Evidence map›Paper›PMID 34272702›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Priyanka R Kulkarni, Amir S Youssef, Aneesh A Argikar

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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

3 authors at 2 institutions in 1 country.

Priyanka R KulkarniTakeda Pharmaceuticals International, Cambridge, MA, USA.
Amir S YoussefGlaxoSmithKline, Collegeville, PA, USA.
Aneesh A ArgikarSynteract Inc, Morrisville, NC, USA. Aneesh.Argikar@synteract.com.
GlaxoSmithKline (United States) · USTakeda (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Indexed as

Drug Dosage CalculationsDrug Elimination RoutesHepatocytesHumansIn Vitro TechniquesKineticsMembrane Transport ProteinsMetabolic Clearance RateModels, TheoreticalProteomicsMembrane Transport ProteinsClearanceExtended clearance modelExtrahepatic metabolismIntrinsic clearanceIn vitro–in vivo extrapolationPhysiologically based pharmacokinetic modelsScaling factorsWell stirred model

Identifiers

PMID34272702
OpenAlexW3182463521

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.