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A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology
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  • Abdallah Derbalah,
  • Hesham Al-Sallami,
  • Chihiro Hasegawa,
  • Abhishek Gulati,
  • Stephen Duffull
Abdallah Derbalah
University of Otago
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Hesham Al-Sallami
University of Otago
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Chihiro Hasegawa
University of Otago
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Abhishek Gulati
Astellas Pharma Global Development Inc
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Stephen Duffull
University of Otago
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Peer review status:ACCEPTED

30 Apr 2020Submitted to British Journal of Clinical Pharmacology
02 May 2020Submission Checks Completed
02 May 2020Assigned to Editor
07 May 2020Reviewer(s) Assigned
30 May 2020Review(s) Completed, Editorial Evaluation Pending
04 Jun 2020Editorial Decision: Revise Minor
13 Jun 20201st Revision Received
15 Jun 2020Submission Checks Completed
15 Jun 2020Assigned to Editor
15 Jun 2020Review(s) Completed, Editorial Evaluation Pending
16 Jun 2020Reviewer(s) Assigned
22 Jun 2020Editorial Decision: Accept

Abstract

Quantitative systems pharmacology (QSP) is a relatively new discipline within modelling and simulation that has gained wide attention over the past few years. The application of QSP models spans drug-target identification and validation, through all drug development phases as well as clinical applications. Due to their detailed mechanistic nature, QSP models are capable of extrapolating knowledge to predict outcomes in scenarios that have not been tested experimentally making them an important resource in experimental and clinical pharmacology. However, these models are complicated to work with due to their size and inherent complexity. This makes many applications of QSP models for simulation, parameter estimation and trial design computationally intractable. A number of techniques have been developed to simplify QSP models into smaller models that are more amenable to further analyses while retaining their accurate predictive capabilities. Different simplification techniques have different strengths and weaknesses and hence different utilities. Understanding the utilities of different methods is essential for selection of the best method for a particular situation. In this paper, we have created an overall framework for model simplification techniques that allows a natural categorisation of methods based on their utility. We provide a brief description of the concept underpinning the different methods and example applications. A summary of the utilities of methods is intended provide a guide to modellers in their model endeavours to simplify these complicated models.