Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices
Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate’s mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.
KEY WORDSbest practices biomarkers life cycle of QSP models model assessment virtual patients
The authors would like to thank Dr. Robert Schuck (pharmacologist, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration) for his comments on leveraging genetics to calibrate the model.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
This article reflects the views of the authors and should not be construed to represent the views or policies of the US Food and Drug Administration.
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