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Mammographic AI Adds Predictive Power to Breast Cancer Risk Models

A study by Kaiser Permanente researchers has shown that integrating mammographic AI with polygenic risk scores and clinical risk models can improve breast cancer risk stratification, guiding both personalized breast cancer screening and chemoprevention.

The study, published in the Journal of the National Cancer Institute, is one of the largest and most diverse to evaluate the ability of the three approaches to predict breast cancer risk.

“Our goal is to improve our ability to assess a woman’s breast cancer risk so we can personalize breast cancer screening recommendations,” said lead author Vignesh Arasu, MD, PhD, a radiologist and research scientist at the Kaiser Permanente Division of Research. “Our study shows that each of the approaches identifies a distinct group of women, and that when all three risk tests are used, we increase our ability to differentiate high-risk and low-risk women and provide more personalized screening recommendations.”

The study included 82,957 women (75% non-Hispanic White, 9% Asian, 7% Latina, and 4% Black) who enrolled in the Kaiser Permanente Research Bank between 2003 to 2020. All the women had a recent negative mammogram, and none had previously been diagnosed with breast cancer or had a genetic mutation known to increase breast cancer risk.

The researchers calculated each woman’s breast cancer risk using three different approaches: The Mirai mammography AI risk score, which looks for risk-related imaging biomarkers; the Breast Cancer Surveillance Consortium version 3 clinical risk score, which considers factors such as age, race or ethnicity, family history of breast cancer, breast density, and body mass index; and the 313-SNP polygenic risk score (PRS) that assesses risk based on the presence or absence of 313 breast cancer-associated single nucleotide polymorphisms.

During 10 years of follow-up, 2471 (3%) women were diagnosed with invasive breast cancer or ductal carcinoma in situ.

Arasu and team report that the C-index for breast cancer prediction when all three risk scores were combined was 0.70.

“This means that if you take any women who actually will get breast cancer in the future and pass her information to a risk model, that model will say she has a higher risk about 70% of the time relative to women who won’t get cancer,” Arasu explained.

The C-index for the combined model was significantly higher than that for individual models with only the clinical risk score (0.62), which is used by most clinical practices, the PRS (0.61), or the Mirai score (0.66).

The C-index for the triple model was also significantly higher than that for a model that combined the clinical and the polygenic risk scores (0.66).

The increase in the C-index of 0.04 “represents a moderate but meaningful improvement in discrimination when incorporating all three risk domains compared with the clinical plus polygenic model alone,” Arasu told Inside Precision Medicine.

He added: “The C-index reflects overall model performance across all possible risk thresholds and is commonly used as an initial, global assessment when integrating new predictors, such as AI-derived measures, to gauge their added value. However, future work will focus on defining clinically actionable risk thresholds to better characterize how these improvements translate into real-world tradeoffs between benefit and harm.”

Importantly, the improvements in risk prediction were consistent over time and across four common self-reported racial/ethnic subgroups of Asian, Black, Latina, and White women, which the authors say indicates that incorporating mammographic AI and ancestry-adjusted PRS into clinical risk prediction models may benefit all women.

The study also found that among the women at highest risk for developing breast cancer, the clinical risk score alone identified 19% of the women who went on to develop breast cancer over a 10-year period while the combined model identified 26% of these women.

Although all three model types are becoming increasingly accessible and part of care, Arasu cautioned that “more research is needed to see the benefit of using all three scores before a program that uses all three should be implemented.”

He added: “We already are planning the next research step, which will be to study the combined model in the on-going national WISDOM clinical trial in the next 1-2 years. The WISDOM trial already uses a PRS and clinical risk score to assess risk. Now, we will be adding mammographic AI.”

“By identifying more accurately which women are truly at high-risk, we hope to find more breast cancers as early as possible, when they are most easily treatable,” said Arasu. “For women who are very high risk, there is also the potential to discuss, in addition to annual mammography, risk reduction with a medication, such as tamoxifen.”

The post Mammographic AI Adds Predictive Power to Breast Cancer Risk Models appeared first on Inside Precision Medicine.

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Highly Sensitive ctDNA Test Improves Detection of Residual Pancreatic Cancer

A highly sensitive blood test that detects traces of tumor DNA in patients with localized pancreatic cancer identified substantially more patients with residual disease than conventional liquid biopsy testing, according to a prospective study published in Clinical Cancer Research. The findings suggest that more sensitive detection of circulating tumor DNA (ctDNA) could improve risk stratification after chemotherapy and surgery and help identify patients who remain at high risk for recurrence despite reassuring imaging results.

Researchers at Northwestern Medicine evaluated digital droplet polymerase chain reaction (ddPCR), a liquid biopsy approach that detects specific KRAS mutations, against standard next-generation sequencing (NGS), which surveys hundreds of cancer-associated genes but with lower sensitivity. Because KRAS mutations drive more than 90% of pancreatic cancers, the investigators hypothesized that focusing on this single, biologically important target would allow detection of extremely low levels of circulating tumor DNA that broader sequencing approaches often miss.

