Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene-gene and/or gene-environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP-SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene-gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity.

Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI.

BACKGROUND & AIMS: Although most drug-induced liver injury (DILI) cases resolve after the offending medication is discontinued, time to recovery varies among patients, with 6 -12% developing a chronic disease. Herein, we investigated clinical factors and drug properties as potential risk determinants that influence the time course for DILI recovery and developed a model to predict its trajectory. METHODS: We applied an accelerated failure time model to 294 cases collected by the International Drug-Induced Liver Network Consortium (iDILIC). Factors included in the multivariate recovery score model were selected through univariate analysis. The model was externally validated using 257 cases from the Spanish DILI Registry and 191 cases from the LiverTox database. RESULTS: Higher serum bilirubin and alkaline phosphatase (ALP) at DILI onset, a longer time to onset, and non-significant drug metabolism were associated with a longer recovery and were included in the recovery score model. We defined high- and low-risk groups based on the scores assigned by the model. The estimated probability of recovery by 6 months was 0.46 (95% CI 0.26-0.61) for the high-risk group and 0.93 (95% CI 0.58-0.99) for the low-risk group in the iDILIC. Model performance was validated in both validation sets. The high- and low-risk cases identified by the model showed a significantly different time course for recovery, with a majority of low-risk cases recovering sooner. CONCLUSION: The trajectory of biochemical recovery from DILI is predicted by the extent of drug metabolism, serum bilirubin and ALP at DILI onset. The model can be used to compute an estimated DILI recovery and, when a significant delay is predicted, clinicians may consider additional investigations such as histologic evaluation or extended follow-up. LAY SUMMARY: In this study, we investigated whether drug properties and clinical factors are associated with the time it takes to recover from drug-induced liver injury (DILI). We found that total bilirubin, alkaline phosphatase level at DILI onset, time to onset, and extent of drug metabolism were consistently associated with recovery time. Using these factors, we built a model to predict the trajectory of recovery from DILI and validated this model in 2 independent cohorts. Our findings offer important insights into the factors influencing the trajectory of recovery from DILI. Additional investigations and longer follow-ups can be planned in those for whom a delayed recovery is predicted.

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of DILI presentations by a single drug suggests that both drugs and host factors may contribute to the phenotype. However, factors determining the DILI types have not been yet elucidated. Identifying such factors may help to accurately predict the injury types based on drugs and host information and assist the clinical diagnosis of DILI. Using prospective DILI registry datasets, we sought to explore and validate the associations of biochemical injury types at the time of DILI recognition with comprehensive information on drug properties and host factors. Random forest models identified a set of drug properties and host factors that differentiate hepatocellular from cholestatic damage with reasonable accuracy (69-84%). A simplified logistic regression model developed for practical use, consisting of patient's age, drug's lipoaffinity, and hybridization ratio, achieved a fair prediction (68-74%), but suggested potential clinical usability, computing the likelihood of liver injury type based on two properties of drugs taken by a patient and patient's age. In summary, considering both drug and host factors in evaluating DILI risk and phenotypes open an avenue for future DILI research and aid in the refinement of causality assessment.

The Development of a Database for Herbal and Dietary Supplement Induced Liver Toxicity.

The growing use of herbal dietary supplements (HDS) in the United States provides compelling evidence for risk of herbal-induced liver injury (HILI). Information on HDS products was retrieved from MedlinePlus of the U.S. National Library of Medicine and the herbal monograph of the European Medicines Agency. The hepatotoxic potential of HDS was ascertained by considering published case reports. Other relevant data were collected from governmental documents, public databases, web sources, and the literature. We collected information for 296 unique HDS products. Evidence of hepatotoxicity was reported for 67, that is 1 in 5, of these HDS products. The database revealed an apparent gender preponderance with women representing 61% of HILI cases. Culprit hepatotoxic HDS were mostly used for weight control, followed by pain and inflammation, mental stress, and mood disorders. Commonly discussed mechanistic events associated with HILI are reactive metabolites and oxidative stress, mitochondrial injury, as well as inhibition of transporters. HDS⁻drug interactions, causing both synergistic and antagonizing effects of drugs, were also reported for certain HDS. The database contains information for nearly 300 commonly used HDS products to provide a single-entry point for better comprehension of their impact on public health.