Evidence map›Paper›PMID 41892047›Full record

ReviewBiosensors2026

Organs-on-Chips in Drug Development: Engineering Foundations, Artificial Intelligence, and Clinical Translation.

Nilanjan Roy, Luca Cucullo

Abstract readReview
In one paragraph

Review in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
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.

Nilanjan RoyDepartment of Computer Sciences, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI 53706, USA.
Luca CuculloDepartment of Foundational Medical Studies, Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA.ORCID 0000-0002-2827-7162

Funding

Repurposing Metformin to Offset Stroke Risk and Injury in Comorbid Populations of SmokersR01NS117906 · NINDS · TEXAS TECH UNIVERSITY HEALTH SCIS CENTER · PI ABBRUSCATO, THOMAS J, CUCULLO, LUCA · 2020 to 2024
$2.9M
NINDS NIH HHS R01 NS117906
6 · The paper itself

Abstract

Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010-2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK-PD, and precision medicine.

Indexed as

Artificial IntelligenceBiosensing TechniquesDrug DevelopmentLab-On-A-Chip DevicesHumansMicrofluidicsMicrophysiological SystemsTranslational Research, Biomedicalartificial intelligencebiosensorsmicrofluidicsmicrophysiological systemsmulti-omicsorgan-on-a-chipprecision medicinevalidation

Identifiers

PMID41892047
PMCPMC13024081

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.