The U.S. FDA’s decision to phase out mandatory animal testing requirements for certain drugs marks a watershed moment in the evolution of drug development. For the first time in nearly a century, developers will have a regulatory pathway to bring therapies to market using validated human-relevant models — including organoids, tissue chips, and computational simulations — in place of traditional animal studies. This shift reflects not only scientific progress but also growing ethical expectations and operational demands for faster, more predictive drug development. Yet the transition must be guided by rigorous standards, collaborative validation efforts, and global regulatory alignment to ensure that innovation does not outpace safety. The future of drug development will depend not on abandoning rigor — but on redefining it.
A Transformational Moment for Drug Development
The U.S. Food and Drug Administration’s (FDA) recent announcement that it will begin phasing out the requirement for animal testing in the development of certain new drugs marks a historic inflection point for the pharmaceutical industry. For the first time in nearly a century, drug developers will have a regulatory pathway to bring therapies — beginning with monoclonal antibodies (mAbs) — to clinical trials and market approval without relying on animal models for preclinical safety data. The announcement, detailed in the FDA’s official press release,1 signals a deliberate shift toward embracing cutting-edge technologies like organoids, microphysiological systems (MPS), and artificial intelligence (AI) to assess drug safety and efficacy with human-relevant models rather than surrogate animal species.
This is a significant departure from the regulatory framework that has governed drug development since the passage of the 1938 Food, Drug, and Cosmetic Act. That legislation, spurred by the public health crisis of the Elixir Sulfanilamide tragedy, mandated animal testing as a safeguard against human harm. Over the decades that followed, this requirement became a foundational element of FDA regulation and was expanded upon with additional rules for laboratory practices, safety testing, and product quality standards, all of which centered animal testing as a key source of safety data.2
While the original rationale for animal testing was grounded in public health protection, both the scientific and ethical landscapes have evolved dramatically. Advances in laboratory science have enabled the development of sophisticated in vitro models using human cells and tissues, as well as computational tools capable of simulating complex biological processes. These technologies offer the potential for safety and efficacy assessments that are more directly relevant to human biology, overcoming long-standing challenges with interspecies variability. At the same time, growing public concern over animal welfare and ethical research practices has intensified calls for alternatives to animal testing, with organizations like Humane Society International and Michelson Medical Research Foundation leading advocacy efforts.3,4
Taken together, these forces have created both the opportunity and the imperative to rethink the role of animal testing in drug development. The FDA’s new approach represents a necessary evolution — one that holds tremendous promise for improving drug development efficiency, enhancing human relevance, and reducing animal suffering. But realizing these benefits will require a thoughtful and measured transition, supported by rigorous standards, careful validation of new models, and ongoing collaboration between regulators, industry, and scientific innovators.
The Long Road to Reform
How Animal Testing Became the Gold Standard
The origins of mandatory animal testing in drug development trace back to one of the most notorious public health disasters in U.S. history. In 1937, the Elixir Sulfanilamide tragedy claimed the lives of more than 100 people — many of them children — after a toxic solvent was used in a medicinal preparation without adequate safety testing. The public outcry that followed prompted swift legislative action, resulting in the passage of the Food, Drug, and Cosmetic Act the following year. For the first time, pharmaceutical manufacturers were required to demonstrate the safety of their products before marketing them, and animal testing quickly became the primary tool for generating this safety data.2
Over the following decades, the practice of animal testing was formalized and expanded. In the 1970s, the FDA implemented Good Laboratory Practice (GLP) regulations, establishing standardized procedures for conducting nonclinical laboratory studies, including those involving animals. These rules were introduced in response to growing concerns about the reliability, reproducibility, and integrity of safety data, particularly in the wake of scandals involving fraudulent laboratory practices. GLP regulations were designed to ensure that data submitted to the FDA met minimum standards of quality and transparency, further entrenching animal testing within the drug development process.5
A role for animal testing role was also codified in specific regulatory frameworks for exceptional circumstances. In 2002, the FDA introduced the Animal Rule, which allows drug approval based on animal efficacy studies in cases where human trials would be unethical or infeasible, such as in the development of countermeasures for bioterrorism or emerging infectious diseases. The Animal Rule recognizes the unique value of animal models in certain high-risk scenarios, even as it underscores the limitations of applying human-centered standards of proof to all drugs indiscriminately.6
Building Momentum for Change
Despite this long-standing reliance on animal testing, scientific and ethical critiques of the practice have steadily gained momentum over the past several decades. Central to these critiques has been the rise of the Three Rs principle — replacement, reduction, and refinement — first articulated in 1959 and increasingly embraced in research ethics guidelines worldwide. The Three Rs promote the use of alternatives to animal testing where possible (replacement), minimizing the number of animals used in experiments (reduction), and improving experimental techniques to reduce animal suffering (refinement). These principles have been influential in reshaping laboratory practices and setting the stage for regulatory reform.
