How Pharma Can Transition to Non-animal Studies for Investigational Drugs

The draft guidance describes the Center for Drug Evaluation and Research’s (CDER’s) recommendations for validating NAMs to support a drug application. The four core validation principles are:

1. Context of Use: Define the NAMs’ regulatory purpose
2. Human biological relevance of NAMs for assessing toxicity
3. Technical characterization: Establishing scientific confidence through robust, reliable, and reproducible methods
4. Fit-for-Purpose: Assurance that NAMs help in regulatory decision-making

To get more insights on the guidance, read this Biospace article.

The FDA guidance builds on its 2025 Roadmap to Reducing Animal Testing in Pre-clinical Safety Studies. The Roadmap signaled that drug developers should use NAMs as the default, not the exception. Partnering with federal agencies such as the National Institutes of Health, the National Toxicology Program and the Department of Veterans Affairs, the FDA aims to accelerate the validation and adoption of these innovative methods through the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM).

Indeed, global regulators are increasingly supporting the use of NAMs, but harmonization remains a challenge. Take Europe for example. The EMA has set recommendations for 3Rs (reduce, refine, and replace) approaches and NAMs, is moving to revise guidelines for regulatory acceptance of these methods, has formed the joint Committee for Medicinal Products for Human Use/Committee for Veterinary Medicinal Products (CHMP/CVMP) 3Rs Working Party and is contributing to developing NAMs as part of the EMA Innovation Task Force. In addition, several ICH guidelines already accommodate the use of NAMs, including the main guidance for biotechnology derived pharmaceuticals (ICH S6(R1) and the guideline on detection of toxicity to reproduction (ICH S5(R3). However, in these guidelines, the scenarios where NAM-based approaches are considered justified are limited and/or leave significant regulatory uncertainty regarding acceptance of a NAM-based approach

For decades, animal testing has been the standard practice in the pharmaceutical industry for evaluating the safety and efficacy of drugs before they enter the clinic. However, animal tests often do not accurately predict drug effects in humans. Additionally, there are ethical concerns associated with animal testing, particularly regarding the use of costly and difficult-to-source non-human primates (NHPs). Thus, the pharmaceutical industry has been striving for the 3Rs of animal testing for decades.

A range of modeling and simulation tools supports the move away from animal testing. AI-driven modeling and simulation provide rapid, cost-effective solutions for both large and small molecules. The pharmaceutical industry is already deploying established in silico models for a range of applications. One landmark case of successful use of NAMs is the drug, Kimmtrak. This immunotherapy agent (bispecific fusion protein) was approved without any in vivo toxicology data. It targets a human-specific mechanism (anti-CD3 effector domain specific for human CD3) with no binding or activation of T cells in preclinical species. Thus, no relevant animal model was available.

One of the biggest challenges drug developers face in this era of tight funding from investors is making “go/no go” decisions on which candidates to progress through the development process. In silico models can help make earlier assessments on the safety liability of drug candidates. For example, Certara’s ToxStudio® software suite brings together a suite of cutting-edge NAMs to address three critical areas in preclinical safety:

• Cardiac safety (QT prolongation risk): The Cardiac Safety Simulator software uses AI-driven modeling and simulation to assess the pro-arrhythmic risk of drugs with enhanced quantitative structure activity relationship (QSAR) models that predict cardiac ion channel current inhibition, even in the absence of in vitro data.
• Off-target safety: Secondary Intelligence™ software identifies and ranks off-target interactions using quantitative analysis, enabling earlier and more accurate risk assessments.
• Drug-Induced Liver Injury (DILI): Libra™ AI-powered software uses QSAR modeling to identify potential DILI hazards. It was trained on a set of 682 DILI positive and 648 DILI negative compounds. Libra uses the chemical fingerprints of test compounds to return a binary prediction of ‘DILI’ or ‘non-DILI’, as well as the probability associated with that prediction. Thus, by using Libra, discovery scientists can assess the risk of DILI, even prior to compound synthesis.

Figure 2.Opportunities for in silico NAMs throughout drug development

Mechanistic modeling uses in vitro data to simulate human pharmacokinetics and pharmacodynamics (PK/PD). This approach helps determine appropriate dose levels and reduces the need for animal studies.

