A Model to predict severity of drug‐induced liver injury in humans

Abstract

Drug-induced liver injury (DILI) is a major public health concern, and improving its prediction remains an unmet challenge. Recently, we reported the Rule-of-2 (RO2) and found lipophilicity (logP 3) and daily dose 100 mg of oral medications to be associated with significant risk for DILI; however, the RO2 failed to estimate grades of DILI severity. In an effort to develop a quantitative metrics, we analyzed the association of daily dose, logP, and formation of reactive metabolites (RM) in a large set of Food and Drug Administration-approved oral medications and found factoring RM into the RO2 to highly improve DILI prediction. Based on these parameters and by considering n 5 354 drugs, an algorithm to assign a DILI score was developed. In univariate and multivariate logistic regression analyses the algorithm (i.e., DILI score model) defined the relative contribution of daily dose, logP, and RM and permitted a quantitative assessment of risk of clinical DILI. Furthermore, a clear relationship between calculated DILI scores and DILI risk was obtained when applied to three independent studies. The DILI score model was also functional with drug pairs defined by similar chemical structure and mode of action but divergent toxicities. Specifically, for drug pairs where the RO2 failed, the DILI score correctly identified toxic drugs. Finally, the model was applied to n 5 159 clinical cases collected from the National Institutes of Health's LiverTox database to demonstrate that the DILI score correlated with the severity of clinical outcome. Conclusions: Based on daily dose, lipophilicity, and RM, a DILI score algorithm was developed that provides a scale of assessing the severity of DILI risk in humans associated with oral medications. (HEPATOLOGY 2016;64:931-940) A lthough animal studies are mandatory for investigational new drug approvals by regulatory agencies, (1,2) a retrospective analysis revealed such tests to fail in predicting risk for drug-induced liver injury (DILI) in about 45% of clinical trials. (3) The findings reinforce the need to improve the prediction power of DILI model more reliably and to define contributing factors. (4) Recently, we demonstrated that oral medications of high lipophilicity (i.e., logP 3) administered at daily doses of 100 mg are associated with an increased risk of developing DILI, which led us to develop the concept of the "Rule-of-2" (RO2). (5) It was stated that the RO2 added value in predicting risk for developing DILI but fell short of foreseeing severity. (6,7) Incorporating additional factors, especially those relevant to mechanisms of DILI, (8) facilitates the development of a quantitative metrics by considering relevant factors. Covalent binding of reactive metabolites (RM) is a frequently cited mechanism and was shown to cause liver injury through direct toxicity events (9) or by endorsing immune reactions. (10) This prompted us to investigate the relationship between the RO2 and RM for the development of a quantitative measure in predicting risk for DILI.Numerous drug...

Drug-induced liver injury: Towards early prediction and risk stratification

“…Chen et al[34] combined the rule-of-two with the capacity to produce reactive metabolites and implemented a model to assess the risk of DILI onset and severity. Both dose-based and Cmax based-scores were calculated.…”

“…Therefore, we applied the score developed by Chen et al[34] to DOACs and found intriguing data (Table 2). Based on these results, different issues emerge: (1) no DOAC appears to be associated with risk of severe liver damage (they all received a score well below the threshold of 7); (2) the highest score emerged for dabigatran; (3) the risk does not appear to be strongly influenced by dose or Cmax (there is only a small increase in Cmax-based score), or chemical motifs; (4) DOACs pose a lower risk as compared to warfarin (the dose-based risk score is 4.67, according to Chen et al[34]).…”

“…1From official European Summary of Product Characteristics; 2Only in EU; 3Calculated based on formulas reported by Chen et al[34]; 4Calculated based on formulas reported by Chen et al[34] and assuming no RM formation; 5Data obtained from Drug Bank (; source: ALOGPS). ACS: Acute coronary syndrome; BCRP: Breast cancer resistance protein; DVT: Deep vein thrombosis; NVAF: Non valvular atrial fibrillation; ND: Not determined; RM: Reactive metabolites; DITdP: Drug-induced torsade de pointes.…”

Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury

“…The project has developed several predictive models 11-14 . For example, we developed a “rule-of-two” model based on the observations that oral medications of high daily dose and lipophilicity were associated with significant risk of DILI 12 ; the model identified drugs that caused severe DILI but produced a number of false negatives.…”

“…We also constructed a computational model that predicts human hepatotoxicity based on patterns of gene expression observed in rats following short-term in vivo exposures 11 . More recently, we developed a DILIScore model that is able to quantitatively assess a drug’s severity for DILI based on three sets of information (i.e., daily dose, lipophilicity, and the presence of reactive metabolites 14 . )…”