Literature DB >> 31532188

Moving beyond Binary Predictions of Human Drug-Induced Liver Injury (DILI) toward Contrasting Relative Risk Potential.

Michael D Aleo, Falgun Shah, Scott Allen1, Hugh A Barton, Chester Costales, Sarah Lazzaro, Louis Leung, Andrea Nilson, R Scott Obach, A David Rodrigues, Yvonne Will.   

Abstract

The hepatic risk matrix (HRM) was developed and used to differentiate lead clinical and back-up drug candidates against competitor/marketed drugs within the same pharmaceutical class for their potential to cause human drug-induced liver injury (DILI). The hybrid HRM scoring system blends physicochemical properties (Rule of Two Model: dose and lipophilicity or Partition Model: dose, ionization state, lipophilicity, and fractional carbon bond saturation) with common toxicity mechanisms (cytotoxicity, mitochondrial dysfunction, and bile salt export pump (BSEP) inhibition) that promote DILI. HRM scores are based on bracketed safety margins (<1, 1-10, 10-100, and >100× clinical Cmax,total). On the basis of well-established clinical safety experience of marketed/withdrawn drug candidates, the background analysis consists of 200 drugs from the Liver Toxicity Knowledge Base annotated as Most-DILI- (79), Less-DILI- (56), No-DILI- (47), and Ambiguous-DILI-concern (18) drugs. Scores were generated for over 21 internal and 7 external drug candidates discontinued for unacceptable incidence/magnitude of liver transaminase elevations during clinical trials or withdrawn for liver injury severity. Both hybrid scoring systems identified 70-80% Most-DILI-concern drugs, but more importantly, stratified successful/unsuccessful drug candidates for liver safety (incidence/severity of transaminase elevations and approved drug labels). Incorporating other mechanisms (reactive metabolite and cytotoxic metabolite generation and hepatic efflux transport inhibition, other than BSEP) to the HRM had minimal beneficial impact in DILI prediction/stratification. As is, the hybrid scoring system was positioned for portfolio assessments to contrast DILI risk potential of small molecule drug candidates in early clinical development. This stratified approach for DILI prediction aided decisions regarding drug candidate progression, follow-up mechanistic work, back-up selection, clinical dose selection, and due diligence assessments in favor of compounds with less implied clinical hepatotoxicity risk.

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Year:  2019        PMID: 31532188     DOI: 10.1021/acs.chemrestox.9b00262

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  8 in total

Review 1.  The Promise of AI for DILI Prediction.

Authors:  Andreu Vall; Yogesh Sabnis; Jiye Shi; Reiner Class; Sepp Hochreiter; Günter Klambauer
Journal:  Front Artif Intell       Date:  2021-04-14

2.  Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Authors:  Eni Minerali; Daniel H Foil; Kimberley M Zorn; Thomas R Lane; Sean Ekins
Journal:  Mol Pharm       Date:  2020-06-08       Impact factor: 4.939

3.  An in vitro coculture system of human peripheral blood mononuclear cells with hepatocellular carcinoma-derived cells for predicting drug-induced liver injury.

Authors:  Shingo Oda; Yuka Uchida; Michael D Aleo; Petra H Koza-Taylor; Yusuke Matsui; Masanori Hizue; Lisa D Marroquin; Jessica Whritenour; Eri Uchida; Tsuyoshi Yokoi
Journal:  Arch Toxicol       Date:  2020-08-20       Impact factor: 5.153

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

Review 5.  A property-response perspective on modern toxicity assessment and drug toxicity index (DTI).

Authors:  Vaibhav A Dixit; Pragati Singh
Journal:  In Silico Pharmacol       Date:  2021-05-15

6.  Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.

Authors:  Ting Li; Weida Tong; Ruth Roberts; Zhichao Liu; Shraddha Thakkar
Journal:  Front Bioeng Biotechnol       Date:  2020-11-27

7.  Hepatotoxicity reports in the FDA adverse event reporting system database: A comparison of drugs that cause injury via mitochondrial or other mechanisms.

Authors:  Payal Rana; Michael D Aleo; Xuerong Wen; Stephen Kogut
Journal:  Acta Pharm Sin B       Date:  2021-06-07       Impact factor: 11.413

Review 8.  The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development.

Authors:  Paul A Walker; Stephanie Ryder; Andrea Lavado; Clive Dilworth; Robert J Riley
Journal:  Arch Toxicol       Date:  2020-05-06       Impact factor: 5.153

  8 in total

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