FDA Contemplates Changes in Trial Design, Drug Development and Use of AI in Clinical Trials
Curator: Stephen J. Williams, Ph.D.
Several pharma companies just told the FDA what AI in clinical trials should look like.
It is not what most people expect.
I went back through the RTCT comment letters from Novartis, Lilly, Bayer, Daiichi and others. Read together, they are the clearest signal yet on how AI will get adopted in drug development over the next few years.
Three things they make clear.
Adoption starts at the data layer, not the decision layer. Every letter drew the same line: AI supports qualified clinical judgment, it does not replace it. Daiichi was explicit that AI should not autonomously make dose escalation or safety decisions. But underneath that line, they want automation everywhere. Reconciliation, coding consistency, discrepancy detection, safety surveillance. That is where AI enters trials first, and it is where the near-term value is.
The bar is oversight, not sophistication. Lilly put it best: the FDA should select for the most mature oversight of AI, not the most mature AI. Across the letters, the requirements are validation, audit trails, data lineage, change control, human review that is evidenced rather than assumed. A brilliant model with weak governance does not clear this bar. A well governed one doing unglamorous work does.
The industry is already building the plumbing. Lilly and Bayer, independently, both proposed a DMF-style pathway letting AI vendors disclose model documentation directly to the FDA with sponsors referencing it.
Put those together and the future looks less like AI making trial decisions and more like AI making trial data trustworthy fast enough for humans to decide sooner.
The sponsors who lead this will not be the ones with the most sophisticated models. They will be the ones whose data operations were modernized and able to clean enough to move in real time.
FDA News Release
FDA Announces Major Steps to Implement Real-Time Clinical Trials
Agency unveils real-time trial proofs-of-concept and upcoming pilot program
For Immediate Release:
April 28, 2026
On May 27, 2026, the FDA posted a notice in the Federal Register extending the comment period for the Request for Information until June 29, 2026.
The U.S. Food and Drug Administration today announced two major steps as part of an initiative to advance the implementation of real-time clinical trials (RTCT). First, the agency unveiled the successful initiation of two proof-of-concept clinical trials that will report endpoints and data signals to the agency in real time. Second, the agency released a Request for Information (RFI) regarding a proposed pilot program for RTCT that will launch this summer.
Early-phase clinical trials are a bottleneck in drug development, often characterized by high uncertainty, limited patient populations, and inefficient decision-making processes. Data is typically reported from sites to sponsors, who analyze and subsequently submit data to the FDA. With improvements in AI and data science, sponsors and trial sites have the opportunity to conduct real-time trials in a way that enhances safety monitoring and radically increases efficiency.
“For 60 years, we’ve been conducting clinical trials in the same way, where key data signals can take years to reach the FDA. The lag time can delay regulatory decisions unnecessarily and slow down the drug development timeline,” said FDA Commissioner Marty Makary, M.D., M.P.H. “We are boldly advancing a modern approach whereby FDA scientists can view safety signals and endpoints in real time as a trial progresses. This will help us accelerate promising therapies, and build toward our ultimate goal of running real-time, continuous trials across all phases of drug development.”
The FDA is announcing the successful initiation of proof-of-concept RTCTs by AstraZeneca and Amgen. AstraZeneca is conducting a Phase 2 multi-site trial, TRAVERSE, in patients with treatment-naïve mantle cell lymphoma, with participation from The University of Texas MD Anderson Cancer Center and University of Pennsylvania. Amgen is conducting a Phase 1b trial, STREAM-SCLC, in patients with limited-stage small cell lung carcinoma and final site selection is in process. For each trial, the FDA met with the sponsor on the establishment of criteria for reporting signals in real time. The agency has since received and validated signals for AstraZeneca’s trial through Paradigm Health, thereby establishing the feasibility of the technical framework required for real-time signal sharing.
The FDA seeks to build on these proofs-of-concept with a broader pilot program. Today’s RFI seeks input on potential pilot program design and implementation, as well as evaluation metrics and success criteria.
“Real-time trials have been talked about for years. We demonstrated that it is not only possible, but also potentially transformative for the clinical trials ecosystem,” said Chief AI Officer Jeremy Walsh. “We have to consider our processes from the standpoint of a patient awaiting a potentially powerful treatment.”
Real-time clinical trials are an important step towards the agency’s goal of facilitating continuous trials. At present, most clinical development occurs in discrete phases. Because each defined phase of clinical development is run according to a protocol and typically as a separate study, there is generally a hiatus in the development program after one phase ends and the next begins. This slows the pace of product development. Because real-time trials allow the FDA to view key insights in real time, this hiatus could be eliminated or reduced to a minimum, enabling “continuous” trials.
The agency will accept comments on the RFI until May 29, 2026. The agency intends to disseminate final selection criteria in July and complete pilot selections in August.
https://www.youtube.com/live/hPT6X4SKOjw?si=ZfLrn3NmYvyqFILm
https://www.ajmc.com/view/fda-will-require-only-1-study-to-approve-new-drugs-speeding-up-process
News|Articles|February 19, 2026
FDA Will Require Only 1 Study to Approve New Drugs, Speeding Up Process
Author(s)Julia Bonavitacola
Fact checked by: Christina Mattina
A commentary by FDA officials Vinay Prasad, MD, MPH, and Martin Makary, MD, MPH, details the new system for drug approvals in the US.
FDA Commissioner Martin Makary, MD, MPH, and his top deputy Vinay Prasad, MD, MPH, announced in a commentary published in The New England Journal of Medicine that the FDA will revamp its method of approving drugs for use in the US.1 The commentary announced that the agency’s historic reliance on 2 clinical trials will end, with only 1 pivotal trial needed for a drug to be approved for use nationwide.
“Going forward, the FDA’s default position is that 1 adequate and well-controlled study, combined with confirmatory evidence, will serve as the basis of marketing authorization of novel products,” the FDA officials wrote in their commentary.
The FDA had previously worked under guidelines stating that “adequate and well-controlled investigations” were needed before a drug could be approved for widespread use, which were interpreted as generally requiring 2 clinical investigations.2 These guidelines had been in place since 1998, with only supplementary guidance published in 2019 and 2023. The newly announced shift marks the first substantial change in the methods of FDA approvals since the FDA obtained the authority to grant marketing authorizations.1
Makary and Prasad noted that these guidelines had been flexible in the past—specifically in oncology, where 1 study was often enough for a drug approval—but were confusing to drug manufacturers seeking to understand when only 1 trial would be acceptable. Moving forward, the default of using only 1 trial to grant a drug approval should clear up questions surrounding the necessary number of trials.
