Codon Optimization Isn’t Equal: Benchmarking Gene Design for Antibody Expression



Image of Justin Byers

Justin Byers

Founder and CEO
Axio BioPharma

Panelist

Image of Justin Byers

Justin Byers

Justin Byers is the founder and CEO of Axio BioPharma. He holds a BS in biochemistry and molecular biology from Illinois State University and has held leadership roles at Illumina, Danaher, and Fujifilm. Throughout his career, Byers has led commercial, operational, and cross-functional initiatives supporting biologics programs from early development through manufacturing. He has worked closely with scientific teams to scale workflows, improve process rigor, and align technical execution with strategic objectives. At Axio, Byers oversees corporate strategy, partnerships, and scientific direction. His focus is positioning the company at the intersection of structured data and biologics workflow execution. Axio is accelerating biologics development through mAb production services for R&D while partnering with innovators and CDMOs to ensure the data required for rigorous decision making and a digitally enabled future is generated, structured, and accessible.



Image of Daniel Lin-Arlow, PhD

Daniel Lin-Arlow, PhD

Chief Scientific Officer and Co-founder
Ansa Biotechnologies

Panelist

Image of Daniel Lin-Arlow, PhD

Daniel Lin-Arlow, PhD

Daniel Lin-Arlow, PhD, is a scientist-entrepreneur with deep expertise in synthetic biology and biophysics. Motivated by firsthand challenges in obtaining DNA constructs for metabolic engineering in graduate school, he is deeply committed to providing scientists with the DNA constructs they need for their research. As Ansa’s founding CEO, Lin-Arlow grew the company from two employees in 2018 to more than 70 by 2024, raising over $130 million in venture capital and grant funding to support technology development and commercialization. He transitioned to the role of chief scientific officer in 2024, where he leads the development of new applications of the company’s technologies. Lin-Arlow received his PhD from the University of California, Berkeley for his work in Jay Keasling’s lab for developing the DNA synthesis technology commercialized by Ansa. Prior to graduate school, he was a scientific associate at D.E. Shaw Research where he studied the biophysical properties of G protein-coupled receptors, including how drugs bind and modulate their activity. Dan began his scientific career at MIT, where he earned dual SB degrees in math with computer science and biology, and developed computation tools for the analysis of regulation of gene expression at the Broad Institute of MIT and Harvard. Lin-Arlow is a co-inventor of nine patent families and has co-authored scientific publications in Nature, Science, Cell, PNAS, and Nature Biotechnology.



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Antibody expression titers are key drivers of screening efficiency in discovery, developability, manufacturing economics, and development timelines. Although it is possible to address poor antibody expression by increasing overall batch size and optimizing downstream processes, the root cause often lies in the underlying DNA sequences. Controlled benchmarking studies are helpful for systematically evaluating DNA construct design decisions that impact titers.

In this GEN webinar, Justin Byers and Daniel Lin-Arlow, PhD, examine how enzymatic DNA synthesis and DNA construct design mitigate antibody expression challenges.

Byers will walk through a controlled benchmarking study of codon-optimization approaches, including details of the study design and how structured, gene-to-protein workflows can help identify optimal constructs before they become downstream problems. He will show that under matched CHO and HEK293 conditions, antibody constructs codon-optimized with an AI codon language model had consistently higher transient expression titers than other approaches. The AI codon-optimized sequences contained “complex” features such as repeats and GC skew that challenge traditional gene synthesis processes but were readily manufactured by Ansa’s DNA synthesis platform. These results suggest that complex sequence features can be important for optimal gene expression, which makes the ability to manufacture them as relevant as the codon strategy.

Lin-Arlow will present Ansa’s enzymatic DNA synthesis technology and the benefits to clients working on antibody production, cell and gene therapies, and other synthetic biology applications. Key takeaways include:

  • An AI-powered codon optimization strategy that measurably improves transient antibody expression yield
  • Why controlled side-by-side benchmarking under standardized conditions is the only reliable way to objectively evaluate DNA construct design choices
  • How integrating rigorous sequence evaluation upstream compresses timelines and reduces the risks of expression failures late in development
  • How Ansa’s fully enzymatic DNA synthesis addresses complex sequences, including: High or low GC content, secondary structures, inverted terminal repeats (ITRs), and homopolymers
  • The Ansa On-Time Guarantee—DNA orders shipped on time, or the complete order is free

A live Q&A session will follow the presentation offering you a chance to pose questions to our expert panelists.

Produced with support from:

ANSA Biotechnology logo

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Multiple Sclerosis Myelin Loss Revealed by Transcriptomic Analysis in Mice

More than one million people across the United States live with multiple sclerosis (MS), a disease that affects the brain, optic nerves, and spine. MS is characterized by overwhelming fatigue, muscle spasms, and vision problems, which can flare up and then subside over days, months, or even years. Studying the underlying damage to the nervous system is key to identifying new treatment paradigms for MS. 

A new study published in Nature Communications titled, “A comparative transcriptomic analysis of mouse demyelination models and multiple sclerosis lesions,” compares two prevailing models, cuprizone (CPZ) and lysophosphatidylcholine (LPC), for the study of myelin loss and regeneration in an MS mouse model. 

