Drug-Induced Liver Injury

Almario, E. E. Navarro; Borlak, Jürgen; Suzuki, Ayako; Chen, Minjun

doi:10.1155/2017/2461694

Cited by 7 publications

(4 citation statements)

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“…After approval and market release, however, exposure to much larger patient populations can reveal a drug’s hidden risk. Indeed, already-approved drugs are most commonly withdrawn from the market due to intolerable numbers of IADR cases. − Even if a drug is not withdrawn, it may be labeled with a “black box” warning by the FDA, significantly curtailing its profitability …”

Section: Introductionmentioning

confidence: 99%

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Hughes

Flynn

Dang

et al. 2021

Chem. Res. Toxicol.

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Section: Introductionmentioning

confidence: 99%

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Hughes

Flynn

Dang

et al. 2021

Chem. Res. Toxicol.

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“…Safety problems are one of the primary causes of drug candidate attrition. − Furthermore, idiosyncratic adverse drug reactions (IADRs) frequently only arise after approval, incurring significant resources. , Many IADRs present as drug-induced liver injury, the leading driver of drug withdrawal from the market. − Although IADRs are poorly understood, many are linked to bioactivation: enzymatic conversion of drugs to electrophilically reactive metabolites. − Reactive metabolites covalently bind to nucleophilic sites within biological macromolecules, including DNA − and off-target proteins. − Conjugation to DNA is frequently mutagenic, , and conjugation to proteins can disrupt their functions , or incite adverse immune responses leading to IADRs. − …”

Section: Introductionmentioning

confidence: 99%

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Hughes

Dang

Kumar

et al. 2020

J. Chem. Inf. Model.

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Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined systemtermed the metabolic forestfor generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

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“…In fact, DILI accounts for the majority of acute liver failure cases in the United States, although drug-induced hepatotoxicity can also mimic many other liver diseases as well [ 4 - 6 ]. DILI is also the primary reason for drug withdrawal from the market [ 7 - 9 ]…”

Section: Introductionmentioning

confidence: 99%

Models of Drug Induced Liver Injury (DILI) – Current Issues and Future Perspectives

Kuna

Božić

Kizivat

et al. 2018

CDM

104

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Background: Drug-induced Liver Injury (DILI) is an important cause of acute liver failure cases in the United States, and remains a common cause of withdrawal of drugs in both preclinical and clinical phases.Methods: A structured search of bibliographic databases – Web of Science Core Collection, Scopus and Medline for peer-reviewed articles on models of DILI was performed. The reference lists of relevant studies was prepared and a citation search for the included studies was carried out. In addition, the characteristics of screened studies were described.Results: One hundred and six articles about the existing knowledge of appropriate models to study DILI in vitro and in vivo with special focus on hepatic cell models, variations of 3D co-cultures, animal models, databases and predictive modeling and translational biomarkers developed to understand the mechanisms and pathophysiology of DILI are described.Conclusion: Besides descriptions of current applications of existing modeling systems, associated advantages and limitations of each modeling system and future directions for research development are discussed as well.

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Drug-Induced Liver Injury

Cited by 7 publications

References 0 publications

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Modeling the Bioactivation and Subsequent Reactivity of Drugs

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Models of Drug Induced Liver Injury (DILI) – Current Issues and Future Perspectives

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