Immunogenicity refers to the body producing an immune response to a foreign substance. If a drug produces significant immunogenicity, it can pose both efficacy and safety issues. Certara’s IG Simulator (QSP platform) predicts immunogenicity using human data, validated by pharma partners, and supports regulatory submissions without animal data.

The 2022 FDA Modernization Act 2.0 opened the door for replacing expensive animal testing with cheaper, more effective human-relevant methods. However, drug developers need confidence that NAMs will be accepted by regulators. Without clear guidance from the agency and consistent review practices, sponsors will default to animal testing, even when NAMs are more scientifically valid. Here are some ways that you can confidently incorporate NAMs into your development strategy.

Transitioning from animal-based testing to NAMs requires a “weight of evidence” approach. Instead of directly replacing each animal test, sponsors should weigh multiple factors, including the disease being treated, clinical need, and drug target information, as well as clinical, modeling, and in vitro data. Together, these data streams form a more complete and human-relevant picture of drug safety and efficacy.

The FDA will continue expanding the use of NAMs by validating them with real-world clinical data and updating its guidance for including them in drug submissions. It also plans to support pilot programs focused on NAMs, lead efforts to align with international regulatory standards, and build shared data repositories to collect and compare study results.

Additionally, the FDA will train its reviewers to assess NAM evidence and track their effectiveness in streamlining drug development. The pressure will be mounting for drug development programs to adopt NAMs as the US National Institutes of Health (NIH) just announced an end to funding for animal-only studies.

Regulators now expect holistic submission packages. Sponsors should combine NAMs to develop their drug’s evidence base. The NAMs they use must be reproducible, standardized, and benchmarked against clinical outcomes. To ensure both regulatory and patient trust, NAMs must confer predictive power that meets or exceeds traditional animal tests.

The FDA’s eventual goal is to eliminate animal testing for all drugs. But the FDA Roadmap prioritizes reducing non-human primate testing for monoclonal antibodies (mAbs), potentially eliminating an average of 144 NHPs per drug at $50K each. The nonclinical testing of mAbs presents unique challenges due to their precise target specificity, which often means there are no suitable non-human species for study, or NHPs may be the sole viable option.

To facilitate regulators accepting NAMs in mAb programs, companies can focus on a few key steps. These recommendations are particularly relevant for “me too” mAbs that target the same proteins as existing approved therapies. If the target and antibody are already well understood, more animal testing may not add much value.

By carefully comparing the new mAb to approved ones, examining its structure, binding mechanism, potency, and behavior in the body, and utilizing in vitro data and other robust evidence, companies can demonstrate that the safety risks are already well understood. This makes it possible to reduce or skip some animal studies, especially for long-term and reproductive toxicity. The goal is to only use animal studies when truly needed. Otherwise, sponsors should leverage existing clinical data as well as lab and in silico model results to fill knowledge gaps.

To successfully adopt a NAM-based approach, sponsors should connect with the FDA early to discuss their plans. They should also consider participating in pilot programs offered by the agency.

It’s important to use well-validated tools, like the Simcyp physiologically-based pharmacokinetic (PBPK) Simulator and QSP models, and organ-on-chip systems. Sponsors should also clearly document all assumptions and data. Submissions should include human-relevant evidence that ties closely to clinical outcomes.

Early-phase study designs, especially first-in-human trials, should reflect learning from NAMs. Because of minimized animal testing, sponsors should take particular care in monitoring dosing and safety in these studies. Finally, sponsors should align with international guidelines, like those from the ICH, to streamline global regulatory submissions.

We recently gave a webinar on this topic and asked our attendees about their current use of NAMs (Figure 6). The fact that over 40% of respondents reported not having used any NAM means that we must educate drug developers about the potential benefits of these emerging tools. This also suggests that we will see rapid growth in the non-animal alternative testing market in the coming years.

Navigating this shift away from animal testing requires more than just new tools. It demands the right strategy and experience. Certara’s Non-Animal Navigator™ solution positions sponsors to lead confidently in this new era of drug development. To learn more about this topic, watch our webinar.