Katrina Adams, PhD, Gallagher Assistant Professor at University of Notre Dame, studies the role of the loss and regeneration of myelin on MS progression. As a fatty substance protects nerve cells, myelin envelopes the axons of the brain as they route the electrical signals that carry information throughout the nervous system. The damage and swelling that follow myelin loss in MS form distinct “lesions,” which vary in size, number and location in the nervous system. 

“Our analysis of these two models of myelin loss and regeneration provides a road map based on robust scientific evidence that we hope will advance the study of MS and related diseases,” said Adams. 

While both CPZ and LPC models degrade myelin, the timeline and localization of myelin loss varies. CPZ causes widespread loss of myelin over several weeks while LPC induces a lesion within days. This new research, which was funded by the National Multiple Sclerosis Society, points to specific scenarios in which one model is better suited, depending on which aspect of MS is under investigation. 

“If you’re studying the myelin-producing cells and what’s happening to them in MS—are they stressed, dying or trying to repair?—CPZ is better, since the loss of myelin is more gradual,” Adams said. “For studying the immune cells that respond to the myelin loss, LPC may be better, since the immune response is more aggressive than in CPZ.” 

The team also analyzed the resulting lesions from each preclinical model alongside data obtained from human MS tissue samples. Genetic maps of each type of tissue using single-cell RNA sequencing were constructed to examine the genetic changes that occurred in response to demyelination. 

“By matching each model to features seen in diseased tissue from real patients, we can be sure that we’re targeting things that are actually causing disease in human patients,” Adams said. “There are so many potential paths to follow, so we want to make sure that the path chosen has direct relevance to MS patients.” 

In addition to phenotypic differences, the genetic changes in diseased cells vary between the two models, an area of future exploration for the Adams research group. 

Since MS flare-ups are primarily triggered by the immune system’s reaction to lesions, current clinical treatments focus on quelling this autoimmune response. The regeneration of lost myelin within MS lesions remains a promising yet unrealized drug target. 

“The strategic use of these two preclinical models is essential for translating insights into therapies that might restore lost myelin,” Adams said. “We need to better understand the very process of demyelination in order to treat one of the root causes of this debilitating disorder.” 

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Drug Target for Fragile X Syndrome Identified Through Preclinical Study

UCLA Health researchers have identified a potential drug target for treating fragile X syndrome (FXS), the most common genetic cause of intellectual disability and autism that affects roughly one in 2,000 boys.

Fragile X syndrome is caused by a mutation in a single gene, FMR1, that results in the loss of a protein critical for normal brain development and function. Headed by Carlos Portera-Cailliau, MD, PhD, professor of neurology at UCLA and member of the UCLA Brain Research Institute, the researchers, the team’s work in genetically engineered mice lacking the Fmr1 gene identified the synaptic protein EPAC2 as a potential therapeutic target for fragile X syndrome. Their study showed that blocking EPAC2 in the fragile X mouse model restored abnormal patterns of brain activity and improved several FXS-associated behavioral symptoms.

Pertera-Cailliau is senior and corresponding author of the researchers published paper in Neuron, titled “Translatome profiling reveals opposing alterations in inhibitory and excitatory neurons of fragile X mice and identifies EPAC2 as a therapeutic target.”

Fragile X syndrome is a prototypical neurodevelopmental disorder (NDD) characterized by intellectual disability, social anxiety, atypical sensory processing characterized heightened sensitivity to sensory input such as sound and touch, and a higher risk of seizures. Many also meet the criteria for an autism spectrum disorder diagnosis. “Symptoms of fragile X syndrome (FXS), the leading monogenic cause of intellectual disability and autism, are thought to arise from an excitation/inhibition (E/I) imbalance,” the authors stated.

FXS is caused by mutations in the FMR1 gene, resulting in near complete loss of the fragile X messenger ribonucleoprotein (FMRP), an RNA-binding protein in neurons that plays different roles in cell compartments including the nucleus, axons and dendrites, including regulating mRNA translation at synapses, they explained. As it is caused by a change in a single gene, fragile X syndrome has long been considered a promising candidate for targeted therapies yet clinical trials to date have not produced an effective treatment. “Since the discovery of the genetic basis of FXS in 1991, several clinical trials have been undertaken—without success—and no specific treatments for FXS are currently available,” the investigators continued. “Thus, there is an urgent need to rethink therapeutic strategies for FXS.”

For their newly reported study the researchers used genetically engineered knockout (KO) mice that lack Fmr1 to simulate fragile X syndrome. Using genetic sequencing, they found that levels of the gene EPAC2 were increased in the brain of fragile X mice. This was of potential interest as a target for therapy because the gene’s protein, EPAC2, is localized to synapses and is known to be important for learning and memory.

The researchers then demonstrated that blocking EPAC2 in the fragile X mouse model, either genetically, or using an EPAC2 inhibitor compound, restored cortical circuit function and improved multiple behavioral symptoms associated with fragile X syndrome, including heightened sensitivity to touch, difficulties with social interaction and their susceptibility for seizures. “Perhaps the most exciting result is that treatment with an EPAC2 antagonist can rescue several behavioral phenotypes in Fmr1 KO mice,” the authors stated.

