate giant Mars to base 600 jobs at 400 North Aberdeen Street within the Fulton Market campus. Other biotech spaces are in the works: In North Chicago, Rosalind Franklin University of Medicine and Science plans to nearly double the size of its Helix 51 biomedical incubator to just under 13,000 square feet by adding 6,000 square feet of new lab and office space, citing growing demand from early-stage biotechs. The expansion is expected to create space for up to 10 additional companies. Also in North Chicago, home-grown AbbVie announced plans to build two new API manufacturing facilities totaling $380 million at its campus in the Chicago suburb. The facilities—designed to support production of next-generation neuroscience and obesity treatments—are set to be fully operational in 2029. However, AbbVie opted to build its planned $1.4-billion biomanufacturing campus not in North Chicago but 821 miles southeast in Durham, NC. Across Illinois, biotech stakeholders have applauded Gov. J.B. Pritzker (D) for proposing to sweeten the state’s Research & Development Tax Credit program by allowing companies to transfer their credits for cash. “This is a transformative step for our startup and growth-stage ecosystem,” stated John Conrad, president and CEO of the Illinois Biotechnology Innovation Organization (iBIO). Pritzker is seeking a third term in November vs. Darren Bailey (R).

The Windy City and vicinity rank sixth in both NIH funding (2,658 awards totaling $1.607 billion) and jobs (94,000, according to statewide industry group Illinois Biotechnology Innovation Organization or iBIO). The region places ninth in patents (5,569 families) and VC ($917.677 million in 2025, says iBIO, zero in Q1 2026,

10. Seattle

AGC Biologics, Element Research Center facility in Bothell, WA — AGC Biologics, a global CDMO, expanded its regional research footprint last fall by signing a 37,575-square-foot lease at Element Research Center in Bothell, WA. [AGC Biologics]

Seattle and the Greater Puget Sound’s strong base of academic and other nonprofit research institutions helped the region achieve consecutive years of Nobel laureates: Mary E. Brunkow, PhD, of the Institute for Systems Biology in Seattle co-won the 2025 prize in Physiology or Medicine a year after David Baker, PhD, director of the Institute for Protein Design at University of Washington (UW), co-won the 2024 prize in Chemistry. A UW spinout, Seattle-based 3D tissue model developer Curi Bio, closed in December on a $10-million Series B financing led by South Korean contract research organization DreamCIS. In April, Achieve Life Sciences (based in Seattle and Vancouver, BC) announced an up-to-$354 million private placement whose purposes include funding a Phase III trial and future commercialization of e-cigarette cessation candidate cytisinicline, while Athira Pharma landed up to $236 million in conjunction with acquiring exclusive rights from Sermonix Pharmaceuticals to the Phase III metastatic breast cancer candidate lasofoxifene. AGC Biologics, a global CDMO, expanded its regional research footprint last fall by signing a 37,575-square-foot lease at Element Research Center in Bothell, WA. However, Astellas Pharma told Washington state officials in April it will shutter the Seattle site of its Universal Cells subsidiary by 2028, with 50 employees to be impacted via layoffs or transfers to South San Francisco, CA, or Westborough, MA.

Seattle and its suburbs placed highest at eighth in both NIH funding (eighth with 1,892 awards totaling $1.572 billion) and VC ($1.06 billion in 2025, zero in Q1 2026, according to industry group Life Science Washington). The region was ninth in lab space (11.46 million square feet, according to regional real estate firm Flinn Ferguson Cresa) and 10th in both jobs (48,765 according to Life Science Washington) and patents (5,416 families).

The post Top 10 U.S. Biopharma Clusters 2026 appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

<![CDATA[Review the origins and advancements of ECT.]]>

June 1, 2026

Pharma’s Trial Problem: Outdated Systems, Broken Data, and the Coming AI Reset

Erik Terjesen
Managing Director
Silicon Foundry, a Kearney Company

Clinical development has become the most resource-intensive stage of drug innovation. Across the industry, clinical trials consume 60–70% of total R&D spending, a proportion that continues to rise as trials grow more complex, more data-heavy, and more operationally demanding. The irony is that while science has advanced dramatically, the underlying model for running trials still reflects assumptions from a pre-digital era. The result is an ecosystem in which timelines stretch, costs multiply, and meaningful efficiency gains remain elusive.

AI has reached a level of maturity capable of reshaping this landscape, but its potential remains constrained by a fundamental issue the industry has been slow to confront. The data used to power these systems was never designed with AI in mind. In fact, the true crisis in clinical development today is structural and deeply rooted in how trial data is organized, contextualized, and interpreted.

Why trial models are failing

Clinical trials were built for physical sites, paper workflows, and slow-moving systems. Modern trials look nothing like that. They are distributed, data-heavy, biomarker-driven, and increasingly adaptive, yet they still run on infrastructure designed for a simpler era.

For years, clinical operations have been organized around sites and checklists rather than continuous insight. Data moves in bursts, workflows remain fragmented, and systems rarely talk to one another. Precision medicine expanded what trials could ask of data, but the way trials actually operate has barely evolved.

The problem isn’t only speed or scale. It’s also the quiet erosion of efficiency in places trial plans rarely account for. Across the industry, leaders describe a growing layer of “invisible waste”: repeated handoffs, duplicative manual work, incompatible data structures, and everyday operational friction that steadily stretches timelines and drives up costs, even though it seldom appears in formal project plans.

AI changes the equation, but only if trial data can support it.

Why AI stumbles in pharma

There is no shortage of AI talent, tools, or ambition in the life sciences sector. What is scarce is data that AI can meaningfully learn from. Most early AI-for-clinical-trials initiatives failed not because the models were immature, but because the data they were fed was not curated with clinical intent.

Two challenges define this crisis:

1. General-purpose models cannot interpret clinical nuance.

Models trained on large public corpora can identify patterns, but they lack clinical judgment. If the data is unstructured, inconsistently labeled, or lacks contextual metadata, the model will draw the wrong conclusions with absolute confidence. The well-known “ruler problem”—in which an AI system learned to detect malignant skin lesions based on the presence of a ruler beside the lesion—illustrates how easily models latch onto irrelevant signals.

2. Pharma’s internal data is both rich and unusable.

Organizations hold decades of trial data, but these assets are rarely AI-ready. Different study teams, CROs, and geographies used different standards. Biomarker and imaging data are often stored in systems that cannot communicate with EDC or safety platforms. And clinical notes, PDFs, and unstructured documents require interpretation that models cannot perform without curated training sets.

AI amplifies the quality of the data it is given. If the input is clinically inconsistent, overgeneralized, or disconnected from the trial context, the outputs will be clinically meaningless.

Recognizing this, many pharmas are now investing heavily in curated internal datasets, governance frameworks, and senior AI leadership, often in the form of newly created chief AI officer roles. These leaders are tasked with not just deploying tools, but rebuilding the data infrastructure from which future AI insights will emerge.

The new AI toolkit for clinical trials

Once the data foundation is strong, AI becomes a force multiplier across the entire trial lifecycle. Several categories show particularly high near-term impact potential.

Clinical-grade language models: Purpose-built models that ingest curated internal datasets can help draft protocols, refine eligibility criteria, flag operational risks, and interpret historical trial performance. Unlike general-purpose systems, these models are tuned to reason the way experienced clinical scientists do.

Multimodal AI for patient stratification and endpoint optimization: Integrating imaging, labs, digital biomarkers, and historical trial outcomes enables more precise cohort selection and improves the likelihood of detecting true therapeutic effect. These tools help convert today’s complex data streams into actionable insights.

Synthetic and hybrid control arms: While still emerging, these approaches reduce dependence on large traditional control cohorts by incorporating real-world evidence and model-generated comparators when appropriate. The result is faster recruitment and more efficient statistical design.

AI agents for operations: Operational agents can triage site queries, assist with eligibility adjudication, coordinate scheduling, and draft routine documentation. They are particularly helpful in reducing the administrative burden that slows trial execution.

The most underestimated category, and the one with the most long-term potential, is clinical-driven AI, where the model is trained to interpret clinical data the way a researcher with a PhD or a clinician would. This approach addresses the core issue of context, which is essential for decision-making in regulated environments.

From site-centric to data-centric trials

Trials are gradually evolving away from rigid site-based infrastructure and toward data-centric execution. AI accelerates this shift by enabling continuous monitoring, adaptive decision-making, and greater representation across diverse populations. The next phase of this transition requires progress in several areas:

Reliable digital biomarkers collected via wearables and sensors that feed directly into the trial data ecosystem.
Real-world evidence integration that allows trial designs to incorporate external data while maintaining regulatory rigor.
Improved cohort diversity, supported by AI-driven recruitment models that identify and engage underrepresented populations.

Always-on trial oversight, where adaptive protocols adjust based on real-time data rather than periodic interim reviews.

As these elements mature, trials will resemble dynamic learning systems rather than static sequences of predefined events.

Pharma cannot do this alone

The clinical-trial innovation ecosystem is now incredibly fragmented. A myriad of startups, many founded within the last five years, are attempting to solve different slices of the trial process. Some focus on recruitment; others on protocol simulation, operational automation, predictive enrollment, or digital biomarker analysis.

This fragmentation creates noise but also opportunity. The organizations that succeed will be those that adopt a hybrid strategy, in which internal data expertise is paired with carefully selected external partners. Evaluating early-stage companies requires disciplined technical assessment and an understanding of which partners can meet enterprise requirements in a regulated environment.

