Drug Hepatotoxicity

Stine, Jonathan G.; Chalasani, Naga

doi:10.1016/j.cld.2016.08.008

Cited by 26 publications

(11 citation statements)

References 80 publications

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“…Fibrosis occurs in multiple organs, which is represented as the abnormal hyperplasia of connective tissue. Many factors lead to hepatic fibrosis, such as viral hepatitis, alcohol abuse, and drugs [92][93][94]. Inflammation and fibrosis are two processes that are activated in response to injury as reparative mechanisms.…”

Section: Roles Of Trem In Hepatic Fibrosismentioning

confidence: 99%

Function of TREM1 and TREM2 in Liver-Related Diseases

Sun

Feng

Tang

2020

Cells

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TREM1 and TREM2 are members of the triggering receptors expressed on myeloid cells (TREM) family. Both TREM1 and TREM2 are immunoglobulin superfamily receptors. Their main function is to identify foreign antigens and toxic substances, thereby adjusting the inflammatory response. In the liver, TREM1 and TREM2 are expressed on non-parenchymal cells, such as liver sinusoidal endothelial cells, Kupffer cells, and hepatic stellate cells, and cells which infiltrate the liver in response to injury including monocyte-derived macrophages and neutrophils. The function of TREM1 and TREM2 in inflammatory response depends on Toll-like receptor 4. TREM1 mainly augments inflammation during acute inflammation, while TREM2 mainly inhibits chronic inflammation to protect the liver from pathological changes. Chronic inflammation often induces metabolic abnormalities, fibrosis, and tumorigenesis. The above physiological changes lead to liver-related diseases, such as liver injury, nonalcoholic steatohepatitis, hepatic fibrosis, and hepatocellular carcinoma. Here, we review the function of TREM1 and TREM2 in different liver diseases based on inflammation, providing a more comprehensive perspective for the treatment of liver-related diseases.

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Section: Roles Of Trem In Hepatic Fibrosismentioning

confidence: 99%

Function of TREM1 and TREM2 in Liver-Related Diseases

Sun

Feng

Tang

2020

Cells

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“…DTI also provided the most informative feature selection for paradigms that require dimensionality reduction (such as SVM, ANN or kNN). Because Ethanol consumption increases toxicity of acetaminophen through induction of CYP2E1 (chronic intake) and formation of N-acetyl-pbenzoquinone imine as well as reduction of glutathione stores necessary for N-acetyl-p-benzoquinone imine elimination (Stine & Chalasani, 2017). Isoniazid toxicity through slow acetylation is genetically predetermined and racial predisposition has been extensively researched .…”

Section: Predictive Modelsmentioning

confidence: 99%

Prediction of clinically relevant drug‐induced liver injury from structure using machine learning

Hammann

Schöning

Drewe

2018

J of Applied Toxicology

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Drug‐induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical manifestations vary, and toxicity may or may not appear dose‐dependent. We present several machine‐learning models (decision tree induction, k‐nearest neighbor, support vector machines, artificial neural networks) for the prediction of clinically relevant DILI based solely on drug structure, with data taken from published DILI cases. Our models achieved corrected classification rates of up to 89%. We also studied the association of a drug's interaction with carriers, enzymes and transporters, and the relationship of defined daily doses with hepatotoxicity. The results presented here are useful as a screening tool both in a clinical setting in the assessment of DILI as well as in the early stages of drug development to rule out potentially hepatotoxic candidates.

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“…The liver functions at the intersection of numerous metabolic pathways and is equally subject to the effects of active metabolites as well as drugs, other agents, and the potential interactions between them [ 9 , 23 , 24 ]. Patient genetics, demographics, comorbidities, behavior, and environment all may play a role in precipitating DILI [ 9 , 23 , 25 – 27 ]. DILI may also occur with delayed onset, adding complexity to assessment of the temporal interplay between all these factors [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Using an Automated Algorithm to Identify Potential Drug-Induced Liver Injury Cases in a Pharmacovigilance Database

et al. 2021

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Introduction Drug-induced liver injury (DILI) is the most frequent cause of acute liver failure in North America and Europe, but it is often missed because of unstandardized diagnostic methods and criteria. This study aimed to develop and validate an automated algorithm to identify potential DILI cases in routine pharmacovigilance (PV) activities. Methods Post-marketing hepatic adverse events reported for a potentially hepatotoxic drug in a global PV database from 19 March 2017 to 18 June 2018 were assessed manually and with the automated algorithm. The algorithm provided case assessments by applying pre-specified criteria to all case data and narratives simultaneously. Results A total of 1456 cases were included for analysis and assessed manually. Sufficient data for algorithm assessment were available for 476 cases (32.7%). Of these cases, manual assessment identified 312 (65.5%) potential DILI cases while algorithm assessment identified 305 (64.1%) potential DILI cases. Comparison of manual and algorithm assessments demonstrated a sensitivity of 97.8% and a specificity of 79.3% for the algorithm. Given the prevalence of potential DILI cases in the population studied, the algorithm was calculated to have positive predictive value 56.3% and negative predictive value 99.2%. The time required for manual review compared to algorithm review suggested that application of the algorithm prior to manual screening would have resulted in a time savings of 42.2%. Conclusion An automated algorithm to identify potential DILI cases was developed and successfully implemented. The algorithm demonstrated a high sensitivity, a high negative predictive value, along with significant efficiency and utility in a real-time PV database. Supplementary Information The online version contains supplementary material available at 10.1007/s12325-021-01856-x.

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Drug Hepatotoxicity

Cited by 26 publications

References 80 publications

Function of TREM1 and TREM2 in Liver-Related Diseases

Function of TREM1 and TREM2 in Liver-Related Diseases

Prediction of clinically relevant drug‐induced liver injury from structure using machine learning

Using an Automated Algorithm to Identify Potential Drug-Induced Liver Injury Cases in a Pharmacovigilance Database

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