At the same time, technological innovation has begun to challenge the scientific necessity of animal models for many aspects of drug development. Advances in in vitro systems, such as organoids and tissue chips, allow for the study of human cells in three-dimensional structures that mimic the function of real organs. MPS, often referred to as "organs-on-chips," integrate these cellular systems with fluid dynamics and mechanical forces to better replicate the in vivo environment. Additionally, computational modeling and AI have enabled researchers to simulate complex biological processes, predict drug behavior, and optimize compound selection before human testing. These tools promise to generate data that is not only ethically preferable but potentially more relevant to human physiology than traditional animal models.7
This convergence of ethical advocacy and scientific progress created the conditions for legislative action. In 2022, Congress passed the FDA Modernization Act 2.0, a landmark update to the original Food, Drug, and Cosmetic Act. This legislation removed the federal requirement that new drugs must undergo animal testing before entering human clinical trials, opening the door for validated non-animal methods to be used as part of regulatory submissions. The FDA Modernization Act 2.0 did not mandate the abandonment of animal testing but provided flexibility for drug developers to use alternative approaches where scientifically appropriate.
This legislative shift signaled a growing recognition that drug development needed to evolve in step with modern science. The FDA’s recent announcement to phase out animal testing requirements for certain drug classes — beginning with mAbs — is the latest and most significant step in this long trajectory. It reflects a broader movement toward making drug development faster, more humane, and ultimately more predictive of human outcomes, while still grounded in rigorous standards of safety and efficacy.
Inside the FDA's New Pathway Beyond Animal Testing
The FDA’s new policy represents a carefully structured departure from its historical reliance on animal testing, rather than an abrupt or universal elimination of the practice. At its core, the policy creates a clear pathway for certain drugs to progress through the regulatory system without the traditional requirement for animal testing. Instead, the FDA will now accept non-animal data generated through validated alternative models as evidence of safety and efficacy, provided those models are scientifically appropriate for the drug in question.1
Under this new framework, developers of mAbs can submit preclinical data derived from human-relevant models, such as organoids, tissue chips, MPS, and advanced computational models, including those powered by AI and machine learning. These technologies are explicitly called out in the FDA’s strategy roadmap as key enablers of the transition away from animal testing. The agency emphasizes that the use of these models must meet high standards of validation and reproducibility, ensuring that data generated from non-animal systems is robust enough to support decision-making about human trials.7
mAbs represent a logical starting point for this new regulatory approach for several scientific and safety-related reasons. Unlike small molecule drugs, which often interact with multiple biological targets and can have off-target effects, mAbs are typically designed to bind with high specificity to a single human antigen. This means that many of the systemic toxicity concerns that necessitate animal testing for small molecules are less relevant for mAbs. Additionally, because antibodies are species-specific by design, their activity in non-human animal models is often limited or even misleading, making human-derived models not only ethically preferable but also scientifically superior in many cases.