Pharma organizations also face a structural talent challenge. The best AI engineers often gravitate toward startups rather than large enterprises. This dynamic reinforces the need for partnership models that combine internal governance with external innovation rather than relying exclusively on one or the other.

What AI can (and cannot) fix

While AI can dramatically shorten timelines and improve decision-making, it is not a cure-all. It will not rescue a flawed trial design, replace human oversight, or eliminate the need for regulatory rigor. What it can do is accelerate the work around those elements, optimizing how protocols are developed, how patients are selected, how data is interpreted, and how milestones are achieved. The organizations that reap the greatest benefit will be those with disciplined data stewardship and a willingness to rethink long-held operational assumptions.

Erik Terjesen is the managing director at Silicon Foundry, a Kearney Company

The post Pharma’s Trial Problem: Outdated Systems, Broken Data, and the Coming AI Reset appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

Targeted Protein Degradation Broadens Its Scope

Like any complex system, the cell depends on a tightly regulated quality control network to maintain order and prevent the accumulation of harmful proteins. This network governs protein homeostasis, including the synthesis, folding, trafficking, and ultimately the clearance of proteins. When these processes fail, aberrant or misfolded proteins can accumulate and drive disease.

Targeted protein degradation (TPD) therapeutics seek to harness this intrinsic quality control machinery to selectively eliminate disease-causing proteins. Central to this approach is the principle of induced proximity, in which a designed molecule brings a target protein into close contact with a cellular effector, triggering its removal through endogenous degradation pathways.

Two major systems underpin these processes. The ubiquitin-proteasome system governs the degradation of intracellular, soluble proteins, where targets are tagged with ubiquitin by a cascade of enzymes, including E3 ubiquitin ligases, and directed to the proteasome for destruction. In parallel, lysosome-mediated pathways handle larger, membrane-bound, extracellular, or aggregated proteins by routing them through endocytic or autophagic mechanisms for degradation.

Building on these natural systems, a growing toolkit of TPD modalities has emerged. For example, proteolysis-targeting chimeras (PROTACs) exploit the ubiquitin-proteasome system, while newer approaches such as lysosome-targeting chimeras, including sortilin-based lysosome targeting chimeras (SORTACs), extend degradation to extracellular and membrane-associated proteins. Molecular glues, by contrast, stabilize interactions between E3 ligases and target proteins without requiring a bifunctional design, further expanding the scope of induced proximity strategies. Additional degrader technologies are being developed.

Although first described more than 25 years ago, TPD is now entering a phase of rapid maturation and increasing therapeutic relevance. By operating through catalytic, event-driven mechanisms rather than traditional occupancy-based inhibition, these approaches offer the potential to address previously “undruggable” targets, overcome resistance mechanisms, and deliver more durable clinical responses. At the same time, key challenges remain, including expanding access to extracellular targets, improving target validation strategies, and navigating an increasingly complex and data-rich development landscape.

Tackling the extracellular frontier

Early TPD efforts have primarily targeted cytosolic proteins, leaving extracellular and membrane-bound targets (estimated to comprise about 40% of the human proteome) largely unaddressed.

“Many key drivers of disease, including inflammatory cytokines, protein aggregates, and secreted factors, remain inaccessible to conventional PROTAC-based approaches,” says Simon Glerup, PhD, co-founder and CSO, Draupnir Bio, a spinout from Aarhus University (Denmark).

Lab team photo — Simon Glerup, PhD, co-founder and CSO, Draupnir Bio and Lab: Lab photo from left: Jonas Lende, Casper Larsen, Simon Glerup, Marianne Kristensen, Camilla Gustafsen, Amanda Simonsen, Line Slemming.

The company is addressing this gap by utilizing its proprietary SORTAC platform, a modular, small-molecule technology designed to degrade extracellular proteins by harnessing the natural lysosomal clearance pathway. Glerup notes that “these targets are central to diseases such as neurodegeneration and inflammation, yet remain difficult to drug with existing modalities.”

SORTACs are bifunctional small molecules composed of a sortilin-binding module linked to a target-binding ligand, enabling formation of a ternary complex between an extracellular disease protein and the lysosomal receptor sortilin, which drives internalization and degradation in lysosomes. Glerup elaborates, “Unlike antibody-based or intracellular TPD approaches, SORTACs combine the advantages of small molecules (such as potential oral delivery and tissue penetration) with catalytic, event-driven pharmacology. The platform has demonstrated hallmark TPD properties, including ternary complex formation and catalytic turnover, with in vitro and in vivo degradation of therapeutically relevant targets.”

Glerup emphasizes that SORTACs enable degradation of both soluble and membrane-associated proteins and leverage receptor recycling to drive sustained target clearance.

The company has launched a multi-partner Danish initiative, DESYNA (Degradation of Extracellular α-SYNuclein Aggregates) in collaboration with Aarhus University, focusing on Parkinson’s disease. Accumulation of α-synuclein aggregates is a key driver of disease, and the approach aims to selectively degrade these pathogenic species and halt their progression.

Glerup believes extracellular TPD represents the next major wave of innovation in the field. “By extending TPD beyond the cell’s interior, the cytosol, SORTAC has the potential to unlock a large and previously inaccessible target space. With growing validation and collaborative efforts such as DESYNA, there is strong reason for optimism that this approach can deliver transformative therapies for diseases that currently lack effective treatment options.”

Enabling TPD workflows

Advancing TPD depends on a coordinated ecosystem of tools that support target validation, mechanistic interrogation, and translational predictions. Within this context, attention is increasingly focused on the central challenge of translating mechanistic promise into consistent patient benefits.

Hannah Maple, PhD
Senior Director
Bio-Techne

“I think we are on the brink of seeing TPD and induced proximity truly usher in a new era in drug discovery as we await the first clinical approval of a PROTAC degrader,” says Hannah Maple, PhD, senior director at Bio-Techne®. At the same time, she notes that “one of the challenges with this as a new drug modality is to gain a deeper understanding of where the maximum patient benefit lies from a target and indication perspective.”

That uncertainty places renewed emphasis on target validation strategies. Maple elaborates, “Driving efficacy versus standard of care in a predictable way remains a challenge, despite in many cases strong mechanistic rationale for degradation versus inhibition of a particular target. For this reason, I would keep target validation high on the list of key challenges for the field as it relates to driving clinical impact and patient benefit with this technology.”

To support this critical transition, Maple says Bio-Techne has established long-standing collaborations with leading research groups to co-develop new technologies and support training of the next generation of TPD scientists. The company has also built an integrated portfolio of tools spanning biological reagents, chemical probes, and assay platforms with TPD-focused capabilities across its R&D Systems portfolio brand, including the Tocris small-molecule products.

Maple provides an example. “Some of the most useful categories of tools for target exploration and validation in the context of TPD are the R&D Systems’ Tocris Tag Degradation Platforms and self-labeling protein tag platforms.” These approaches involve fusing a small protein tag to the protein of interest and pairing it with a complementary small-molecule ligand that binds the tag. The tag ligand is typically bifunctional and can be developed to recruit an E3 ligase to the protein-of-interest, eliciting degradation in a controllable, tunable manner.

Within this ecosystem, protein-level tools support target interrogation and validation. Maple highlights self-labeling tag systems as particularly valuable. “Through our R&D Systems brand, we have built a leading portfolio of these technologies, and very recently launched BromoCatch, a next-generation self-labeling tag platform that was co-developed with [the lab of Alessio Ciulli, PhD] at the Centre for Targeted Protein Degradation, University of Dundee. BromoCatch represents a powerful, modular platform that uses a low molecular weight protein tag. The benefit of this approach is to minimally perturb the native localization or function of the protein being tagged, versus prior larger tags that could cause undesired functional effects.”

BromoCatch illustration — BromoCatch is a small, rationally designed self-labeling tag platform for targeted protein analysis, manipulation, and degradation.
[Bio-Techne]

BromoCatch is a small, rationally designed self-labeling tag platform for targeted protein analysis, manipulation, and degradation.
[Bio-Techne]

Complementing these approaches, the R&D Systems portfolio provides targeted degradation reagents such as dTAG-13, a heterobifunctional degrader used in tag-based systems to selectively eliminate engineered proteins of interest, offering a chemical alternative to genetic knockdown approaches.

Maple reports that another impactful technology of Bio-Techne’s R&D Systems portfolio is their Simple Western^TM automated western blot instruments. She explains, “TPD heavily relies on western blotting, but scaling screening campaigns using this as a primary assay is a huge time and resource drain, with variable data quality and poor reproducibility. Simple Western technology allows researchers to get reliable, reproducible and quantitative degradation data on a fully automated instrument.”

Enhancing pipeline intelligence

Flavio Lima Bianchi
Lead Research Analyst
Beacon by Hanson Wade

Keeping pace with the fast-moving TPD landscape can be daunting. “Part of the problem is that reliable data is hard to come by, particularly in regards to the advancements coming out of China, with developers still relying on their own, in-house methods to generate viable, orally bioavailable lead candidates at the cost of significant time and investment,” observes Flavio Lima Bianchi, lead research analyst at Beacon by Hanson Wade.

As evidence of this challenge, Bianchi notes that despite PROTACs comprising roughly a third of the overall TPD landscape, “to date less than five percent of PROTACs have managed to progress into the clinic and only a select few drugs have reached late-state, pivotal studies.”

The company is addressing these limitations in several ways. “We aggregate all available TPD data and render it into an easily searchable and digestible format. Too often is information siloed within organizations and, perhaps more importantly, failed degraders are rarely published or are quietly swept under the rug.”