“EPAC2 emerged as an attractive target because it was consistently altered across multiple types of brain cells in our analysis,” said the study’s first author Anand Suresh, PhD, a post-doctoral fellow in the laboratory of Portera-Cailliau. “When we blocked it, either genetically or with a drug compound, we saw meaningful improvements in both brain circuit function and behavior.”

EPAC2 is expressed almost exclusively in the brain, which means drugs targeting it are less likely to cause unwanted effects elsewhere in the body. Suresh said this is an important consideration as researchers continue preclinical studies. “This bodes well for future preclinical trials and safety studies in humans, as compounds that target EPAC2 should not have off-target effects,” the authors stated in their report.

For their study the UCLA investigators used an RNA sequencing technique to examine gene activity separately in two major classes of brain cells: those that excite and those that inhibit neural activity. Fragile X syndrome is thought to arise from an imbalance between these two systems. The analysis revealed striking differences in how the genetic mutation underlying Fragile X syndrome affects each cell type but also identified a small set of genes, including the one that encodes EPAC2, that were dysregulated in both.

The researchers also found that EPAC2 levels appear to rise gradually as the brain matures, suggesting it may be a particularly relevant target for older children and adults with Fragile X syndrome, rather than only in early development. They concluded, “Our results should encourage the development of novel EPAC2 inhibitors for the treatment of FXS. More generally, our study exemplifies how transcriptomic approaches in animal models of neuropsychiatric conditions can be used to prioritize potential novel therapeutic targets.”

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Reporter’s Notebook: The Day the Scientific Debate Died

When the news broke on May 5 that U.S. Food and Drug Administration (FDA) officials had blocked the publication of two major COVID-19 vaccine safety studies in 2025 after being accepted for publication in medical journals, many researchers saw more than a scientific dispute. They saw it as further evidence that America’s most powerful public health agencies were devolving into ideological warfare, institutional instability, and political distrust.

By the time FDA commissioner Martin Makary, MD, resigned a week later on May 12, 2026, there was a growing conflict among scientists, political appointees, public health officials, and outside activists not only over vaccines and public health policy but also over something even more fundamental: who gets to determine what constitutes legitimate science and whether scientific disagreement itself would still be permitted to occur in public.  

A decade in the making 

Makary’s departure came amid a broader transformation of the FDA and HHS from relatively stable, technocratic agencies into politicized institutions shaped by pandemic-era conflicts. Under Obama-era leaders Robert Califf, MD, and Margaret Hamburg, MD, the FDA emphasized regulatory continuity and evidence-based policymaking, while the Department of Health and Human Services (HHS) focused largely on healthcare administration and implementing the Affordable Care Act. 

That changed during the first Trump administration and accelerated during COVID-19, when disputes over vaccines, masking, emergency authorizations, and therapeutics turned the FDA into a political flashpoint. Leadership turnover has increased, tensions between political appointees and career scientists have deepened, and public trust has fractured along ideological lines.  

The Biden administration attempted to restore institutional stability by returning Califf to the FDA and appointing lawyer and politician Xavier Becerra, JD, to HHS, but the agencies remained mired in conflicts over pandemic policy and public health authority. Under HHS Secretary Robert F. Kennedy Jr. in the second Trump administration, those tensions intensified further through staffing and funding cuts, ideological battles over vaccines and food policy, and growing distrust inside federal health agencies.  

Makary initially aligned with parts of the administration’s “Make America Healthy Again” (MAHA) agenda, particularly on food reform and criticism of segments of the pharmaceutical industry. But reports suggest he became caught between competing pressures from the White House, HHS leadership, industry groups, conservative activists, and public-health officials. He ultimately resigned amid disputes over vaping regulation, drug approvals, and broader public health policy during a sweeping restructuring of federal health agencies. Reports also indicated he was already at risk of removal and that his departure was not directly tied to controversy over the blocked COVID publication.  

Instead, the move signals the White House’s continued support for Robert F. Kennedy Jr., “MAHA,” and a shift toward more centralized control over food-safety strategy, inspections, and outbreak response—changes that could affect how aggressively the FDA enforces nutrition standards and responds to contamination events. More broadly, the episode has done little to ease concerns among scientists and public-health experts that political considerations are increasingly shaping regulatory decisions and narrowing the space for independent scientific debate within federal health agencies. 

Seeing double(think) 

One of the two was posted online as a medRxiv preprint in 2025 by lead author Joann F. Gruber, PhD, and senior author Steven A. Anderson, PhD, and examined updated Covid-19 vaccines in adults over 65, the population most vulnerable to severe disease and death from the virus. The analysis drew on data from 7.6 million Medicare FFS beneficiaries who received a COVID-19 vaccination in 2023–2024—either the Pfizer-BioNTech (3.68 million) or Moderna (3.84 million) mRNA vaccine or the Novavax protein-based vaccine (30,000)—and found no new vaccine safety signals. 

But before the study could move through peer review, publication was halted by the FDA, according to a spokesperson for the HHS. As reported by the New York Times, an HHS spokesperson said the studies were withdrawn “because the authors drew broad conclusions that were not supported by the underlying data. The FDA acted to protect the integrity of its scientific process and ensure that any work associated with the agency meets its high standards.”  