While the removal of the animal testing requirement for mAbs is the most immediate and tangible element of the FDA’s policy change, the agency has outlined a broader roadmap for expanding the use of non-animal models across additional drug classes and development stages. The FDA plans to implement pilot projects that will test and evaluate new non-animal methodologies, working closely with industry, academia, and technology developers to validate emerging tools. The roadmap also calls for the creation of standardized validation frameworks for new models, ensuring that data generated from organoids, MPS, or in silico simulations is comparable in rigor and reliability to traditional animal data.7
A critical feature of the new approach is its reliance on risk-based decision making. Rather than mandating the elimination of animal testing across the board, the FDA will assess each drug candidate on a case-by-case basis, considering factors such as the mechanism of action, therapeutic area, and potential toxicity profile. This allows for flexibility while maintaining a strong emphasis on patient safety and data quality.
Ultimately, the FDA’s new policy does not represent a wholesale rejection of animal testing but a rebalancing of regulatory expectations to reflect the current state of science. By opening the door to human-based laboratory models and creating a clear roadmap for their validation and implementation, the agency is setting the stage for a future in which drug development is not only faster and more ethical but also more predictive of real-world human outcomes.
Proof in Practice: Non-Animal Models Already Delivering Results
Recent years have seen growing validation of non-animal testing models in drug development:
Liver-on-a-chip: Used to model human liver metabolism and toxicity, reducing reliance on animal studies for hepatotoxicity screening.
Human cardiac organoids: Applied to study drug-induced cardiotoxicity in precision medicine and safety assessments.
Computational pharmacokinetics: AI-driven modeling systems predicting human absorption, distribution, metabolism, and excretion (ADME) with increasing accuracy.
Corrositex: An FDA-approved synthetic skin model for chemical corrosivity testing, replacing traditional rabbit-based assays.
Lung-on-a-chip: Mimics human lung tissue for studying respiratory toxicity, inhaled drug delivery, and disease modeling — including applications in COVID-19 research.
Blood–brain barrier (BBB) models: Advanced microfluidic systems used to replicate the protective barrier between the bloodstream and the brain — critical for neuropharmaceutical development and CNS toxicity testing.
Skin-on-a-chip: Used for dermal absorption, irritation, and wound healing studies — increasingly replacing animal testing in dermatological product development.
Multi-organ "body-on-a-chip" platforms: Integrated systems that connect multiple organ models (liver, kidney, heart, gut) to simulate systemic human physiology and drug distribution.
EpiDerm™ / EpiSkin™ models: Widely adopted 3D human skin tissue models for testing skin irritation, corrosion, phototoxicity, and permeation — approved in many regulatory settings.
Why This Shift is Good Science — and Good Business
Better Models, Better Medicines
Perhaps the most compelling argument in favor of reducing reliance on animal testing lies in its potential to advance the scientific rigor and relevance of drug development. For decades, a central challenge in pharmaceutical research has been the limited ability of animal models to accurately predict human responses to new therapies. While animal studies have historically provided useful information about toxicity and pharmacokinetics, interspecies differences in physiology, metabolism, and immune responses have often led to false positives and false negatives — drugs that appeared safe or effective in animals but failed in human trials, or vice versa.
By enabling the use of human-derived models, the FDA’s new approach aims to close this translational gap. These models are built from human cells, engineered to replicate key features of human tissues and organs, and often incorporate advanced features like perfusion systems and mechanical stress to mimic the in vivo environment more accurately. This human-centric approach has the potential to improve the predictiveness of preclinical data, enhancing confidence in early-stage drug candidates and reducing costly late-stage failures.
Moreover, the new framework aligns with the growing emphasis on precision medicine — tailoring therapies to the unique genetic and physiological profiles of individual patients. Advanced in vitro and in silico models offer a platform for testing drugs in the context of specific disease states, genetic mutations, or even patient-derived cells, accelerating the development of targeted therapies and personalized treatment strategies. These models also offer new opportunities for studying rare diseases, for which animal models may not exist or may be poorly representative of human pathology.
As computational power increases and machine learning algorithms become more sophisticated, in silico modeling is poised to play an increasingly important role in drug development. Predictive toxicology, virtual screening, and pharmacokinetic modeling can complement laboratory-based studies, reducing the need for animal testing while enhancing the depth and granularity of safety assessments.
