Evidence map›Paper›PMID 30076203›Full record

ReviewDrug metabolism and disposition: the biological fate of chemicals2018

Emerging Kidney Models to Investigate Metabolism, Transport, and Toxicity of Drugs and Xenobiotics.

Piyush Bajaj, Swapan K Chowdhury, Robert Yucha, Edward J Kelly, Guangqing Xiao

Open access · hybridAbstract readReview
In one paragraph

Review in Drug metabolism and disposition: the biological fate of chemicals, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 8% 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 104 citations in OpenAlex.

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  8. Novel emerging cell and organoid systems for the study of drug metabolism and toxicity in humans.Drug metabolism and disposition: the biological fate of chemicals · 2025
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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

5 authors at 2 institutions in 1 country.

Piyush BajajDrug Safety Research and Evaluation (P.B.) and Drug Metabolism and Pharmacokinetics Department (S.K.C., R.Y., G.X.), Takeda Pharmaceutical International Co., Cambridge, Massachusetts; and Department of Pharmaceutics, University of Washington, Seattle, Washington (E.J.K.) Piyush.Bajaj@takeda.com.
Swapan K ChowdhuryDrug Safety Research and Evaluation (P.B.) and Drug Metabolism and Pharmacokinetics Department (S.K.C., R.Y., G.X.), Takeda Pharmaceutical International Co., Cambridge, Massachusetts; and Department of Pharmaceutics, University of Washington, Seattle, Washington (E.J.K.).
Robert YuchaDrug Safety Research and Evaluation (P.B.) and Drug Metabolism and Pharmacokinetics Department (S.K.C., R.Y., G.X.), Takeda Pharmaceutical International Co., Cambridge, Massachusetts; and Department of Pharmaceutics, University of Washington, Seattle, Washington (E.J.K.).
Edward J KellyDrug Safety Research and Evaluation (P.B.) and Drug Metabolism and Pharmacokinetics Department (S.K.C., R.Y., G.X.), Takeda Pharmaceutical International Co., Cambridge, Massachusetts; and Department of Pharmaceutics, University of Washington, Seattle, Washington (E.J.K.).
Guangqing XiaoDrug Safety Research and Evaluation (P.B.) and Drug Metabolism and Pharmacokinetics Department (S.K.C., R.Y., G.X.), Takeda Pharmaceutical International Co., Cambridge, Massachusetts; and Department of Pharmaceutics, University of Washington, Seattle, Washington (E.J.K.).
University of Washington · USTakeda (United States) · US

Funding

XENOBIOTIC BIOTRANSFORMATION AND DISPOSITIONP30ES007033 · NIEHS · UNIVERSITY OF WASHINGTON · PI Nicole Ann Errett · 1995 to 2026
$42.5M
Using iPSCs to model patient outcomes in nephrotic syndromeUH3TR000504 · NCATS · UNIVERSITY OF WASHINGTON · PI HIMMELFARB, JONATHAN · 2014 to 2016
$6.1M
A Microphysiological System for Kidney Disease Modeling and Drug Efficacy TestingUG3TR002158 · NCATS · UNIVERSITY OF WASHINGTON · PI HIMMELFARB, JONATHAN · 2017 to 2018
$2.9M
NCATS NIH HHS UG3 TR002158NCATS NIH HHS UH3 TR000504NIEHS NIH HHS P30 ES007033
6 · The paper itself

Abstract

The kidney is a major clearance organ of the body and is responsible for the elimination of many xenobiotics and prescription drugs. With its multitude of uptake and efflux transporters and metabolizing enzymes, the proximal tubule cell (PTC) in the nephron plays a key role in the disposition of xenobiotics and is also a primary site for toxicity. In this minireview, we first provide an overview of the major transporters and metabolizing enzymes in the PTCs responsible for biotransformation and disposition of drugs. Next, we discuss different cell sources that have been used to model PTCs in vitro, their pros and cons, and their characterization. As current technology is inadequate to evaluate reliably drug disposition and toxicity in the kidney, we then discuss recent advancements in kidney microphysiological systems (MPS) and the need to develop robust in vitro platforms that could be routinely used by pharmaceutical companies to screen compounds. Finally, we discuss the new and exciting field of stem cell-derived kidney models as potential cell sources for future kidney MPS. Given the push from both regulatory agencies and pharmaceutical companies to use more predictive "human-like" in vitro systems in the early stages of drug development to reduce attrition, these emerging models have the potential to be a game changer and may revolutionize how renal disposition and kidney toxicity in drug discovery are evaluated in the future.

Indexed as

AnimalsBiological TransportDrug DiscoveryHumansKidney Tubules, ProximalXenobioticsXenobiotics

Identifiers

PMID30076203
PMCPMC6199623
OpenAlexW2886398600

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.