He continues, “Beacon leverages a mixture of publicly available and proprietary data obtained directly from developers to track every single TPD program globally and to lift the lid on both the successes and the failures, enabling developers to make better, more informed decisions.”

While investigators relying on in-house methods may spend significant time searching available information, Bianchi emphasizes that their platform extends well beyond data access. “Beacon TPD is a subscription-based intelligence platform, providing users the ability to search comprehensive, curated preclinical, clinical, and commercial data across the induced proximity landscape. Aside from this primary search and retrieve function, Beacon’s additional functionalities include analyst reports, conference summaries, weekly newsletters and alerts, all designed to keep users abreast of the latest development within their field of interest.”

Broadening TPD horizons

Bio-Techne’s Maple envisions TPD expanding well beyond its original scope. “I think about TPD as one portion of a broader induced proximity revolution. The basic principles and technological breakthroughs that have driven TPD can be applied to targeted protein localization, stabilization, modulation, etc. This opens new optionality from a therapeutic standpoint and is also opening entire new fields of basic research enabled by these new principles and chemical tools.”

The post Targeted Protein Degradation Broadens Its Scope appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

Organ Chips Move Towards Mainstream Drug Development, with Hurdles Ahead

In April 2025, the U.S. Food and Drug Administration (FDA) released a strategic roadmap to make animal testing the exception for preclinical safety and toxicity studies within the next three to five years. Central to that vision is the adoption of validated new approach methodologies (NAMs), including organ-on-chip systems. The National Institutes of Health reinforced that shift the same month by requiring that all new notices of funding involving animal models incorporate human-focused approaches such as organ chips and other NAMs. Similar changes are emerging globally. In November 2025, the U.K. government published its roadmap to largely phase out animal testing in research while accelerating the development and validation of alternative methods.

For organ-on-chip developers, growing interest from federal agencies is a welcome trend. They are currently generating the data necessary to show that their technologies can work in stringent regulatory environments. However, there are still outstanding questions around validation standards, regulatory expectations, and how NAM data will be evaluated in submissions. At the same time, adoption remains slow, with drug developers continuing to rely largely on established animal models, which command billions in investment compared to the much smaller organ-chip sector.

Still, it is clear that momentum is building behind NAMs. And in response, organ-chip developers are stepping up to ensure that their platforms can produce results when the time comes.

From space flight to lab scale-up

When the Artemis II astronauts launched their historic 10-day journey around the Moon in April 2026, they carried some unusual cargo: organ chips containing cells from their bone marrow. The chips are part of the AVATAR (A Virtual Astronaut Tissue Analog Response) investigation, which is using organ-on-chip devices to study the effects of deep-space radiation and microgravity on human health.

Emulate's Organ Chip — Emulate’s organ chips played a pivotal role in the recent Artemis II lunar mission. The so-called AVATAR experiment could change how space agencies study the effects of radiation and microgravity impact human health. [Emulate

Before the trip, cells from the astronauts were harvested to create two sets of bone marrow chips: one set traveled beside the crew aboard their spacecraft, while another remained on Earth. The idea was to compare both sets of chips when the astronauts returned to Earth. More broadly, the AVATAR project also aims to provide proof-of-concept for including human organ chips in future missions.

In 2025, Emulate announced that its organ-chip technology was selected to accompany the astronauts on their lunar fly-by. It is an exciting project for Emulate, which commercializes human organ-chip technology developed at the Wyss Institute for Biologically Inspired Engineering at Harvard University. But it is only one of several activities that the company has been involved in the recent past. The company’s liver organ chips were one of the first to be accepted for the FDA’s Innovative Science and Technology Approaches for New Drugs (ISTAND) program, which supports tools that fall outside the scope of existing qualification programs but may still be useful for drug development.

In a conversation with GEN, Lorna Ewart, PhD, Emulate’s chief scientific officer, described 2025 as a pivotal year both externally—with announcements from multiple federal agencies promising increased support for organ chips—and internally, with the launch of Emulate’s new instrument, AVA, in June 2025 to address what Ewart describes as “key operational challenges” with the company’s first-generation platform. AVA has a higher throughput than its predecessor, enabling microfluidic workflows across 96 parallel organ chips or “emulations” in a single run. The company claims that it is the first organ-on-chip workstation to combine high-throughput microfluidic tissue culture with automated imaging in a self-contained environment.

Interest in the instrument to date has come primarily from large pharmaceutical companies and mid-sized biotech firms, who need to run large numbers of chips in parallel. But, Ewart says, there is also strong interest from academic institutions and government agencies. Some of that interest is driven by AVA’s much smaller footprint. Compared to Emulate’s first-generation system, AVA is a compact benchtop system that does not require multiple incubators. The company has also reduced the size of each emulation, or chip equivalent, by about 50%, meaning that the new platform requires fewer cells and uses less media, helping to keep experimental costs down. “Academics are actually quite excited about getting their hands on it and looking at it as a core lab instrument where multiple labs will be able to use it.”

AVA also addresses concerns about reproducibility, a consistent source of worry for drug developers, and one that Emulate has made a priority. The company has shared data showing that its liver-chip biology is reproducible both internally and externally in laboratories using AVA. The company has also taken steps to minimize technical variability within experiments as well as bias when running AVA at scale. “We need to make sure that the first chip array looks the same as chip array eight,” Ewart says. “If it doesn’t, there’s variability across those different [chip arrays] that will impact the way that a user can design, what we would refer to as a fully burdened experiment.”

More complex, automated models

When it first launched, U.K.-based organ-on-chip company CN Bio started with a liver-on-a-chip platform, but has since expanded to include various organ models, including intestine, lung, and kidney. The company’s commercial platform is built on technology developed in the laboratory of Linda Griffith, PhD, at the Massachusetts Institute of Technology.

Tomasz Kostrzewski, PhD
Chief Scientific Officer
CN Bio

Currently, CN Bio has applications in multiple arenas, including safety, toxicology, and disease modeling. “For example, in the toxicology space, we have a very well-known and well-utilized model of drug-induced liver injury,” Tomasz Kostrzewski, PhD, the company’s CSO, tells GEN. That model is being utilized by several global clinical research organizations to offer assays as a service. The company also has a multi-organ system that links its intestine and liver chip models, which can be used to predict the oral bioavailability of drugs, and a range of disease models for metabolic liver disease, chronic obstructive pulmonary disease, and more.

Perhaps one of the biggest challenges, from Kostrzewski’s perspective, is the misconception among some stakeholders that organ chips can fully replace animal models today. That is not a position that the organ-chip community has advocated for, he says. The focus should be on “using these tools to answer the right question and [in] the right context of use at the right time alongside all those other approaches that are out there.”

Development plans in the near future involve making incremental improvements that refine CN Bio’s platform over time. “One key area that we’re working on is immunology and adding in more complex immune cultures into our chips,” Kostrzewski says. Recently, “we presented some of the first data [incorporating] peripheral immune cells in our liver model and looking at the toxicity of monoclonal antibodies.” Some customers are building “neuronal blood brain barrier models on our platform” with an eye towards “understanding how drugs can penetrate across that barrier.” In parallel, the company is expanding into new organ systems, including kidney models, via partnerships.

The company is also turning to automation to help customers scale their work. CN Bio’s open design integrates well with standard robotic systems, making it well-suited for high-throughput workflows, Kostrzewski says. Customers could run more chips in parallel as part of larger screening studies with more consistency and less human intervention. There is also the potential to incorporate sensing capabilities, much like those used in biomanufacturing, to monitor system performance in real time and generate functional readouts.

In addition, the company is working to demonstrate to drug developers that organ chips can generate valuable translational data that predicts clinical outcomes. That certainly has been true for CN Bio as “we have a number of molecules that we have helped take to the clinic” that have been proven successful, says Kostrzewski. And there are customers using its organ chips “to make no-go decisions” regarding potential drug programs. “That’s the ultimate proof that these technologies do what they say,” he says.

CN Bio’s PhysioMimix supports studies of metabolic liver disease, chronic obstructive pulmonary disease, and drug delivery in the brain. There are also efforts to develop additional organ systems using the technology. [CN Bio]

Digital twin and multi-organ models

Hesperos’ co-founders, James Hickman, PhD, and Michel Shuler, PhD, have been involved in the organ-chip space since its early conception. In fact, the technology that underpins the company’s services emerged from work that both scientists were doing independently in their laboratories. Today, the company provides drug development services using its Human-on-a-Chip^® single- and multi-organ systems in areas such as neurodegenerative disease.

In April, the company published a study in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association focused on familial Alzheimer’s disease (fAD). Specifically, scientists at Hesperos and the University of Central Florida (UCF) used a neuromuscular junction (NMJ) multi-organ chip to show that fAD-associated mutations caused specific impairments in NMJ functions that occurred independently of brain pathology. Building on that work, Hesperos scientists and their collaborators are trying to understand what therapeutics could potentially be useful for both the peripheral and central nervous systems, as well as which would need to be specific for each.

Last year, the company also demonstrated what they claim is the first true digital twin capability using an organ-on-chip platform. That capability is described in an Advanced Science paper where the scientists explain how a multi-organ system comprising human liver, spleen, endothelial tissues, and blood was used to replicate the full lifecycle of Plasmodium falciparum, the parasite responsible for malaria. They plan to publish additional studies on their work on digital twins. Additionally, like Emulate, Hesperos is also participating in the FDA’s ISTAND program.

