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Complete populations of virtual patients for in silico clinical trials


Sinisi, S; Alimguzhin, V; Mancini, T; Tronci, E; Leeners, B (2021). Complete populations of virtual patients for in silico clinical trials. Oxford University Studies in the Enlightenment:Epub ahead of print.

Abstract

Motivation

Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g. reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patients (VPs), i.e. populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.
Results

We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours. We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalized Mean Absolute Error of 15%, 20% and 35% (90% of the latter dataset is covered within 20% error).

Availability and implementation. Our open-source software is available at https://bitbucket.org/mclab/vipgenerator

Abstract

Motivation

Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g. reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patients (VPs), i.e. populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.
Results

We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours. We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalized Mean Absolute Error of 15%, 20% and 35% (90% of the latter dataset is covered within 20% error).

Availability and implementation. Our open-source software is available at https://bitbucket.org/mclab/vipgenerator

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Reproductive Endocrinology
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications
Language:English
Date:2021
Deposited On:11 Feb 2021 08:57
Last Modified:11 Feb 2021 08:58
Publisher:Liverpool University Press
ISSN:0435-2866
OA Status:Closed
Publisher DOI:https://doi.org/10.1093/bioinformatics/btaa1026
PubMed ID:33325489

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