Literature DB >> 22875368

In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym™: a mechanistic, mathematical model of DILI.

Brett A Howell1, Yuching Yang, Rukmini Kumar, Jeffrey L Woodhead, Alison H Harrill, Harvey J Clewell, Melvin E Andersen, Scott Q Siler, Paul B Watkins.   

Abstract

Drug-induced liver injury (DILI) is not only a major concern for all patients requiring drug therapy, but also for the pharmaceutical industry. Many new in vitro assays and pre-clinical animal models are being developed to help screen compounds for the potential to cause DILI. This study demonstrates that mechanistic, mathematical modeling offers a method for interpreting and extrapolating results. The DILIsym™ model (version 1A), a mathematical representation of DILI, was combined with in vitro data for the model hepatotoxicant methapyrilene (MP) to carry out an in vitro to in vivo extrapolation. In addition, simulations comparing DILI responses across species illustrated how modeling can aid in selecting the most appropriate pre-clinical species for safety testing results relevant to humans. The parameter inputs used to predict DILI for MP were restricted to in vitro inputs solely related to ADME (absorption, distribution, metabolism, elimination) processes. MP toxicity was correctly predicted to occur in rats, but was not apparent in the simulations for humans and mice (consistent with literature). When the hepatotoxicity of MP and acetaminophen (APAP) was compared across rats, mice, and humans at an equivalent dose, the species most susceptible to APAP was not susceptible to MP, and vice versa. Furthermore, consideration of variability in simulated population samples (SimPops™) provided confidence in the predictions and allowed examination of the biological parameters most predictive of outcome. Differences in model sensitivity to the parameters were related to species differences, but the severity of DILI for each drug/species combination was also an important factor.

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Year:  2012        PMID: 22875368     DOI: 10.1007/s10928-012-9266-0

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  76 in total

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2.  Acarbose alone or in combination with ethanol potentiates the hepatotoxicity of carbon tetrachloride and acetaminophen in rats.

Authors:  P Y Wang; T Kaneko; Y Wang; A Sato
Journal:  Hepatology       Date:  1999-01       Impact factor: 17.425

3.  Methylmercury inhibits the in vitro uptake of the glutathione precursor, cystine, in astrocytes, but not in neurons.

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4.  Histopathological changes in the liver following a paracetamol overdose: correlation with clinical and biochemical parameters.

Authors:  B Portmann; I C Talbot; D W Day; A R Davidson; I M Murray-Lyon; R Williams
Journal:  J Pathol       Date:  1975-11       Impact factor: 7.996

5.  The Type 1 Diabetes PhysioLab Platform: a validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse.

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Journal:  Clin Exp Immunol       Date:  2010-05-18       Impact factor: 4.330

6.  Identification of the thiophene ring of methapyrilene as a novel bioactivation-dependent hepatic toxicophore.

Authors:  Emma E Graham; Rachel J Walsh; Charlotte M Hirst; James L Maggs; Scott Martin; Martin J Wild; Ian D Wilson; John R Harding; J Gerald Kenna; Raimund M Peter; Dominic P Williams; B Kevin Park
Journal:  J Pharmacol Exp Ther       Date:  2008-05-01       Impact factor: 4.030

7.  Bacterial- and viral-induced inflammation increases sensitivity to acetaminophen hepatotoxicity.

Authors:  Jane F Maddox; Chidozie J Amuzie; Maoxiang Li; Sandra W Newport; Erica Sparkenbaugh; Christopher F Cuff; James J Pestka; Glenn H Cantor; Robert A Roth; Patricia E Ganey
Journal:  J Toxicol Environ Health A       Date:  2010

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10.  Novel mechanisms of protection against acetaminophen hepatotoxicity in mice by glutathione and N-acetylcysteine.

Authors:  Chieko Saito; Claudia Zwingmann; Hartmut Jaeschke
Journal:  Hepatology       Date:  2010-01       Impact factor: 17.425

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  35 in total

Review 1.  An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters.

Authors:  Kyunghee Yang; Kathleen Köck; Alexander Sedykh; Alexander Tropsha; Kim L R Brouwer
Journal:  J Pharm Sci       Date:  2013-05-07       Impact factor: 3.534

Review 2.  Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.

Authors:  Richard J Weaver; Eric A Blomme; Amy E Chadwick; Ian M Copple; Helga H J Gerets; Christopher E Goldring; Andre Guillouzo; Philip G Hewitt; Magnus Ingelman-Sundberg; Klaus Gjervig Jensen; Satu Juhila; Ursula Klingmüller; Gilles Labbe; Michael J Liguori; Cerys A Lovatt; Paul Morgan; Dean J Naisbitt; Raymond H H Pieters; Jan Snoeys; Bob van de Water; Dominic P Williams; B Kevin Park
Journal:  Nat Rev Drug Discov       Date:  2019-11-20       Impact factor: 84.694

3.  Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

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Journal:  J Chem Inf Model       Date:  2017-04-10       Impact factor: 4.956

Review 4.  Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction.

Authors:  Antonio Segovia-Zafra; Daniel E Di Zeo-Sánchez; Carlos López-Gómez; Zeus Pérez-Valdés; Eduardo García-Fuentes; Raúl J Andrade; M Isabel Lucena; Marina Villanueva-Paz
Journal:  Acta Pharm Sin B       Date:  2021-11-18       Impact factor: 11.413

5.  Characterizing the Effects of Race/Ethnicity on Acetaminophen Pharmacokinetics Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Todd J Zurlinden; Brad Reisfeld
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-02       Impact factor: 2.441

6.  In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.

Authors:  David J Klinke; Marc R Birtwistle
Journal:  Curr Opin Chem Eng       Date:  2015-11-01       Impact factor: 5.163

7.  A novel approach for estimating ingested dose associated with paracetamol overdose.

Authors:  Todd J Zurlinden; Kennon Heard; Brad Reisfeld
Journal:  Br J Clin Pharmacol       Date:  2015-12-09       Impact factor: 4.335

8.  Maximizing the impact of microphysiological systems with in vitro-in vivo translation.

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Journal:  Lab Chip       Date:  2018-06-26       Impact factor: 6.799

9.  Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips.

Authors:  Anna Herland; Ben M Maoz; Debarun Das; Mahadevabharath R Somayaji; Rachelle Prantil-Baun; Richard Novak; Michael Cronce; Tessa Huffstater; Sauveur S F Jeanty; Miles Ingram; Angeliki Chalkiadaki; David Benson Chou; Susan Marquez; Aaron Delahanty; Sasan Jalili-Firoozinezhad; Yuka Milton; Alexandra Sontheimer-Phelps; Ben Swenor; Oren Levy; Kevin K Parker; Andrzej Przekwas; Donald E Ingber
Journal:  Nat Biomed Eng       Date:  2020-01-27       Impact factor: 25.671

Review 10.  The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development.

Authors:  Zhengying Zhou; Jinwei Zhu; Muhan Jiang; Lan Sang; Kun Hao; Hua He
Journal:  Pharmaceutics       Date:  2021-05-12       Impact factor: 6.321

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