“We’re able to detect very high sensitivity in the blood for pancreas cancer,” said senior author Akhil Chawla, MD, clinical associate professor of surgery at Northwestern University Feinberg School of Medicine and a complex surgical oncologist at Northwestern Medicine. “We’re looking for extremely low levels of the DNA in the plasma.”

The prospective study followed 106 patients with localized pancreatic cancer from diagnosis through chemotherapy and surgical resection. Blood samples were collected before treatment, after chemotherapy, and following surgery to determine whether changes in KRAS ctDNA reflected treatment response and predicted patient outcomes.

At diagnosis, ddPCR detected tumor-derived KRAS DNA in 65% of patients, compared with just 17% using conventional NGS. The differences became even more striking after treatment. Following chemotherapy, ddPCR detected ctDNA in 60% of patients, while NGS detected it in only 5%. After surgery, ddPCR remained positive in 56% of patients compared with 9% using standard sequencing.

“What this publication is going to show is that yes, we can detect it with both, but we are missing a significant number of patients with standard sequencing,” Chawla said.

According to the study, patients whose disease was detected only by ddPCR represented a previously hidden intermediate-risk group. These patients had a median overall survival of 27 months after diagnosis, compared with 41 months among patients who tested negative by both assays. The findings suggest that standard liquid biopsy testing may underestimate the presence of minimal residual disease in many patients who appear to have responded well to therapy.

“We’re missing up to 60% of patients,” Chawla said. “Even at the time of diagnosis, and after treatment—particularly where we think it looks like on a CT scan after a patient’s undergone chemotherapy and had their surgical resection—everything looks great, and even the blood test that looks at ctDNA looks great. In sixty percent of patients we were still able to detect low levels.”

He adds, “Our goal is to get rid of that disease forever. Unfortunately, even with the work that we’ve done, we’ve shown that 60% to 70% of patients have recurrence of that disease after chemotherapy and surgery.”

The study also demonstrated that ctDNA dynamics over the course of treatment carried important prognostic information. Rather than relying solely on whether ctDNA was present or absent, investigators found that changes in ctDNA levels reflected treatment response.

“We’ve been able to see it even with chemotherapy and surgery,” Chawla said. “Patients that have that significant decline in the marker—not just the presence or absence, but even if they go from a high level to a medium to low level—those patients actually are benefiting from that treatment.”

Conversely, patients whose ctDNA levels remained stable or increased during treatment experienced substantially worse outcomes.

Unlike broad NGS panels, which search for hundreds of genetic alterations simultaneously, ddPCR focuses on a limited number of mutations with far greater analytical sensitivity. Chawla emphasized that the innovation was not a new laboratory technology but rather a new application of an established one.

“DDPCR can detect a single mutation with 1,000 times more sensitivity,” he said. “When we do that deep dive in pancreas cancer, what we’re really trying to do is identify whether this biomarker can, number one, be detectable in a high percentage of patients; number two, is it prognostic… and number three, can it really tell us how well a treatment is working?”

Because the assay targets the same KRAS mutations that are the focus of emerging targeted therapies, Chawla believes the approach could become increasingly valuable as those therapies enter clinical practice.

“I think there’s a lot of value in this test, particularly as we enter the age of KRAS-targeted treatments,” he said. “Our biomarker actually targets the exact same gene.”

Currently, surveillance after surgery relies primarily on CT imaging performed every few months, leaving clinicians with limited tools for detecting microscopic residual disease before recurrence becomes radiographically apparent.

“What the standard of care today is, is we just get CT scans every three months to give us an idea of when this comes back—we’re just kind of sitting on our hands and waiting,” Chawla said. “This blood test gives us an opportunity to be more active in that surveillance, potentially be more adaptive in our treatment.”

 

The post Highly Sensitive ctDNA Test Improves Detection of Residual Pancreatic Cancer appeared first on Inside Precision Medicine.

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Stockholm3 Blood Test Detects More High-Risk Prostate Cancers Early 

A new blood test may help address one of the key challenges in prostate cancer detection—identifying aggressive forms of the disease early on. According to research led by a team at Karolinska Institutet, the Stockholm3 blood test detected more clinically significant cancer cases than the well-established, but problematic, Prostate Specific Antigen (PSA) screening test. 

The study appears in the Annals of Internal Medicine. Swedish researchers collaborated with teams from Europe and the U.S. on this work.

Prostate cancer is one of the top cancers among men globally, with an estimated 1.5 million new cases and 397,000 deaths annually. PSA testing has long been used for early detection. But, although PSA is prostate-specific, it is not cancer-specific. Elevated PSA levels can be caused by benign conditions as well as cancer and up to 50% of diagnosed aggressive prostate cancers are in men with low PSA values—below today’s cutoffs of PSA 3 ng/ml or PSA 4 ng/ml.

