Drug development continues to face a critical translation gap: while many candidates show promise in animal studies, the majority do not succeed in human trials. Differences between species can limit how well animal models predict human toxicity, efficacy, or complex biological responses, leading to costly setbacks and delayed therapies for patients. Addressing this challenge doesn’t mean abandoning traditional research, it means strengthening it. By leveraging human-based NAMs, researchers can generate more relevant insights, improve confidence in preclinical findings, and better inform decisions before therapies reach the clinic.
What Are New Approach Methodologies?
New approach methodologies, or NAMs, have the potential to transform our understanding of human health and disease pathways. These are human-based testing platforms that include in vitro systems (organoids, organ-on-chip devices, human cell cultures), in silico models (computational simulations, AI-driven predictions), and in chemico approaches (chemistry-based assays that predict biological interactions). Each methodology offers distinct advantages, but the real transformation happens when they're used in combination.
Unlike traditional approaches that rely on observing disease and drug effects in whole organisms, NAMs allow researchers to isolate specific biological mechanisms, test them under controlled conditions, and scale findings.
The Combinatorial Advantage
The power of NAMs multiplies when methodologies work together. Consider drug-induced liver injury, a leading cause of clinical trial failures and post-market drug withdrawals. An in vitro liver model might detect cellular toxicity. An in silico model could predict which metabolites cause the damage. An in chemico assay might reveal the specific chemical reactions triggering cell death. Separately, each tool provides insight. Together, they create a powerful understanding that's both predictive and explanatory.
This combined approach solves a critical problem: no single test can capture the complexity of human biology. The “complement system”, a cascade of proteins involved in immunity and inflammation, illustrates this perfectly. Complement system dysregulation plays roles in diseases from Alzheimer's to kidney disease, yet studying it requires understanding:
- Protein to protein interactions across 50+ complement components
- Tissue-specific activation patterns
- Genetic variants affecting expression and function
- Drug-target engagement in human contexts
- Off-target effects across multiple organ systems
Traditional models struggle with this complexity because complement proteins differ substantially across species.
Why Traditional Models Fall Short
Animal models do not always work effectively at translating results to human biology. A target that's safe in rats might be toxic in humans simply because the protein's structure varies by a few amino acids.
Beyond biological differences, animal models face practical constraints. They can be expensive, time-consuming, and low-throughput. Testing a single compound in mice might take months and cost tens of thousands of dollars. Scaling to test hundreds of compounds, or exploring combinatorial therapies, becomes prohibitively costly.
The Hard Part: Validation and Integration
Developing NAMs is one challenge. Getting them accepted is another. Regulatory agencies can require extensive validation showing that new methods are reliable, reproducible, and relevant to human health outcomes.
Utilization of NAMs also poses its own hurdles. Most research infrastructure (i.e. labs, workflows, expertise) is built around animal studies. Transitioning to NAMs requires new equipment, new skills, and new ways of thinking about experimental design.
Conclusion
Despite these challenges, NAMs are already transforming corners of biomedical research. Companies are using organ-on-chip platforms for preclinical safety testing. In addition, regulatory agencies have accepted several NAMs as replacements for specific animal tests.
The future requires a coordinated effort across various organizations along with investment in validation studies, standardization of protocols, and training the next generation of scientists in human-relevant methodologies.
