Evidence map›Paper›PMID 10223169›Full record

ReviewClinical pharmacokinetics1999

Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations.

T Lavé, P Coassolo, B Reigner

Abstract readComparative StudyReview
PubMed Publisher
In one paragraph

Review in Clinical pharmacokinetics, 1999. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–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

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

3 authors.

T LavéDepartment of Cardiovascular Research, F. Hoffmann-La Roche Ltd, Basel, Switzerland. thierry.lave@roche.com
P Coassolo
B Reigner

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article reviews the methods available for predicting hepatic metabolic clearance in humans, and discusses their application to the processes of drug discovery and development. The application of these techniques has increased markedly during the past few years because of the improved availability of human liver samples, which has increased the opportunities to use in vitro studies to predict human clearance. The techniques available involve both empirical and physiologically based approaches. Allometric scaling using in vitro data from animals and humans combines certain aspects of both approaches. An evaluation of data retrieved from the literature indicates that, together with in vitro human data, allometric scaling based on a combination of in vitro and in vivo preclinical data can accurately predict clearance in humans. With this approach, 80% of the predictions were within a 2-fold factor of actual human clearance values, with an overall accuracy of 1.6-fold. The uncertainties and inaccuracies in predicting human clearance are related to: (i) the specific method that is used to make the prediction; (ii) the experimental design and the model used to determine the in vitro clearance; (iii) protein binding within the in vitro test system; and (iv) various in vivo factors such as the involvement of extrahepatic metabolism and active transport processes, interindividual variability and nonlinearity in pharmacokinetics. In contrast to purely empirical approaches, the physiological approach to predicting clearance gives an opportunity to integrate some of these complexities and, therefore, should provide more confidence in the prediction of clearance in humans.

Indexed as

PharmacokineticsAnimalsBody WeightHumansLiverPredictive Value of TestsSpecies Specificity

Identifiers

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.