“There are a number of tests in development to improve on PSA alone. A lot of them, like Stockholm3, are fairly ingenuous and do a good job,” Mark Pomerantz told Inside Precision Medicine. He is a medical oncologist at the Dana-Farber Cancer Institute in Boston. Until one of these newer tests emerges as a winner, though, MRI is the gold standard for evaluating men with high PSA. “With MRI we can see the prostate in some detail,” Pomerantz said, “But it is expensive.”

The Karolinska-led researchers analyzed data from 12,670 men aged 50–74 from the population-based STHLM3-MRI study, which compared MRI-targeted and standard biopsy in men with elevated PSAs. In this more recent Annals study, the men were tested first with both PSA and Stockholm3 and followed for two years via national cancer registries, which allowed researchers to also identify cancer cases missed during the initial screening.

Stockholm3 detected 90 percent of aggressive cancer cases, compared to 74 percent for PSA.

“The test incorporates plasma protein biomarkers, genetic risk information from a polygenic risk score, and clinical factors such as age, family history, and prior biopsy history,” the study’s lead author Thorgerdur Palsdottir, told Inside Precision Medicine. Palsdottir is a researcher at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet. 

He added, “Together, these components provide a more comprehensive estimate of a risk of harboring clinically significant prostate cancer than PSA alone.” 

The components, he explained, map onto distinct biological axes rather than a single one. The kallikreins (PSA, free PSA, hK2) reflect prostate epithelial and tumor secretory activity, and the free-to-total PSA relationship helps separate benign enlargement from cancer. The polygenic score and family history capture inherited susceptibility—a man’s baseline predisposition—rather than anything about a tumor. GDF-15 is a stress-response marker that has been associated with more aggressive disease across several cancers. 

During the study follow-up, 443 men were diagnosed with clinically significant, i.e. aggressive, prostate cancer. Stockholm3 missed significantly fewer serious cancer cases than PSA, while the proportion of men incorrectly classified as high-risk was similar between the tests.

“These results point toward a potential change in how prostate cancer screening can be conducted. A more precise blood test could enable earlier detection of aggressive disease while reducing the number of unnecessary follow-up examinations and procedures,” said Palsdottir.

He adds that longer-term follow-up is needed to fully assess the effects on mortality and long-term outcomes. “The next important step is to evaluate longer-term outcomes, including disease progression, metastatic disease, and prostate cancer mortality.”

The post Stockholm3 Blood Test Detects More High-Risk Prostate Cancers Early  appeared first on Inside Precision Medicine.

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Boys, Masculinity, and the Looksmaxxing Trend  

By now, you’ve probably heard of the term looksmaxxing. Think pieces about the trend have popped up all over the internet. And in a recent episode of Saturday Night Live, comedians poked fun at lookmaxxing influencers obsessed with having the perfect male physique.

While this new social media craze may seem silly, it’s impacting more boys than you might think. In a study conducted last year that surveyed over 3,000 young men (ages 16–25) from the United States, United Kingdom, and Australia, nearly two-thirds of participants were regularly engaging with masculinity influencers.

Teen boys are being encouraged to change the way they look in order to fit a certain standard of attraction. The growing amount of looksmaxxing content they see online can have real effects on their self-esteem and mental health.   

What is looksmaxxing?  

Looksmaxxing originated nearly a decade ago in incel forums where men blamed their lack of romantic partners on the belief that female sexual selection is primarily based on physical qualities. So men who aren’t born with traits desirable to women are doomed to fail romantically. While traditional incels wallow in this fate, looksmaxxers seek to enhance their appearance to become more attractive. Their community claims that there is a universal standard for what the ideal man (and woman) should look like.

This is determined by a rating system called the PSL scale — the name being an amalgamation of three prominent misogynistic incel forums of the 2010s. There are many factors that go into the scaling, such as eye shape, jaw size, nose angle, and body fat percentage. Along this scale, you can land in four categories: subhuman, normie, Chadlite, and Chad (the ultimate catch).

During the pandemic, looksmaxxing went mainstream, merging with “manosphere” content on social media platforms like TikTok and Instagram. The trend became less about the ability to attract women and more of a competition among boys and men as they engaged in mog-offs — online contests where people have their faces analyzed and compared by facial recognition software to determine who’s better looking.

Self-improvement practices have gained popularity among boys. Some are considered to be softmaxxing, like developing skincare routines or eating high-protein diets, and others to be hardmaxxing, like using growth hormones or getting cosmetic surgery.

Prominent young influencers like Clavicular represent the extreme side of looksmaxxing. He practices bonesmashing (using a hammer on facial bones to try to form more angular features), injects himself with testosterone, and takes meth to maintain a low body fat percentage while still having a muscular physique.

