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Updated: Jan 19, 2026
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
Published on: January 22, 2011
Model Informed Drug Development: Novel Oncology Agents are Lost in Translation
Kapil Mayawala1, Dinesh P de Alwis2, Jeffrey R Sachs1
1Quantitative Pharmacology and Pharmacometrics, PPDM, Merck & Co., Inc., Kenilworth, New Jersey.
Abstract:
Excitement around and investment in oncology drug development are at unprecedented levels. To maximize the health impact and productivity of this research and development investment, quantitative modeling should impact key decisions in early clinical oncology including Go/No-Go decisions based on early clinical data, and dose selection for late stage studies.See related article by Bottino et al., p. 6633.
Insights
Quantitative modeling can improve oncology drug development by informing critical decisions. This approach maximizes research investment and enhances the health impact of new cancer therapies.
Area of Science:
- Oncology
- Clinical Pharmacology
- Biostatistics
Background:
- Oncology drug development is experiencing significant investment and excitement.
- Maximizing the return on investment in oncology R&D is crucial.
- Quantitative modeling offers a strategic approach to enhance decision-making.
Purpose of the Study:
- To highlight the importance of quantitative modeling in early clinical oncology.
- To demonstrate how modeling can guide key development decisions.
- To advocate for the integration of modeling in Go/No-Go and dose selection processes.
Main Methods:
- Review of current practices in oncology drug development.
- Discussion of the role of quantitative modeling in decision-making.
- Case examples illustrating the application of modeling in Go/No-Go and dose selection.
Main Results:
- Quantitative modeling provides a data-driven framework for critical decisions.
- Early clinical data analysis using modeling can optimize Go/No-Go decisions.
- Modeling aids in selecting appropriate doses for late-stage studies, improving efficiency.
Conclusions:
- Integrating quantitative modeling into early clinical oncology is essential.
- Modeling enhances the productivity and health impact of oncology R&D investments.
- Strategic application of modeling supports more informed and efficient drug development.
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