Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Area of Science:

• Oncology
• Translational Medicine
• Biomedical Research

Background:

• Despite advances in understanding cancer biology, including carcinogenesis and the cancer immunity cycle, oncology drug development faces low success rates.
• Key translational drug development objectives in oncology require a more robust framework for effective decision-making.

Purpose of the Study:

• To outline key translational drug development objectives in oncology.
• To summarize literature evidence on using mathematical modeling for a unifying translational framework.
• To provide recommendations for building mathematical models to predict clinical efficacy and toxicity of antineoplastic agents.

Main Methods:

• Literature review summarizing the application of mathematical modeling in oncology drug development.
• Analysis of existing evidence on preclinical to clinical translation frameworks.
• Synthesis of strategies for developing predictive mathematical models.

Main Results:

• Mathematical modeling can provide a unifying framework to address critical questions in oncology drug development.
• Such models can facilitate the prediction of clinical efficacy and toxicity for investigational antineoplastic agents.
• Evidence suggests these models are valuable for go/no-go decisions in preclinical development and for planning early clinical studies.

Conclusions:

• A rigorous, unifying preclinical to clinical translational framework based on mathematical models is essential for improving oncology drug development.
• Mathematical modeling offers a powerful tool to enhance the prediction of drug performance and guide development strategies.
• Implementing such frameworks can significantly improve the efficiency and success rate of bringing new oncology drugs to patients.