Both Gruber, whose work with the FDA’s Center for Biologics Evaluation and Research (CBER) began in 2017, and Anderson, a veteran of CBER having joined in 2001, left the FDA at some point in 2025. It’s worth noting that Anderson’s team posted a second preprint on influenza vaccines that mirrored the COVID-19 vaccines study, using the same patient population, on the same day, which was also posted online as a medRxiv preprint on January 5, 2025, and was accepted in the peer-reviewed journal Vaccine on March 25, 2025, and made available online April 8, 2025. 

On June 25, 2025, Makary and former CBER director Vinay Prasad, MD, PhD, in conjunction with manufacturers, added class safety warnings for myocarditis and pericarditis to COVID-19 mRNA vaccines’ prescribing information. The exact timing of when the accepted Gruber and Anderson study was pulled from publication has yet to be reported. That it occurred before June 2025 to prevent contradiction with Makary’s and Prasad’s safety update to the COVID-19 mRNA vaccine is entirely possible. Anderson’s team posted a second preprint on influenza vaccines that mirrored the COVID-19 vaccines study, using the same patient population, on the same day, which was also posted online as a medRxiv preprint on January 5, 2025, and was accepted in the peer-reviewed journal Vaccine on March 25, 2025, and made available online April 8, 2025. 

Both Gruber, whose work with the FDA’s Center for Biologics Evaluation and Research (CBER) began in 2017, and Anderson, a veteran of CBER having joined in 2001, no longer work at the FDA. According to their LinkedIn profiles, Gruber left in June 2025 and Anderson in December 2024. 

Gold-standard science 

I spoke with several leading epidemiologists to assess whether there was substance to the HHS statement. All immediately noted the lack of specificity in the agency’s criticism, particularly the vague references to “gold-standard science.” 

An epidemiologist with experience conducting vaccine safety studies, requesting anonymity, told Inside Precision Medicine, “If someone wants to criticize the study, there should either be a very clear articulation of what exactly they mean when they invoke terms like ‘gold-standard science’—specifically, which methods are acceptable, which are not, and why—or they should bring to the table the scientific credibility that would justify dismissing this kind of work outright. Frankly, neither of those things has happened. Broad, nonspecific attacks like these actually undermine the critique itself.”  

The epidemiologists I spoke to emphasized that the study’s methods were not novel but reflected established approaches used in vaccine surveillance and prior scientific work, including self-controlled case series and cohort studies. A Harvard University researcher familiar with the study said the framework was specifically designed for this purpose. “The system/infrastructure (FDA BEST), data source, study design, and analytic approach are all fit-for-purpose for the study question,” the Harvard researcher told me. “Self-controlled designs are robust and control for non-time-varying factors, especially in adults.” 

Céline Gounder, MD, an infectious disease specialist, epidemiologist, and editor-at-large for public health at KFF Health News, said the report used one of the strongest available methods for post-market vaccine surveillance. “This study used one of the best methods we have to check if vaccines cause side effects, and it found that the updated COVID vaccines are safe,” Gounder told me. “Pulling this study from publication doesn’t protect good science. It’s not radical transparency, and it’s not gold-standard science.” 

Gounder also noted that the study analyzed data from more than seven million people and found no new safety concerns. “That’s a careful conclusion backed by solid data,” she said. “Blocking a study because you don’t like the answer is censorship.” 

Indeed, the consensus among interviewees was that the study appeared adequately powered and appropriately cautious in its conclusions. “This study includes a large number of people, and from what I can see, it appears adequately powered for the conclusions they’re making,” said the epidemiologist with vaccine expertise. “Importantly, the authors are framing the findings appropriately. They are not claiming more than the data support. Their framing is essentially ‘No new safety signals identified.’ That’s a careful and reasonable way to present findings like this.” 

The study also openly acknowledged limitations, including possible outcome misclassification and residual uncertainty, while describing how these issues were addressed in the analysis. “Seasonality can be a concern with the study design, but it was adjusted for in the study,” said the Harvard researcher. “Claims data are well equipped for studying the exposure and outcomes of interest… In this study, misclassification was accounted for.”

Steven Goodman, MD, PhD, associate dean of clinical and translational research and professor of epidemiology and population health and medicine at Stanford University, told Inside Precision Medicine the study was informative and aligned with broader evidence supporting the low-risk profile of COVID vaccines in adults over 65. Goodman also highlighted the restraint of the authors’ interpretations. “They do not make a statement about the risk-benefit balance, which they can’t because they didn’t study the benefit, but they note that the FDA felt that the balance was positive,” he said. “Their main conclusion was, ‘Our study contributes to growing evidence on the safety of COVID-19 vaccines.’ It is hard to argue with that.” 

Goodman added, “All studies have strengths and limitations, i.e., none establish a scientific truth all by themselves. But this is fundamentally good science that adds valuable information to the COVID vaccine safety picture in adults >65.” 

