QP-27

K. Gadkar, S. Ramanujan; Genentech, South San Francisco, CA

BACKGROUND: The area of Quantitative Systems Pharmacology (QSP) is seeing increasing adoption and efforts in pharmaceutical research. QSP, however, represents a different approach than traditional pharmacometric M&S, and thus, involves different technical considerations.
METHODS: QSP models are typically less driven by fitting of individual datasets but involve integration of diverse datasets to enable the mathematical representation of the biology of interest, expanding the scope of intended applications. Further, QSP models are often used for testing biological/clinical hypotheses and for predictions in scenarios or patient populations where clinical data is limited. These novel aspects of QSP necessitate different technical workflows and approaches. Here we present a robust workflow that, in its entirety or in sections, has been successfully applied in QSP-based efforts to address many of the novel challenges these efforts face.
RESULTS: This workflow involves: (1) initial data evaluation and scope specification; (2) model structure identification and implementation; (3) calibration & validation of “reference” virtual subjects and (4) of alternate virtual subjects and virtual populations; (5) model-based prediction; and (6) iteration with laboratory and clinical data acquisition. Technical approaches for each of these stages are discussed, including: aggregation of diverse data; selection of modeling formalism; development and identifiability of model structure(s); parameter optimization, sensitivity, and uncertainty/variability; resulting robustness of associated predictions; and experimental design guidance.
CONCLUSION: In addition to proposing a systemic model development framework, we also review published systems modeling efforts that illustrate this workflow and the technical approaches discussed.