ArticlePharmaceutical research2015
MITOsym®: A Mechanistic, Mathematical Model of Hepatocellular Respiration and Bioenergetics.
Article in Pharmaceutical research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed, 41 citations in OpenAlex.
- Quantitative systems toxicology (QST) reproduces species differences in PF-04895162 liver safety due to combined mitochondrial and bile acid toxicity.Pharmacology research & perspectives · 2019Trial
- Using Quantitative Systems Toxicology to Investigate Observed Species Differences in CKA-Mediated Hepatotoxicity.Toxicological sciences : an official journal of the Society of Toxicology · 2018Trial
- RENAsym, a quantitative systems toxicology platform model, designed to predict drug-induced kidney injury indicates polymyxin B and valproate overdose toxicity potential while simultaneously demonstrating acetaminophen and valproate safety at therapeutic levels.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Quantitative Systems Toxicology Predicts Ivacaftor-Induced Oxidative Stress Contributes to CFTR Modulator Hepatotoxicity.Clinical pharmacology and therapeutics · 2026Article
- Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery.Communications medicine · 2025Article
- Drug metabolizing enzymes and transporters, and their roles for the development of drug-induced liver injury.Expert opinion on drug metabolism & toxicology · 2025Review
- Review
- Kinetic Mathematical Modeling of Oxidative Phosphorylation in Cardiomyocyte Mitochondria.Cells · 2022Review
- Comparing the Liver Safety Profiles of 4 Next-Generation CGRP Receptor Antagonists to the Hepatotoxic CGRP Inhibitor Telcagepant Using Quantitative Systems Toxicology Modeling.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- Investigating bile acid-mediated cholestatic drug-induced liver injury using a mechanistic model of multidrug resistance protein 3 (MDR3) inhibition.Frontiers in pharmacology · 2022Article
- The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment.Archives of toxicology · 2021Article
- Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction.Acta pharmaceutica Sinica. B · 2021Review
- The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development.Pharmaceutics · 2021Review
- Dynamic Modeling of Mitochondrial Membrane Potential Upon Exposure to Mitochondrial Inhibitors.Frontiers in pharmacology · 2021Article
- Assessing Effects of BHV-0223 40 mg Zydis Sublingual Formulation and Riluzole 50 mg Oral Tablet on Liver Function Test Parameters Utilizing DILIsym.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.Nature reviews. Drug discovery · 2020Review
- Analyzing the Mechanisms Behind Macrolide Antibiotic-Induced Liver Injury Using Quantitative Systems Toxicology Modeling.Pharmaceutical research · 2019Article
- Quantitative Systems Toxicology Analysis of In Vitro Mechanistic Assays Reveals Importance of Bile Acid Accumulation and Mitochondrial Dysfunction in TAK-875-Induced Liver Injury.Toxicological sciences : an official journal of the Society of Toxicology · 2019Article
- Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.Clinical and translational science · 2018Article
- Modelling the impact of changes in the extracellular environment on the cytosolic free NAD+/NADH ratio during cell culture.PloS one · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMITOsym, a new mathematical model of hepatocellular respiration and bioenergetics, has been developed in partnership with the DILIsym® model with the purpose of translating in vitro compound screening data into predictions of drug induced liver injury (DILI) risk for patients. The combined efforts of these two models should increase the efficiency of evaluating compounds in drug development in addition to enhancing patient care.
methodsMITOsym includes the basic, essential biochemical pathways associated with hepatocellular respiration and bioenergetics, including mitochondrial oxidative phosphorylation, electron transport chain activity, mitochondrial membrane potential, and glycolysis; also included are dynamic feedback signals based on perturbation of these pathways. The quantitative relationships included in MITOsym are based primarily on published data; additional new experiments were also performed in HepG2 cells to determine the effects on oxygen consumption rate as media glucose concentrations or oligomycin concentrations were varied. The effects of varying concentrations of FCCP on the mitochondrial proton gradient were also measured in HepG2 cells.
resultsMITOsym simulates and recapitulates the reported dynamic changes to hepatocellular oxygen consumption rates, extracellular acidification rates, the mitochondrial proton gradient, and ATP concentrations in the presence of classic mitochondrial toxins such as rotenone, FCCP, and oligomycin.
conclusionsMITOsym can be used to simulate hepatocellular respiration and bioenergetics and provide mechanistic hypotheses to facilitate the translation of in vitro data collection to predictions of in vivo human hepatotoxicity risk for novel compounds.
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Registered trials
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.