In a conversation with GEN, Hickman described the broader adoption of organ-on-chip technology as a mixed bag, with some people being more open to the technology and others showing more resistance. He noted that many in the community are still accustomed to using animal models, which may make them more reticent to change, but also acknowledged that animal testing is a multi-billion-dollar business. “There are a lot of people with a vested interest in keeping animal experimentation going,” he says. That means that although people may be interested in alternatives like organs-on-chips, from a practical perspective, it may be difficult for them to disengage from their reliance on animal models.

He also pointed to the FDA’s evolving guidance on alternative technologies—and the lack of clarity—as one of the biggest hurdles. “People are still trying to get their hands around the FDA announcements on moving away from animal models,” and trying to understand what the agency wants to see, Hickman explained. “We have a pretty good idea of what that [might be needed and] we work with a couple of people [to] generate data along those lines,” he says. “The biggest thing is to start getting [clearer guidance] in terms of what they will accept in lieu of safety data.” There are also questions around whether good laboratory practice (GLP) requirements for these new approach methodologies need to mirror those for animal studies, given the differences between the systems. “Doing GLP is really expensive,” Hickman said, and requiring the same standards could effectively put many companies out of the running to conduct safety studies because they can’t afford it.

Equally important is addressing the limited investment in organ chip and other alternative technologies. Hickman estimates that commercial NAM entities collectively generate hundreds of millions in revenue, compared to tens of billions secured by large animal CROs. Although federal agencies have committed to supporting NAMs, providing millions in funding, greater investment is needed for these alternative technologies to come into their own. Hickman added, “It’s a matter of trying to increase that capacity to really start showing that it’s a force in the industry versus a shiny new toy that people haven’t quite figured out what to do with.”

The post Organ Chips Move Towards Mainstream Drug Development, with Hurdles Ahead appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

State of the Diagnostic Industry: Recombinants on the Rise

Discovery of a fragile foundation

Four years ago in GEN, Scripps Laboratories predicted that the clinical diagnostic industry was on the verge of a recombinant protein revolution. At the time, in vitro diagnostic (IVD) assay developers were opposed to using recombinant proteins as replacements for proteins derived from human or animal tissues, glands, organs, and fluids, so-called “native” proteins. The pushback was vigorous, even palpable.

David A. George
Director, Product Research
Scripps Laboratories

Today, the transition to recombinants is underway, as they are being approved and adopted in IVD assays around the globe. I witnessed firsthand the shortage of native starting materials and helped drive this shift by developing recombinants suitable for the IVD industry. Recombinants are now the most responsible option in many diagnostic areas for laboratories that care about long‑term risk management, supply chain resilience, sustainable sourcing, and price stability.

The IVD industry relied far too long on a surprisingly fragile supply network. Many of the proteins used in diagnostic assays are purified from starting materials obtained from human donors, or from abattoirs in the case of animal-sourced materials. For decades, this system appeared satisfactory: native materials were available, performance was good, and IVD assays were being produced to meet global demand. The system appeared sustainable, and there was no visible reason to change; that is, until there was.

Native sourcing becomes unsustainable

The erosion of the native starting material supply chain was not a single, isolated event. It occurred over many years, even decades. Today, native raw materials for critical proteins in several diagnostic areas are unavailable in the quantities needed to support the growing IVD industry.

Going back 10 to 15 years, human hearts and livers were becoming increasingly expensive and difficult to obtain. In addition, the quality of the donor organs made available to material manufacturing companies was deteriorating severely. Many organs were either resected or visibly diseased. The poor-quality hearts yielded less and less of the cardiac biomarkers creatine kinase MB (CK-MB), troponin I (TnI), and troponin T (TnT). Similarly, yields of the iron-storage protein ferritin from human livers decreased precipitously.

Pituitary glands have a similar story of declining availability and spiking costs. Pituitaries are the source of follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin (PRL), and thyroid-stimulating hormone (TSH). These hormones are essential to testing in reproductive medicine (FSH, LH, PRL) and thyroid disease (TSH). The pituitary gland is small, the size of a pea, and each human has only one. Several thousand glands are needed, from several thousand donors, for a single pituitary gland-extraction batch. Given the growing size of the reproductive and thyroid testing markets, such large-scale consumption of this limited resource was not sustainable.

Animal-derived proteins are not immune to such supply chain disruptions. Changes in how porcine stomachs are processed at abattoirs around the world significantly reduced the intrinsic factor content available for purification. Porcine intrinsic factor has a high affinity for vitamin B12 and has been used for decades in metabolic diagnostics as the binding reagent in B12 assays. With the new stomach excision process resulting in lower yields, producing native intrinsic factor has become more challenging and expensive.

One telling indicator that some areas of the native protein model are under strain is the behavior of IVD assay manufacturers themselves. Many long-standing hormone customers have implemented a “last time buy” strategy, purchasing native hormones in quantities to last three to five years. This tactic may bridge a short-term gap, but it signals a deeper, industry-wide revelation: continuing to build assay portfolios on such vulnerable raw materials is not aligned with long-term risk management.

From skepticism to necessity

When companies began presenting recombinant alternatives to the IVD industry, the reception was cool. Many companies would not entertain a discussion about recombinants, let alone consider evaluating them. The conventional wisdom was that native proteins were inherently superior in immunoassays, particularly for structurally complex proteins, like the 24-subunit ferritin molecule, or for glycosylated, two-subunit proteins, like FSH, LH, and TSH. To be fair, the recombinants available 10 or 20 years ago were not produced with the IVD industry in mind and did not perform up to industry standards.

In only a few years, the IVD industry’s attitude toward recombinants has shifted dramatically. A willingness to evaluate them as replacements for native proteins has spread across the globe. The same diagnostic laboratories that refused to have a conversation about recombinants four years ago are now proactively soliciting their suppliers for recombinant alternatives to native proteins. Many global IVD leaders have implemented a mandate to switch to recombinant proteins wherever a native protein may be considered at risk of a raw material shortage. Furthermore, when a new assay is being developed, a “recombinant-first” approach is now the norm.

I have also witnessed a cultural element to the shift. Some of the larger IVD companies have said that their scientific staff was reluctant to switch away from native proteins, but that the transition to recombinants is happening, regardless. This, too, demonstrates a broader understanding in the industry of the fallibility of the old native model.

Recombinants taking over

The most swift and dramatic transition to recombinant hormones is occurring in the fields of reproductive biology (FSH, LH, PRL) and thyroid disease (TSH). Historically, recombinant forms of these hormones performed poorly, so the resistance to evaluating recombinants was strong. As the supply of pituitary glands contracted, however, assay manufacturers were forced to confront the vulnerability of their supply chain. Fortunately, having inside knowledge of the pituitary supply constraints, our laboratory set out early to develop recombinant forms of these hormones. By the time the supply crisis hit, we were prepared with a full line of IVD-assay-tested recombinant hormones.

Structure of recombinant bovine chymosin — Credit: vdvornyk/ iStock / Getty Images Plus

The response in the industry has been decisive and far-reaching. Most customers for native hormones have now tested, approved, and switched to recombinant versions. This change did not occur because native hormones suddenly became unusable, but because their supply became incompatible with the magnitude, reliability, and planning requirements of the industry. By contrast, recombinant hormones can be produced at scale in controlled systems with consistent quality and predictable availability.

Cardiovascular diagnostics are following a similar path. Recombinant TnI, TnT, CK-MB, and myoglobin are being adopted quickly as replacements for the native forms derived from human hearts. The supply of suitable organs cannot keep pace with industry demand, as cardiovascular disease is on the rise globally and the growth of point-of-care testing continues. Recombinant cardiac markers offer a solution to organ supply shortages, meeting the industry’s high demand for these proteins, while maintaining the performance characteristics IVD laboratories expect.

In anemia and metabolic diagnostics, the switch has not been immediate, but it is underway. Recombinant apoferritin (ferritin without iron) and recombinant human intrinsic factor are available to replace the native proteins, and they are being evaluated and approved. The global supply of native ferritin and intrinsic factor is diminishing, but the situation is not as dire as with heart- and pituitary-derived proteins. Thus, the transition is progressing, but is not as far along.

Keys to producing recombinants

To justify switching to a recombinant protein, the recombinant must perform comparably to the native protein it is intended to replace. Early recombinants did not perform well, resulting in the skepticism seen initially. In antibody-based assays, even subtle structural differences can translate into poor recognition, reduced sensitivity, or altered calibrator performance. Overcoming these issues requires more than simply expressing a protein in a convenient host; it requires a project development and testing strategy tailored to the nuances of IVD assay development.

At Scripps, our intention was to devise and implement a strategy that would produce recombinants suitable for the IVD industry. The process involves appropriate gene, expression vector, and host cell line selection; tagless protein expression; early and extensive testing in antibody-based systems, including clinical analyzers; and a willingness to revisit any or all of these elements if the desired recombinant is not produced.

This development strategy addresses the concern about recombinant protein performance in the IVD industry. When a recombinant biomarker performs well and can be supplied consistently, without relying on the unstable supply framework of donor materials, the recombinant becomes not just an acceptable option, but the preferred one.

Looking ahead

The IVD industry is at an inflection point, bending toward global acceptance of recombinant biomarkers. The constraints on native tissue supply and quality will not ease; in fact, they will likely intensify. Simultaneously, industry expectations surrounding ethical sourcing, supply chain stability, risk mitigation, and long-term cost control will become more stringent. Given this environment, continued reliance on donor materials is difficult to justify and is perhaps foolish.

Recombinant proteins offer a way forward that unites consistent assay performance with sound business judgement. Disconnected from unreliable tissue supply networks, recombinants support sustainable and ethical sourcing practices, providing IVD assay manufacturers with a stable foundation for planning and growth. The experience of recent years—in reproductive biology, cardiology, and thyroid disease in particular—has shown that when recombinants are developed with clinical assay performance in mind, they can match or even exceed the standards set by native proteins.