“The FDA’s historical reliance on 2 clinical trials rather than 1 was intended to provide credible causal evidence that a therapy could improve clinical outcomes with acceptable safety in a world where biologic understanding was more limited than it is today,” the FDA officials wrote. “Two trials should be seen as just 1 of many interlocking facets of clinical credibility, and in 2026 there are powerful alternative ways to feel assured that our products help people live longer or better than requiring manufacturers to test them yet again.”
This move is another step in Makary’s attempts to shorten FDA reviews, which started when he began his tenure last year.3 These include mandating the use of artificial intelligence for staffers and offering new medications a 1-month drug assessment if the FDA believes that the drug serves a national interest.
About 60% of first-of-a-kind drugs have been approved based on a single study in the past 5 years due to legislative initiatives that encouraged flexibility in reviewing drugs for conditions that were hard to treat. The drugs more likely to be affected by this new standard are for common diseases rather than those for rare diseases or cancers, which were already more often receiving approval based on a single trial.
This announcement comes a day after the FDA announced that it will now review Moderna’s seasonal mRNA flu vaccine application, which it had previously refused to look at due to perceived safety and efficacy concerns.4 The increased scrutiny of vaccines presents a contrast to the newly streamlined default standard for FDA approvals.
References
- Prasad V, Makary MA. One pivotal trial, the new default option for FDA approval—ending the two-trial dogma. N Engl J Med. 2026;394(8):815-817. doi:10.1056/NEJMsb2517623
- Demonstrating substantial evidence of effectiveness with one adequate and well-controlled clinical investigation and confirmatory evidence. FDA. Updated November 30, 2023. Accessed February 19, 2026. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/demonstrating-substantial-evidence-effectiveness-one-adequate-and-well-controlled-clinical
- Perrone M. FDA will drop two-study requirement for new drug approvals, aiming to speed access. AP News. Updated February 18, 2026. Accessed February 19, 2026. https://apnews.com/article/fda-drug-approval-studies-makary-prasad-a5aaa5501ae15f264bbd20d0dffa4dc4
- Steinzor P. FDA reverses course, will review Moderna’s mRNA flu vaccine. AJMC®. February 18, 2026. Accessed February 19, 2026. https://www.ajmc.com/view/fda-reverses-course-will-review-moderna-s-mrna-flu-vaccine
Pharm Stat. 2022 Aug 26;22(1):96–111. doi: 10.1002/pst.2262
Should the two‐trial paradigm still be the gold standard in drug assessment?
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- Author information
- Article notes
- Copyright and License information
PMCID: PMC10087480 PMID: 36054079
Abstract
Two significant pivotal trials are usually required for a new drug approval by a regulatory agency. This standard requirement is known as the two‐trial paradigm. However, several authors have questioned why we need exactly two pivotal trials, what statistical error the regulators are trying to protect against, and potential alternative approaches. Therefore, it is important to investigate these questions to better understand the regulatory decision‐making in the assessment of drugs’ effectiveness. It is common that two identically designed trials are run solely to adhere to the two‐trial rule. Previous work showed that combining the data from the two trials into a single trial (one‐trial paradigm) would increase the power while ensuring the same level of type I error protection as the two‐trial paradigm. However, this is true only under a specific scenario and there is little investigation on the type I error protection over the whole null region. In this article, we compare the two paradigms by considering scenarios in which the two trials are conducted in identical or different populations as well as with equal or unequal size. With identical populations, the results show that a single trial provides better type I error protection and higher power. Conversely, with different populations, although the one‐trial rule is more powerful in some cases, it does not always protect against the type I error. Hence, there is the need for appropriate flexibility around the two‐trial paradigm and the appropriate approach should be chosen based on the questions we are interested in.
Making preclinical evidence predict the clinic for 2- and 4-Legged Patients | CSO, Instem | Co-Founder, Digital Preclinical Society | Co-Chair, VQN | President, 3Rs Collaborative
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For fifty years, preclinical safety ran on one rule: do the animal study, then justify the exception. Last week the #FDA‘s #OncologyCenterofExcellence released a draft guidance that quietly turns that rule around. Read the headlines and it is an animal-reduction story. One relevant species instead of two. A single three-month study instead of separate one- and three-month studies. A structured risk assessment in place of a study. And yet the reduction is not the point. The precondition is.
Each of those moves is permitted only when you already understand the product, the #targetbiology, and the #toxicity well enough to defend the omission. The study used to be the default. Now the knowledge is the default, and the study is what you add when the knowledge runs out. That is a higher bar, not a lower one. Dropping a study you cannot defend is easy. Defending the decision is the work.
Here is the part that should focus the mind. Across 7,565 drugs and five species, only one of 26 system organ classes showed strong cross-species concordance for both small molecules and biologics. Running the study was never the same as understanding the risk. I wrote this week’s Digital Record on what the guidance actually asks of sponsors, and why the teams that win the timeline treat their evidence as an asset, not an archive. If you lead nonclinical safety or translational science for an oncology biologic or a conjugate, read it as an evidence standard, then ask the harder question of your own programs: which study are you running out of habit, and could you defend dropping it on Monday. Know more. Run less. Defend both.
Issue #3 is free here: https://lnkd.in/eSYzkh_x
FDA Reports Meeting Year One Goals for Reducing Animal Drug Testing
June 8, 2026
On May 29, 2026, the Food and Drug Administration (FDA) released its latest guidance related to FDA’s intent to reduce unnecessary animal testing for nonclinical safety assessments, Oncology Pharmaceuticals: Streamlined Nonclinical Safety Studies for Biologics and Conjugated Products. The guidance is intended to help reduce unnecessary animal testing by incorporating an integrated knowledge-based risk assessment with a focus on three-month toxicology studies for certain oncology pharmaceuticals.
Sponsors may propose alternative approaches for a three-month general toxicology study for product classes not described in the guidance, provided such approaches are sufficient to address product safety. Such approaches for a three-month general toxicology study may include a non-sacrificial toxicology study, an alternative study design to reduce animal numbers, or a weight of evidence (WoE) risk assessment for products with well-understood targets to replace animal studies. These approaches could be supplemented with new approach methodologies (NAMs), as appropriate.
FDA’s recommendations cover general toxicology and WoE Assessment. For general toxicology, FDA stated that animal toxicology studies should use pharmacologically relevant species or WoE risk assessment in its absence. FDA stated that if pharmacological activity is similar to humans in both rodent and non-rodent species, then general toxicology may be conducted in a single rodent species and supplemented with WoE risk assessment as appropriate.
