Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices

AbstractSystems 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 comm on 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 an d 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 quali ty of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QS...
Source: The AAPS Journal - Category: Drugs & Pharmacology Source Type: research