I have seen the industry’s view of recombinants evolve from skepticism to necessity. Focusing on tagless expression and rigorous early testing, recombinants can be produced not as lesser-quality replacements, but as robust solutions. As assay developers and IVD executives look ahead to the next decade of innovation, recombinants are no longer a speculative option. They are the most responsible path toward assuring the continued availability of the tests that patients and clinicians rely on daily.

David A. George is director of product research at Scripps Laboratories.

The post State of the Diagnostic Industry: Recombinants on the Rise appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

Next Generation Biopharma Innovation

Researchers are digging deeper into biology’s complexity. In preclinical research, the traditional in vivo models are simply not enough to fuel the engine with the relevant translational data needed to progress successfully to the clinic.

As research needs evolve in immunology and immune-oncology—as focus on neuroscience increases and metabolic drugs such as GLP-1-based therapeutics become more prevalent—in vivo model suppliers are being requested to up the game on new platforms. In response, these suppliers are expanding their humanization platforms while developing advanced models that can be used to study complex and overlapping disease biology.

Regulatory factors also affect this market. The continued focus on the reduction of the use of animals by U.S. and European regulatory authorities has further opened the door to new approach methodologies (NAMs). NAMs are not new. Organ-on-chip or microphysiological systems, organoids, and iPSCs have been available for years. Finally, these systems are entering the limelight. Although the NAM market still requires more standardization across platforms, these systems are starting to impact preclinical research.

Building translational engines

The Jackson Laboratory (JAX) recently launched its latest humanized model, the NSG^®-SGM3-IL15-MHC I/II DKO (S15-DKO). The S15-DKO represents their latest advancement in generating PBMC-humanized mice, supporting broad engraftment of immune cell subtypes such as CD4+ and CD8+ T cells, CD33+ myeloid cells, and CD16+/CD56+ natural killer (NK) cells. The knockout of the murine MHC Class I/II receptors delays the onset of Graft vs. Host Disease (GvHD).

S15-DKO model is JAX’s latest advancement in generating PBMC-humanized mice, supporting broad engraftment of immune cell subtypes. The knockout of the murine MHC Class I/II receptors delays the onset of Graft vs. Host Disease (GvHD). [The Jackson Laboratory]

The model also supports the engraftment of rare immune cell subsets, including gd T cells and CD19+/CD38+ B cells that retain the memory state of the donor PBMCs.

Another advanced model for CD34+ hematopoietic stem cell (HSC) humanization, the NSG-FLT3-IL15 mouse generates a cellular-diverse human immune system encompassing myeloid cells, mature NK cells, functional dendritic cells, and T cells.

Both models are available in naïve strains, or off-the-shelf pre-characterized PBMC- and HSC-engraftment, along with full preclinical services tailored to immuno-oncology and autoimmune drug discovery.

“With the FDA’s renewed focus on reducing reliance on non-human primates in biologic development, demand for validated, translational preclinical models has never been higher,” said Luke Dimasi, senior director, JAX.

The genetically humanized FcRn platform and the newly expanded Tg32 hALB mouse address this need. Lacking murine Fcgrt and albumin while expressing their human counterparts, the Tg32 hALB is the first model for studying the pharmacokinetics and pharmacodynamics of human albumin therapeutics, as well as human IgG and Fc-domain-based biologics. Preclinical mAb testing services are available.

“Our offering extends beyond the vivarium,” Dimasi emphasized. JAX’s iPSC repository continues to grow with engineered lines carrying disease-relevant mutations linked to Alzheimer’s, Parkinson’s, ALS, and frontotemporal dementia. In 2025, JAX added HALO-tagged and TET-inducible lines to the collection. The acquisition and integration of the New York Stem Cell Foundation (NYSCF) brings complementary patient-derived iPSCs to the portfolio.

“As the field moves towards new approach methodologies (NAMs), we are evolving alongside it,” Dimasi pointed out. “Our in vivo mouse capabilities give us decades of deeply validated biological context. We are now layering human iPSCs and AI-computational phenotyping on top of that foundation to build a convergent translational engine that no single approach could deliver alone.”

Developing relevant models

According to Jason Rashkow, PhD, product manager for research models, Charles River Laboratories, the company’s comprehensive collection of spontaneously developing rat models spans metabolic disease, diabetes, hypertension, and heart failure, providing strong translational relevance across cardiometabolic indications.

Custom diet preconditioning services allow researchers to tailor disease progression to specific study objectives through strategic model selection and diet design. Standardized preconditioning offerings are planned. “This approach will accelerate study initiation, giving researchers faster access to these metabolic disease models,” said Rashkow.

The increasing prevalence of GLP-1-based therapeutics and next-generation incretin and poly-agonist therapies expanding into cardiometabolic indications such as heart failure with preserved ejection fraction (HFpEF) is accelerating demand for advanced disease models. The combination of established disease models, standardized preconditioning approaches, and custom solutions reflects the complexity of modern metabolic drug development.

In addition, optimization of the generation of CD34+ HSC-humanized mice continues. These models, developed on the severely immunodeficient NCG strain, support research in immuno-oncology, autoimmune disease, vaccine research, and related fields.

As immuno-oncology research needs shift, so does the need for models that enable the study of NK cell-based therapies, tumor microenvironment reprogramming, and cancer vaccines. “Although variant NCG models expressing human cytokines or HLA transgenes begin to meet these needs, transgenes can influence humanization requirements,” Rashkow noted.

To counteract this, the company expanded access to a peripheral blood mononuclear cell (PBMC) engrafted NCG variant strain carrying a double knockout for murine MHC class I and class II, which significantly delays the onset of GvHD, allowing for longer-term studies in the context of mature T cells.

To better support researchers studying HLA-A2-restricted immune responses in vivo, humanization optimization of a NCG variant expressing human HLA-A*02:01 was completed. Further development of the humanization protocols for other variant strains will support next-generation immunotherapy discovery and translational research.

Lastly, the expanded aged C57BL/6 mouse offerings support researchers investigating age-related disease. As a licensed distributor of JAX^® Mice to researchers in Europe and Asia, Charles River Europe can now provide aged C57BL/6J mice up to 90 weeks of age. In North America, Charles River offers aged C57BL/6N mice up to 77+ weeks of age.

Improving translational fidelity

“Improved translational fidelity, increased demand for study-ready systems that better align with clinical endpoints, and the need to model complex and overlapping disease biology are driving model development,” related Michael Seiler, PhD, vice president of portfolio management, Taconic Biosciences.

Complex modalities such as checkpoint inhibitors and engineered cell therapies require more complete immune system function and deeper phenotyping. Expansion of the FcResolv^® NOG portfolio and huSelect services reduces murine immune interference and donor variability. Advanced flow cytometry panels support deeper, standardized

immune profiling.

Animals Alternatives — With the goal of improving translation relevance, Taconic develops in vivo models that reflect complex and overlapping disease biology in immunology, immuno-oncology, neurobiology, and cardiometabolic indications. [Taconic Biosciences]

Planned launches include platforms and models designed to support immuno-oncology, biologics, engineered cell therapies, infectious disease, and autoimmune research, with a focus on more complete and functional human immune system biology. Gene and protein analysis services are available.

In neuroscience, the shift is toward better alignment with clinical disease biology, particularly in Alzheimer’s disease and neuroinflammation, along with increased focus on blood-brain barrier (BBB) biology and CNS delivery. Parkinson’s disease model offerings include aSyn KI/KO, PINK1 KO, and LRRK2 KO rat models.

Future models include BBB-focused platforms such as TFRC and CD98, ARTE10 crosses with BBB models, and neuroimmunology-focused NOG variants, including IL-34 and TREM2-related models.

The rapid growth of obesity therapeutics, including GLP-1 and next-generation incretin approaches, is accelerating demand for more predictive metabolic and liver models in cardiometabolic disease. A range of models are aimed at obesity, MASH, cardiovascular disease, and DMPK applications.

Taconic is expanding its capabilities in transgene characterization, CRISPR off-target analysis, and tiered Custom Model Generation Solutions. The acquisition of TransCure bioServices significantly bolsters support of integrated in vivo study services, particularly in humanized immune system and immuno-oncology research. “We now offer a more seamless, end-to-end solution from model selection through study execution and data generation,” said Seiler.

“We continue to evolve toward integrated solutions rather than standalone models. This includes expanded CMS and CMGS capabilities, humanization-as-a-service, deeper phenotyping and multiomic analysis, and partner-enabled data generation,” Seiler added.

Importantly, the move toward integrating in vivo models with complementary technologies such as organoids, iPSCs, and AI-enabled analysis will influence how models are developed and deployed within research workflows.

Standardizing NAMs

The field is clearly shifting toward ready-to-use biology, producing a strong demand for standardized NAM platforms and services that deliver consistent, high-quality results. To facilitate scientists, MIMETAS continues to develop robust OrganoReady^® models and advanced services, including immune-competent and vascularized systems across multiple organs.

“Last year, we strengthened our fee-for-service capabilities and advanced several models to deliver high-quality biology in a consistent, scalable way,” said Paul Vulto, PhD, co-CEO and co-founder, MIMETAS. “We made strong progress in our kidney tubuloid research program, CAR T-related applications, and a BBB model under unidirectional flow.”

