Evidence map›Paper›PMID 42797292›Full record

ReviewPharmaceutics2026

PK-Informed Microphysiological Systems: From Dynamic Dosing to Quantitative In Vitro-In Vivo Translation.

Su Jeong Kang, Sunghyun Bong, Min Jeong Jo, Jae Min Lee, Moon Sup Yoon, Seonmin Park, Yeseung Lee, Yuseon Shin, Hye Jin Lee, Chun-Woong Park and 1 more

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Su Jeong KangCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Sunghyun BongCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Min Jeong JoCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Jae Min LeeCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Moon Sup YoonCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Seonmin ParkCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Yeseung LeeCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Yuseon ShinCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Hye Jin LeeCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.
Chun-Woong ParkCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.ORCID 0000-0001-5329-8443
Dae Hwan ShinCollege of Pharmacy, Chungbuk National University, Cheongju 28160, Republic of Korea.ORCID 0009-0009-6023-9930

Funding

Chungbuk National University BK21 Program 2025Ministry of Education 2026-RISE-11-014-03Ministry of Science and ICT RS-2025-02273102Ministry of SMEs and Startups RS-2025-23523734
6 · The paper itself

Abstract

Conventional in vitro drug evaluation relies largely on static concentration-response assays that fail to reproduce the dynamic pharmacokinetic (PK) profiles observed in vivo, contributing to the gap between preclinical findings and clinical outcomes. Recent advances in microphysiological systems (MPSs), particularly microfluidic organ-on-chip platforms, enable programmable concentration-time profiles that more closely mimic physiological drug exposure. These PK-informed platforms allow systematic investigation of schedule dependency, time-dependent pharmacodynamics (PD), and exposure-driven efficacy under controlled flow conditions. Spatially resolved analytical approaches further reveal heterogeneous drug penetration and metabolic responses within tissues, emphasizing the importance of spatiotemporal PK-PD coupling. Integration of multi-organ and vascularized chip systems with physiologically based pharmacokinetic (PBPK) modeling increasingly supports quantitative in vitro-in vivo translation. This review outlines how PK-informed MPSs can generate dynamic in vitro exposure and response data that inform PBPK modeling, thereby supporting quantitative in vitro-in vivo translation of drug disposition and response.

Indexed as

drug dispositiondynamic drug exposurein vitro–in vivo translationmicrophysiological systemsorgan-on-chipphysiologically based pharmacokinetic modelingPK–PD modeling

Identifiers

PMID42797292
PMCPMC13610437

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.