What do, can and should we learn from models to evaluate potential anticancer agents?

Area of Science:

• Oncology
• Drug Development
• Translational Medicine

Background:

• Anticancer agent development is a lengthy (10+ years) and costly (up to $500 million) process.
• A high failure rate of investigational anticancer agents in clinical trials suggests issues with preclinical evaluation.
• Current preclinical models may be flawed, misused, or their data misinterpreted, hindering effective drug translation.

Purpose of the Study:

• To review current preclinical models used for anticancer agent evaluation.
• To provide a perspective on necessary information from preclinical studies.
• To suggest future approaches for obtaining data to improve the translation of novel agents into clinical practice.

Main Methods:

• Review of current practices in preclinical anticancer agent evaluation.
• Analysis of the range of available preclinical models.
• Personal perspective on future data generation needs.

Main Results:

• Preclinical models are critical but often inadequate for predicting clinical efficacy of anticancer agents.
• Misinterpretation or misuse of data from preclinical models contributes to high clinical trial failure rates.
• There is a need for improved preclinical models and data interpretation strategies.

Conclusions:

• Enhancing preclinical models and their application is crucial for improving the success rate of anticancer drug development.
• A re-evaluation of how preclinical data informs clinical trial design is necessary.
• Future research should focus on developing more predictive preclinical models to optimize the transfer of novel agents into clinical practice.