The novel human distal nephron-on-chip model in the OrganoPlate^® replicates physiologic sodium and water transport using primary human kidney cells. This three-dimensional microfluidic platform, as detailed in Kidney360, serves as a high-throughput tool for functional drug screening and investigating distal nephron physiology and disease.¹

A polarized kidney tubuloid in an OrganoPlate chip showcases apical and basolateral access. Immunofluorescence 3D reconstruction demonstrates tubule polarization and barrier formation: blue, DNA; red, acetylated tubulin; and green, Na /KATPase. [MIMETAS]

In addition, a three-dimensional BBB microvasculature model developed on the OrganoPlate Graft 48 UniFlow was evaluated in a recent Fluids Barriers CNS publication. Tri-cultures of endothelial cells, pericytes, and astrocytes were used to demonstrate that this pump-free, unidirectional perfused, three-dimensional BBB model outperformed simpler systems on vascular architecture and barrier function. Its high-throughput nature renders the model suitable for studies of BBB function in health, disease, and therapeutic development.²

This year, the company’s UniFlow technology will be offered for in-lab use, enabling customers to create a stable, perfusable vascularized bed for endothelial tissues. New OrganoServices for gastrointestinal toxicity (GI tox) and drug-induced vascular injury (DIVI), alongside a multi-donor expansion of the OrganoReady Colon Organoid product, are also planned.

A major trend in NAMs is the increased need for standardization and regulatory alignment across the field. With initiatives like IAMPS (Industry Alliance for MicroPhysiological Systems), of which MIMETAS is a founding member, industry innovators will work together to advance regulatory acceptance.

The space is evolving quickly, but Vulto emphasized that their focus remains unchanged: building robust human models that help researchers make better decisions.

Improving organoid access

“Organoids are part of a broader innovation focus to help researchers work with more predictive models, more advanced tools, and more connected workflows across the path from discovery to development,” commented Heather Hargett, PhD, head of cell biology reagents franchise at MilliporeSigma, the U.S. and Canada Life Science business of Merck KGaA, Darmstadt, Germany.

The regulatory landscape is becoming increasingly favorable to NAMs. In March 2026, the FDA issued a draft guidance to establish clear validation principles for NAMs, including organoids and in silico (or AI) models, when submitted in support of drug applications.

Phasing out animal use for research and regulatory purposes is also supported by the European Commission’s Roadmap Towards Phasing Out Animal Testing for Chemical Safety Assessments.

Patient-derived organoids (PDOs) retain individual genetic and phenotypic characteristics, enabling drug response testing across diverse patient backgrounds and disease subtypes. The image shows immunocytochemical (ICC) characterization of human colon PDOs that are positive for the colon-specific marker CA II (green), nuclei (blue) and actin (red). [MilliporeSigma, the U.S. and Canada Life Science business of Merck KGaA, Darmstadt, Germany

HUB’s advanced organoid capabilities are now being combined with the company’s cell culture expertise, manufacturing scale, global commercial reach, and broad life science portfolio to make organoids a more practical and scalable tool in drug discovery and translational research.

Key priorities include expanding the validated organoid biobank across additional therapeutic areas, tissues, disease states, and patient backgrounds. “Last October, we announced a strategic partnership with Promega Corporation,” said Hargett. “By combining our organoid expertise with Promega’s advanced reporter technology, we aim to enable high-throughput screening that helps researchers identify safer and more effective drug candidates.”

The case of petosemtamab, developed by Merus, is a notable example of the real-world impact of organoid technology. Petosemtamab’s efficacy was tested using HUB organoids. The EGFR x LGR5 bispecific antibody has received FDA Breakthrough Therapy Designation for use in combination with pembrolizumab for first-line treatment of PD-L1-positive recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). A global Phase III trial is ongoing. Recently, Genmab acquired Merus for approximately $8 billion USD.

Adopting organoid technology is a capital efficiency strategy, according to Hargett. Patient-derived organoids retain individual genetic and phenotypic characteristics, enabling drug response testing across diverse patient backgrounds and disease subtypes. Organoids support a “fail fast” approach by identifying non-viable candidates earlier, reducing costly late-stage clinical trial failures, and allowing companies to redirect resources toward the most promising programs.

References

Bernardi MDL, Dilmen E, Kurek D et al. A Novel Human Distal Tubuloid-on-a-Chip Model for Investigating Sodium and Water Transport Mechanisms. Kidney360. 2025 Nov 1;6(11):1981-1993. doi: 10.34067/KID.0000000992.
Admiraal J, Emeh PO, Bokkers M et al. Building the blood-brain barrier: a scalable self-assembling 3D model of the brain microvasculature under unidirectional flow. Fluids Barriers CNS. 2026 Jan 23;23(1):29. doi: 10.1186/s12987-026-00765-x.

The post Next Generation Biopharma Innovation appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

The Confidence Gap: Why Drug Discovery’s Data Explosion Hasn’t Solved Its Billion-Dollar Decision Problem

Laurence Arnold, PhD
Head of R&D
Pelago Bioscience

We’ve never had more data in drug discovery. Yet despite this explosion in capability, our industry’s most fundamental challenge remains stubbornly intact: making confident early decisions about which drug programs deserve billion-dollar investments, and which should be shelved.

It costs two to three billion dollars to bring a drug to market, with a 90% failure rate, often higher. These numbers mask something more troubling. We’re not just failing because biology is hard; we’re failing because the mountains of data we’re generating aren’t giving us what we actually need at decision points that matter.

In my view, we don’t have a data volume problem—we have a data relevance problem.

Biological activity is not relevance

Traditional drug discovery relies on a “dissect and build” approach: isolate one variable, measure it in a controlled environment, then extrapolate. It’s disciplined. It’s reproducible. And it has delivered important medicines.

But the persistently high failure rate in drug development tells us we’re reaching the limits of this approach. In reality, biology operates through cascading networks, feedback loops, and context-dependent equilibria. These are dynamic biological systems where cause and effect rarely follow straight lines.

We’ve successfully drugged only about 650 of 20,000 potentially druggable proteins. Not because scientists lack talent, but because for most targets, we don’t have robust ways to measure what matters—the initiating molecular event in a biologically relevant context.

We’re good at measuring activity. What we struggle with is measuring relevance.

An assay telling you a compound binds to your target protein is useful, but does it bind in living cells? In the disease context that matters? With the pharmacokinetics to reach patients? A compound brilliant in a purified enzyme assay might never reach its target in cells, or it might hit off-targets producing effects through entirely different mechanisms.

The result? Ever-expanding data sets that still don’t answer the critical question in modern drug discovery: Are we making the right decision?

The cost of borrowed confidence

There’s a human dimension here that rarely makes it into industry discussions. Despite what is often repeated in drug discovery circles, scientists in R&D are rewarded for being right, not for being bold.

Most scientists think in terms of “future hindsight”: will we look back and realize we missed something obvious? The responsibility isn’t to push programs forward at all costs. It’s to execute each step well, knowing that most will fail. Success stories often appear bold in retrospect. In practice, they are usually built on careful, incremental decisions that gradually improve the odds.

So, teams do their jobs with discipline and rigor. They hit milestones, generate data, and advance programs. Everyone knows 90% of projects will fail, but this one has shown activity in the assay, has a plausible mechanism, and has momentum. The data might not be perfect, but it’s good enough to keep going.

Until it isn’t. And the failure comes late, after years of effort and hundreds of millions spent.

Of course, failure is how science advances. But many of these failures were avoidable earlier. Hard-working teams just didn’t have data that would let them make the call with confidence when it mattered most, before massive resources were committed.

What decision-ready evidence looks like

The best experiment isn’t always the one that moves your program forward—it’s the one that tells you when to stop.

Think of it as taking a stepladder to look over a thick hedge rather than hacking through it with an axe. You might not learn everything about what’s inside it, but you’ll know much faster whether there’s anything worth pursuing on the other side.

The pharmaceutical industry has been built on a model of going through the hedge, but the resource cost and timelines are increasingly untenable. So, what would an alternative, evidence-driven discovery model look like?

Evidence-driven discovery requires a hierarchy of questions. Before optimizing potency or selectivity, can you prove that engaging this target in this context produces therapeutically relevant effects? Not in an abstract system, but in actual disease biology.

This is about front-loading proof of concept before investing in optimization. Measure the initiating molecular interaction early, free from tags or unnatural expression control, in cells and tissues that approximate disease.

It also requires new frameworks for proof of target engagement. We’re seeing this with technologies that measure binding in native cellular contexts, patient-derived models, and translational designs that test hypotheses much earlier in preclinical development. The goal isn’t replacing traditional assays, but knowing which programs deserve that investment.

Ultimately, the win comes from making the right decision at each stage, even when that decision is to stop.

The path forward

Successful programs will establish coherent lines of evidence from initial target engagement through preclinical models to human proof of concept—and they will do it fast enough to fail early when evidence doesn’t align.

This means rigorously testing hypotheses in the real biological context of disease before perfecting molecules or committing billions of dollars.

Some will argue this is unrealistic—that you need optimized compounds, and that shortcuts lead to false negatives killing promising programs. These concerns aren’t wrong; they’re just insufficient when the old model demonstrably isn’t working.

The real question is whether the risk of earlier translational testing exceeds spending nine years and a billion dollars on a target that was never going to work.

Making the call with confidence

Here’s what I tell my team: Your job isn’t to get a drug to the clinic. Your job is to do each step exceptionally well, building evidence you can defend. Because if we’re systematic about gathering the right evidence early, and if we’re honest about what the data is—and isn’t—telling us, the statistics start working in our favor.

The industry is moving toward evidence-first approaches—technologies validating targets in relevant contexts, translational frameworks testing hypotheses earlier, and computational tools trained on quality data.

