Translational preclinical efficacy models

Bridging the gap between preclinical discovery and clinical success

The growing prevalence of obesity, MASLD, cardiovascular disease, diabetic kidney disease, sarcopenia and neurodegenerative disorders is driving demand for more effective therapies. However, translating promising preclinical findings into successful clinical outcomes remains a significant challenge.

Predictive models for better decision-making

Preclinical models often fail to capture the complexity of human disease biology. As a consequence, promising drug candidates may fail due to lack of efficacy or safety concerns that more predictive models could have identified much sooner. The result is costly late-stage failures, delayed decisions, and missed opportunities for patients. Improving the translational relevance of preclinical research is one of the most effective ways to accelerate decision-making, reduce development risk, and bring new therapies to patients faster.

That is the gap TNO helps to close. Decades of research have enabled us to develop translational models that bridge the gap between preclinical research and clinical reality. By providing robust insights into efficacy, safety, and mechanisms of action, we help you advance promising therapies with greater confidence and efficiency.

How we work

We help pharma, biotech, and academic partners bridge the gap between exploratory research and clinical application. Four principles guide every study:

• Predictive power. Our models are validated against human data and benchmarked against clinical readouts, increasing confidence that preclinical findings will translate to the clinic.
• Mechanistic understanding. We look beyond descriptive endpoints to uncover biological pathways, disease drivers, and functional biomarkers that explain how and why a treatment works.
• Tailor-made study designs. Every research question demands a different approach. We design each study around your objectives, combining the right models, read-out technologies, and analytical approaches.
• Co-development and innovation. We work closely with partners to develop novel disease models, while integrating emerging technologies such as digital pathology and advanced omics.