A WoE risk assessment may include multiple factors. The factors include nonclinical and clinical data generated with the investigational product (e.g., pharmacology, safety, and pharmacokinetics), a literature-based assessment of potential toxicities with the molecular target, toxicity findings in animals and humans associated with the same class of pharmaceuticals, and other data. The Center for Drug Evaluation and Research’s (CDER) oncology review divisions will determine when the WoE risk assessment is sufficient to address the safety risks based on the totality of evidence.
This guidance builds upon FDA’s initial Roadmap to Reducing Animal Testing in Preclinical Safety Studies (April 2025) to provide a strategic, stepwise approach using NAMs, such as organ-on-a-chip systems, computational modeling, and advanced in vitro assays (e.g., organoids and microphysiological systems). The principles come from a growing scientific recognition that animals are inadequate models of human health, i.e., over 90% of drugs that appear safe and effective in animals do not go on to receive approval in humans. In addition, the time and cost of long-term animal studies delay therapies reaching patients, e.g., developing a monoclonal antibody costs $600-750 million and may take up to nine years, with typical programs using 144 non-human primates at costs reaching $50,000 per animal.
FDA’s one-year progress report, Reducing Animal Testing in Nonclinical Studies Year One Progress and the Path Forward (April 2026) declared that the necessary foundations or goals had been met or exceeded, resulting in additional guidance. Some of those goals included:
- On July 31, 2025, FDA made the Innovative Science and Technology Approaches for New Drugs pilot program permanent. The program employs the Drug Development Tool Qualification regulatory framework, providing a clear, predictable pathway for developers to gain formal FDA acceptance of NAMs.
- In August 2025, FDA and the National Institutes of Health formalized a partnership in a Memorandum of Understanding to accelerate the standardization, qualification, and adoption of human-relevant alternative methods.
- In October 2025, the CDER / Office of New Drugs Streamlined Nonclinical Studies and Acceptable New Approach Methodologies database went live, providing a searchable, regularly updated inventory of specific drug development contexts where streamlined nonclinical programs are acceptable.
- In November 2025, FDA researchers from the National Center for Toxicological Research and CDER, collaborating with Emulate Inc. and the University of North Carolina at Chapel Hill, published challenges and solutions in measuring commonly used biomarkers for drug-induced liver injury in a liver-on-a-chip platform.
- On December 2, 2025, FDA released draft guidance, Monoclonal Antibodies: Streamlined Nonclinical Safety Studies.
- On December 8, 2025, FDA crossed a technological threshold by qualifying the AI-Based Histologic Measurement of NASH its first AI-based drug development tool for use in metabolic dysfunction-associated steatohepatitis clinical trials.
- On March 18, 2026, FDA published a draft guidance document: General Considerations for the Use of New Approach Methodologies that established four core validation principles to transform the abstract question of “When is an alternative acceptable?” into concrete, actionable requirements. On the same date, FDA’s Level 2 update to “Pyrogen and Endotoxins Testing: Questions and Answers” guidance provided the flexibility for manufacturers to transition from Limulus Amoebocyte Lysate (LAL) reagents for bacterial endotoxin testing to transition from harvesting horseshoe crabs for production of LAL reagent to recombinant agents.
We will continue to monitor FDA’s continuing implantation of a framework to reduce animal testing in nonclinical studies.
This blog was drafted by Brian Malkin, a Spencer Fane attorney on the FDA Pharmaceutical and Biologics Market Team. For more information, visit spencerfane.com.
F
or decades, preclinical testing in mice, rats, and nonhuman primates has been a crucial part of drug development, an important although not infallible way to ensure some measure of safety for human participants in clinical trials. In 2022, Congress passed the FDA Modernization Act 2.0, stating that the agency was no longer compelled by law to require animal testing.1 However, the act did not prohibit the FDA from requiring animal testing, and animal toxicity data remained an essential step in the path towards human trials.
But on April 10, the FDA announced plans to phase out these requirements, stating that they would be “reduced, refined, or potentially replaced” with New Approach Methodologies (NAMs). These methods include human-derived cell models, such as organoids and organ-on-a-chip systems, as well as in silico approaches such as pharmacokinetic modeling and toxicity-predicting machine learning algorithms. Within the year, certain monoclonal antibodies, which are most commonly used in the treatment of cancer and serious autoimmune disease, could be evaluated using a “primarily non-animal-based testing strategy.” According to the roadmap accompanying the FDA announcement, “In the long-term (3–5 years), FDA will aim to make animal studies the exception rather than the norm for pre-clinical safety/toxicity testing.”
This announcement has been met with both hopefulness and concern in the scientific community. On one hand, there is broad support for moving away from animal testing, which is ethically fraught, expensive, and not always predictive of human biological responses. On the other hand, scientists and pharmaceutical industry professionals have expressed that NAMs are not yet advanced enough to fully replace animal models.2,3 Moreover, there are concerns that dramatic reductions in budgets for scientific research and the firing of thousands of workers at the FDA and NIH will stall further development of NAMs and interfere with the functioning of the very systems that would be responsible for validating, standardizing, and monitoring the efficacy of these technologies.
Alex Rubinsteyn, a University of North Carolina at Chapel Hill researcher who uses machine learning approaches to inform the development of personalized cancer vaccines, supports reducing animal testing but is uncertain about how this will play out in practice in the current climate. “I think this could become a disaster,” he said. “But it could also potentially unlock a much faster rate of progress.”
Few would dispute the shortcomings of current animal testing pathways: About 90 percent of drugs that make it through preclinical trials never obtain FDA approval for use in humans, largely due to insufficient efficacy or safety.4 Joseph Wu, who studies patient-specific in vitro models of cardiovascular disease at Stanford University, said that this high failure rate is partly due to the inherent differences between human and rodent biology. Furthermore, he noted, “The heterogeneity that exists among humans cannot be captured by using a traditional mouse model.”
Additionally, despite attempts to streamline evaluations of drugs for currently untreatable diseases, “[Drug development] is still really slow and really expensive,” said Rubinsteyn. “And it’s unmatched to clinical realities for certain kinds of disease: There are sufficiently deadly diseases where you really would want to go much faster than you’re allowed to go.”
How Do In Vitro and In Silico Approaches Stack Up Against In Vivo?
Despite the inherent flaws in animal testing, many researchers say that NAMs are not yet advanced enough to fully replace traditional drug safety studies. For example, in a late 2024 report to the FDA Science Board, members of the NAMs subcommittee—convened in 2023 to provide recommendations on integrating NAMs into regulatory processes—wrote that, “Technical limitations to current NAMs exist…today, no assays fully capture the critical hazard endpoints for assessing all currently existing human or animal organ systems; therefore, NAMs cannot fully eliminate the use of integrated physiological systems such as in animal and human trials.”2
In a published response to the FDA announcement, the president of the National Association for Biomedical Research, Matthew Bailey, echoed these sentiments. “No AI model or simulation has yet demonstrated the ability to fully replicate all the unknowns about many full biological systems.”
