Evidence map›Paper›PMID 39030888›Full record

ArticleCPT: pharmacometrics & systems pharmacology2024

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator.

Liam Curry, Sarah Alrubia, Frederic Y Bois, Ruth Clayton, Eman El-Khateeb, Trevor N Johnson, Muhammad Faisal, Sibylle Neuhoff, Kris Wragg, Amin Rostami-Hodjegan

Abstract read
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Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Liam CurryCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0009-0000-9941-1536
Sarah AlrubiaCentre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.
Frederic Y BoisCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0000-0002-4154-0391
Ruth ClaytonCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.
Eman El-KhateebCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0000-0002-8365-6528
Trevor N JohnsonCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0000-0003-0778-0081
Muhammad FaisalCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.
Sibylle NeuhoffCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0000-0001-8809-1960
Kris WraggCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.
Amin Rostami-HodjeganCertara Predictive Technologies (CPT), Simcyp Division, Sheffield, UK.ORCID 0000-0003-3917-844X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Indexed as

Computer SimulationModels, BiologicalPharmacokineticsSoftwareCrohn DiseaseDrug DevelopmentHumansObesityObesity, MorbidPharmaceutical PreparationsPharmaceutical Preparations

Identifiers

PMID39030888
PMCPMC11533108

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.