Automation and AI Will Drive Next-Gen CAR T Manufacturing

The remarkable clinical success of CAR T therapies in blood cancers has validated the promise of engineered cell therapies. But according to Adam Janvier, PhD, head of cell therapy at eXmoor Pharma, the industry faces a crucial challenge: transforming highly personalized, labor-intensive manufacturing into scalable, commercially viable production systems.

“The next big thing that really needs to come through is how we can start tackling solid tumors,” Janvier says. “Until solid tumor is truly tackled, we’re always going to be missing that key next step of what CAR T has the promise to do.”

Although scientific hurdles remain, Janvier emphasizes that manufacturing constraints are equally pressing. Today’s autologous CAR T therapies rely on harvesting a patient’s own immune cells, engineering them outside the body, and reinfusing them after a manufacturing process that can stretch to two weeks. For critically ill patients, that timeline can prove devastating.

“We might fail a process because the donation just isn’t good enough,” he explains, referring to inconsistent starting material collected from heavily pretreated cancer patients. “Then we’ve got a four to six week vein-to-vein time due to manufacturing, testing, and logistics, where the patient might pass away during the period.”

The dual risks of manufacturing failure and lengthy turnaround times are pushing developers and contract development and manufacturing organizations (CDMOs) toward alternative strategies. Among the most promising are allogeneic, or off-the-shelf, CAR T therapies and emerging in vivo approaches that could eliminate ex vivo manufacturing altogether.

“There’s a lot of work going on now with in vivo CAR T,” Janvier says. “Instead of taking a blood donation as starting material, there is growing evidence that we could use the reprogramming technology directly with the patient, generating functional CAR T cells in situ.” Although such approaches remain early-stage, they represent a potential paradigm shift by reducing manufacturing time, simplifying logistics, and lowering costs.

Analytics and quality control also remain major bottlenecks. Current CAR T testing workflows rely heavily on expensive, time-consuming assays, including flow cytometry, qPCR, and tests to confirm the quality and safety of the lentivirus. Janvier believes that AI could eventually streamline many of these processes.

“One of the exciting technologies coming out is AI-based flow-cytometry approaches,” he says, pointing to emerging platforms that use label-free imaging and machine learning to characterize cells without fluorescent antibodies. “All of a sudden, you’re removing the need for antibodies and fluorophores, reducing the cost,” he says.

Still, Janvier argues that automation might ultimately become the defining factor in whether CAR T therapies can achieve widespread commercial adoption. Current cleanroom manufacturing remains highly manual, requiring specialized staff and flexible—but inefficient—facility layouts. “Once we approach commercial scale, batch costs need to have substantially decreased,” he says. “Automation can really support that.”

Janvier envisions future CDMOs operating sophisticated robotic manufacturing platforms capable of running around the clock while minimizing operator variability and contamination risk. However, implementing such systems will require substantial capital investment and new technical expertise. “These are not going to be inexpensive methods to implement into facilities,” he notes. “They’re going to be a large capital investment, and also a large people investment.”

Beyond manufacturing hardware, Janvier believes structural changes must begin much earlier in therapy development. Many CAR T programs originate in academic laboratories focused primarily on biological innovation rather than manufacturability or commercial scalability. “What can be missed there is the translation starting at the very beginning,” he says. “You need to start with the end in mind.”

That means considering GMP compatibility, scalability, cost-of-goods analysis, and automation readiness long before therapies enter clinical trials. Investors, Janvier adds, are increasingly demanding evidence that therapies can ultimately be manufactured at scale—not simply that the science is compelling.

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Real-World Implementation of EndoConnect in Brazilian Primary Care: Formative Study of Usability, Engagement, and Equity in Digital Endometriosis Care