Looksmaxxing and new beauty standards

The rise of looksmaxxing seems to have a caused a ripple effect among teen boys. While the ideal look has centered on big muscles and washboard abs for decades, there’s now an added pressure on facial beauty that’s typically been reserved for girls.

“With some of the teen boys I work with, most of whom already have self-esteem issues, I think there is a lot more concern about how they look,” observes Alnardo Martinez, LMHC, director of the Pediatric OCD Intensive Program and a mental health counselor at the Child Mind Institute. “They want to have the strong jaw, really big muscles, clear skin, and a perfect haircut.”

However, Martinez notes that it sometimes take a while for boys  to admit that they feel this pressure. They may insist that they don’t really care about that stuff. “But then, maybe a few months later, it comes out that there is a lot of comparison. They’re spending a lot of time in front of the mirror or in the bathroom trying to create this perfect image,” he observes.

What teen boys think about looksmaxxing and self-improvement

We talked to young men who were critical of Clavicular and the impact looksmaxxing can have on teens but were positive about engaging in some form of physical self-improvement.

Wyatt, now 19, remembers comparing his jawline to his peers’ when he was in 7th grade. “I just felt like they had really sharp jawlines. And I was just like, ‘Oh, I want to get closer to that.’” He would also come across TikToks advertising rubber chewing blocks and chin exercises meant to strengthen the jawline.

And so, Wyatt began to do jaw exercises he’d found online, reciting the alphabet while stretching out the muscles. “I would go through my Zoom classes throughout the day and then after that was done, I’d just go into the bathroom and go through the whole exercise. It would take like an hour sometimes,” he recalls. “It turned into more like a self-care, self-improvement session. I would do that every day after my classes. I didn’t feel like I was done with school until I finished my jawline routine.” He took photos to document his progress.  

Wyatt feels like the routine had a positive effect, because he was able to see an improvement. “I felt more satisfied with myself, a little more confident.”

Lev, now 19, remembers wanting to have some control over his body when going through puberty in high school. “Puberty is not a straightforward process. It’s not all peaches and cream. Your body changes, and it can be uncomfortable,” he explains. “But with lifting and strength training, it was very exciting to see this, you know, man energy that came out of it. I wanted to harness that and really take it by the reins. Have some agency as a man.”

And while he rejects the extreme parts of looksmaxxing, Lev does regularly practice self-improvement through weight lifting, skin care routines, and taking GLP-1 weight loss medication.

How looksmaxxing can impact boys’ mental health

Since looksmaxxing places such a strong emphasis on achieving a very specific look, clinicians are concerned about its influence on teens. “Self-esteem is pretty fragile during puberty,” Martinez says. “There’s already a ton of comparison and perceived flaws that teens don’t love about themselves.”

These insecurities can be exacerbated by the type of content teens engage with online, Martinez explains. Along with ChatGPT bots specifically designed to judge aesthetics, Reddit threads such as r/Mewing and websites like Looksmaxxing Forum encourage boys to post pictures of their faces and bodies to get rated by their peers. Boys as young as 13 visit these forums, posting pictures and asking for tips on how to improve their looks.

“These are generally places where people are already pretty harsh and critical. These boys are receiving a lot more ‘confirmation’ around the perceived things that are wrong with them or that they need to change,” Martinez says. “And it just feeds into the already present negative self-image and self-talk.”

He explains that this type of social media engagement can also compound underlying mental health issues like depression and social anxiety. “They might be less likely to go out and talk to people because they’re thinking, ‘Everyone is going to see this one thing that everyone else has told me is wrong with me. So now I can’t go out,’”he says.

Martinez is also concerned that online content can negatively affect teens with body dysmorphic disorder (BDD). “If they think they have a big nose, for example, they might go on these Reddits and ask, ‘What does my nose look like? Is it too big?’ There are trolls out there. Someone is going to say yes and then that’s going to make the BDD symptoms even worse.”

When behaviors might be concerning

In some ways, teen boys taking part in more self-improvement practices could be seen as a good thing. They’re exercising, taking care of their skin, and eating more balanced diets. The issues begin when these types of practices turn into obsession. And given the underlying ideology of looksmaxxing and the nature of social media, things can become unhealthy.

According to Martinez, there are some changes in behavior to look out for that indicate you might want to step in.

One clear change, he says, is a noticeable shift in the amount of time they’re spending on grooming themselves. “Maybe they were someone who would typically just get up and run out the door without washing their face,” he says. “But now they’re spending a lot more time in the bathroom and asking a lot of questions about how they look.”

Another warning sign can be a big change in personality. “Irritability is a big one that we’ll see a lot,” he says. “They’re unhappy with how they look, so this increases a general level of irritation.”