The epidemiologists stressed that vaccine safety science depends on cumulative evidence across multiple studies, methods, and datasets. “Public health surveillance has always operated this way,” the Harvard University researcher said. “No single study claims to be the final word on a topic. You accumulate evidence across multiple studies, multiple methods, and multiple datasets and then interpret the totality of evidence together.” 

The Harvard researcher added, “The results are consistent with what has been reported by others, including in other countries. There is no clear scientific reason for this work being pulled from publication.” 

Truth welcomes questions 

Further, many of the epidemiologists I interviewed stressed that publication does not imply unquestioned acceptance. Instead, they argued that publication serves as the mechanism through which scientific claims challenge, refine, or overturn one another.   

Goodman emphasized that the unpublished manuscript was intended for scientific scrutiny and peer review and said imperfections in such work are neither unusual nor disqualifying. “Is it perfect? No, but this is a preprint, and usually the peer review and editing process improves the analyses, exploring robustness to various assumptions, the reporting, and the interpretation,” he said. “I would presume that the final version would have come out with some more qualifications, limitations, sensitivity analyses and caveats.” 

The experts I contacted emphasized how suppressing publication interrupts the ordinary process through which scientific consensus develops. “This study should be out there, clearly labeled as one piece of evidence among many, with all the necessary caveats attached,” said the epidemiologist. “Then additional studies come in, more data accumulate, and eventually the field interprets the evidence in totality.” 

The unnamed epidemiologist added, “For 250 years, this country has benefited from exactly that: reasonable people openly disagreeing about difficult issues. So why not say, ‘Fine, publish the study,’ and then publish an editorial alongside it explaining the caveats, limitations, and alternative interpretations? That’s how science is supposed to work. You respond to speech you disagree with by adding more speech, not by suppressing speech and certainly not by suppressing scientific speech.” 

Several of the epidemiologists argued that blocking the manuscript conflicts with repeated public calls for open scientific debate from directors at agencies under the purview of HHS, notably Jay Bhattacharya, PhD, Director of the National Institutes of Health (NIH). 

Goodman said that how the FDA handled this study “contrasted with Dr. Bhattacharya’s many public remarks stressing the criticality of open discussion of scientific results and his objections to suppressing science whose results one doesn’t like. The forced withdrawal of this manuscript prevented that process from occurring, shutting down the open discussion Dr. Bhattacharya has called for in innumerable forums.” 

Goodman added that if officials believe the study contains fatal flaws, they should articulate those concerns publicly and subject them to scientific scrutiny, “letting the authors respond and the scientific community decide… Their own critique should be subjected to peer review.” 

The fundamental process of science encourages that disputes over evidence should unfold transparently in scientific journals and public debates. “If someone has objections, they should make those objections publicly and specifically in the scientific literature where others can critique them, evaluate them, or even prove them right,” said the epidemiologist. “That’s how science advances. That’s what real science looks like: gold-, platinum-, titanium-, or whatever rare metal metaphor people want to use for standards. The scientific enterprise in this country has succeeded because ideas are tested openly, criticized openly, and refined openly. This kind of amateur hour behavior at regulatory agencies doesn’t help anybody.” 

Nostrums, not normalcy 

The culling of FDA scientists, be it via resignations or firings in 2025–2026, has continued since Makary’s resignation. Tracy Beth Hoeg, MD, PhD, the head of the FDA’s Center for Drug Evaluation and Research (CDER), was fired Friday (according to a social media post reported by Reuters on Sunday) and replaced by Michael Davis, MD, PhD, who had served as deputy director of CDER for about a year. 

There is no concrete evidence connecting the FDA’s blocking publication of two studies accepted into medical journals to Makary’s departure. But the contradictory messaging within the agency on COVID-19 mRNA vaccinesthe product of Operation Warp Speed, considered a signature accomplishment of the first Trump administrationis obvious. The collapse of confidence within institutions that once relied on scientific independence as their organizing principle. Increasingly, senior scientists and regulators appear unwilling to publicly defend decisions, studies, or processes they privately regarded as scientifically sound. That shift matters more than any single resignation. 

The appointment of Kyle Diamantas as acting head of the FDA, however, is far from a course correction, marking another sharp turn away from independent scientific leadership at America’s top health regulator. A former corporate lawyer for Abbott Laboratories with no medical or research background, Diamantas rose through the agency by advancing the “MAHA” food agenda and cultivating ties to politically aligned health influencers rather than the scientific establishment. A close friend of Donald Trump Jr., the appointment of the 38-year-old Diamantas only reinforces concerns that ideological loyalty is increasingly outweighing scientific expertise inside the FDA. 

For decades, FDA and HHS leadership operated with relative continuity, assuming that disputes would be resolved through open scientific debate. That assumption now appears badly weakened. The blocked vaccine safety study became symbolic not merely because of the substance of the research but also because even many scientists who believed the work was rigorous hesitated to say so publicly. When experts become reluctant to attach their names to conclusions that they consider obvious or well-supported, the problem extends beyond politics or personnel. It reflects a deeper institutional fear inside the scientific establishment itself. 

Makary’s resignation earlier this month therefore represented more than another leadership change in Washington. It exposed how federal health agencies have been pulled into a culture where scientific judgments are increasingly filtered through ideological loyalty, political risk, and reputational self-preservation. The larger danger is not only instability at the FDA or HHS, but the emergence of a scientific culture in which silence becomes safer than candor. Institutions built to evaluate evidence cannot function for long under those conditions.  

