A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology

Derbalah, Abdallah; Sallami, Hesham Al; Hasegawa, Chihiro; Gulati, Abhishek; Duffull, Stephen B.

doi:10.22541/au.158872382.29129480

Cited by 3 publications

(3 citation statements)

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“…A purpose of creating virtual populations is to explore the influence of between‐subject and within‐subject variabilities in the system. Because we contend that this may be difficult to realize effectively by reviewing profiles of state variables for plausibility, depending on the aims of the analysis, one suggestion is to consider a model‐order reduction method 8 approach to render the full systems model into a smaller mechanistic input–output model that can be used to estimate the between‐subject variances in the parameters based on the available data and thereby avoid the issues associated with generating virtual patients.…”

Section: Inferencementioning

confidence: 99%

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models

Duffull

Gulati

2020

CPT Pharmacom & Syst Pharma

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Section: Inferencementioning

confidence: 99%

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models

Duffull

Gulati

2020

CPT Pharmacom & Syst Pharma

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“…a subset of genes whose expression predicts response to a drug treatment. FS methods are data-driven and can inform QSP model structure by identifying the minimal physiologically meaningful representation to enable mechanistic interpretation and prediction [49]. The higher efficiency achieved using FS is clear when comparing a FS ?…”

Section: Dimension Reductionmentioning

confidence: 99%

Two heads are better than one: current landscape of integrating QSP and machine learning

Zhang

Androulakis

Bonate

et al. 2022

J Pharmacokinet Pharmacodyn

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Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

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“…However, due to their size and complexity, the application of QSP models outside of specialised settings is challenging. Derbalah et al 11 argue that reducing QSP models, to create simpler models while retaining the essential mechanistic components, will have an important impact on pharmacometrics moving forward. The authors present formal strategies for QSP model reduction that can facilitate the utility of these complex models (e.g., parameter estimation) while retaining their predictive qualities.…”

mentioning

confidence: 99%

Perspectives on the past, present, and future of pharmacometrics

Bies

Wright

2022

Brit J Clinical Pharma

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In October 1966, the futuristic television show Star Trek aired an episode called "Miri" in which the crew of the starship Enterprise became trapped on a planet in the middle of a viral pandemic. The ship's doctor, clearly well trained in medical chemistry, drug delivery, and biopharmaceutics, quickly developed a remedy. However, the science officer was not able to access the ships computer to conduct the necessary simulations to find a safe and effective dose. They were forced to test possible doses using a trial and error approach, nearly poisoning a crew member in the process. Unknowingly, the writers of this episode correctly predicted the important place that pharmacometrics would eventually assume in drug development.Pharmacometrics is the use of mathematical models to describe and predict drug response. It is now an integral part of Phase 1, 2, and 3 data analysis and an important methodology for preclinical and postmarketing trials. As stated by Gobburu 1 in his insightful commentary

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A Framework for Simplification of Quantitative Systems Pharmacology Models in Clinical Pharmacology

Cited by 3 publications

References 0 publications

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models

Potential Issues With Virtual Populations When Applied to Nonlinear Quantitative Systems Pharmacology Models

Two heads are better than one: current landscape of integrating QSP and machine learning

Perspectives on the past, present, and future of pharmacometrics

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