Similarly, Rubinsteyn noted that while these models can be useful when the space is sufficiently constrained, in other situations, they are still no match for the complexity of biology. “The thing that I work on the most—personalized cancer vaccines—is totally plagued by machine learning models not capturing the relevant realm.”
“We could improve those models with cell lines, but ultimately, the cell lines will be different if we put them in the context of a living organism,” he continued. “[In vivo], the tumor cells will shift what they express. They have to deal with being in contact with other cells in the tissue. They’ll have to pull in vasculature, and they’ll have to deal with the immune environment. So, they’re going to shift how they behave.”
To address this problem, other research groups are building organ-on-a-chip systems as a closer approximation of how cells behave in living tissues. These models can have layers of cells supported by an extracellular matrix with a simplified vascular system, recapitulating some of the cell-cell interactions and mechanical forces present in a living organism.
Yu Shrike Zhang, a Harvard Medical School researcher who uses bioprinting, microfluidics, and other techniques to create improved organ-on-a-chip platforms, noted that these models are quite advanced for certain tissue types.5 “For the liver, the models are pretty precise in general,” Zhang said. “It’s been [studied] for a very long time, and people know exactly how it works.”
Indeed, a liver-on-a-chip model developed by the biotechnology company Emulate, Inc., was able to correctly identify drugs known to be toxic or nontoxic to the liver with a sensitivity of 87 percent and a specificity of 100 percent.6
Using organ-on-a-chip models, Zhang said, “In three to five years, I think we can probably get to a pretty high level in terms of testing [toxicity or biological responses] for individual organs.” Modeling interactions between different organs, however, is a more challenging task. Crosstalk between organ systems is complex and incompletely understood, and organs can be influenced, or influence each other, via changes in metabolism, blood circulation, immune function, endocrine signalling, or nervous system activity.7
Scaling is also a concern, according to Zhang. As size changes, different physical forces and properties increase or decrease in different ways. So, it is not yet entirely clear how to best model a 200-pound human body using organ-on-a-chip systems, some of which may be only a few cell layers thick.
“Things are quite complicated, both biologically and in terms of how these devices operate,” said Zhang. “Being able to really reproduce that organism-level interaction—I think that’s still something that will be really important to look into. Maybe that’s something that’s going to be mature in the next three to five years? I mean, no one knows. But I think that’s probably one of the major limitations right now.”
Refine, Reduce, Replace and the Promise of NAMS
Even scientists who are extremely enthusiastic about NAMs seem to view them as a tool to reduce, not completely replace animal testing. Wu, for example, has spent decades developing in vitro models using cardiomyocytes derived from human induced pluripotent stem cells (iPSCs) to improve our understanding of cardiovascular disease. He has created an extensive biobank of human iPSCs, capturing genetic diversity in health and disease, and even founded a company, Greenstone Biosciences, which aims to accelerate the drug discovery process by combining in vitro and in silico approaches.
However, Wu said, “I’m a proponent of using all models. I’m not a proponent of saying that, ‘Oh, in the future, we should just get rid of mouse models.’” Instead, he said, applying these strategies prior to animal testing could greatly reduce the number of animals that would be needed for each experiment, enabling researchers to identify promising targets and screen for well-defined types of toxicity.
For example, Wu was part of a project led by fellow Stanford University cardiovascular biologist Mark Mercola, in which the team used iPSC-derived human cardiomyocytes and machine learning to classify existing drugs as low risk or intermediate/high risk for causing dangerous arrhythmias. Using area under the curve as a measure of the model’s accuracy—for which 0.5 indicates a random classifier and one is a perfect classifier—the model correctly identified risk with an area under the curve value of 0.95.8 In the future, a system like this one could help researchers spot potentially cardiotoxic compounds early in the drug development process. This has the potential to prevent the investment of time, money, and animal lives into investigating a drug that might treat one disease very well but be ultimately useless because of severe adverse effects.
Furthermore, unlike studies performed in strains of genetically identical mice, research with human cells can provide not only general safety predictions, but they also help identify which individuals might be most at risk for particular side effects and even suggest mechanisms for mitigating these effects.
For example, the chemotherapy drug doxorubicin can lead to heart failure in a subset of patients, but for many years, the mechanism of this cardiotoxicity was not known, and there was no way to predict which patients were at risk. In a study of eight breast cancer patients, Wu and his team showed that iPSC-derived cardiomyocytes from patients who experienced this side effect were more sensitive to doxorubicin toxicity than cells from patients who did not. 9 In the future, this could serve as a tool for screening patients prior to treatment. In a subsequent study, the researchers used a CRISPR-based approach to screen cardiomyocytes for genes that contributed to this vulnerability. One gene, which coded for the enzyme carbonic anhydrase 12, seemed to play a large role: When expression of this gene was inhibited in the cells, they were protected from doxorubicin toxicity.10 An antagonist of this enzyme, Indisulam, was also protective in heart cells. Only after all these experiments did the researchers test the drug in mice.
Continue reading below…
Since then, Wu and his team have used iPSC-derived cells, patient data, and AI to identify a candidate compound for the treatment of marijuana-induced vasculature inflammation, and two potential therapies for cardiac fibrosis.11–13 “These three papers all have mouse models, but they’re toward the end,” said Wu. “They’re only done for validation—the initial screen, initial validation, initial design, all that stuff is done [using] organoids, stem cells, and AI.”
The first candidate is currently in a Phase 1 clinical trial for the treatment of inflammation associated with heart failure, the second is in an open-label study for treating idiopathic pulmonary fibrosis. In the coming years, studies such as these will provide crucial data to answer the question of whether these newer drug development techniques can increase efficiency and reduce failure rates in clinical trials.
An Uncertain Future for Drug Development
Much work remains to be done, however, if animal testing is to be truly replaced in the next three to five years. In addition to the development of the NAMs technologies themselves, the FDA roadmap also calls for the creation of open-access toxicity information databases, developing strategies to validate NAMs, determining appropriate thresholds for eliminating animal testing, figuring out how to standardize these techniques so that they can be compared across many different laboratories, coordinating with other federal agencies, and monitoring how well all of this is working.
“Transitioning from animal-based testing to NAMs for safety will require careful planning, robust science, and collaboration,” the roadmap states.