The post Reporter’s Notebook: The Day the Scientific Debate Died appeared first on Inside Precision Medicine.

Organ-on-Chip Method Designed to Zero In on Connection Between Diabetes and Dementia

A University of Bath-led research effort received £500,000 to develop an organ-on-chip device that replicates connections between the brain, gut, and pancreas. The GlucoBrain project is designed to allow researchers to track how signals move between the organs and uncover why diabetes may lead to changes in memory and cognition.

Collaborators include investigators from the University of Oxford and Johns Hopkins. Their findings could pave the way for new treatments to improve the lives of millions of people affected by diabetes, dementia, or both, notes the team.

Diabetes and Alzheimer’s disease are two of the world’s most pressing health problems, especially in aging societies. While diabetes is widely known to affect the heart, kidneys, and eyes, growing evidence suggests it is also linked with problems in memory, learning, and brain function. However, the biological mechanisms behind this link remain poorly understood.

“Our gut, pancreas, and brain are constantly communicating via a network of signals, helping us regulate hunger and blood sugar,”  says Despina Moschou, PhD, project lead. “But we still don’t fully understand how these signals interact at a cellular level and why glucose levels are linked to cognitive decline. “By creating a connected system on a chip, we can study in real time how signals travel between organs, how diabetes may impair brain function, and how new drugs could help.”

Most current knowledge on the link between diabetes and dementia comes from animal studies, simple cell cultures, and patient studies. While these are useful, they don’t fully and accurately capture all the complex interactions between our organs, hormones, and cells, points out Moschou.

Organ-on-chip technology uses living human cells in miniature devices that mimic how organs work in the body. Unlike cell cultures grown in a petri dish, these devices allow cells to grow in three dimensions, receive a controlled supply of nutrients and interact more naturally. Researchers will also be able to isolate these individual organs and cell types to understand exactly how they communicate at a molecular level.

The three-year project starts in October, bringing together engineers, clinicians, biologists and computer scientists to model the complex disease interactions. The team will first develop individual chip models for the gut, pancreas, and brain, before connecting them into a multi-organ system. They will gradually increase complexity and measure how each organ responds to glucose, hormones and different drug treatments.

Researchers from the University of Oxford will provide core clinical expertise in diabetes and metabolic disease, ensuring models are physiologically accurate. The team from Johns Hopkins University brings specialist expertise in Alzheimer’s disease and brain organoids.

GlucoBrain is a pilot project established to help researchers understand exactly how diseases like diabetes and dementia work at a deeper, biological level. This early-stage research will build the foundations for even more advanced and realistic models, bringing together more organs and cell types, explain team members. By harnessing the power of artificial intelligence, the devices have the potential to reveal new insights into how diseases emerge and develop.

“Not only would these devices give us an unprecedented way to study diseases, but they could help speed up drug discovery and testing, reducing reliance on animal models and making results more relevant to humans,” continues Moschou. “In the long term, they could pave the way for personalized medicine, using a patient’s own cells to identify the most effective treatment.”

The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) Health Technologies Connectivity Awards.

 

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AI Model Predicts Alzheimer’s Progression from a Single MRI Scan

Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer’s disease progression using only a single baseline MRI scan and basic demographic information. The approach, published in Nature Aging, could help make early Alzheimer’s assessment faster, more accessible, and less dependent on costly specialized testing.

Alzheimer’s diagnosis remains complex and resource-intensive

Alzheimer’s disease accounts for approximately 60% to 70% of dementia cases worldwide. Although structural brain changes and cognitive decline are hallmarks of the disease, accurately forecasting who will develop progressive impairment remains difficult.

Current diagnostic workflows often rely on multiple complementary techniques, including PET imaging, cerebrospinal fluid or blood biomarkers, genetic testing, and comprehensive neuropsychological assessments. While effective, these approaches can be expensive, time-consuming, and inaccessible in many healthcare settings.

MRI scans are among the most widely available clinical imaging tools for neurological assessment, but MRI data alone has historically struggled to capture the complexity and heterogeneity of Alzheimer’s disease progression when used in conventional AI frameworks.

To address this challenge, the UCSF team designed a multitask deep learning framework that combines domain-specific imaging knowledge with advanced machine learning methods to predict cognitive outcomes directly from structural MRI scans.

AI framework predicts cognition without invasive testing

Unlike many earlier Alzheimer’s prediction models, the new system does not require longitudinal imaging data, baseline cognitive testing, PET scans, or molecular biomarker analysis.

The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan. The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer’s diagnosis prediction, and estimation of both present and future cognitive performance.

A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions. According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.

Senior study author Ashish Raj, PhD, professor of radiology and biomedical imaging at UCSF, said the goal was to create a system that could be realistically implemented in routine clinical environments.

“Unlike previous approaches, our model does not require baseline cognitive assessment, specialized image pipelines, expensive PET scans, genetic analysis, or fluid proteomics, making it a fast, accurate, and easily implementable tool for most clinical settings,” Raj said in a statement.

