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Genetic Risk Factors in Idiosyncratic DILI
Research Guide

What is Genetic Risk Factors in Idiosyncratic DILI?

Genetic risk factors in idiosyncratic DILI are specific genetic variants, particularly HLA alleles and pharmacogenetic polymorphisms, that predispose individuals to unpredictable, hypersensitivity-mediated drug-induced liver injury.

Idiosyncratic DILI affects susceptible individuals without dose dependency, with HLA associations like HLA-B*57:01 linked to flucloxacillin-induced liver injury (Monshi et al., 2012, 247 citations). Genome-wide studies identify variants influencing immune responses and drug metabolism. Over 10 key papers from 2007-2021 map these risks, including pharmacogenetics reviews (Russmann et al., 2010, 145 citations).

Curated Papers

Key Challenges

Why It Matters

Genetic risk factors enable patient stratification to avoid severe DILI, as HLA-B*57:01 screening prevents flucloxacillin hepatotoxicity in at-risk patients (Monshi et al., 2012). Chalasani and Björnsson (2010, 337 citations) highlight how identifying these factors reduces acute liver failure incidence. Russmann et al. (2010) show pharmacogenetic testing supports personalized dosing, lowering regulatory drug withdrawals.

Key Research Challenges

HLA Association Variability

HLA alleles like B*57:01 show drug-specific links but vary across populations and drugs (Monshi et al., 2012). Replicating associations requires large cohorts due to rarity of idiosyncratic cases (Chalasani and Björnsson, 2010). Functional validation of immune mechanisms remains limited.

Genome-Wide Variant Detection

GWAS struggle with low incidence and polygenic risks in DILI (Russmann et al., 2010). Rare variants escape detection without massive sequencing (Russmann et al., 2009). Integrating pharmacogenetics with metabolomics adds complexity.

Clinical Translation Barriers

Genetic screening lacks standardized protocols for routine use (Chalasani and Björnsson, 2010). Cost-effectiveness and prospective validation hinder adoption (Russmann et al., 2010). Predicting severity from variants needs better models.

Essential Papers

Current Concepts of Mechanisms in Drug-Induced Hepatotoxicity

Stefan Russmann, Gerd A. Kullak‐Ublick, Ignazio Grattagliano · 2009 · Current Medicinal Chemistry · 407 citations

Drug-induced liver injury (DILI) has become a leading cause of severe liver disease in Western countries and therefore poses a major clinical and regulatory challenge. Whereas previously drug-speci...

Risk Factors for Idiosyncratic Drug-Induced Liver Injury

Naga Chalasani, Einar S. Björnsson · 2010 · Gastroenterology · 337 citations

Albumin in decompensated cirrhosis: new concepts and perspectives

Mauro Bernardi, Paolo Angeli, Joan Clària et al. · 2020 · Gut · 331 citations

The pathophysiological background of decompensated cirrhosis is characterised by a systemic proinflammatory and pro-oxidant milieu that plays a major role in the development of multiorgan dysfuncti...

Drug-Induced Liver Injury: Cascade of Events Leading to Cell Death, Apoptosis or Necrosis

Andrea Iorga, Lily Dara, Neil Kaplowitz · 2017 · International Journal of Molecular Sciences · 284 citations

Drug-induced liver injury (DILI) can broadly be divided into predictable and dose dependent such as acetaminophen (APAP) and unpredictable or idiosyncratic DILI (IDILI). Liver injury from drug hepa...

Review article: drug hepatotoxicity

Charissa Chang, Thomas D. Schiano · 2007 · Alimentary Pharmacology & Therapeutics · 265 citations

SUMMARY Background Drug toxicity is the leading cause of acute liver failure in the United States. Further understanding of hepatotoxicity is becoming increasingly important as more drugs come to m...

Human Leukocyte Antigen (HLA)-B*57:01-Restricted Activation of Drug-Specific T cells Provides the Immunological Basis for Flucloxacillin-Induced Liver Injury

Manal Monshi, Lee Faulkner, Andrew Gibson et al. · 2012 · Hepatology · 247 citations

The role of the adaptive immune system in adverse drug reactions that target the liver has not been defined. For flucloxacillin, a delay in the reaction onset and identification of human leukocyte ...