But will this be possible in the chaos currently afflicting many government agencies and the dramatic changes to support for scientific research in the United States? The Trump administration has already terminated 1.8 billion dollars in National Institutes of Health (NIH) grants; the administration’s proposal for the upcoming year would slash the budget of the NIH by 40 percent.14,15
Some of these governmental budget cuts and funding freezes adversely impact the laboratories that have been instrumental in developing the very NAMs technologies the FDA is hoping to promote. For example, Harvard University bioengineer Donald Ingber, a pioneer in organ-chip research and scientific founder of Emulate, Inc., received stop-work orders on two major organ-on-a-chip projects in late April 2025.
Beyond the technologies themselves, planning and collaboration efforts may also be impacted by the major changes at these agencies. So far, 2025 has been marked by many cancelled or postponed scientific meetings at the FDA and NIH, as well as firings of thousands of workers, including many top-level officials and a large portion of communications roles, and the resignation of Peter Marks, director of Center for Biologics Evaluation and Research.
Rubinsteyn, for his part, worries about how reductions in animal testing requirements will play out in such an environment, raising concerns that insufficient oversight could create opportunities for unscrupulous companies to bring potentially unsafe drugs to market.
“I do think that this is, in principle, a positive direction for change,” he said. But depending on how these changes are implemented, “it could go quite wrong.”
Disclosure of conflicts of interest: Yu Shrike Zhang sits on the scientific advisory board and holds options with Xellar Biosystems.
Comparative Study
Toxicol Sci
. 2015 Dec;148(2):355-67. doi: 10.1093/toxsci/kfv189. Epub 2015 Oct 5.
Correlation of In Vivo Versus In Vitro Benchmark Doses (BMDs) Derived From Micronucleus Test Data: A Proof of Concept Study
Lya G Soeteman-Hernández 1, Mick D Fellows 2, George E Johnson 3, Wout Slob 1
Affiliations Expand
- PMID: 26443842
- PMCID: PMC4659532
- DOI: 10.1093/toxsci/kfv189
Abstract
In this study, we explored the applicability of using in vitro micronucleus (MN) data from human lymphoblastoid TK6 cells to derive in vivo genotoxicity potency information. Nineteen chemicals covering a broad spectrum of genotoxic modes of action were tested in an in vitro MN test using TK6 cells using the same study protocol. Several of these chemicals were considered to need metabolic activation, and these were administered in the presence of S9. The Benchmark dose (BMD) approach was applied using the dose-response modeling program PROAST to estimate the genotoxic potency from the in vitro data. The resulting in vitro BMDs were compared with previously derived BMDs from in vivo MN and carcinogenicity studies. A proportional correlation was observed between the BMDs from the in vitro MN and the BMDs from the in vivo MN assays. Further, a clear correlation was found between the BMDs from in vitro MN and the associated BMDs for malignant tumors. Although these results are based on only 19 compounds, they show that genotoxicity potencies estimated from in vitro tests may result in useful information regarding in vivo genotoxic potency, as well as expected cancer potency. Extension of the number of compounds and further investigation of metabolic activation (S9) and of other toxicokinetic factors would be needed to validate our initial conclusions. However, this initial work suggests that this approach could be used for in vitro to in vivo extrapolations which would support the reduction of animals used in research (3Rs: replacement, reduction, and refinement).
Keywords: TK6 cells, benchmark dose
https://www.drugdiscoverynews.com/why-toxicology-is-still-the-toughest-test-for-nam-adoption-17194
Toxicology remains the most challenging field for adopting new approach methodologies (NAMs) as it requires predicting systemic, long-term human health effects that are inherently complex to replicate outside a living organism. While NAMs offer human-relevant data, the industry faces significant hurdles in validating these methods to the same level of trust as traditional animal models.
DDN spoke with Justin Boyd, Product Manager at Sartorius, to explore how NAMs are being applied in practice across drug discovery and safety assessment, and what ultimately determines whether they transition from scientifically compelling tools into routine components of toxicology workflows.
You’ve spent much of your career building biologically relevant cellular models of disease. How does that emphasis on relevance shape how you think about NAMs in toxicology, compared with more traditional animal-based approaches?
I recently joined the vendor side of NAMs. For nearly two decades before that, as a drug hunter, I was less focused on building models and more on applying them. In that context, I thought of NAMs as fit-for-purpose tools to rapidly explore the effects of experimental drugs on the proximal human biology I care about.
Now, as Product Manager of a NAMs portfolio, I still strongly believe in that utility. The strengths of NAMs lie in: (1) conservation of human biology, (2) speed to data-driven decision-making, and (3) cost to execute study. That said, I don’t see NAMs as replacing the value of a whole organism — whether mouse, rat, or non-human primate. A preclinical toxicity study in animals provides a more comprehensive view of how a compound behaves in the context of an intact organism, including systemic interactions that are still not well captured in vitro.
However, NAMs create an opportunity to rank and/or differentiate compounds with higher molecular resolution while remaining “in human.” That kind of insight can meaningfully inform decisions about which compounds are worth advancing into more expensive and time-consuming animal studies.
Ultimately, I think of NAMs for toxicity as key complementary models for evaluating tissue-specific risk to drive decision to go into the animal models, leading to better stewardship of resources for drug discovery and animal welfare.
NAMs are often discussed as ethical or regulatory advances, but from your perspective, where do they most clearly outperform legacy toxicology methods scientifically?
With respect to performance, there are two clear areas where NAMs excel. First, NAMs can recapitulate aspects of human biology more faithfully than preclinical species. This becomes especially important when studying the proximal biology engaged by an experimental drug, where species differences can significantly limit interpretability.
Second, NAMs substantially reduce the time and cost required to reach a decision. From a project or program management perspective, the ability to make informed and confident stage-gate decisions is where the highest value lies. In this context, NAMs enable a more expedient and cost-effective approach to predicting toxicity in the pre-Investigational New Drug (IND) to IND space.
Although, it’s likely that animals will be used at this point, NAMs can and should be deployed to derisk the Good Laboratory Practice (GLP) toxicity studies in animals and potentially reduce the numbers of cohorts and time for treatments.
Many toxicology assays still rely on relatively reductionist systems. How close are we to NAMs that genuinely capture the complexity of chronic diseases like Alzheimer’s or Parkinson’s when it comes to assessing safety?
I think this is a tricky question, and I would start by noting that the complexity of Alzheimer’s (AD) and Parkinson’s disease (PD) pathobiology is part of what limits our ability to clearly distinguish mechanisms that cause disease from those that simply exacerbate progression. As such, “who, when, and how” these diseases are treated and the potential toxicity from treatment remain controversial.
