{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T13:27:49Z","timestamp":1793194069207,"version":"4.5.0"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board, India","doi-asserted-by":"crossref","award":["No. SB\/S9\/Z-03\/2017-XI (2022)"],"award-info":[{"award-number":["No. SB\/S9\/Z-03\/2017-XI (2022)"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between\n                    <jats:italic>in vitro<\/jats:italic>\n                    findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The\n                    <jats:italic>in vitro<\/jats:italic>\n                    liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and\n                    <jats:italic>in vitro<\/jats:italic>\n                    to\n                    <jats:italic>in vivo<\/jats:italic>\n                    extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits:\n                    <jats:italic>i<\/jats:italic>\n                    ) better prediction of intrinsic liver clearance of drugs compared to the conventional model and\n                    <jats:italic>ii<\/jats:italic>\n                    )explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various\n                    <jats:italic>in vitro<\/jats:italic>\n                    liver-on-chip systems. Accurate prediction of\n                    <jats:italic>in<\/jats:italic>\n                    <jats:italic>vivo<\/jats:italic>\n                    clearance from\n                    <jats:italic>in<\/jats:italic>\n                    <jats:italic>vitro<\/jats:italic>\n                    data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic\n                    <jats:italic>in vitro<\/jats:italic>\n                    systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0314083","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T13:43:33Z","timestamp":1736430213000},"page":"e0314083","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":27,"title":["DigiLoCS: A leap forward in predictive organ-on-chip simulations"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2861-4453","authenticated-orcid":true,"given":"Manoja Rajalakshmi","family":"Aravindakshan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chittaranjan","family":"Mandal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Pothen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Schaller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7618-0421","authenticated-orcid":true,"given":"Christian","family":"Maass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"issue":"9","key":"pone.0314083.ref001","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1016\/j.drudis.2019.06.003","article-title":"Impact of organ-on-a-chip technology on pharmaceutical R&D costs","volume":"24","author":"N Franzen","year":"2019","journal-title":"Drug Discovery Today"},{"issue":"1","key":"pone.0314083.ref002","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.nhtm.2014.08.001","article-title":"Animal models in translational medicine: validation and prediction","volume":"2","author":"T Denayer","year":"2014","journal-title":"European Journal of Molecular & Clinical Medicine"},{"issue":"12","key":"pone.0314083.ref003","doi-asserted-by":"crossref","first-page":"5324","DOI":"10.1002\/jps.22726","article-title":"Evolution of a detailed physiological model to simulate the gastrointestinal transit and absorption process in humans, part 1: oral solutions","volume":"100","author":"K Thelen","year":"2011","journal-title":"Journal of Pharmaceutical Sciences"},{"issue":"3","key":"pone.0314083.ref004","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1002\/cpt.37","article-title":"Physiologically based pharmacokinetic modelling in drug discovery and development: a pharmaceutical industry perspective","volume":"97","author":"H Jones","year":"2015","journal-title":"Clinical Pharmacology & Therapeutics"},{"issue":"11","key":"pone.0314083.ref005","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/S0090-9556(24)14938-0","article-title":"Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes","volume":"27","author":"RS Obach","year":"1999","journal-title":"Drug Metabolism and Disposition"},{"issue":"2","key":"pone.0314083.ref006","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1124\/dmd.106.011569","article-title":"Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance","volume":"35","author":"HS Brown","year":"2006","journal-title":"Drug Metabolism and Disposition"},{"issue":"4","key":"pone.0314083.ref007","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1021\/js9702877","article-title":"Physiologically relevant one-compartment pharmacokinetic models for skin. 2. comparison of models when combined with a systemic pharmacokinetic model","volume":"87","author":"MB Reddy","year":"1998","journal-title":"Journal of Pharmaceutical Sciences"},{"key":"pone.0314083.ref008","volume-title":"The AAPS Journal","author":"Y Murata","year":"2022"},{"issue":"10","key":"pone.0314083.ref009","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1007\/s11095-010-0218-3","article-title":"Prediction of human metabolic clearance from in vitro systems: Retrospective analysis and prospective view","volume":"27","author":"D Hallifax","year":"2010","journal-title":"Pharmaceutical Research"},{"issue":"3","key":"pone.0314083.ref010","doi-asserted-by":"crossref","DOI":"10.1098\/rsob.210333","article-title":"Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics","volume":"12","author":"T Shroff","year":"2022","journal-title":"Open Biology"},{"key":"pone.0314083.ref011","unstructured":"European Medicines Agency. 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