Evidence map›Paper›PMID 38844719›Full record

ArticleThe AAPS journal2024

Simulating Healthy Participant Pharmacokinetics for Renal and Hepatic Impairment Studies: Retrospective Assessment of the Approach.

John P Prybylski, Yuchen Wang, Vaishali Sahasrabudhe, Vivek Purohit

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Article in The AAPS journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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4 · The record

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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

4 authors.

John P PrybylskiPharmacometrics and Systems Pharmacology, Pfizer, Groton, Connecticut, USA. john.prybylski@pfizer.com.ORCID 0000-0001-5802-0539
Yuchen WangPharmacometrics and Systems Pharmacology, Pfizer, South San Francisco, California, USA.ORCID 0009-0000-2351-2090
Vaishali SahasrabudheClinical Pharmacology, Pfizer, Groton, Connecticut, USA.ORCID 0009-0007-2592-6490
Vivek PurohitPharmacometrics and Systems Pharmacology, Pfizer, Groton, Connecticut, USA.ORCID 0000-0002-9019-0789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present.

Indexed as

Computer SimulationHealthy VolunteersModels, BiologicalHumansKidney DiseasesLiver DiseasesPharmaceutical PreparationsPharmacokineticsRenal InsufficiencyRetrospective StudiesPharmaceutical Preparationshepaticpharmacokineticspharmacometricsrenalsimulationvirtual cohort

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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.