The Economic Case for Human-Based Preclinical Drug Testing: FDA’s NAMs Roadmap Explained
- Yajush Gupta
- 9 sept.
- 6 min de lecture
Dernière mise à jour : 10 sept.
See how NAMs can improve predictive accuracy, optimize development strategies, and reduce reliance on traditional animal studies in monoclonal antibody programs.
What looks like a breakthrough in animals often turns out to be a dead end in humans - 9 out of 10 times. The FDA now admits the obvious: animal biology simply doesn’t predict human biology. That mismatch isn’t just a scientific problem, it’s an economic sinkhole, burning billions on false positives and late-stage failures.
4Dcell spoke with Dr. Eckhard von Keutz, DVM, PhD, former SVP at Bayer and Chair at the Fraunhofer Institute for Toxicology and Experimental Medicine, to discuss the root causes behind the high failure rate of new drugs in development. He explains: "The chronically high attrition rate of new drug candidates traces back to two major factors: the lack of efficacy because animal disease models often fail to capture the true complexity of human biology and pathology, and safety issues, particularly those driven by human-specific mechanisms or idiosyncratic reactions, where animal models cannot replicate the physiological and genetic diversity of patient populations."
His analysis identifies the core issue: "At their core, both challenges reflect the same underlying problem: the poor predictability of traditional preclinical models when it comes to human outcomes." This assessment aligns directly with the FDA's own conclusions in their roadmap to reduce reliance on animals, moving toward NAMs as the default where feasible.
The Economic Burden of Predictive Failure
Take monoclonal antibodies. By current rules, every program must run months of Good Laboratory Practice (GLP) primate studies. Beyond the ethical concerns of using primates, the FDA’s own roadmap puts the math in black and white: a typical mAb program burns through ~144 monkeys, each costing up to $50,000. That’s $7 million in animal expenses before you’ve even cleared early safety gates, inside a development cycle already carrying a $650–750M price tag. The bigger issue is that these primate studies often generate misleading signals. Safe drugs look unsafe in animals, unsafe drugs slip through and hit humans, and companies spend years chasing ghosts.
These biological differences manifest in concrete economic consequences. The FDA roadmap provides specific examples, noting that animal-based data have been "particularly poor predictors of drug success for multiple common diseases including cancer, Alzheimer's and inflammatory diseases"¹. The complexity becomes evident in real-world cases.
Some medications generally recognized as safe in humans may have never passed animal testing. The TGN1412 case exemplifies this disconnect: this monoclonal antibody "caused a life-threatening cytokine release syndrome in human volunteers despite appearing safe in preclinical monkey studies"¹
A Promising Path
Dr. von Keutz sees promise in addressing these fundamental limitations: "In this context, a new generation of systems, collectively termed New Approach Methodologies (NAMs), has the potential to reshape the preclinical landscape by providing better insights than animals often do."
FDA NAMs Roadmap
The FDA's response centers on New Approach Methodologies (NAMs) that encompass in vitro human-based systems, computational modeling, and other innovative platforms designed to evaluate immunogenicity, toxicity, and pharmacodynamics with enhanced human relevance¹. This shift received legislative support through the FDA Modernization Act 2.0, which "explicitly authorized the use of non-animal alternatives (cell-based assays, computer models, etc.) to support an investigational new drug (IND) application"¹.
The economic implications address the predictive failures identified above. The FDA roadmap indicates the agency will "reduce the routine 6-month primate toxicology testing for mAbs that show no concerning signals in 1-month studies plus NAM tests to three months"¹. The FDA announcement makes the agency’s intent explicit: animal studies should become the exception, not the rule in three to five years. Cardiac toxicity remains one of the toughest safety hurdles. Animal hearts simply don’t predict human arrhythmia or cardiotoxic responses.
Here, NAMs are already delivering. Human stem-cell–derived cardiomyocytes can flag arrhythmia risks, and new platforms go further:
Biological Relevance: Human induced pluripotent stem cell-derived cardiomyocytes express the human-specific characteristics that animal models cannot replicate. Advanced platforms like the SmartHeart® system generate cardiac tissues composed of hiPSC-derived cardiomyocytes and cardiac fibroblasts in a 3:1 ratio that mirrors physiological tissue composition².