But all this data is just noise until it answers the question keeping many of us up at night: Can I make this call with confidence, or am I crossing my fingers and hoping?

We won’t solve the 90% failure rate entirely. Biology is too complex. But we can close the confidence gap by using the right data, at the right time, to answer the key question: Should we keep going?

And sometimes—often, even—the most valuable answer will be no.

Laurence Arnold, PhD, is the Head of R&D at Pelago Bioscience.

The post The Confidence Gap: Why Drug Discovery’s Data Explosion Hasn’t Solved Its Billion-Dollar Decision Problem appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

Novel Therapeutic Modalities Target the Undruggable

From small molecules and protein therapeutics to gene therapies, biotech industry players have placed their bets on a wide range of modalities that push the limits of what was once considered “druggable.”

AI biologics company, Absci, focuses on rational antibody design to bypass labor-intensive experimental screens. The ability to computationally design antibodies from scratch, or de novo, without reference to a known binder, could transform an antibody drug market projected to reach $445 billion within the next five years.

Unveiled in January, the company’s latest protein design model, Origin-1, generated developability-optimized antibodies that achieved nanomolar binding affinity and functional inhibition of IL36RA, a therapeutic target for squamous cell carcinomas. By simulating the delivery of pro-inflammatory cytokine, IL-36, the AI-designed drug candidate boosts intratumor immune response for cancer control.

Origin-1 generates de novo antibodies for “zero-prior” epitopes, or target sites that lack structural data from known protein-protein complexes. Sean McClain, CEO of Absci, emphasizes the approach as a “more expansive” version of de novo design that requires only a monomeric structure as input to generate viable candidates.

Nathaniel Bennett, PhD, co-founder at Xaira Therapeutics, highlights that Absci’s atomic-level experimental validation contributes to the field’s understanding of how AI will play a major role in therapeutic development, particularly for expanding the range of tractable drug targets.

“This is a solid piece of work that shows how AI-driven antibody design continues to mature,” says Bennett, “particularly in settings with limited prior structural information.”

Janani Iyer, PhD, head of AI/ML product at Absci, emphasizes that the targets that most often strike interest from pharma partners are typically less studied and lack epitope structure in the public domain. “We’re focused on building an AI platform technology that unlocks really unmet needs,” she said.

Permanently bound

While highly precise therapeutics, biologics, such as antibodies, are typically constrained to intravenous delivery. A growing number of biotech companies are expanding the capabilities of small molecules, which offer the advantage of convenient oral administration.

Unveiled from stealth last October, Expedition Medicines leverages generative AI to design small-molecule drugs that target shallow pockets using covalent chemistry. The Flagship Pioneering spinout targets a range of traditionally undruggable sensors, regulators, and transcription factors, where disease is driven by interactions across protein surfaces. These small molecules remain inert inside the body until activated by the appropriate protein catalyst.

“Small molecules have historically been more challenging for generative AI, but I think we are at an inflection point, with the right chemistry insights, data, algorithms, and compute finally coming together,” said Molly Gibson, PhD, CEO of Expedition.

She notes that Expedition’s technology contrasts with many of today’s molecular design efforts, which use 3D atomic positions to model reversible interactions in deep pockets.

The company’s tech stack trains AI models on high-throughput mass spectrometry data that measures the potency of each small molecule against 20,000 sites in the proteome. These fit-for-purpose datasets are advantageous over DNA-encoded libraries (DELs), which are burdened by substantial noise that can limit predictive power.

Expedition is focusing on demonstrating clinical proof points. In a partnership with Pfizer, the startup is identifying target molecules correlated with prostate cancer disease progression and treatment resistance. As a long-term goal, the team plans to expand the proteomics platform to additional modalities, such as proximity events that drive protein degradation or stability.

Biologic in a pill

AI drug developer, 1910 Genetics, has recently tackled macrocyclic peptides, a class that aims to combine the oral convenience of small molecules with the high specificity of biologics. Historically, these compounds have struggled to balance cell-membrane permeability with key therapeutic properties such as potency and solubility.

To address this gap, 1910’s AI model, PEGASUS, is trained on a multi-modal dataset that generates billions of cyclic peptides separated by permeability-related characteristics and solvent-dependent computational simulations. PEGASUS was able to demonstrate the first cyclic peptides with more than two polar or ionizable fragments to achieve in vitro cell-membrane permeability.

Jen Asher, PhD, founder and CEO of 1910, describes the model as a “versatile tool” that accelerates the design-make-test cycle by triaging compounds for synthesis, supporting lead optimization, and designing new starting peptides with desired properties.

With a company name that references the year that the first patient was diagnosed with sickle cell disease in the United States, the first condition for which the field identified a molecular basis, 1910 is committed to multi-modality drug discovery. The company’s platform also houses CANDID-CNS, an AI model that predicts small molecule blood-brain barrier (BBB) penetration within Beyond-Rule-of-5 (bRo5) chemical space to advance therapies for neurological disease.

With only about two percent of small-molecule drugs able to cross the BBB, accurate penetration prediction can identify promising candidates that are more likely to succeed in the clinic. The model achieved an 87% success rate for predicting bRo5 small molecule brain penetration and distribution, outperforming a 56% success rate for the industry standard, Pfizer’s CNS Multiparameter Optimization (CNS-MPO) score.

Encrypted message

Jacob Becraft, PhD, CEO at Strand Therapeutics, is placing his bet on programmable mRNA therapeutics for cancers and autoimmune diseases. Strand is among a vibrant genetic medicine ecosystem, where engineered vehicles, such as adeno-associated vectors (AAVs) and lipid nanoparticles (LNPs), deliver therapeutic genetic material into patient cells to produce therapeutic proteins. These medicines must achieve therapeutic potency in the right tissues while avoiding off-target effects. Yet, targeted delivery beyond the liver remains a challenge.

STX-005 illustration — STX-005 extends the same programmable mRNA platform behind STX-001 to in vivo CAR T therapy, using circular RNA and targeted systemic delivery to generate CAR T cells directly inside the body. The approach is designed to produce long-term, cell-specific expression without the ex vivo manufacturing required by conventional CAR T. The program extends the company’s work in targeted, safe, and effective systemic delivery and has potential applications to autoimmune diseases and blood cancers. [Strand Therapeutics]

Strand’s technology addresses this gap by enabling selective mRNA expression within cancer cells while sparing healthy tissue. This approach allows mRNA to be delivered broadly while targeting expression to the intended tumor cells.

“It’s like an encrypted message. It doesn’t matter who picks up my message because they can’t read it,” Becraft said. “If the protein doesn’t get created, then it’s not off-target.” The tech stack challenges the “old school mentality” that mRNA biodistribution is the key metric that defines off-target effects.

Strand’s technology leverages a machine learning–driven approach that applies molecular sensors to detect microRNA expression signatures distinguishing tumor cells from healthy cell types. As an example, liver-specific microRNAs bind to target sites in the 3¢ UTR of the delivered mRNA to suppress its expression in healthy hepatocytes and prevent off-target effects.

Last May, Strand announced the Phase I dose-escalation trial for STX001, a programmable, self-replicating mRNA therapy designed to treat advanced solid tumors by producing IL-12 directly in the tumor microenvironment. Notably, STX001 demonstrated an abscopal response, in which localized treatment of a single tumor led to a systemic immune response that reduced distant tumor sites. The company looks to advance the candidate to Phase II trials.

As the therapeutic toolbox continues to expand, the field is working to close the “undruggable” gap.

The post Novel Therapeutic Modalities Target the Undruggable appeared first on GEN – Genetic Engineering and Biotechnology News.

June 1, 2026

Mass Spectrometry’s Discovery Revolution

Mass spectrometry (MS) has quietly undergone one of the most consequential evolutions in modern drug discovery. Once viewed primarily as a confirmatory analytical tool, it is now reshaping how researchers identify, validate, and optimize therapeutic candidates. Across chemoproteomics, metabolomics, immunopeptidomics, and beyond, MS is increasingly positioned not at the end of the pipeline—but at its beginning, where the most crucial decisions are made.

“Mass spectrometry is no longer just a downstream analytical checkpoint,” says Aaron Robitaille, PhD, the senior director of product & vertical marketing of mass spectrometry at Thermo Fisher Scientific. “It is increasingly serving as a discovery engine.”

This shift reflects a broader transformation across the pharmaceutical industry: from hypothesis-driven experimentation toward data-rich, systems-level interrogation of biology.

Seeing biology more clearly

Drug discovery has always struggled with a fundamental problem: Biology is complex, noisy, and often opaque. Many of the molecules that determine therapeutic success are low in abundance, transient, or entirely unknown. MS addresses this challenge by enabling researchers to observe biological systems with unprecedented depth and specificity.

According to Robitaille, MS now supports nearly every stage of early discovery—from target identification and engagement to pharmacokinetics and mechanism-of-action studies. One of its most transformative applications is chemoproteomics, where researchers can directly measure drug-protein interactions within living cells. This enables scientists to evaluate not just whether a compound binds its intended target, but also whether it interacts with unintended ones.

Crucially, MS is moving upstream in the discovery pipeline. “What makes that important is not merely breadth. It is timing,” Robitaille notes. By enabling high-throughput screening with detailed molecular readouts, MS helps eliminate poor candidates earlier—saving time, cost, and effort.

Technological advances are driving this shift. Historically, researchers faced trade-offs between speed and sensitivity, or between targeted and untargeted analyses. Newer platforms are collapsing these compromises. Hybrid acquisition methods, for example, allow targeted and untargeted data to be collected simultaneously in a single experiment, enabling both hypothesis testing and discovery.

