Abstract

GEN BiotechnologyVol. 2, No. 1 EditorialFree AccessAnimal Alternatives for Drug DevelopmentHana El-SamadHana El-SamadE-mail Address: helsamad@altoslabs.comEditor-in-Chief, GEN Biotechnology.Search for more papers by this authorPublished Online:15 Feb 2023https://doi.org/10.1089/genbio.2023.29081.editorialAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail The federal mandate for animal testing in drug development has been a U.S. law since 1938. In September 2022, this landscape changed when the U.S. Senate passed the FDA Modernization Act 2.0, S.5002. This act was subsequently adopted by the U.S. Congress in December 2022, ushering in an era where the use of animal testing in drug development is no longer required. The bill does not completely ban testing on animals but enables drug developers to use alternatives when feasible. The new policy, also known as the Reducing Animal Testing Act, was attached to the $1.7 trillion omnibus spending bill.The FDA Modernization Act 2.0 is backed by more than 200 organizations (notably, generics giant Teva was the only pharma backer).1 One of the main supporters, People for the Ethical Treatment of Animals (PETA), argued in a statement that the new law “signals a radical shift in the way drugs and treatments are developed.”2 The group's own Research Modernization Deal calls on the National Institutes of Health (NIH) to “stop using animals in areas of research where we know this approach isn't leading to treatments for humans.”3PETA argues that the cost of animal research is too high and that even drugs that have demonstrated safety in animal studies often go on to cause harm in human subjects in clinical testing. PETA also calls on the NIH to “redirect public funding toward sophisticated, non-animal methods,” arguing that “reliance on animal models diverts funds from more promising areas of research and delays the development of effective drugs and treatments.” Proponents of diverting from animal research to other modalities also argue that the availability of disease appropriate animal models is a large bottleneck. For example, among the thousands of known rare genetic diseases, only a few hundred are being actively researched because there is no valid animal model for most of them.Despite the absence of a definitive census to quantify the correlation between animal studies and drug safety and efficacy in humans across the full drug development landscape, it is clear that the situation is far from ideal. The translation between animal models and humans is notoriously one big sinkhole in the drug development landscape's many valleys of death. The process is also slow and costly. But then, what is the alternative?Turning to TechnologyMany technologies have been proposed to supplement or replace animal use in preclinical studies, including human cell-based assays, organ chips and microphysiological systems, as well as sophisticated computer modeling. But are these technologies, thrust into the spotlight, robust enough to convince regulators and the public that a drug has been adequately vetted and de-risked?But are these technologies, thrust into the spotlight, robust enough to convince regulators and the public that a drug has been adequately vetted and de-risked?The field of organoids, as well as organ chip technologies and microphysiological systems, has seen impressive progress recently and became part of the scientific arsenal during COVID-19 for drug and vaccine testing. These technologies have proved useful for understanding how the SARS-CoV-2 virus interacts with human organs and for testing drug efficiency in genetically diverse backgrounds (e.g., using organoid structures built with genetically diverse cells). In addition, the National Institute for Environmental Health Sciences (NIEHS) at the NIH has made an important investment in developing biochemical and cell-based (in vitro) alternatives to toxicology tests carried out in animals.4 In fact, NIEHS has instated the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) to organize, validate, and disseminate these efforts.At the same time, the use of computational tools, modeling, and simulation has seen an exponential growth across all biomedical sectors. From virtual patients to digital twins, a growing number of companies are developing simulation and artificial intelligence (AI) models with the goal of reducing or replacing the need for animal testing.5 In 2016, the FDA formed a modeling and simulation group of almost 200 scientists who are supporting the implementation of computational models in the regulatory review process across FDA, among other goals.6Despite these activities, the question of whether a rigorous scientific platform exists to replace animal testing has a clear answer—No. So, what remains to be done? Unfortunately, the answer here is also fragmented. Short of a rapid and concerted scientific effort to debate this question, and then establish systems, platforms, metrics, and goals, this powerful opportunity to reduce the use of animals, accelerate time to patients, and decrease failure rate in drug development might go unfulfilled.The omnibus spending bill that passed in 2022 had a provision to earmark $5 million for an FDA New Alternative Methods Program designed to curb animal testing. This is a laughably small war chest for such a daunting task. Other governmental agencies need to step in—the NIH is a prime stakeholder, but specifically the newly formed Advanced Research Projects Agency for Health, the mission of which is to “focus on leveraging research advances for real world impact” by “developing entirely new ways to tackle the hardest challenges in health,” would be the ideal meeting place for this effort.This effort would be a tremendous nexus for biotechnology, bioengineering, computational biology, and the latest advances in AI and machine learning. It will open many horizons above and beyond the issue of animal testing. It should be a priority for the scientific ecosystem and for funding bodies.

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