Background: Endometriosis is a chronic gynecological condition affecting approximately 10% of women of reproductive age worldwide and is associated with chronic pelvic pain, infertility, and reduced quality of life. In Brazil’s Unified Health System (Sistema Único de Saúde [SUS]), diagnostic delays frequently range from 7 to 10 years and disproportionately affect socially vulnerable populations, including rural, low-income, Black, and Indigenous women. Digital health interventions have been proposed as scalable solutions; however, most available applications are developed in high-income settings and do not align with the structural and operational realities of low- and middle-income countries (LMICs). Objective: This study aimed to evaluate feasibility, usability, acceptability, and user engagement associated with the real-world implementation of EndoConnect Alpha in primary health care settings, and to explore preliminary patterns of change in symptom burden, knowledge, and care navigation. Methods: A single-arm, prospective, formative implementation study was conducted in 10 primary health care units in Ceará, Brazil. A convenience sample of 60 participants, including women with suspected or confirmed endometriosis and primary care professionals, used the platform over an 8-week period under real-world conditions. Usability (assessed using the System Usability Scale), acceptability (assessed using the Technology Acceptance Model), engagement metrics, and exploratory outcomes were assessed. All analyses were exploratory, with no control group and no causal inference. Results: High usability and acceptability were observed, with strong user engagement, including a 79% completion rate of educational modules and consistent platform use. Observed decreases in pelvic pain and anxiety were identified, alongside increases in disease-related knowledge, self-reported therapy adherence, and reported gynecological referrals. A positive association between usability and acceptability was also observed. These findings should be interpreted as exploratory signals given the study design. Descriptive subgroup analyses suggested more pronounced trends among rural participants and those with a lower education level. Conclusions: The real-world implementation of EndoConnect Alpha demonstrated high feasibility, usability, and acceptability within a public primary care setting in a middle-income country. Observed trends suggest potential benefits, particularly among underserved populations; however, causal inference cannot be established. These findings support further controlled evaluation and highlight the relevance of equity-oriented digital health strategies tailored to LMIC contexts.
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CGT Sector Opts for New Tech for Efficiency and Consistency

A maturing, data-rich and more commercially-focused cell and gene therapy (CGT) industry is finally embracing innovative manufacturing technologies, according to the head of U.K.-based industrial software firm, Autolomous.

Early CGT manufacturing relied heavily on technologies borrowed from biologics manufacturing, systems originally built for producing therapeutic proteins rather than patient-specific therapies. As a result, production processes were often highly manual, fragmented, and operator-dependent. Indeed, in many cases, manufacturing resembled a specialized laboratory process, rather than a scalable industrial operation.

But the situation is changing, says Autolomous CEO, Alexander Seyf, who argues CGT manufacturers are embracing innovation with product quality, cost control, and scalability in mind.

“As the industry moves beyond early clinical programs and toward commercialization, manufacturers are recognizing that traditional approaches simply won’t support the scale, consistency, or economics needed long term.

“There’s now much greater focus on automation, closed processing, and digital integration to improve reproducibility, reduce contamination risk, and lower the cost of goods,” Seyf tells GEN.

Increasingly, CGT firms are opting for purpose-built technologies, he says, citing automated cell handling, closed-system processing, robotic fill-finish, and integrated single-use platforms as examples.

“The broader shift is toward what many describe as ’CGT 4.0:’ applying the principles of Industry 4.0 to cell therapy manufacturing. The aim is to move away from highly manual, artisanal production toward scalable, repeatable manufacturing that can support broader patient access,” he adds.

Data

Data is also driving the adoption of new technologies. In cell and gene therapy production, large volumes of data are collected by different instruments, software platforms, QC systems, environmental monitoring tools, and manual inputs—many of which were never designed to communicate with one another.

In such circumstances, digital technologies provide manufacturers with an infrastructure that can gather process information and ensure it is usable, traceable, and reliable, Seyf says.

“Maintaining data integrity and full chain-of-custody visibility across fragmented systems can become incredibly difficult, particularly as operations scale.

“To solve this, companies are investing heavily in integrated digital architectures that bring manufacturing and quality data together into unified environments. Standardized data models, interoperable software platforms, and automated data capture are becoming increasingly important. Cloud infrastructure also plays a key role because it allows manufacturers to aggregate and analyze data across multiple facilities in real time,” Seyf says.

A typical, modern digital cell and gene therapy manufacturing setup includes systems that manage manufacturing execution, laboratory information, quality, electronic batch records, and cloud-based data platforms.

“Together, these systems help manage scheduling, batch tracking, compliance, traceability, and quality oversight in real time,” Seyf says, adding, “Modern facilities are also increasingly using process analytical technologies and integrated sensors to monitor critical process parameters continuously, rather than relying only on end-point testing.”

“A digital CGT manufacturing system is really about connectivity and visibility across the entire process. It’s not just about replacing paper records with electronic systems—it’s about creating an environment where manufacturing equipment, quality systems, analytics, and logistics are all connected and continuously sharing data.”

Material traceability

The need to keep track of cell and gene therapy raw materials is also fueling the adoption of innovative technologies, with patient-specific therapies being a case in point.