The Thermo Scientific Orbitrap Astral Zoom MS exemplifies this convergence. Built around parallelized acquisition and enhanced ion handling, the system delivers high throughput, deep proteome coverage, and precise quantitation—all in one platform. Its ability to process hundreds of samples per day while quantifying thousands of proteins illustrates how MS is becoming both scalable and decision-ready.

Interrogating biology at scale

For Mike Knierman, biopharma workflow manager at Agilent, the expanding role of MS reflects the growing complexity of new therapies. “Drug discovery today spans multiple therapeutic modalities, including small molecules, monoclonal antibodies, oligonucleotides, and cell-based therapies,” he explains. MS provides a unifying analytical backbone across this diversity.

One of the most significant recent developments, Knierman emphasizes, is MS’s ability to interrogate biology at scale. Techniques such as proteomics, metabolomics (See Sidebar), and lipidomics allow researchers to observe how candidate drugs perturb entire cellular systems, rather than isolated targets. This systems-level insight is essential for understanding the mechanism of action and identifying off-target effects early in development.

Emerging measurements—such as protein turnover—are also enabling new therapeutic strategies. These include targeted protein degradation approaches, which require a detailed understanding of dynamic protein lifecycles rather than static abundance.

Agilent’s Revident LC/Q-TOF platform reflects this trend toward intelligent, high-resolution analysis. Designed for accurate-mass performance with built-in diagnostics, the system incorporates features that automate quality control and maintain data consistency. Its ultra-fast detector supports a wide dynamic range without sacrificing resolution, enabling confident identification and quantitation in complex biological samples.

Equally important are workflow innovations. The platform’s Intelligent Reflex capabilities automate routine checks—such as calibration verification and carryover detection—reducing manual intervention and ensuring consistent performance. In drug discovery environments where throughput and reproducibility are crucial, these features help maintain data integrity while accelerating timelines.

Ultimately, Knierman highlights MS as a driver of “biology-driven discovery,” where decisions are guided by comprehensive molecular data rather than limited readouts.

A shift in discovery models

Todd Stawicki, senior global market development manager for pharma, SCIEX, places MS within a broader transformation of drug discovery itself. The industry is moving away from traditional in vivo models toward more complex in vitro systems—such as organoids and tissue-based assays—in an effort to reduce impacts to laboratory animals and rising global regulatory efforts.

This shift dramatically increases the number and complexity of experimental endpoints. “Many or most of these endpoints are best served by mass spectrometry,” Stawicki notes. As a result, MS is becoming indispensable for analyzing the rich datasets generated by these models.

MS is also deeply embedded throughout the discovery lifecycle. In the early stages, it supports proteomics and complements genomic studies. It plays a central role in hit identification and lead optimization, and remains crucial in ADME (absorption, distribution, metabolism, and excretion) and DMPK (drug metabolism and pharmacokinetics) studies.

Analysis of a system suitability test and rat plasma matrix — Analysis of a system suitability test (SST, top) and rat plasma matrix (bottom) injections on the SCIEX 7500+ system for three drug compounds shows coefficients of variation (%CV) of three to five percent across more than 10,000 injections of rat plasma. [SCIEX]

Technological innovation continues to expand MS’s capabilities. Acoustic ejection-based MS, for example, enables rapid, label-free screening, while advanced systems—like the SCIEX 7500+ system—address one of the field’s most persistent challenges: balancing sensitivity with dynamic range.

As new drug modalities become more potent and targeted, they often exist at extremely low concentrations in complex biological matrices. This creates a dual requirement for high sensitivity and a broad quantitation range. The SCIEX 7500+ system meets this need, enabling accurate measurement across diverse tissues and concentration levels.

Robustness is another key consideration. SCIEX Mass Guard technology, for instance, enhances system uptime, ensuring that high-throughput workflows can run reliably over extended periods. In an environment where delays can be costly, this operational stability is as important as analytical performance.

Balancing throughput and insight

Shimadzu’s perspective underscores the importance of versatility in modern MS workflows. “Mass spectrometry has become one of the most versatile analytical tools in drug discovery,” says Lihini Mendis, PhD, LCMS product specialist at Shimadzu Scientific Instruments, noting that it now supports everything from early screening to preclinical development.

Triple-quadrupole MS systems can be used in drug discovery for bioanalysis and studies of drug metabolism and pharmacokinetics. [Shimadzu Scientific Instruments]

A major recent trend is the push toward higher throughput without compromising data quality. Rapid LC-MS methods and triple quadrupole systems are increasingly used to process large sample volumes efficiently, particularly in quantitative workflows such as bioanalysis and DMPK studies.

At the same time, qualitative MS capabilities are expanding. High-resolution instruments, combined with advanced fragmentation techniques, allow researchers to gain deeper structural insights into complex molecules such as lipids and metabolites. This dual capability—quantitative precision and qualitative depth—enables scientists to answer both “how much” and “what exactly” within the same experiment, Mendis explains.

Shimadzu’s portfolio reflects this balance. Single-quadrupole systems provide accessible, high-throughput screening, while triple-quadrupole platforms emphasize stability and reproducibility for quantitative analysis. High-resolution instruments extend capabilities into accurate-mass analysis and structural elucidation, all while maintaining user-friendly operation.

The overarching goal is not complexity for its own sake, but meaningful data that supports confident decision-making. By focusing on workflow efficiency and reliability, Shimadzu aims to streamline the path from data acquisition to actionable insight.

A proteoform-centric vision

While incremental improvements in speed and sensitivity have driven much of MS innovation, Bruker’s recently introduced timsOmni system points toward a more fundamental shift: a move toward protein-centric analysis at the level of intact proteoforms—structurally distinct variants of proteins that arise from genetic mutations, alternative splicing, or post-translational modifications.

The platform introduces a multimodal trapping approach that enables precise control over ion reactions, supporting a wide range of fragmentation techniques. This flexibility allows researchers to tailor experiments to extract detailed structural information from complex biomolecules.

Rather than focusing solely on peptides or simplified representations of proteins, the system emphasizes intact protein analysis. This is particularly important for identifying proteoforms. These variants often play critical roles in disease but are difficult to detect using conventional approaches.

The timsOmni platform enables detailed mapping of such variations, including modifications, such as acetylation and glycosylation, that influence protein function and cellular signaling. By combining high sensitivity with advanced fragmentation methods, it allows researchers to generate comprehensive sequence information and localize modifications with precision.

Importantly, this capability extends beyond discovery into biopharma development and quality control. The ability to characterize therapeutic antibodies and other biologics at the proteoform level has significant implications for both efficacy and safety.

Supporting software further enhances this capability by translating complex spectral data into actionable insights. Advanced algorithms enable de novo sequencing, charge state assignment, and modification identification, making it easier for researchers to navigate the complexity of proteoform analysis.

Accelerating insights

As therapeutic modalities become more complex, the need for faster, more precise characterization tools has never been greater. David Curtin, vice president and general manager, biologics business, Waters Analytical Sciences, Waters Corporation, highlights how emerging platforms are enabling researchers to generate deeper insights earlier in the development cycle—when those insights can have the greatest impact.

As one example, Curtin describes the Xevo CDMS platform as a breakthrough in capability and accessibility. As the first dedicated benchtop charge-detection mass spectrometry system, it enables measurement across a wide spectrum of mega-mass biomolecules. Crucially, it supports “characterization in process development when decisions matter most,” Curtin says, allowing teams to act on high-quality data in real time.

Speed is one of its most transformative advantages. “Xevo CDMS delivers accurate analysis in less than 10 minutes,” Curtin explains. This represents a dramatic improvement over traditional workflows that could take hours, days, or even weeks when outsourced. The result is a shift to same-day decision-making, fundamentally changing how process development is executed and optimized.

Efficiency is another key differentiator. Curtin notes that “the system requires up to 100 times less sample than current methods,” addressing a long-standing limitation in biopharma research. With reduced sample demands, scientists can run more experiments per batch, leading to “lower cost, higher yields, fewer impurities, and faster time to market,” he says.

Beyond operational improvements, the platform unlocks new scientific possibilities. Curtin emphasizes that it delivers direct mass and charge measurements for individual 100-kilodalton to 150-megadalton molecules, including complex structures such as glycosylated proteins, viral vectors like AAV, and lipid nanoparticles. In many of these cases, “CDMS isn’t just a better option; it’s the only option,” Curtin says.

Ultimately, Curtin underscores the broader impact: researchers are now generating “fast, accurate orthogonal data” that validates existing approaches while opening entirely new lines of inquiry. Scientists, he says, are “asking and answering questions they couldn’t tackle before”—a powerful indicator of how this technology is advancing the development of therapies for diseases including cancer, heart disease, and Alzheimer’s.

From data to decisions

Across all these perspectives, a common theme emerges: MS is no longer defined by its ability to generate data, but by its ability to inform decisions. This clarity is transforming drug discovery. By revealing off-target effects, validating mechanisms of action, and identifying biomarkers at early stages, MS helps reduce uncertainty and improve success rates. It allows researchers to prioritize the most promising candidates and eliminate those unlikely to succeed.

As Robitaille puts it, the ultimate value of modern MS lies in “the ability to see meaningful biology early enough to act on it.” In an industry where time, cost, and complexity are ever-increasing, that capability might prove to be one of the most important advances of all.

The post Mass Spectrometry’s Discovery Revolution appeared first on GEN – Genetic Engineering and Biotechnology News.