Evidence map›Paper›PMID 41420310›Full record

ArticleCPT: pharmacometrics & systems pharmacology2026

Beyond the Michaelis-Menten: Evaluation of a tQSSA-Based IVIVE Approach for Predicting In Vivo Intrinsic Clearance From Hepatocyte Assays.

Ngoc-Anh Thi Vu, Yun Min Song, Sang Kyum Kim, Hwi-Yeol Yun, Soyoung Lee, Jae Kyoung Kim, Jung-Woo Chae

Erratum issuedAbstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ngoc-Anh Thi VuCollege of Pharmacy, Chungnam National University, Daejeon, Korea.ORCID https://orcid.org/0000-0003-0106-4607
Yun Min SongBiomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Korea.ORCID https://orcid.org/0000-0001-7562-5935
Sang Kyum KimCollege of Pharmacy, Chungnam National University, Daejeon, Korea.ORCID https://orcid.org/0000-0001-5769-0792
Hwi-Yeol YunCollege of Pharmacy, Chungnam National University, Daejeon, Korea.ORCID https://orcid.org/0000-0001-8793-2449
Soyoung LeeCollege of Pharmacy, Chungnam National University, Daejeon, Korea.ORCID https://orcid.org/0000-0001-5799-4489
Jae Kyoung KimBiomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Korea.ORCID https://orcid.org/0000-0001-7842-2172
Jung-Woo ChaeCollege of Pharmacy, Chungnam National University, Daejeon, Korea.ORCID https://orcid.org/0000-0001-6026-7063

Funding

Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University)Basic Science Research Program through the National Research Foundation of Korea (NRF)Chungnam National UniversityInstitute of Information & Communications Technology Planning Evaluation (IITP)Korea Environmental Industry & Technology Institute (KEITI) through Core Technology Development Project for Environmental Diseases Prevention and Management RS-2021-KE001333Korea government (MSIT) RS-2022-00155857Korea government (MSIT) RS-2022-NR069643Korea government (MSIT) RS-2022-NR070856Korea government (MSIT) RS-2023-00278597Korea government (MSIT) RS-2025-25397599Korea Institute for Advancement of Technology (KIAT) RS-2024-00434342Korea Machine Learning Ledger Orchestration for Drug Discovery Project (K-MELLODDY)Korea Ministry of Environment (MOE)Ministry of Education RS-2025-25397599Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea RS-2024-00460694Ministry of Trade, Industry, and Energy (MOTIE)National Research Foundation of Korea (NRF)Senior Health Convergence Research Center based on Life Cycle (Chungnam National University)The Korea Institute of Toxicology (KIT) Research Program 2710008763The Korea Institute of Toxicology (KIT) Research Program KK-2401-01
6 · The paper itself

Abstract

The classical Michaelis-Menten model, under the standard quasi-steady-state approximation (sQSSA), is widely used in in vitro-in vivo extrapolation (IVIVE) studies using hepatocyte or human liver microsomal (HLM) assays to predict intrinsic hepatic clearance (

Indexed as

HepatocytesModels, BiologicalHumansMetabolic Clearance RateMicrosomes, Liverintrinsic clearanceIVIVEMichaelis–Mentensaturation metabolicsQSSAtQSSA

Identifiers

PMID41420310
PMCPMC12823819

What OpenQuestion holds

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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.