Meeting Public Expectations and Raising Industry Standards
Beyond the scientific advantages, the move away from mandatory animal testing resonates deeply with evolving societal values around ethics, animal welfare, and corporate responsibility. Public concern over the treatment of animals in laboratory settings has grown steadily over the past several decades, fueled by advocacy groups who have long argued that the ethical costs of animal testing should not be ignored, particularly when viable alternatives exist.
By enabling validated non-animal testing pathways, the FDA’s new policy positions the pharmaceutical industry to align more closely with public expectations for ethical research and development practices. This shift may improve the perception of drug developers among consumers, investors, and regulators, fostering greater trust in the integrity of the drug development process.
Operationally, reducing reliance on animal testing may also yield important cost and timeline efficiencies. Animal studies can be time-consuming, expensive, and logistically complex, requiring specialized facilities, ethical approvals, and extensive monitoring. In contrast, in vitro and in silico models can often be scaled more easily, automated, and integrated with digital platforms, enabling faster data generation and more agile decision-making. While the initial investment in developing and validating these models may be substantial, the long-term efficiencies could prove significant, particularly for companies developing large portfolios of mAbs or other biologics.
The Hard Part: Getting It Right Without Cutting Corners
Risks of Moving Too Fast
While the FDA’s new approach represents a necessary evolution for drug development, its success will depend on careful and deliberate implementation. Chief among the risks is the possibility of moving too quickly — embracing new in vitro and in silico models without the rigorous validation necessary to ensure patient safety and regulatory confidence. As promising as organoids, MPS, and computational tools may be, these technologies vary widely in their maturity, standardization, and predictive power. Not all models are created equal, and without clear, validated frameworks for their application, there is a danger that poorly characterized systems could undermine both scientific credibility and public trust.
Industry readiness is another critical concern, particularly for smaller biotechnology companies and contract research organizations (CROs) that may lack the resources or expertise to rapidly adopt new testing paradigms. While large pharmaceutical companies often have internal capabilities to develop and validate alternative models, many smaller developers rely on external partners for their preclinical studies. If CROs and other service providers are slow to adapt, this could create bottlenecks or lead to uneven adoption across the industry. Additionally, a fragmented ecosystem where some companies embrace non-animal testing while others remain dependent on traditional models could complicate regulatory review and hinder innovation.
Global regulatory harmonization presents yet another challenge. While the FDA’s policy change is a landmark step, it is not yet universally mirrored by other major regulatory bodies, such as the European Medicines Agency (EMA) or Japan’s Pharmaceuticals and Medical Devices Agency (PMDA). Many drug developers operate globally, and divergent regulatory expectations could lead to confusion, duplication of effort, or delays in bringing new therapies to market. Until there is broader international alignment, companies may face complex decisions about which testing strategies to pursue, balancing ethical goals with pragmatic considerations about market access and regulatory approval.
Building Trust with Rigorous Standards
The key to overcoming these challenges lies in the establishment of rigorous standards and governance mechanisms to guide the development, validation, and application of new testing models. The FDA’s roadmap outlines a commitment to creating structured validation frameworks for in vitro and in silico systems, ensuring that these methods are scientifically robust, reproducible, and appropriate for their intended uses.7 Without such standards, there is a risk that enthusiasm for innovation could outpace the safeguards necessary to protect patients and preserve the integrity of the drug development process.
Industry consortia, academic collaborations, and public–private partnerships will play a vital role in this effort. Shared data repositories, consensus protocols, and collaborative validation studies can accelerate the development of new models while distributing the burden of proof across multiple stakeholders. Organizations like the Innovative Medicines Initiative in Europe or the FDA’s own Predictive Toxicology Roadmap provide useful precedents for these types of collaborative initiatives. Moreover, open science principles and transparent reporting of both successes and failures in model development will be essential for building a robust evidence base.