These behaviors paired with an unusual uptick in time spent on social media, Martinez explains, can be a sign that something’s wrong and support is needed.

How to support your child

If you’re worried that your child might be engaging in looksmaxxing-related behaviors to an unhealthy degree, says Martinez, there are a few things you can do:

  • Open communication. Martinez suggests approaching your child with curiosity. “You could start the conversation by saying something like, ‘So have you heard about this? What do you think about it? Have you ever had any thoughts yourself about how you look or desires to change your body or face?’ And then give them some space to be open and vulnerable about it. Validate their experience.” 
  • Find out where your child is getting their information. “Read it together, talk about it, and see what your child thinks about it,” Martinez advises. “And if it’s promoting something dangerous, then you can talk to them about how those practices can be harmful and what could actually happen if they do some of those things.”
  • Encourage male role models. “There’s a patient I work with now who doesn’t have a present dad,” Martinez explains. “His mom tries to talk to him about things like body image, but he feels like she doesn’t understand and can’t relate. So having someone that he can talk to and be open about this stuff with, especially someone who can also share their own struggles, can be really helpful.”
  • Seek help from a mental health professional. This is especially important if you find out that your child has been engaging in extreme forms of looksmaxxing such as bonesmashing or starvemaxxing. Martinez recommends looking for a clinician who specializes in body image or body dysmorphic disorder.

A lot of parenting comes down to open communication around what your kids are seeing and what they’re feeling. We all have things about our bodies that we might not like and wish we could change, says Martinez, and it can help to normalize those feelings. “And then you can discuss how they can make changes in healthy ways,” he suggests. “Go over what’s a realistic change and what’s a dangerous change.”

The post Boys, Masculinity, and the Looksmaxxing Trend   appeared first on Child Mind Institute.

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Digitize or Fall Behind

Bioprocessing companies risk slowing scientific progress unless they embrace digital-data capture and greater collaboration, according to Alexander Seyf, CEO of Autolomous, a company developing digital manufacturing solutions for cell and gene therapies.

Speaking about the industry’s biggest challenges, Seyf describes poor data management as the “elephant in the room,” arguing that too much crucial information remains trapped in paper records, spreadsheets, and isolated systems.

“Everybody wants to have AI,” Seyf says. “But where do you have your data? If it’s in binders, there’s not much you can do.”

According to Seyf, the path toward more efficient manufacturing, stronger clinical outcomes, and meaningful AI applications begins with digitizing information from the earliest stages of research. He believes many organizations make the mistake of waiting until their science is mature before investing in digital infrastructure. “The sooner you start, the better it is,” he says. “Pen and paper do not prevail, and pen and paper do not transfer.”

Seyf argues that the consequences extend far beyond operational inefficiencies. When data remain inaccessible or fragmented, researchers lose opportunities to learn from past experiments, identify patterns, and accelerate scientific discovery. He stresses that the industry must become more willing to share non-commercially sensitive knowledge, particularly in areas such as rare diseases and advanced therapies, where patient populations are limited. “We are all here to serve patients,” he says. “Protect your intellectual property, but also share the learnings.”

One of his strongest criticisms is directed at the scientific community’s tendency to focus almost exclusively on successful outcomes. Seyf believes failed studies and unsuccessful trials often contain lessons that could prevent others from repeating the same mistakes. “A lot of publications want to publicize only the good news,” he says. “That’s fundamentally wrong. We need to learn from failures.”

To illustrate his point, Seyf compares the biotechnology sector with the aviation industry. Modern airlines routinely share information about incidents and technical problems to prevent future accidents, creating a culture of collective learning and safety. “If something goes wrong, everybody in the world knows about it and knows how it was managed,” he says. “We are also dealing with people’s lives. The only way for us to improve is to share.”

Seyf also highlights the growing role of AI in healthcare. Although consumer AI systems have benefited from vast amounts of publicly available information, healthcare still operates with a relatively small pool of accessible data, he says. Expanding that foundation, he argues, could unlock major advances in diagnosis, drug development, and personalized medicine. “Imagine what we could do,” he says. “The progression of science is unlimited.”

For commercial bioprocessors, his recommendation is straightforward: digitize from day one. Capturing research, development, manufacturing, and clinical data in digital formats not only improves collaboration but also preserves institutional knowledge when employees move on. “Every time a scientist leaves, the knowledge goes with them,” Seyf says. “But when it is digital, the knowledge stays with the company.”

As cell and gene therapies continue to evolve, Seyf believes the industry faces a choice. It can continue operating in silos, or it can embrace transparency, digitalization, and collaboration to speed innovation and deliver better outcomes for patients. “The reason humanity has progressed,” he says, “is because we shared.”

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Standardizing Personalized CRISPR Gene-Editing Therapies

The revolutionary success of Baby KJ, the first patient to be treated with a personalized CRISPR gene-editing therapy, is spurring the industry to develop platforms for standardizing the manufacturing of future individualized therapies.