Large imaging datasets improved robustness and generalizability

To train and validate the framework, the researchers used imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including MRI scans, demographic information, diagnoses, and cognitive assessments.

The team also incorporated MRI data from the Human Connectome Project Young Adult cohort, which contains scans from healthy younger adults with minimal age-related brain atrophy. According to the authors, exposing the model to healthy brain anatomy improved its ability to distinguish pathological neurodegeneration from normal aging.

An external validation cohort from the Dallas Lifespan Brain Study was additionally used to test the generalizability of the framework across independent datasets.

The researchers reported that the multitask framework outperformed existing AI methods, including standard transfer-learning approaches, in predicting clinically relevant outcomes. The model generated accurate predictions for Alzheimer’s diagnosis, tissue segmentation, current cognitive function, and future cognitive decline using only baseline MRI data.

The study also reported improvements in computational efficiency and processing speed compared with more complex MRI morphometry pipelines commonly used in neuroimaging research.

First author Daren Ma, MSc, a machine learning specialist in the Raj Lab at UCSF, said the framework could help clinicians identify at-risk patients earlier and streamline referrals for advanced neurological evaluation.

“We reported meaningful gains in speed and performance over other pipelines, which could prove valuable in developing a quick clinical prediction of cognitive impairment prior to referring the patient to a more advanced imaging lab and/or a full neuroradiology report,” Ma said.

Potential implications beyond Alzheimer’s disease

The researchers believe the framework could eventually be adapted for other neurodegenerative disorders characterized by structural brain changes and progressive cognitive decline.

Potential future applications include Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and Huntington’s disease. The ability to estimate cognitive impairment using minimal baseline data may also prove useful in community healthcare settings where access to specialist neuropsychological testing is limited.

In addition, the model may have implications for clinical trial design. Identifying likely disease progressors early could help reduce trial size requirements and improve patient selection for studies evaluating disease-modifying therapies.

“The ability to correctly predict progressors from non-progressors using only baseline data can dramatically reduce sample sizes and cost,” Raj said.

The authors emphasized, however, that further validation will be necessary before the model can be broadly implemented in routine clinical practice. Future iterations of the framework may incorporate additional clinical measurements where available, including longitudinal MRI imaging, PET scans, genetics, and blood or cerebrospinal fluid biomarkers.

The study highlights the growing role of AI-driven imaging analysis in neurology and suggests that clinically accessible tools such as MRI may eventually support earlier and more scalable prediction of Alzheimer’s disease progression.

The post AI Model Predicts Alzheimer’s Progression from a Single MRI Scan appeared first on Inside Precision Medicine.

Microbiome Therapy Could Help Drug-Resistant Melanoma Patients

Microbiotica, a microbiome-focused biotech based in Cambridge in the U.K., has achieved good Phase Ib results in a trial of its microbiome therapy for patients with advanced melanoma skin cancer.

The therapy, currently known as MB097, is designed to be given to patients who have not previously responded to immunotherapy in addition to a checkpoint inhibitor pembrolizumab. MB097 was developed to reverse the drug resistance seen in these patients and is based on research looking into the gut microbiome of melanoma patients who do respond to this kind of immunotherapy.

The primary endpoint of the trial, which included 41 patients from the U.K., France, Italy, and Spain, who had previously shown resistance to anti-PD-1 drugs, was safety and tolerability of MB097. Several secondary endpoints including response rate, duration of response, and overall survival were also included. The therapy, which contains nine beneficial strains of gut bacteria, met both its primary and secondary endpoints in the study, according to the company, although precise details will be released at a scientific conference later this year.

“There is increasing evidence that the microbiome plays a crucial role in patients’ response to immune checkpoint inhibitors. Clinical benefit has been reported with fecal microbiota transplantations, while MB097 capsules taken orally each day affords an easy and reproducible way of modifying the microbiome,” said the national coordinating investigator for the study, Pippa Corrie, MD, PhD, a clinician and researcher from Cambridge University Hospitals NHS Foundation Trust, in a press statement.

“The MELODY-1 study results show that MB097 is well tolerated, with encouraging early signs of efficacy in a very difficult to treat metastatic melanoma patient population with primary resistance to anti-PD-1 based immunotherapy, in whom there is a significant unmet need.”

Up to half of all advanced melanoma patients fail to respond to anti-PD-1 immunotherapy, leaving them with very few options. A growing body of research, including a 2021 study showing fecal transplant can overcome resistance to anti-PD-1 immunotherapy, shows that the gut microbiome plays an important role in whether a patient’s immune system mounts an effective anti-tumor response when given these therapies.

The make-up of MB097 is based on detailed research looking at strains of bacteria linked to effective response to immunotherapy. Preclinical work showed that the bacteria in the therapy directly activate cytotoxic T cells and counter immunosuppressive tumor macrophages. If larger controlled trials confirm these initial results MB097 could become a standard add-on to immunotherapy.

Microbiotica has another clinical program in ulcerative colitis, which also reported good results earlier this year in another Phase Ib trial. In total, 63% of those in the treatment group achieved clinical disease remission versus 30% in the placebo group and all were also taking standard therapy for the autoimmune disease.

