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Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System.

ABSTRACT: Integrating quantitative systems pharmacology (QSP) into pharmacokinetics/pharmacodynamics (PKPD) has resulted in models that are highly complex and often not amenable to further exploration via estimation or design. Because QSP models are usually depicted using nonlinear differential equations it is not straightforward to apply some model reduction techniques, such as proper lumping. In this study, we explore the combined use of linearization and proper lumping as a general method to simplification of a nonlinear QSP model. We illustrate this with a bone biology model and the reduced model was then applied to describe bone mineral density (BMD) changes due to denosumab dosing. The methodologies used in this study can be applied to other multiscale models for developing a mechanism-based structural model for future analyses.

SUBMITTER: Hasegawa C

PROVIDER: S-EPMC6157701 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System.

Hasegawa Chihiro C Duffull Stephen B SB

CPT: pharmacometrics & systems pharmacology 20180813 9

Integrating quantitative systems pharmacology (QSP) into pharmacokinetics/pharmacodynamics (PKPD) has resulted in models that are highly complex and often not amenable to further exploration via estimation or design. Because QSP models are usually depicted using nonlinear differential equations it is not straightforward to apply some model reduction techniques, such as proper lumping. In this study, we explore the combined use of linearization and proper lumping as a general method to simplifica ...[more]

PMID: 30043496

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Project description:We present a conceptual approach to determine the optimal solution to delivering a health technology, consistent with the objective of maximizing patient outcomes subject to resources available to a publicly funded health system. The article addresses two key policy questions: 1) adding system values through appropriate planning of health services delivery and 2) considering the tradeoff between patient outcomes and costs to the health system through appropriate use of health technologies for conditions with time-dependent treatment outcomes. We develop a health technology optimization framework that considers geographical variation and searches for the best delivery method through a pairwise comparison of all possible strategies, factoring in controlled variables including disease epidemiology, time or distance to hospitals, available medical services, treatment eligibility, treatment efficacy, and costs. Taking variations of these factors into account would help support a more efficient allocation of health resources. Drawing identified strategies together then creates a map of optimal strategies. We apply the proposed method to a policy-relevant health technology assessment of endovascular therapy (EVT) for treating acute ischemic stroke. The best strategy for providing EVT relies on the geographical location of stroke onset and the decision maker's preference for either patient outcomes or economic efficiency. The proposed method produced an optimization map showing the optimal strategy for EVT delivery, which maximizes patient outcomes while minimizing health system costs. In the illustrative case study, there were no tradeoffs between health outcomes and costs, meaning that the delivery strategies that were clinically optimal for patients were also the most cost-effective. In conclusion, the health technology optimization approach is a useful tool for informing implementation decisions and coordinating the delivery of complex health services such as EVT.

Dataset Information

Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System.

Publications

Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System.

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