In some cases, NAMs, particularly complex in vitro models with multiple cell types and structures, can recapitulate complex non-cell autonomous biology, such as the impact of inflammation on neuronal health. Moreover, computation-based NAM tools can help predict the trajectory of biology and stratify at-risk populations for toxicity outcomes.
So, when asking how close we are to NAMs that genuinely capture the complexity of chronic diseases like AD and PD, I would say they are, in many ways, as close to recapitulating that complexity as our current understanding allows us to define it.
Drug-induced nephrotoxicity remains a major clinical challenge. From your experience working with human kidney microtissues, why has traditional animal toxicology struggled to predict renal risk in humans?
It sounds cliché, but animals are not humans. In the case of the kidney, there are two key drivers of translational gaps.
First, the expression of key kidney genes and their protein products — particularly those governing transport and metabolism — differs significantly between preclinical species and humans. Second, baseline renal metabolism itself varies across species, further compounding these differences.
Given that the primary function of the kidney is to clear waste, toxins, and excess fluids from the blood, these species-specific differences directly impact our ability to predict nephrotoxicity using traditional animal models.
You’ve worked extensively with 3D human epithelial tissue models. What does moving from 2D cultures to 3D systems fundamentally change in how we understand toxicity mechanisms?
The difference between traditional 2D cultures and 3D systems, in the context of toxicity, is relatively straightforward. By recapitulating tissue structure, 3D models allow us to move beyond simply asking whether a compound is toxic, to understanding where that toxicity occurs and to what extent.
Understanding the relationship between exposure (where a polarized, functional cell sees a compound) and response is uniquely addressed in our systems compared to 2D. This is particularly important in epithelial tissues, where basolateral versus apical exposure can lead to very different toxicity outcomes. In skin, intestine, and lung, for example, cells may be exposed either from the basolateral side via systemic circulation or from the apical side through local administration or environmental contact. That distinction is fundamentally lost in 2D systems.
Do you see NAMs primarily as screening tools, or are they mature enough to inform dose selection, risk stratification, and IND-enabling decisions?
I believe NAMs have always been able to inform dose selection, risk stratification, and IND-enabling decisions. In fact, screening may not be the best deployment of NAMs due to scalability challenges and cost. The appropriateness of a NAM’s utility is dependent upon the limitations of the human biology you can explore within the NAM and the modality of the therapeutic. If the NAM contains the biology that you are targeting and the therapeutic modality is compatible with the model, then the NAM should be appropriate for dose selection, risk stratification and IND decisions.
One advantage you’ve previously highlighted is integrating human tissue models with live-cell analysis. Why is temporal resolution — seeing toxicity unfold in real time — so important?
There is both a practical and a biologically relevant dimension to the importance of temporal resolution in toxicity responses. From a practical standpoint, when developing any assay, identifying the time point at which the signal is maximal is essential for ensuring robustness and is a key part of assay optimization. In the context of toxicity, being able to observe the behavior and toxicity signals over time will enable you to identify the most appropriate time of incubation for maximal signal response.
Biologically, however, toxicity is not a single event — it manifests in different ways depending on mechanism. If you use tool compounds that induce toxicity through different mechanisms, knowing the kinetics of the toxicity response can help resolve whether your assay can distinguish direct and indirect mechanisms leading to toxicity.
In that sense, time to toxicity signal can be as informative as the signal itself, particularly when evaluating unknown compounds. In the context of advanced cell models for toxicity, often the exposure times can be prolonged (days to weeks) to predict clinical outcome.
NAMs can be scientifically compelling but still fail to gain traction. From a product and commercialization standpoint, what determines whether a NAM actually gets embedded into routine toxicology workflows?
This is the $100+ million question. Adoption of any platform is influenced by a range of factors — cost, fit-for-purpose utility, biological relevance, format, and ease of use among them. In practice, different players in the field tend to emphasize the aspects they value most, often based on their own balance of biological relevance versus scalability.
At the moment, traction tends to emerge organically through a “let’s try it and see if it works” approach. This is not unique to NAMs. However, toxicology is a particularly high-bar area, where established gold standards inherently challenge any new model system more than exploratory or discovery settings do. That makes sense: Toxicology groups are ultimately responsible for generating a weight of evidence that supports progression to the clinic.
In that context, NAMs introduce both opportunity and friction. While they offer potentially better predictive insight, they also require additional effort to validate against established approaches — often more effort than is required to continue using what is already accepted. Because of this, I would argue that regulators are the key gatekeepers of NAM adoption in toxicology. Ultimately, they define what is essential versus optional in the data package required to advance into the clinic. In my view, the biggest lever for accelerating adoption is therefore not customer preference, but regulatory acceptance.
What is the incentive to explore better models of toxicology if existing ones are “good enough” to reach regulatory endpoints? We could discuss the ethics and scientific rationale around choosing better, more predictive models. But if NAMs remain encouraged rather than required, it is difficult to expect meaningful acceleration in their uptake. I really hope that regulators recognize that there’s a big difference between accepting NAMs and requiring them. Making NAMs essential for toxicity studies for IND filing would catalyze their adoption far more effectively than incremental product refinement alone.
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About the Author
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Bree Foster, PhD
- Bree Foster is a science writer at Drug Discovery News with over 2 years of experience at Technology Networks, Drug Discovery News, and other scientific marketing agencies. She holds a PhD in comparative and functional genomics from the University of Liverpool and enjoys crafting compelling stories for science.
he 90% myth
Posted: by Chris Magee on 9/06/25
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Why do 90% of new drugs fail?
If you’ve read anything on animal testing, you’ll have read something to the effect that ‘more than 90% of drugs tested in animals fail in humans’. Is that some damning indictment of animal models? Absolutely not. Let’s unpack this a bit.
Note: The 90% statistic refers to regulatory safety testing. Other sorts of animal use, like discovering decapod sentience through ‘curiosity-driven’ basic research, will be discussed in another article since the applications are so broad and the application of the research so complex that percentages are usually meaningless.
Tl;DR:
The drug attrition rate, which isn’t 90%, isn’t due to the use of animals and there’s no such thing as a drug that’s developed and tested using only animals before heading to human trials.
Let’s start at the beginning.
How drugs get licensed
In drug trials, all drugs intended for humans are tested on humans: they are tested and refined through three stages of clinical trial before being licensed for public use. Phase 1 of human testing looks primarily at safety, whereas phases 2 and 3 are for safety and then efficacy. Each stage uses more human volunteers than the last and the later stages might include those with a particular medical condition. There is also a post-licensing stage 4, where new treatments are introduced to the wider population, for instance into the clinic by specialist doctors.