Seyf tells GEN, “Maintaining chain-of-identity and chain-of-custody is particularly important in CGT manufacturing, especially for autologous therapies where every batch is tied to an individual patient. That requires seamless integration between manufacturing systems, analytics platforms, and logistics operations.”

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Oligo Therapy Firms Moving Toward Less Expensive, High Production Methods

A wide range of new technologies offers the next generation of oligonucleotide manufacturers a choice in how they will commercially manufacture their products. That’s the view of a technical expert with experience working with small biotechs and large pharmaceutical companies.

According to Barrie Cassey, PhD, a former technical director at GSK, the tide of oligonucleotide therapies now reaching the clinical stage is driving companies to commit to cheaper high-production techniques, such as enzymatic ligation.

“What’s driving this technology is the ability to make [oligonucleotide therapies] at scale,” he says.

According to Cassey, most oligonucleotide drugs are currently produced by solid-phase techniques, but the capacity for commercial manufacturing is limited globally, with most therapies being for rare disease patients. However, looking at products in Phase III, 40% target large patient populations, Cassey says, and this rises to around 50% for products in Phase II.

“If you extrapolate, assuming 70% of these Phase III assets become commercial (which is pretty conservative), your total demand could rise to six to seven tons globally, which is more than all global capacity today,” he explains.

Overall, within eight years, global manufacturing demand could reach as high as 30 tons—far outstripping current manufacturing capabilities, Cassey says. Moreover, drugs targeting large patient populations (e.g., statins) have traditionally needed to be cheap to manufacture, he says.

To get around this, large pharmaceutical companies are increasingly looking at new techniques for oligonucleotide manufacture, such as experimenting with fragmentation versus whole molecule approaches. One company has invested $250 million in a manufacturing plant using enzymatic ligation, Cassey adds.

Overall, Cassey says, the take-home message is that new techniques are closer than many people think, with some companies already offering them for preclinical manufacture.

“These technologies are almost ready to go and, if someone wanted to use them in twelve months, it might be possible,” he says. “It’s just about getting the right conditions for everything to coalesce together.”

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Open-Source Up- and Downstream Modeling in a Unified Environment

A broad European industry/academic consortium, Inno4Vac, has developed a robust, open-source modeling platform that unifies upstream and downstream models and predicts product stability, all within a single environment. Unlike many open-source modeling programs, this one, called CADET-Hub, doesn’t need mad programming skills to use.

As Reinhard Liebers, PhD, a project manager at the European Vaccine Initiative, tells GEN, this work shows “that mechanistic modeling, digital twins, and stability prediction can be integrated into one collaborative platform to accelerate bioprocess development, improve process understanding, and reduce experimental workload.”

CADET-Hub is an integrated, modular, cloud-based modeling platform designed to help the biotech industry especially vaccine manufacturers quickly reconfigure their manufacturing platforms in response to emerging demands such as quickly-mutating pathogens. Its developers add that it can be used to help optimize a broad spectrum of bioprocesses.

Integration and ease of use—even for non-programmers—are standout features for this new platform. A key motivation for developing CADET-Hub was “to make the setting up and fine-tuning of biomanufacturing processes more efficient as well as reduce the technical overhead of setting up local software environments for bioprocess modeling,” according to a team led by Eric von Lieres, PhD, head of modeling and simulation at Forschungszentrum Jülich, and Liebers, in a recent paper. Therefore, each of the modules is web-based “in principle, with options for local deployment if desired, and a graphical user interface under development,” they tell GEN.

The upstream models are based on computational fluid dynamics and metabolic models for bioreactor simulations. The downstream models offer insights into centrifugation, filtration, and chromatography. CADET-Hub includes a dedicated stability forecasting model.

The research around CADET-Hub supports a shift from empirical trial-and-error development toward predictive, data-driven biomanufacturing with integrated upstream, downstream, control, and stability models,” Liebers says.

Overview of CADET-Hub and the broader CADET-Hub environment, which integrates standalone modeling, the open-source CADET framework within a JupyterHub-based online platform. [Inno4Vac]

Case studies

The team of authors around Liebers and von Lieres tested CADET-Hub under three scenarios.

As a way to model anion exchange capture of a recombinant subunit vaccine, they report that it functioned effectively as a collaborative workflow to share experimental data and mechanistic models, capturing meaningful nonlinear and multicomponent effects.