The company now plans to move both its programs to larger controlled studies with a view to moving closer to market approval with both therapies.

The post Microbiome Therapy Could Help Drug-Resistant Melanoma Patients appeared first on Inside Precision Medicine.

What to expect from Google this week

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs. 

But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.

So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential. 

Here are three things I’ll be paying particular attention to over the next two days.

An attempted coding comeback

Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.

That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.

Science and health

Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.

I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-based health tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain? 

The drama

While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.

That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.

The Download: Musk v. Altman week 3, and Trump’s tech trading

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Musk v. Altman week 3: Musk and Altman traded blows over each other’s credibility. Now the jury will pick a side.

In the final week of the Musk v. Altman trial, lawyers attacked the credibility of the two tech leaders. Sam Altman was accused of lying and self-dealing, while Elon Musk was portrayed as a power-seeker trying to control artificial general intelligence.

The case unearthed new details about the two arch-rivals and OpenAI’s contested nonprofit status, as well as a golden trophy of a donkey’s ass awarded to an employee who challenged Musk.

Read the full story on the explosive final week of the trial.

—Michelle Kim

Michelle Kim, who’s also a lawyer, has been in court throughout the Musk v. Altman trial. Read her coverage of week 1 and week 2, plus a Q&A on what it was like in the room

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Trump traded hundreds of millions in tech stocks before favorable policy moves
He bought shares in Nvidia, AMD, and Arm ahead of policy boosts. (Quartz)
+ And touted Palantir on Truth Social after buying its stock. (CNBC)
+ His crypto venture and Iran’s top exchange tapped the same networks. (Reuters $)

2 SpaceX plans to list on the Nasdaq stock exchange as soon as June 12
It wants to raise up to $75 billion at a $1.75 trillion valuation. (Reuters $)
+ BlackRock may invest up to $10 billion in the offering. (The Information $)
+ Cerebras’ blockbuster IPO has boosted hopes for the listing. (CNBC)
+ Which is set to dwarf many of the biggest IPOs on ⁠record. (Reuters)

3 Chinese AI groups have pulled ahead of US rivals in video generation
ByteDance and Kuaishou’s models lead in realism and scale. (FT $)
+ AI is fueling China’s short-drama boom. (MIT Technology Review)
+ While its AI labs are betting big on open source. (MIT Technology Review)

4 Iran says it will charge Big Tech for using undersea internet cables
The cables beneath the Strait of Hormuz carry vast digital traffic. (CNN)
+ Tech bosses met at Uber HQ on Saturday to discuss Iran’s future. (404 Media)

5 Samsung has a “last chance” to stop a massive strike over AI
Over 45,000 employees could walk out for 18 days this week. (CNBC
+ They want a bigger share of the AI boom. (FT $)
+ Samsung and its largest labor union will resume talks on Tuesday. (Reuters $)

6 Old oil and gas wells could become a new source of clean energy
US states plan to convert them into geothermal energy assets. (Wired $)
+ A balcony solar boom is coming to the US. (MIT Technology Review)

7 The ChatGPT era has triggered a 30% surge in grades at a top university
Grades inflated in text-heavy courses but remained flat in others. (Axios)
+ Princeton has changed its honor code because of AI cheating. (WSJ $)
+ And real cheating rates may be far higher. (The Times $)

8 Ex-Google CEO Eric Schmidt was fiercely booed during an AI speech
His graduation speech praising AI agents sparked uproar. (The Verge)
+ A populist backlash is building against AI. (MIT Technology Review)

9 Arm faces a US antitrust probe over its chip tech licenses
Regulators are investigating whether it has an illegal monopoly. (Bloomberg $)
+ Qualcomm has accused Arm of anticompetitive conduct. (Reuters $)

10 ArXiv will ban researchers who submit AI slop
Offending authors face year-long bans from the pre-print server. (TechCrunch)

Quote of the day

“When someone offers you a seat on the rocket ship, you do not ask which seat. You just get on.” 

—Ex-Google CEO Eric Schmidt extolls the virtues of AI agents in a graduation speech at the University of Arizona, prompting a chorus of boos.

One More Thing

a gloved hand holding up a microfluidic chip

WYSS INSTITUTE AT HARVARD UNIVERSITY


Is this the end of animal testing?

In a clean room in his lab, Sean Moore peers through a microscope at a bit of human intestinal tissue growing on a plastic chip. It’s one of 24 so-called “organs-on-chips” his team bought three years ago. The technology is designed to mimic human biology—and could reduce the need for animal testing.

The appeal is not only ethical. Around 95% of drugs developed through animal research ultimately fail in people, and early studies suggest organ-on-a-chip systems may offer more accurate insights into how diseases behave and how drugs work. But the field still faces major technical and cost challenges before it can replace animal research.

Find out how organ-on-chip technology could reshape drug testing.

—Harriet Brown

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Listen to the captivating first recordings of whale songs from 1949.
+ Meet the feline guardians of New York’s corner stores in this photo collection.
+ A newly discovered floor plan allowed historians to pinpoint the location of Shakespeare’s only property in London.
+ A music fan spent decades secretly recording 10,000 local shows. Now the entire collection is available online.