Human-Based Models Accelerating Immuno-Oncology Drug Development

Human-Based Models in Preclinical Drug Development: A Growing Focus on Translational Relevance

As pressure grows to generate stronger preclinical evidence earlier in development, biotech companies are increasingly exploring human-based models to improve translational relevance and reduce clinical risk in immuno-oncology programs.

Across biotech, one challenge keeps surfacing in investor meetings, conferences, and founder discussions: early-stage companies are increasingly expected to generate stronger preclinical evidence before securing funding, while generating that evidence itself often requires significant capital. For many innovative biotech programs, this has become a real Catch-22.

Traditional preclinical studies, particularly CRO-led in vivo work, can become expensive very early in development. At the same time, investors and partners are looking not only for efficacy data, but also for translational relevance and a credible path toward clinical success.

The Limitations of Conventional Preclinical Models

Animal models have played a central role in drug development for decades and remain important in many regulatory and research workflows. However, in fields such as immuno-oncology, they do not always fully reflect human biology. Tumor–immune interactions, cytokine signaling, stromal architecture, and the tumor microenvironment can differ significantly between animal models and human patients.

This has increased interest in human-based preclinical systems, including organ-on-chip platforms, patient-derived organoids, and other ex vivo tumor models. By studying drug responses directly in human biological tissue, these approaches can provide more biologically relevant insights into how therapies interact with tumor cells, immune cells, and the surrounding microenvironment.

“In immuno-oncology, treatment response is heavily shaped by complex interactions within the tumor microenvironment — interactions that are often difficult to reproduce in conventional preclinical systems. As the field continues to evolve, there is growing need for human-based models that better preserve these biological dynamics and improve translational relevance earlier in development.”

— Dr. Amir Aref, Director, Center for Personalized & Precision Cancer Therapeutics, Harvard Medical School and Massachusetts General Hospital

Why This Matters in Immuno-Oncology

For immuno-oncology especially, biological context matters. Treatment response is influenced not only by tumor cells themselves, but also by immune cell behavior and interactions within the tumor microenvironment — dynamics that are often difficult to capture in simplified in vitro systems alone.

As a result, there is growing demand for preclinical models that preserve more of the complexity of human tumors while remaining practical and scalable for research use.

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