That’s the topic of a talk by Kok-Seong Lim, PhD, a pharmaceutical leader in CMC development, at the Bioprocessing Summit in Boston.

“Baby KJ was the first proof that individualized gene editing therapy was doable, and, at the same time, in the background, there are manufacturing platforms now being set up that maybe we’re not hearing so much about in the media,” he says.

According to Lim, manufacturers seeking to develop standardized platforms for personalized CRISPR gene-editing therapies using liquid nanoparticles (LNP), the same technology used for Baby KJ, will need to “lock in” their lipid formulation they’re going to use for future manufacturing, which may vary depending on the target organ and therapeutic indication.

After selecting their raw materials, they will also need to lock in their manufacturing process parameters, such as the microfluidic mixing conditions and lipid compositions. Likewise, he says, although the target gene may need to be customized for different patients, certain core components, such as the mRNA encoding the CRISPR-Cas enzyme, could remain unchanged across multiple patients.

This type of standardization may help establish a more scalable and reproducible manufacturing platform for personalized gene-editing therapies, he believes.

Going forward, Lim says, eventually companies may need to look at standardizing their regulatory CMC data package for regulatory filing, such as determining the appropriate extent of their impurity profiling and the overall scope of stability studies.

“Impurity profiling may not need to be as extensive for individualized and personalized treatments because they’re manufactured for a single patient only and the stability requirements may only need to support the timeframe needed for the patient’s treatment,” he says.

Lim adds that the Innovative Genomics Institute (IGI), Penn Medicine, and their collaborators, who treated Baby KJ, are currently working toward clinical trials to treat the next group of patients, but details of the specific LNP configurations for each future patient have not been disclosed.

As well as talking about LNPs, Lim will also discuss AAV technology for personalized CRISPR gene-editing therapies. The technology, he explains, is less popular within the industry than LNPs, due to concerns about potential toxicity, side effects, and manufacturing complexity, but it still merits consideration as a platform technology when it delivers patient benefits.

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Blueprint to Fill the Manufacturing Talent Gap

As biopharmaceutical companies expand facilities and reshore some manufacturing operations, the industry faces a shortfall of trained workers for its manufacturing facilities. While the Bureau of Labor Statistics recently predicted some 19,000 jobs would be created, PhRMA last year predicted the creation of 100,000 new jobs. Both predictions leave a gap between those jobs and the trained workforce.

To close the gap, Manus, a next-gen industrial biotechnology company, and BioMADE have developed an apprenticeship program that can become a blueprint for other companies to develop their own training. “The program can be scaled so development for other [companies] can be faster, down the road,” says Maren Wehrs, PhD, program manager at BioMADE.

“We are trying to build a fairly comprehensive training program that spans fermentation operations as well as downstream purification,” Christine Santos, PhD, CTO, Manus, tells GEN.

Focus: Hands-on learning

“It’s focused on hands-on experiential learning,” Santos continues, “with an extensive curriculum that will include deep dives on the practical aspects of running the equipment, such as so sterilization, safety, contamination control, process monitoring, and analytics. It will also delve into some of the technical aspects, like scale-up principles, as well as decision-making, problem-solving, teamwork, and communications.”

The work occurs at a Manus pilot facility in Augusta, GA. The first cohort starts in July and completes at year’s end, with another cohort beginning in January. After 18 months, “We hope to have a blueprint for an apprenticeship program that could be deployed at any other facility,” Santos says, including new BioMADE pilot facilities or those of other companies.

“We would offer access to the curriculum and the blueprint for [others] to deploy. We’ve spent the past few months formalizing the curriculum,” Santos says. It was developed with input from the University of Georgia, but apprentices needn’t be enrolled in a university program to participate.

Manus’ interest in apprenticeships stems from its 2018 acquisition of a decommissioned NutraSweet manufacturing facility in Augusta for its cell factories and bioprocesses.

“We had the task of recommissioning the facility and rebuilding the workforce to operate it,” Santos recounts. “We were able to rehire some of the NutraSweet employees [and regain their institutional knowledge], but to build out further, we had a huge challenge finding workers who were trained for biomanufacturing operations. We had to invest in a lot of hands-on training.”

This program is one of a few offered directly by a biomanufacturer. More commonly, companies participate in workforce training consortia to develop potential manufacturing workers.

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Trait Combining Key to More Effective Vector Production Hosts

HEK293 cells may be the most common host used in viral vector production, but they are far from ideal, says the author of a new study, who argues that gene therapy firms will need more effective alternatives to support commercial growth.

The study, by a team at University College Dublin and services firm APC, examined the manufacturing systems used to make the recombinant adeno-associated viruses (rAAVs) on which many gene therapies rely.

And the key finding is that not one of the eight commercial cell lines used to date—including the most widely-used line, HEK293—is ideal.

