Evidence map›Paper›PMID 40614771›Full record

ReviewSeminars in liver disease2025

Integrated Patient Digital and Biomimetic Twins for Precision Medicine: A Perspective.

Mark T Miedel, Mark E Schurdak, Andrew M Stern, Alejandro Soto-Gutierrez, Eric von Strobl, Jaideep Behari, D Lansing Taylor

Abstract readReview
In one paragraph

Review in Seminars in liver disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

7 authors.

Mark T MiedelDepartment of Pharmacology and Chemical Biology, Organ Pathobiology and Therapeutics Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
Mark E SchurdakDepartment of Computational and Systems Biology, Organ Pathobiology and Therapeutics Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
Andrew M SternDepartment of Computational and Systems Biology, Organ Pathobiology and Therapeutics Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
Alejandro Soto-GutierrezDepartment of Pathology, Center for Transcriptional Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Eric von StroblDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Jaideep BehariDivision of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
D Lansing TaylorDepartment of Computational and Systems Biology, Organ Pathobiology and Therapeutics Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.

Funding

Pittsburgh Liver Research CenterP30DK120531 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Shuchang Silvia Liu · 2019 to 2026
$10.9M
Qualification of Patient-Derived Biomimetic Liver MPS as Drug Discovery Tools for Drug Metabolism, Toxicity, Drug Efficacy Testing and Clinical Trial Cohort SelectionU2CTR004863 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Alejandro Soto-Gutierrez · 2024 to 2026
$6.1M
University of Pittsburgh Microphysiology Systems Database CenterU24TR002632 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GOUGH, ALBERT H, SCHURDAK, MARK E · 2018 to 2022
$5.1M
Novel Determinants for Progression of Non-Alcoholic Fatty Liver Disease to Hepatocellular Carcinoma and Other Health OutcomesR01CA255809 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BEHARI, JAIDEEP, YUAN, JIAN-MIN · 2021 to 2025
$3.7M
Implementing A QSP Platform to Predict and Test Drugs for Metabolic Associated Fatty Liver Disease Genetic Variants in an iPSC-cell Based Human Biomimetic Liver Microphysiology SystemR01DK135606 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Mark T. Miedel · 2024 to 2026
$1.9M
A vascularized patient-derived iPSC liver acinus microphysiology system as an innovative precision medicine platform for optimizing clinical trial design for nonalcoholic fatty liver diseaseUG3TR003289 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BEHARI, JAIDEEP, SOTO-GUTIERREZ, ALEJANDRO · 2020 to 2021
$1.5M
Re-engineering a human 3D liver tissue model for non-alcoholic fatty liver disease for drug screeningUH3TR004124 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Mark T. Miedel · 2024 to 2026
$716k
InCell 6000 High Content Instrument for Cellular Systems Biology ProgramS10OD012269 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TAYLOR, D. LANSING · 2013 to 2013
$502k
NCATS NIH HHS U24 TR002632NCATS NIH HHS U2C TR004863NCATS NIH HHS UG3 TR003289NCATS NIH HHS UH3 TR004124NCI NIH HHS R01 CA255809NIDDK NIH HHS P30 DK120531NIDDK NIH HHS R01 DK135606NIH HHS S10 OD012269
6 · The paper itself

Abstract

A new paradigm for drug development and patient therapeutic strategies is required, especially for complex, heterogeneous diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD). Heterogeneity in MASLD patients is driven by genetics, various comorbidities, gut microbiota composition, lifestyle, environment, and demographics that produce multiple patient disease presentations and outcomes. Existing drug development methods have had limited success for complex, heterogeneous diseases like MASLD where only a fraction of patients respond to specific treatments, prediction of a therapeutic response is not presently possible, and the cost of the new classes of drugs is high. However, it is now possible to generate patient digital twins (PDTs) that are computational models of patients using clinomics and other "omics" data collected from patients to make various predictions, including responses to therapeutics. PDTs are then integrated with patient biomimetic twins (PBTs) that are patient-derived organoids or induced pluripotent stem cells that are then differentiated into the optimal number of organ-specific cells to produce organ experimental models. The PBTs mimic key aspects of the patient's pathophysiology, enabling predictions to be tested. In conclusion, integration of PTDs and PBTs has the potential to create a powerful precision medicine platform, yet there are challenges.

Indexed as

BiomimeticsPatient-Specific ModelingPrecision MedicineDrug DevelopmentHumansInduced Pluripotent Stem CellsOrganoids

Identifiers

PMID40614771
PMCPMC12312794

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.