When modeling downstream filtration using ion exchange chromatography, CADET-Hub used explicit storage vessel models to close local material balances and transfer component concentrations in a structured way. It showed how upstream variability “can be propagated downstream to predict its impact on subsequent unit operations.” But, they add, “Experimental validation of the filtration model is still required.”

For process optimization and control, they point out, “Vaccine production requires a combination of sequence control and nonlinear model predictive control.” Overall, the case study showed a 50% improvement in upstream yield and a 20% improvement in downstream productivity that is attainable through model-based optimization.

To predict product stability under various conditions, the scientists combined hierarchical and advanced kinetic models within a Bayesian framework. They produced a “robust estimation of vaccine shelf life,” while meeting regulatory expectations and ICH Q12 principles.

“The next steps are to expand CADET-Hub toward fully end-to-end digital bioprocess models; to improve scalability, industrial deployment, and user interfaces; and to continue regulatory validation and adoption,” Liebers says.

CADET-Hub was created as part of the Inno4Vac vaccine development project and received funding from the Innovative Medicines Initiative/European Union/European Federation of Pharmaceutical Industries and Associations (IMI2/EU/EFPIA).

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<![CDATA[Experts discuss ketamine safety, monitoring, neurotoxicity concerns, and key considerations for psychiatrists treating depression at the ASCP Annual Meeting.]]>

Precancerous Adenomas Alter Gut Microbiome for Long Period

Research shows people who have precancerous polyps removed have an altered gut microbiome for around 12 years after undergoing surgery, which may explain why there is a significant risk of progressing to colorectal cancer in these individuals despite the surgery.

“Early detection and resection of adenomas through colonoscopy are critical strategies for preventing colorectal cancer. However, individuals with a history of adenoma resection remain at a higher risk of developing colorectal cancer than those without adenomas,” write lead author Mingyang Song, MBBS, ScD, associate professor of clinical epidemiology and nutrition at Harvard University, and colleagues in Cell Host & Microbe.

“Meta-analyses of stool metagenomes from diverse populations have identified consistent microbial biomarkers largely characteristic of later-stage colorectal cancer… Studies investigating the gut microbiome of adenoma patients and along the adenoma-carcinoma continuum have also reported distinct, dynamic microbial shifts from early neoplasia to advanced colorectal cancer.”

This earlier research suggests a link between the gut microbiome and colorectal cancer. It is possible some of these changes arise early in the disease process and act as drivers of the cancer, but what these changes look like remains unclear.

In this study, the researchers analyzed stool samples from 354 women who had colorectal adenomas removed an average of 12 years earlier and compared them to 354 matched controls without polyps. The team used shotgun metagenomic sequencing to analyze fecal metabolites and the composition of the gut microbiome.

The investigators then compared the microbiome data from the women who had adenomas removed and the controls to those from 1,045 people with colorectal cancer enrolled in earlier colorectal cancer studies.

Women who had precancerous polyps removed still showed some gut microbiome changes similar to those seen in colorectal cancer patients. The similarity was modest: only about 7% of the microbiome differences could be attributed to their disease history while 93% came from individual factors like diet, genetics, and lifestyle.

Overall, 31 bacterial species showed consistent changes in both adenoma and cancer cases compared to controls. For example, levels of Faecalibacterium prausnitzii, a bacteria thought to be protective of the gut due to anti-inflammatory properties, were low and levels of potentially harmful bacteria Flavonifractor plautii were enriched.

“The fact that colorectal cancer-associated gut microbial and metabolic features are still detectable a decade later suggests the gut microbiome may be part of sustained colorectal cancer risk,” said first author Ana Nogal, PhD, postdoctoral research fellow at Harvard, in a press statement. “Diet and lifestyle were closely tied to these microbes, raising the possibility that these habits could influence the gut environment in people at higher risk.”

The post Precancerous Adenomas Alter Gut Microbiome for Long Period appeared first on Inside Precision Medicine.

Opinion: MIT president: Why so many optimistic scientists are losing heart

Most successful scientists are optimists. They have to be, since the vast majority of experiments fail. In graduate school, I remember sitting in the lab at Rockefeller University in New York at 3 a.m., surrounded by stacks of culture dishes for growing cancer cells, none quite showing me what I hoped to find. But glimmers of interesting changes in the cells promised future success and made me feel the experiments wanted to work. That optimism drove me to keep trying. One day, they did work and I uncovered a new insight about a process in those cancer cells that no one had described before.

In 2026, there seem to be plenty of good reasons to be optimistic about science: Breakthroughs are everywhere.

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