Comprehensive Assessment: These platforms enable measurement of contractility, Membrane action potential and calcium handling: three fundamental aspects of cardiac function that capture biological complexity. The tissues demonstrate "in vivo function recapitulated in vitro" with ejection fractions reaching 30% and contraction strains of 25% after 7 days in culture².
Standardization: Modern human cardiac tissue systems demonstrate high reproducibility, addressing the need for reliable prediction of human outcomes.
Throughput Capabilities: Scalable platforms generate multiple tissues per experiment, with some systems producing 96 tissues per plate in formats compatible with automated workflows².
Validation Standards
Building Regulatory Confidence in NAMs
The transition to human-based testing requires rigorous validation to ensure regulatory confidence. The FDA roadmap outlines specific validation requirements, including retrospective analyses comparing NAM predictions to known human outcomes, prospective validation trials, and standardization efforts ensuring reproducibility across laboratories¹.
A significant validation milestone demonstrates NAM potential: the Emulate Liver-Chip correctly identified 87% of hepatotoxic drugs that caused liver injury in patients and was accepted into FDA's Innovative Science and Technology for Advancing New Drugs (ISTAND) pilot program¹. The economic benefits of human-based testing extend beyond immediate cost reductions. The FDA roadmap recognizes several advantages:
Improved Decision-Making: Businesses using predictive methods will be "positioned to make better business decisions by making more informed go/no go decisions regarding which therapeutics to advance, which could ultimately lower drug costs"¹.
Enhanced Mechanistic Understanding: Human-specific biology can reveal toxicity mechanisms that animal models cannot detect. This is demonstrated by the development of "in vitro cytokine release assays (CRAs) using human blood and immune cells to screen therapeutic antibodies" following the TGN1412 incident¹.
Risk Mitigation: Systems that better predict human responses represent substantial risk reduction for development investments.
Moving Toward Better Predictive Systems
The FDA's roadmap establishes a clear implementation pathway. The agency will "encourage sponsors to submit NAM data in parallel with animal data to build a repository of experience" and will "offer regulatory relief (e.g. fewer animal study replicates) to those who do so"¹.
The document indicates FDA will "identify a few pilot cases where, based on strong rationale, an animal study is waived in favor of a NAM"¹. The technical requirements align with systems that can provide enhanced insights. The FDA roadmap outlines specific criteria¹:
Reproducibility: Systems must demonstrate consistent results across laboratories through "multi-site validation studies"¹.
Standardization: The document emphasizes the need to "develop standardized protocols for these methods so that results are replicable across laboratories"¹.
Integration Capability: Platforms should be compatible with computational modelling approaches, as the roadmap notes that "an integrative strategy" might combine "a human organ chip for toxicity, a PBPK model for PK, and an AI immunogenicity predictor"¹.
The Vision: Comprehensive Transformation
The FDA roadmap envisions a comprehensive transformation where "no conventional animal testing will be required for mAb safety, and eventually all drugs/therapeutics" with a "comprehensive integrated NAM toolbox (human cell models + computational models)" becoming "the new standard"¹.
This transformation represents systematic change necessary to address fundamental predictive limitations. Enhanced understanding of human-specific mechanisms enables more sophisticated approaches to drug optimization. The documented limitations of animal models, combined with substantial costs of current testing paradigms and availability of validated human-based alternatives, create compelling arguments for this evolution.
The economic implications extend beyond cost comparisons to encompass improved predictive accuracy, enhanced mechanistic insights, and regulatory advantages. Organizations that successfully integrate validated human-based testing capabilities will be better positioned to benefit from this transformation, addressing the fundamental predictive failures at the core of drug development today. As Dr. von Keutz notes, "By adopting human-relevant models early, companies can make more informed go/no-go decisions, ultimately saving both time and capital while advancing safer, more effective therapeutics."
Want to learn more about applying NAMs in your drug development? Contact us to start the conversation.
Disclaimer: The framework and expert insights presented here discuss human-based models in a general context. References are not intended to endorse or highlight any specific commercial products. While 4Dcell supports and applies these approaches, the experts cited are commenting on the broader field rather than any specific product.
References
3.   Frost & Sullivan Market Report - Therapeutic Monoclonal Antibodies (as cited in FDA Roadmap).
4.   NC3Rs - Reducing Animal Use in Monoclonal Antibody Development (as cited in FDA Roadmap).
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