Lead author, James Conheady, from APC, tells GEN, “Current rAAV production methods using existing cell lines struggle to meet clinical demands, contributing to the expensive price-tag associated with rAAV-based gene therapies.

“Novel cell lines may be able to produce rAAVs at higher yields and/or with improved quality, which ultimately could help make these therapies more accessible to the people who need them.”

Shortcomings

To date, eight different host cell systems have been used to produce rAAVs, with each having strengths and weaknesses.

For example, some cell lines generate rAAV capsids that do not contain the desired genetic material. These empty vectors are a problem because they generate an immune response without providing a therapeutic effect.

Other cell lines struggle to make enough capsids. For example, the recommended dose for systemically delivered gene therapies is upwards of 1 × 1014 vg/kg of a patient’s bodyweight. The yield per production run for HEK293 cells is only around 1010.

Cost is another issue.

According to Conheady and co-authors, the GMP-grade plasmids and transfection reagents used to modify cell lines such that the vectors they produce contain the genes of interest account for a significant proportion of the price of the resulting therapies.

Alternative systems

Given these shortcomings, it is no surprise that the search for more effective alternative hosts is already underway.

Conheady says, “At the end of the day, rAAV manufacturers are all looking for the same things from their upstream process—high titers, improved full/empty ratios, and transduction rates.”

Current cell line development efforts are focused on combining desirable traits, Conheady adds, with characteristics such as resistance to apoptosis, diminished antiviral immune response, and secretion profiles being among the most sought after.

“Many of the traits identified in this review are aligned with modifications that have been shown to be beneficial in the context of rAAV production in HEK293 cells. For example, secretion of vector particles from the cell into the production medium can greatly simplify downstream operations and can be influenced by knocking out genes involved in endosomal trafficking.

“The ideal cell line should also be resistant to transfection and virus-induced apoptosis, to produce significant vector quantities. Knockout of the pro-apoptotic BAX and BAK1 genes has been shown to improve vector yields,” he says.

Whether industry will ever see these efforts pay off and agree on the “ideal” cell line remains to be seen, according to Conheady.

“Manufacturers will require significant grounds to agree on a standardized approach, a novel cell line may need to vastly outperform all others in relation to yield and quality characteristics—as the saying goes, ‘you stick with what you have until you have better’.”

The post Trait Combining Key to More Effective Vector Production Hosts appeared first on GEN – Genetic Engineering and Biotechnology News.

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LLMs are stuck in a groupthink groove. This startup is trying to get them out.

Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9.

That won’t work every time—but if it did for you, you may wonder if I have superpowers. I don’t.

The truth is that most large language models are stuck in a rut. They are far more predictable and far less creative in their responses than you might expect. That’s fine for tasks like coding or research, but groupthink is a problem when you’re brainstorming or planning your next vacation.

The Australian startup Springboards has a solution. It built an LLM called Flint, which has been trained to come up with a wider variety of responses than mainstream LLMs to open-ended questions such as “Where should I go in Europe?”

“Most language models are fighting hallucinations,” says Springboards cofounder and CEO Pip Bingemann. “We welcome them.”

Bingemann introduced me to the random number game when he first showed me his company’s new model. It felt like watching an illusionist with a deck of cards. “This is our sales trick, and it works every single time,” he says.

After ChatGPT and Claude both gave their 7s, Bingemann turned to Flint. It too came back with 7: “Aha, of course that was going to happen, but it’s okay—7 is a legitimate answer.” He restarted the session and prompted again: ChatGPT gave 7, Claude gave 7, Flint gave 3.7916.

Run your way

It’s not just numbers. When Bingemann asked ChatGPT and Claude to name a type of car, he predicted that it would be a Toyota or a Honda—and he was right. Flint came up with a Ford F-150. “There’s all this lost information that doesn’t get served up in these models,” he says. “They’re just as capable of saying a Buick or a Tesla. They just don’t—they’re biased.”

Bingemann sent one last prompt to each of the three models: “Give me a tagline for a campaign for New Balance running shoes. Just the tagline.” Claude: “Run your way.” ChatGPT: “Run your way.” Flint: “Built to last, run to win.” It won’t win any awards, but at least it’s different.

This weird limitation of LLMs is starting to get more attention. In November a team of researchers put out a paper, titled “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond),” that exposed a remarkable degree of repetition not only in the answers from individual LLMs but between them as well. They found that different LLMs converged on very similar answers when prompted with open-ended questions.

It’s not clear exactly why this happens, but the researchers speculate it’s because most LLMs today are trained in similar ways on similar data to do similar tasks. The team won the best paper award at NeurIPS, a major AI conference.

When the researchers asked 25 different LLMs (including models from the top US firms as well as open-source models from China and elsewhere) 50 times each to write a metaphor about time, most of the 1,250 responses were a version of “Time is a river” or “Time is a weaver.”

