This episode of the Pharmaron DMPK Insights Podcast Series sees Simon Taylor (Vice President of Drug Discovery at Pharmaron) and Dr. Emile Chen (former Director of Modeling and Translational Biology at GlaxoSmithKline) explore the fascinating relationship between drug concentration and effect (PKPD).
PKPD is key to a range of essential decisions in the drug development process, including initial modality selection, molecule optimization, translational science, and clinical dosage planning.
Simon and Emile utilize their extensive experience in the field to outline the underlying concepts and practical applications of PKPD, as well as explore the concept and application of model-based target pharmacology assessment in conjunction with physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) modeling to enhance decision-making.
The podcast also investigates a range of other key topics, including:
- The core principles of PKPD and their relevance to the discovery project lifecycle
- The application of PKPD in guiding decision-making, including case study examples
- The use of a powerful combination of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling and machine learning (ML) in determining the ideal combination of properties for the targeted pharmacology
References and further reading
- Chen, E.P., Bondi, R.W. and Michalski, P.J. (2021). Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery. Journal of medicinal chemistry, (online) 64(6), pp.3185 3196. DOI: 10.1021/acs.jmedchem.0c02033. https://pubs.acs.org/doi/10.1021/acs.jmedchem.0c02033.
- Gobburu, J.V.S. and Chen, E.P. (1996). Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic - Pharmacodynamic Analysis. Journal of Pharmaceutical Sciences, 85(5), pp.505–510. DOI: 10.1021/js950433d. https://jpharmsci.org/article/S0022-3549(15)50057-9/abstract.
- Gobburu, J.V.S. and Chen, E.P. (1996). Artificial Neural Networks As a Novel Approach to Integrated Pharmacokinetic - Pharmacodynamic Analysis. Journal of Pharmaceutical Sciences, 85(5), pp.505–510. DOI: 10.1021/acs.jmedchem.3c02169. https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c02169.
- Chen, E.P., et al. (2022). Applications of Model-Based Target Pharmacology Assessment in Defining Drug Design and DMPK Strategies: GSK Experiences. Journal of Medicinal Chemistry, 65(9), pp.6926–6939. DOI: 10.1021/acs.jmedchem.2c00330. https://pubs.acs.org/doi/10.1021/acs.jmedchem.2c00330.
- Chen, E.P. (2025). Putting Pharmacokinetics and pharmacodynamics to work in Drug discovery: A Practical guide for pharmaceutical Scientists. Available at: https://www.wiley.com/en-us/Putting+Pharmacokinetics+and+Pharmacodynamics+to+Work+in+Drug+Discovery%3A+A+Practical+Guide+for+Pharmaceutical+Scientists-p-9781119650225.
Acknowledgments
Produced from materials originally authored by Pharmaron Beijing Co., Ltd.
About Pharmaron
Pharmaron (Stock Code: 300759.SZ/3759.HK) is a premier R&D service provider for the life sciences industry. Founded in 2004, Pharmaron has invested in its people and facilities, and established a broad spectrum of research, development and manufacturing service capabilities throughout the entire drug discovery, preclinical and clinical development process across multiple therapeutic modalities, including small molecules, biologics and CGT products. With over 17,000 employees, and operations in China, the U.S., and the U.K., Pharmaron has an excellent track record in the delivery of R&D solutions to its partners in North America, Europe, Japan and China.
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