Evidence map›Paper›PMID 22011931›Full record

ArticlePharmaceutical research2012

Novel in vitro-in vivo extrapolation (IVIVE) method to predict hepatic organ clearance in rat.

Ken-ichi Umehara, Gian Camenisch

Abstract read
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Article in Pharmaceutical research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.4field-weighted citation impact, top 6% 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

13 citing papers in PubMed, 63 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Ken-ichi UmeharaDrug-Drug Interaction Section (DDI) Drug Metabolism and Pharmacokinetics (DMPK), Novartis Institutes of Biomedical Research (NIBR) Novartis Pharma AG,, WSJ-153.2.02., Novartis Campus, CH-4002, Basel, Switzerland. ken-ichi.umehara@novartis.com
Gian Camenisch
Novartis (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDrug elimination in the liver consists of uptake, metabolism, biliary excretion, and sinusoidal efflux from the hepatocytes to the blood. We aimed to establish an accurate prediction method for liver clearance in rats, considering these four elimination processes. In vitro assays were combined to achieve improved predictions.

methodsIn vitro clearances for uptake, metabolism, biliary excretion and sinusoidal efflux were determined for 13 selected compounds with various physicochemical and pharmacokinetic properties. Suspended hepatocytes, liver microsomes and sandwich-cultured hepatocytes were evaluated as in vitro models. Based on the individual processes, in vivo hepatic clearance was calculated. Subsequently, the predicted clearances were compared with the corresponding in vivo values from literature.

resultsUsing this in vitro-in vivo extrapolation method good linear correlation was observed between predicted and reported clearances. Linear regression analysis revealed much improved prediction for the novel method (r(2) = 0.928) as compared to parameter analysis using hepatocyte uptake only (r(2) = 0.600), microsomal metabolism only (r(2) = 0.687) or overall hepatobiliary excretion in sandwich-cultured hepatocytes (r(2) = 0.321).

conclusionsIn this new attempt to predict hepatic elimination under consideration of multiple clearance processes, in vivo hepatic clearances of 13 compounds in rats were well predicted using an IVIVE analysis method based on in vitro assays.

Indexed as

Models, BiologicalAnimalsBiliary TractCells, CulturedHepatocytesLiverMaleMicrosomes, LiverPharmaceutical PreparationsRatsRats, Sprague-DawleyPharmaceutical Preparations

Identifiers

PMID22011931
OpenAlexW2025284778

What OpenQuestion holds

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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.