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. 2021 Aug;48(4):509-523.
doi: 10.1007/s10928-021-09742-3. Epub 2021 Mar 2.

Reduction of quantitative systems pharmacology models using artificial neural networks

Abdallah Derbalah  1 , Hesham S Al-Sallami  2 , Stephen B Duffull  2
Affiliations

Reduction of quantitative systems pharmacology models using artificial neural networks

Abdallah Derbalah et al. J Pharmacokinet Pharmacodyn. 2021 Aug.

Abstract

Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output relationship. In this study, we explore the use of artificial neural networks for approximating an input-output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together through a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and provides substantial speed boost for use of such models in simulation and control purposes.

Keywords: Machine learning; Model-order reduction; Neural networks; Quantitative systems pharmacology.

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