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Our CSO shares her thoughts on alternative research methods
Julie Frearson, Senior Vice President and Chief Scientific Officer, recently sat down with us to share her insights on how new alternative methods (NAMs) are transforming drug discovery and development, and what innovation awaits in 2025.
Investments and partnerships are being made to broaden NAMs to animal testing. How will they advance drug discovery and development and impact the design of clinical and nonclinical trials?
Julie: Novel approach methodologies using human cell complex in vitro models and in silico methods have already made significant contributions to how we assess the likelihood of a new therapeutic being safe and effective in humans. This happens in daily decision making within the discovery and early safety phases of the drug development journey. With continued advancements addressing cost, throughput, and robustness of the in vitro technology stack (induced Pluripotent Stem Cells (iPSCs), organoid biology, 3D culture models, and microphysiological systems, we can expect their use to increase and broaden in application.
These models are also used to retrospectively investigate toxicities identified in animals and in human clinical trials. The adoption of NAMs in regulated safety assessment will depend on the identification of current unmet needs by standard in vivo workflows.
We expect that over time, regulatory agencies will become increasingly accepting of NAM-based datasets as part of IND submissions when a specific context of use is well defined. Moving forward we are looking at an evolution in how NAMs are deployed."
Julie Frearson
Earlier this year, Charles River launched the Alternative Methods Advancement Project (AMAP). Tell us more about this initiative and its aim to pursue scientific and technological innovations to reduce reliance on animal testing.
Julie: AMAP is an initiative dedicated to pursuing scientific and technological innovations that help us collaborate and develop new alternative methods to animal testing. AMAP will help us align our activities in strategic partnerships, product and services improvements, and advocacy, ensuring we have a cohesive strategy towards reduction of animal use across our portfolio.
Safety, efficiency, and accessibility are critical to the drug discovery and development journey. How can NAMs, like Virtual Control Groups (VCGs), gain industry and regulatory acceptance so that organizations like ours can make them a standard research option for clients?
Julie: For a NAM to be considered viable by a regulatory agency, it needs to be proven at two main levels: validation — to prove the method is consistent and reliable, and qualification — to prove the method is biologically relevant and the results are readily interpretable by a domain expert reviewer. Agencies like the FDA have special programs designed to support the qualification of a new method for drug development, like the iStand program. Typically, these efforts require partnership between different industry stakeholders to cover the innovation, context of use definition, and the regulatory science expertise needed to develop a method towards acceptance.
Recently we announced a collaboration with Sanofi to explore the use of virtual control groups (VCGs) in nonclinical toxicology, as opposed to a clinical setting where virtual controls are already in use. We look forward to this and other such partnerships, providing the data to support regulatory acceptance in the future. We believe well-designed and appropriately deployed VCGs as part of an animal study have the potential to significantly reduce the industry’s animal use footprint.
Artificial intelligence (AI) and machine learning models demonstrate promising potential in microphysiological systems (MPS). In what ways do we integrate MPS with AI versus traditional animal testing in toxicology studies, and how does this translate to drug development?
Julie: AI methods can advance the field of in silico toxicology by bringing together different representations of drugs, biological characterizations and read-outs, and in vitro data. Early predictive models of hERG, drug-induced liver injury (DILI), genotoxicity, carcinogenicity, and skin sensitization have been developed. Furthermore, AI could power our ability to connect in vitro to in vivo exposure levels of a drug—a vital step to increase the application and utility of in vitro models in drug development. Bringing further promise to the field, FDA scientists recently used AI to develop synthetic animal toxicology data based on legacy animal studies.
Data access and quality is critical to the ongoing application of AI to predictive models of complex biological processes. While developments in large language models (LLMs) and generative AI allow us to ingest data from many sources and formats to support model training, data bias is a problem when considering only legacy datasets. Complex in vitro models, including those working in MPS platforms, offer the opportunity to create large, highly quantitative, ethically derived and intentionally designed data sets, at a reasonable cost, for future model building.
As we turn the page on 2024 and set our sights on 2025, what excites you most about the year ahead?
Julie: My view is that the technology is already here. We need to focus on appropriate implementation of these methods to ensure they have a routine, meaningful impact on drug discovery and development over the next decades. There is a real momentum building around NAMs with multiple agencies focusing their attention on this topic.
For example, the NIH launched the TraCe-MPS and Complement-Animal Research in Experimentation focused on context of use and qualification of multiple methods and the combination power of complex in vitro models and AI respectively; and ARPA-H announced their new CATALYST program on in silico models for efficacy and safety; in addition to an FDA-sponsored NAMs sub-committee which has recently recommended to FDA’s Science Board to create a central office for NAMs. recommended to FDA’s Science Board to create a central office for NAMs.
I am also excited that Retrogenix®, our off-target microarray protein platform for biologics offering an alternative to in vivo based tissue cross reactivity studies, is being considered for the iSTAND program. Data from this platform has already been part of more than 80 IND submissions and we look forward to the opportunity to get it fully qualified to further increase our sponsors’ and reviewers’ confidence in the data its produces and the associated decision making.