Image: Sanford Health
Adverse reactions noted after a drug is licensed are fed back to the medicine’s regulator, the Medicines and Healthcare Devices Regulatory Authority (MHRA), which might do things like update the safety information in the booklet that comes with the medicines. Lots of medicines have their safety advice updated as the medicine is used in a greater number of patients.
This is because drugs are licensed on the grounds of what they do in general, e.g. shrink a tumour or lower blood sugar. However, to what extent they work in an individual will vary greatly depending on dozens of factors from genetics to weight to hormones – even to the time of day. This is why there are specialist doctors for different diseases, as well as GPs, who take a patient-centric perspective on the medical tools available for that person (or animal). Hence, very few medicines are withdrawn – it’s usually a case of finessing the practice and guidance on using them safely and optimally and adapting their use for specific patients.
Preclinical testing
Before drugs can go to human trials, they must pass a standard battery of safety tests using both animal and non-animal methods. These tests tend to be specified by bodies like the OECD (mainly for chemicals) and the International Conference on Harmonisation (mainly for pharmaceuticals), which can pool knowledge about how to use the best methods of safety testing, whatever these may be.
Non-animal methods of drug testing can perform well but tend to be limited in scope to one organ system or one effect, whereas animal models tend to give a broader picture of how drugs will act in a whole living body and across a dozen organs at once.
For some applications, non-animal methods are enough to conclude that drug development shouldn’t proceed, and the compound is therefore eliminated before hitting either the human or animal testing stages.
With those drugs that do proceed, animals are very good at ‘predicting’ if a drug will be ‘safe’ in the first human trials.
There are different statistical tools that can be used to determine this safety. Bayesian modelling (figure 1 below) can find ‘true positives’ (PPVs) and ‘true negatives’ (NPV) i.e. a percentage certainty that something will be safe in stage 1 human trials. Likelihood ratios (figure 2) offer a probability of safety.
| Bayesian modelling, NPV safety prediction | ||
| Organ category | Dog to human | Mouse to human |
| Pulmonary | 96% | 95% |
| Biochemical | 95% | 93% |
| Renal | 95% | 96% |
| Ophthalmology | 94% | 96% |
| Haematology | 93% | 92% |
| Cutaneous | 91% | 82% |
| Musculoskeletal | 91% | 92% |
| Cardiovascular | 91% | 75% |
| Nervous system | 90% | 93% |
| Liver | 88% | 89% |
| Gastrointestinal | 76% | 69% |
Figure 1: IQ Consortium translational database
| Likelihood ratios | |||
| Pre-test probability | Pre-test odds | Post-test odds | Post-test probability |
| 10% | 0.11 | 3.16 | 76% |
| 20% | 0.25 | 7.11 | 88% |
| 30% | 0.43 | 12.18 | 92% |
| 40% | 0.67 | 18.95 | 95% |
| 50% | 1.00 | 28.43 | 97% |
| 60% | 1.50 | 42.65 | 98% |
| 70% | 2.33 | 66.34 | 99% |
| 80% | 4.00 | 113.72 | 99% |
| 90% | 9.00 | 255.87 | 100% |
Figure 2: Data from https://pubmed.ncbi.nlm.nih.gov/24329742/
Different species of animal do more or less well at translating to humans depending on the target organs, the type of thing being tested and the size of its molecules. These species differences are well-known, as is the fact that you can increase your certainty that something will be safe or not if a rodent and a non-rodent species both yield similar results.
Thus, the normal testing regime uses species like rats, plus a non-rodent species, usually a dog or primate. Around three-quarters of tests involve suffering in the mildest category, such as a blood test, with a quarter in the moderate category and very few in severe. This is because most of the information about the possible dangers of a new drug comes from a post-mortem of the animal that reveals changes to the internal organs and tissues, rather than observing whether a live animal gets sick or not.
The fact that animals are good predictors of safety in humans is important because 40% of potential new drugs are ultimately removed due to failing these pre-human tests. This means that 40% of possible new drugs would have killed or seriously injured humans in phase 1 trials without the pre-human tests (which would be about 900 people a year in the UK).
Preclinical results shape the human trial
But this is not the whole picture. The preclinical tests of all descriptions, including effects seen in animals, human cells, tissue samples and more, help to inform the design of human clinical trials in the first place. For instance, one or several of the tests might hint at potential issues with the liver, so extra measures can be taken to minimise that risk during the human trial.
All drugs have potential side effects, and their use is always a balance of risk vs potential benefit for the individual patient. All of this means that a large number of drugs proceed to human trials as ‘safe enough to try’ but with a question mark over whether their risks will be manageable or not.
An example of this management is paracetamol, which works better as a painkiller if taken regularly every 4-6 hours to allow it to build up in the tissues and bloodstream. However, we all know not to take a day’s dose all at once.
So, what of the 90%?
Drugs ‘fail’ at every stage of development and for several reasons. For every 100 possible drugs that even get to the animal testing stage, some 5,000 other compounds have already been eliminated. Drugs continue to be removed all the way through human testing too, in ever smaller numbers as we zero in on something that’s going to work.
Percentages thus become less and less helpful for understanding what is happening. Having eliminated 5,000 candidates, for instance, we can be left with 10. If three of those 10 fails, then that’s 30%, which sounds massive, but it’s only 0.06% of the huge pile of 5,000 possible drugs we started with.
In the same way, the 90% statistic is easy to misunderstand.
As we’ve seen, 40% of possible drugs are removed as dangerous by the pre-human safety tests that are mainly in animals. The 90% that ‘fail’, then, is 90% of the 60% that pass preclinical trials. Also, by ‘failure’ it means to have failed for the purpose intended – many drugs can later be repurposed even if they fail in their intended application.
What all this means is, for every 100 potential new drugs at the start of the process, 6 will become drugs in the pharmacy, 40 will be removed by preclinical tests and 54 will be removed for other reasons.
Exactly what those reasons are is the critical point.
Of those 54:
- C40-50% (26 drugs) will not be effective at the safe dose (something the animal test isn’t looking for);
- C25-30% (15 drugs) suspected or known toxicities cannot be managed;
- C10-15% (8 drugs) don’t absorb into the body or get to their target organ properly; and
- c10% (5 drugs) fail due to a lack of commercial need or misplaced strategic planning.
In this way, lots of drugs fail to make it to the chemists’ shelves, but this has very little to do with the efficacy of the animal model as a safety screen for stage 1 clinical trials. Animals do that job very well.
