A New Era: Human-First Drug Development

On July 4, 2025, the National Institutes of Health (NIH) unveiled a groundbreaking policy shift poised to redefine the future of biomedical research.

NIH announced that it is moving away from traditional animal-only models and will prioritize cutting-edge, human-based research technologies such as artificial intelligence (AI) that offer greater clinical relevance, precision, and ethical responsibility.

A cornerstone of this initiative is the creation of the Office of Research Innovation, Validation, and Application (ORIVA). ORIVA will lead the development, validation, and integration of alternative research methods, such as organ-on-chip platforms, advanced computational modeling, real-world human data analysis, and AI-driven tools.

This bold strategy reflects a growing body of evidence that human-focused models are not only more predictive, but also more scalable and directly applicable to advancing human health.

Why It Matters: The Translation Imperative

The impetus for this shift arises from a persistent challenge in drug development: the inability of animal models to consistently predict human outcomes.

Time and again, therapies that show promise in animal studies for diseases such as Alzheimer’s and cancer ultimately fail in human clinical trials. In fact, only 10% of drugs entering Phase I clinical trials succeed in becoming approved drug therapies.¹

This disconnect has resulted in billions of dollars lost, delayed breakthroughs, and critical gaps in patient care. An estimated 70% of R&D expenses are attributed to failed projects, resulting in an annual industry cost of about $50–$60 billion for failed oncology trials, alone.² ³

Recognizing this, NIH’s updated strategy emphasizes the need for tools that more faithfully mirror human biology, rather than relying on animal surrogates. Innovative models, including AI-driven simulations, now offer far greater accuracy and reproducibility.

Technologies such as AI are rapidly establishing themselves as the new gold standard in translational science.

VeriSIM Life: Leading the Charge

VeriSIM Life’s AI-powered platform is uniquely positioned to deliver on this new NIH mandate. Our BIOiSIM™ technology uses sophisticated computational models to simulate human physiological and pharmacological responses with unparalleled depth and accuracy.

While the average ROI from a drug development program is only 5.9%⁴, including the risk of drug failures, our hybrid AI platform has shown it can consistently deliver ROIs of over 60%⁵. We achieve this by shortening development  timelines, reducing R&D expense, and by significantly reducing the risk of development failure. Our platform can deliver strong improvements to a company's earnings and stock price.

Instead of relying on species that poorly reflect human biology, VeriSIM builds AI-driven mechanistic, multi-scale models trained on real-world data, validated against known outcomes, and capable of generating insights that are directly translatable to clinical settings.

As NIH calls for a shift toward integrated, human-relevant tools, VeriSIM stands out as a proven solution that not only meets these standards but enhances them. Our platform helps pharmaceutical and biotech companies to de-risk their pipelines, reduce development costs, and accelerate time to market, all while eliminating the need for many forms of animal testing.

From Lab Animals to Digital Models: Replace. Refine. Revolutionize.

VeriSIM’s platform has already enabled researchers to design, test, and refine drug candidates entirely in silico, using human biology as the reference point from day one. The company’s hybrid AI platform has been applied to develop its own drug assets for pulmonary hypertension (PH) and idiopathic pulmonary fibrosis (IPF), achieving Orphan Drug Designation in just three months and accelerating development timelines by more than two years.

Today, four programs powered by VeriSIM’s technology are in clinical trials—clear evidence of its real world impact and translatability.

By reducing the reliance on animal experiments and prioritizing predictive accuracy in humans, VeriSIM is reshaping the drug development paradigm, making it faster, more cost effective, and ethically aligned with the future of science. This approach does not just fit into NIH’s new framework for innovation in translational research, it embodies it.

The Future is VeriSIM Life

VeriSIM’s predictive AI modeling approaches 90% accuracy in predicting which drugs will succeed in clinical trials. We have shown this in numerous case studies across many different therapeutic areas.⁶

Compare this success rate with the pharmaceutical industry’s clinical success rate of only 10%.⁷

Our hybrid AI platform can reduce drug development time by years and has been shown to save millions of dollars in R&D expenses, not only by shortening drug development time, but by reducing wasted funding spent on drugs which fail.

While NIH’s decision to prioritize human-based research signals a profound shift in the biomedical landscape, the next step requires a platform that delivers results. VeriSIM has shown across numerous case studies that its hybrid AI platform can help a pharmaceutical company to successfully develop new drugs consistently and profitably.

1. Asher Mullard, "Parsing clinical success rates," Nature Reviews Drug Discovery, 2016.
2. Asher Mullard, "The high, and redundant, cost of failure in cancer drug development," JAMA Network Open, 2023.
3. Jentzsch V, Osipenko L, Scannell JW, Hickman JA. “Costs and causes of oncology drug attrition with the example of insulin-like growth factor-1 receptor inhibitors JAMA Network Open, 2023.
4. "Deloitte's 15th Annual Pharmaceutical Innovation Report: Pharma R&D Returns Continue Upward for Second Consecutive Year," Deloitte. 2025.
5. VeriSIM Life, Inc.
6. VeriSIM Life, Inc.
7. Asher Mullard, "Parsing clinical success rates," Nature Reviews Drug Discovery, 2016

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