Lessons from the past can also inform the path forward. The implementation of GLP regulations in the 1970s provides a powerful example of how industry and regulators can work together to create systems of quality assurance for preclinical testing. GLP established a framework for ensuring that animal studies were conducted with consistency, integrity, and transparency.5 A similar approach will be necessary for non-animal models — not just to validate individual systems but to build a culture of data integrity and accountability that transcends specific technologies.
A Roadmap for Sustainable Implementation
The FDA’s decision to phase out mandatory animal testing for certain drugs is not a singular event but the beginning of a long-term process of regulatory evolution. The agency has outlined a phased approach to implementation, with pilot programs, data collection initiatives, and stakeholder engagement designed to guide the transition and inform future policy development.1,7 This deliberate strategy reflects both the complexity of the challenge and the FDA’s recognition that sustainable change must be grounded in evidence, collaboration, and flexibility.
Central to this roadmap is the FDA’s commitment to ongoing data collection and analysis. As drug developers begin to submit data from alternative models in lieu of animal studies, the agency will carefully evaluate the performance of these methods in regulatory decision-making. This feedback loop will enable the FDA to refine its guidance, identify gaps in model predictivity, and promote the continued advancement of non-animal technologies.1
The industry has a vital role to play in supporting this transition. Investment in research and development for new preclinical models, validation studies, and cross-sector collaborations will be essential. Companies that lead in developing, sharing, and standardizing innovative human-relevant models stand to shape the regulatory landscape and gain strategic advantages in efficiency, cost reduction, and public trust. Smaller companies, meanwhile, may benefit from forming partnerships with academic groups, CROs, and technology providers to access the expertise and infrastructure needed for non-animal testing.
Finally, aligning these efforts with global regulatory bodies will be crucial for maintaining consistency and avoiding unnecessary duplication of testing requirements. The FDA’s leadership in this area may catalyze similar reforms by agencies such as the EMA and PMDA, but proactive engagement and international collaboration will be necessary to harmonize standards, accelerate global adoption, and ensure that the benefits of this transition are realized worldwide.
The Future of Drug Development Starts Now
The FDA’s move to phase out mandatory animal testing for certain drugs represents an essential evolution for a modern biopharmaceutical industry — one that reflects both scientific progress and shifting societal values. This is not a shortcut or a relaxation of standards. On the contrary, it is an opportunity to elevate the scientific foundation of drug development by leveraging next-generation preclinical tools and cutting-edge laboratory technologies.
The transition away from animal testing will not be without challenges. But with careful governance and collaborative effort, this shift has the potential to deliver safer medicines, developed more efficiently, and with greater ethical responsibility.
This is a pivotal moment for developers, regulators, and technology providers alike. The future of drug development will belong to those who embrace innovation while respecting the principles of scientific integrity. By working together to establish robust standards, share data transparently, and align global regulatory expectations, the biopharma community can ensure that this historic transformation delivers on its full promise — not only for patients but for the future of medical science itself.
References
1. FDA Announces Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs. U.S. Food and Drug Administration. 10 Apr. 2025.
2. Junod, Suzanne White. “FDA and Clinical Drug Trials: A Short History.” U.S. Food and Drug Administration. Originally published in A Quick Guide to Clinical Trials. Madhu Davies and Faiz Kerimani, eds. Washington: Bioplan, Inc.: 2008.
3. Block, Kitty and Sara Amundson. “We’re calling on the FDA to save animals by modernizing drug testing.” Humane World for Animals. 15 May 2024.
4. Michelson, Gary and Aysha Akhtar. “Finding cures faster: Bring the FDA into the 21st century with advanced testing.” STAT News. 4 Mar. 2022.
5. Good Laboratory Practice and Compliance Monitoring. OECD. Accessed 11 Apr. 2025.
6. “Animal Rule Information.” U.S. Food and Drug Administration. 15 Dec. 2024.
7. Roadmap to Reducing Animal Testing in Preclinical Safety Studies. U.S. Food and Drug Administration. Accessed 11 Apr. 2025.