(I asked some of my colleagues the same question and six people gave me six different answers. My highlight: “Time is a favorite sweatshirt, shaped by a lifetime of wear.”)

When you look for it, you see repetition everywhere, says Kieran Browne, cofounder and CTO at Springboards. “The way that most chat interfaces are designed, it makes it feel like you’re having a personal conversation,” he says. “I think most people don’t really realize the extent to which they are getting the same stuff as everybody else.”

Take another example: “What should I name my band?” Most models will say something involving “glass,” “neon,” “velvet,” or “static,” says Browne.  

When I tried it, ChatGPT spat out a list of 56 band names. At the top was “Glass Harbor.” Skimming through, I found “Static Empire,” “Neon Hearts,” and “Velvet Echo.” I asked Gemini; it gave me 15 suggestions, including “Static Horizon.”

Some of the suggestions looked pretty cool, though. ChatGPT’s “Sofa Astronauts” caught my eye, so I googled it—and found that a band called Sofa Astronauts already exists. 

(OpenAI says that training models to give reliable and coherent answers can lead them to converge around familiar, high-probability responses and that pushing harder for novelty can lead to weaker or less reliable responses. It also notes that the “Artificial Hivemind” paper studied models from 2024 that have since been updated.)

Creative catapult

Springboards has developed a tool backed by a selection of LLMs, including ChatGPT and Claude, that creative professionals in advertising or marketing can use to brainstorm ideas. The tool lets you drag around text produced by different models, picking the bits that you like and combining them into something new—in theory. Springboards is pitching Flint as an alternative model that users of its tool can select when looking for more variety.

Zoe Scaman, founder of the business strategy startup Bodacious and chief strategy officer at 77X, a direct-to-fan marketing platform set up by Luka Dončić of the LA Lakers, has been trying it out. “I find it really useful for throwing me in completely different directions,” she says. “I use it if I want to catapult myself all over the place.”

In one test, Scaman pitted Flint against Claude, Gemini, and ChatGPT by giving each of the models a classic MBA case study: How would you reinvent a finance company for today’s youth? The three mainstream models all went down the same path, she says: “You know, we need to teach financial literacy in a fun and funky way—well, that’s nothing new.”

But Flint came up with something different, suggesting that the whole concept of wealth accumulation should get a rebrand. “That was really interesting,” says Scaman.

She notes that Flint is still a prototype and doesn’t work all the time. “It sometimes falls over when you start pushing it too far,” she says. “But I think that the premise behind it is really powerful.”

Taking the temperature

Springboards built Flint on top of Qwen 3, an open-source model from the Chinese tech giant Alibaba. “We’re a small team,” says Browne. “Training a foundation model is not on the table for us. It’s just too expensive.”

Most LLMs have settings that let you adjust the level of randomness in their output. The most common is called temperature. “Obviously, that was one of the first things we explored, because that’s what people tell you: If you want more creativity, you turn up the temperature,” says Browne.

But changing those settings can also make models incoherent. Dialing up the temperature on one of OpenAI’s models to its maximum setting made it produce responses that switched from English into code halfway through a sentence, says Browne.

Springboards realized that parameters were blunt instruments for what it wanted to do. It does not make sense to dial up the randomness across the board; you only want to boost it at specific points in its output, he says.

For example, when you ask a chatbot “Where should I go in Europe?” the model only needs to tweak the randomness just before it names a destination, not for every word in its response.

To make Flint do this, Springboards trained its version of Qwen 3 to identify the points in its output where more variety was possible and fill those spots with words or phrases that were a little more random.

“Flint’s programmed to throw an oddball in. It’s more of an invitation to think wider,” says Maximilian Weigl, cofounder and chief strategy officer at Uncommon, a marketing firm. “That’s super interesting.”

Weigl’s team uses Flint alongside ChatGPT, Claude, and Gemini. “You can’t really create something boundary-breaking with tools that pull you back to the average,” he says. 

And yet Weigl notes that nine times out of 10 the average is fine. You don’t always need to reach for extremes with something like Flint, he says: “Most people are fine with good enough. They want to see mass-market familiar things.”

Weigl also cautions against using any LLM too much. “I have a big problem when people rely on the output from any AI, including Flint,” he says. “If I saw people on my team copy-pasting something from AI, I’d be like, ‘That’s not your job! Think, talk to other people, use your own voice.’”

For now, Flint is aimed at advertisers and marketers because those are Springboards’s customers. But Bingemann and Browne insist that a lack of variety is a problem for anyone using chatbots.

The idea is to give people the choice and leave it to them to decide if the result is good or not, says Bingemann. “Variety is great when you’re trying to spark ideas,” he says. “Let’s go down this route instead of letting the machines do it all and ending up in a gray, boring world.”

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