Knowledge Mapping of Drug-Induced Liver Injury: A Scientometric Investigation (2010–2019)

Lixin Ke, Cuncun Lu, Rui Shen et al. · 2020 · Frontiers in Pharmacology · 198 citations

This scientometric study comprehensively reviewed the publications related to DILI during the past decade using quantitative and qualitative methods. This information would provide references for s...

Reading Guide

Foundational Papers

Start with Russmann et al. (2009, 407 citations) for DILI mechanisms overview, then Monshi et al. (2012, 247 citations) for HLA-B*57:01 evidence, and Chalasani and Björnsson (2010, 337 citations) for risk factors.

Recent Advances

Study Russmann et al. (2010, 145 citations) for pharmacogenetics advances and Ke et al. (2020, 198 citations) for DILI knowledge mapping.

Core Methods

Core techniques: HLA genotyping, drug-specific T-cell assays (Monshi et al., 2012), GWAS for variants (Russmann et al., 2010), and cohort risk modeling (Chalasani and Björnsson, 2010).

How PapersFlow Helps You Research Genetic Risk Factors in Idiosyncratic DILI

Discover & Search

Research Agent uses citationGraph on Monshi et al. (2012) to map HLA-B*57:01 networks, revealing 247 citing papers on flucloxacillin DILI. exaSearch queries 'HLA pharmacogenetics idiosyncratic DILI' for 250M+ OpenAlex papers. findSimilarPapers expands from Chalasani and Björnsson (2010) to 337-citation risk factor studies.

Analyze & Verify

Analysis Agent runs readPaperContent on Russmann et al. (2010) to extract pharmacogenetic variants, then verifyResponse with CoVe checks claims against GRADE B evidence. runPythonAnalysis processes citation data in pandas for variant frequency stats, verifying HLA association reproducibility.

Synthesize & Write

Synthesis Agent detects gaps in HLA-drug pairs beyond flucloxacillin using gap detection on Monshi et al. (2012). Writing Agent applies latexSyncCitations to compile reviews with 10+ papers, latexCompile for publication-ready drafts, and exportMermaid for genetic risk pathway diagrams.

Use Cases

"Analyze HLA variant frequencies in DILI cohorts from key papers"

Research Agent → searchPapers 'HLA DILI genetics' → Analysis Agent → runPythonAnalysis (pandas aggregation of variant tables from Monshi et al. 2012 and Russmann et al. 2010) → statistical summary CSV with p-values.

"Write LaTeX review on genetic risks for flucloxacillin DILI"

Synthesis Agent → gap detection → Writing Agent → latexEditText (add intro) → latexSyncCitations (Monshi et al. 2012, Chalasani 2010) → latexCompile → PDF with cited genetic mechanisms.

"Find code for GWAS analysis in pharmacogenetic DILI studies"

Research Agent → paperExtractUrls (Russmann et al. 2010) → paperFindGithubRepo → githubRepoInspect → Python scripts for HLA imputation and risk modeling.

Automated Workflows

Deep Research workflow scans 50+ DILI papers via searchPapers, structures HLA risk report with GRADE grading. DeepScan applies 7-step CoVe to validate Monshi et al. (2012) claims against Chalasani (2010). Theorizer generates hypotheses on unstudied HLA-drug pairs from citationGraph.

Try Doxa for Genetic Risk Factors in Idiosyncratic DILI Research

Frequently Asked Questions

What defines genetic risk factors in idiosyncratic DILI?

They are HLA alleles like B*57:01 and pharmacogenetic variants causing hypersensitivity without dose dependence (Monshi et al., 2012; Russmann et al., 2010).

What are key methods for identifying these factors?

Methods include GWAS, HLA genotyping, and T-cell activation assays as in flucloxacillin studies (Monshi et al., 2012; Russmann et al., 2010).

What are major papers on this topic?

Monshi et al. (2012, 247 citations) links HLA-B*57:01 to flucloxacillin DILI; Chalasani and Björnsson (2010, 337 citations) reviews risks; Russmann et al. (2010, 145 citations) covers pharmacogenetics.

What open problems exist?

Challenges include population-specific HLA variability, rare variant detection, and prospective clinical validation (Chalasani and Björnsson, 2010; Russmann et al., 2010).