What is exciting about new approaches – whether they use animals or not – is that they may be able to chip away at the other reasons for failure (more on this later).
Different targets have different success rates
One other complication is that ‘failure’ rates are not uniform.
Currently, translation from preclinical findings to clinical success varies a lot depending on the disease area. Eye treatments are about 35% successful, vaccines are about 40%. The most complex diseases of the most complex organs have, as you’d expect, a much higher failure rate which skews the averages and gives you this slightly bogus 90% figure by some methods of counting. However, there is no evidence that implicates animal models as the major reason for failure. In fact, researchers who found a c95% drug attrition rate also found that 86% of positive results in animals translated into positive results in humans.
This accords perfectly with the IQ Consortium translation database, of animal to human translation, recreated as a table in figure 1 above, which also averages out at 86%
So, where do NAMs fit in?
The term ‘New Approach Methodologies‘ refers to the subset of non-animal technologies concerned with regulatory testing – i.e. the tests required by governments. Non-animal technologies have been in development and used in drug testing since the early 1970s, being applied alongside animal models to try to design better drugs, better clinical trials and spot potentially dangerous compounds. They have a more limited range of applications than a whole-body system, but can nevertheless be a quick, cheap and useful way of spotting red flags or pointing to a way forward. They are a standard part of the toolkit for drug testing, with their use accelerating exponentially in the past 20 years as technology improves. We have ever-better non-animal tests, which are still limited but can tell us enough in some cases to guide a decision on what compounds to try to turn into medicines.
Organs on chips
Some of these techniques are relatively new approaches like organ-on-a-chip technologies. First conceived in the late 1990s, the first successful chip was developed in 2010. These devices, roughly the size of an AA battery, are made from a flexible, translucent polymer. Inside are tiny tubes, each less than a millimetre in diameter, lined with living cells taken from a particular human or animal organ.
These can spot toxicities ranging from liver issues with new drugs to the effects on animals of industrial chemicals. They can be used early to avoid animal use and some emerging technologies could prevent up to 10% of drugs that would ultimately fail from entering animal trials in the first place. In a study completed in late 2022, for instance, liver chips identified compounds that were deemed safe enough to try by animal models, but would ultimately harm humans in wider testing, with 87% accuracy.
That doesn’t mean it can spot 87% of drug toxicities, but 87% of those that would have failed later and specifically for liver-related safety reasons. Given that 40% of compounds are removed prior to human testing, 30% later fail due to unmanageable toxicity and 30% of those do so due to effects on the liver, using this test routinely would help to reduce the number of drugs that later failed human trials for unmanageable toxicity by around a third, or 4-5 drugs for every 100 entering testing.
However, if also used early in the drug testing process they might also spot toxicities that would previously have needed an animal to detect, and this might be enough to halt testing. Liver toxicity is the reason for 14% of failures during preclinical tests so this would amount to a further 5 compounds per 100 that would not progress to the animal stage. As you can see from liver chip vendor Emulate’s own graphic, their chip reduces animal use, and is applied before animal trials. There would still, by their model, be an 82% failure rate and, of course, most drugs don’t fail for liver-related reasons.
Source: https://emulatebio.com/toxicology/
The UK authorises around 35 new drugs for use each year, yet for every drug approved another 9 fail, which would be around 315 trials, some 10% of which could be halted before hitting the animal or human stage, potentially preventing thousands of research animals from being born. This would undoubtedly save pharma companies money since human trials get more expensive the more they progress – from $ 25 million in Phase 1 to $ 54 million in Phase 3.
The UK’s national centre for Refining, Reducing or Replacing animal use has a project to replace ‘second species’ animals like dogs and primates with computer models that have passed its proof-of-principle stage and are well into development, albeit with another three years of development left to run.
Even if this doesn’t work, it will tell us what we need to do to get it to work. As Jonas Salk, who used primates to create a polio vaccine, once said “There is no such thing as a failed experiment because learning what doesn’t work is a necessary step to learning what does.”
New targets
Animal numbers will inevitably continue their steady march, with an occasional lurch, downward in terms of numbers, but it’s important to understand how all this fits together. Whilst it’s very easy to predict the future in general terms – clean energy, personalised medicine, healthier food – actually getting there is a bit of a slog.
The other big reason for drug failure beyond the liver, for instance, is Torsades de Pointes. French for “twisting of the points” it’s a dangerous heart arrhythmia that’s the reason for a very similar proportion of preclinical and clinical failures as liver problems. It makes heart chips the next big target for validation, with sincere hopes that they can be made to work as well as liver chips.
However, this is the low-hanging fruit on offer in terms of organ chips, with diminishing returns as the targets get harder, and the target systems get more complicated. A test for the heart or a kidney is one thing, a test for the Central Nervous System is quite another. In addition, heart arrhythmia and liver issues are the biggest single areas of failure for safety reasons, but the remaining 40% of reasons affect many other organs, each of which will need its own new animal or non-animal testing strategy.
Where next?
There is no one approach, then, that will create a revolution. We need new approaches, and we need new improvements to old approaches. My latest laptop, for instance, isn’t conceptually different from the first laptop I owned but it’s a lot lighter and faster due to hundreds of innovations across all of its components. Improvements to clinical outcomes will come from organ chips, big data and AI, but also from higher standards of scientific rigour, new animal models, more powerful technology and the synergies that arise from using it all together.
Happily, there are very few regulatory barriers to adopting new non-animal technologies, the ethical framework for using new animal models is well-understood and nobody is opposed to using non-animal methods over animals. In addition, whatever the costs of failure during clinical trials, the cost of preclinical R&D and discovery clocks in at $403 million, making it easily the most expensive single stage in the drug development process. Hence, the greatest savings in cost or animal use associated with improvements in technology may have nothing to do with the requirements of the regulator and can be implemented as soon as new technologies mature.
We do need to accelerate the validation of new animal and non-animal methods now that they’re emerging with rapidly increasing frequency. The OECD, the international association for sharing solutions to common problems, is the curator of scientific guidelines for the testing of chemicals. It makes the point that resources should be made available to test the reproducibility and reliability of new methods developed by single labs so that, if they work, they can be applied more widely, and more quickly. Inherent to their thinking is a bias against animal use.
We also need to make sure that politicians aren’t distracted by ideological sideshows or lured towards counterproductive policy directions, like deadlines that amount to deregulation of harmful industries. whose products are only harmful when metabolised in a whole body. There are concrete measures that governments, or prospective governments, could be proposing but politicians of all stripes need to understand where to apply funding and focus to have a positive impact on man, animals and the environment.
https://www.understandinganimalresearch.org.uk/news/the-90-myth
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