Using Bioinformatics to Align With FDA Phase Out of Animal Testing
- 23rd April 2025
- Posted by: Breige McBride
- Category: Bioinformatics
The U.S. Food and Drug Administration (FDA) recently announced plans to phase out animal testing requirements for monoclonal antibodies (mAbs) and other related biological drugs. As part of the announcement, they shared their “Roadmap to Reducing Animal Testing in Preclinical Safety Studies.” Both the announcement and roadmap focus on using New Approach Methodologies (NAMs) to reduce, refine and potentially replace the FDA’s animal testing requirement. NAMs detailed in the roadmap include In Vitro Human-Derived Systems, in silico tools and computational modelling. Bioinformatics is highlighted as a key in silico tool.
As a leading bioinformatics provider, we have created this blog as a resource for drug developers who are looking to utilise bioinformatics in their efforts to align development activities with the FDA’s plans.
Using Bioinformatics to Phase Out Animal Testing
Ways that bioinformatics can reduce or replace the need for animal testing in drug discovery and development (including mAB development) are detailed below. The second example “Bioinformatics and in silico Off-target Screening” is taken directly from page 4 of the FDA’s Roadmap to Reducing Animal Testing in Preclinical Safety Studies.
Drug Target Identification
Strategic use of bioinformatics reduces the need for animal models in drug target identification. For example, gene expression and proteomic data derived from healthy and diseased human tissues can be analysed to identify potential drug targets. In fact, integrating results from human data enables researchers to prioritise targets that are most relevant to human biology and associated in the pathology of the disease of interest, further reducing the need for animal models.
Bioinformatics and in silico Off-target Screening
Using databases of human proteins and AI, one could screen a product’s sequence for any unintended targets (such as cross-reactivity to human tissues). In silico tools can analyse whether the drug might bind to similar epitopes in the human proteome, highlighting potential safety concerns that would traditionally be checked via animal tissue cross-reactivity studies or broad receptor binding panels.
Antibody Sequence Prediction and Design
Understanding the relationship between an antibody’s sequence, its resulting structure, and its function allows scientists to rationally design therapeutic antibodies. This process reduces the reliance on early-stage animal testing by enabling in silico screening and optimization.
Bioinformatics can leverage large-scale databases of known antibody sequences to predict both the three-dimensional structure and potential binding properties of novel antibodies.
Key tools such as sequence alignment and homology modelling play a central role in identifying antibody candidates with high affinity for specific targets. These methods not only accelerate the discovery pipeline but also enhance the precision of antibody-based drug development.
Predicting Immunogenicity
An essential aspect of mAb development is determining whether a candidate antibody might provoke an unwanted immune response in humans. Bioinformatics offers powerful tools to assess the immunogenic potential of mAbs by identifying regions within the antibody sequence that could trigger an immune reaction.
By analysing mAb sequences against databases of known immunogenic epitopes, researchers can predict problematic regions early in the design process. These in silico assessments reduce the reliance on initial in vivo testing and help overcome the limitations associated with species differences, particularly in complex systems like human immunity.
Repurposing Existing Datasets
Companies can repurpose their existing data to ask a different question, reducing the need for further animal experiments. For example, if gene expression data has been generated in an animal model of inflammation to assess efficacy, this data may be repurposed to assess on and off target and on and off tissue effects to highlight potential safety issues allowing refinement of the downstream testing strategy.
Data Integration
Bioinformatics enables the integration of various data types, such as genomic, proteomic, and structural data. In turn, these integrated datasets can be used to create more accurate models of how antibodies will behave in humans, reducing or even negating the need for animal testing for this purpose.
These are just some of the ways that drug developers can use bioinformatics to support them in phasing out animal testing. However, every drug discovery and development project comes with its own unique challenges. To find out the specific ways bioinformatics can be used to reduce the need for animal testing in your discovery or development project, contact us today using the form below. Our bioinformatics specialists will be happy to discuss your project and detail how we can help.
Author: Breige McBride, Marketing Manager, Fios Genomics
Reviewed by Fios Genomics bioinformatics experts to ensure accuracy
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