A novel quantitative computer-assisted drug-induced liver injury causality assessment tool (DILI-CAT).

BACKGROUND AND AIMS: We hypothesized that a drug's clinical signature (or phenotype) of liver injury can be assessed and used to quantitatively develop a computer-assisted DILI causality assessment-tool (DILI-CAT). Therefore, we evaluated drug-specific DILI-phenotypes for amoxicillin-clavulanate (AMX/CLA), cefazolin, cyproterone, and Polygonum multiflorum using data from published case series, to develop DILI-CAT scores for each drug. METHODS: Drug specific phenotypes were made up of the following three clinical features: (1) latency, (2) R-value, and (3) AST/ALT ratio. A point allocation system was developed with points allocated depending on the variance from the norm (or "core") for the 3 variables in published datasets. RESULTS: The four drugs had significantly different phenotypes based on latency, R-value, and AST/ALT ratio. The median cyproterone latency was 150 days versus < 43 days for the other three drugs (median: 26 for AMX/CLA, 20 for cefazolin, and 20 for Polygonum multiflorum; p<0.001). The R-value for the four drugs was also significantly different among drugs (cyproterone [median 12.4] and Polygonum multiflorum [median 10.9]) from AMX/CLA [median 1.44] and cefazolin [median 1.57; p<0.001]). DILI-CAT scores effectively separated cyproterone and Polygonum multiflorum from AMX/CLA and cefazolin, respectively (p<0.001). As expected, because of phenotypic overlap, AMX/CLA and cefazolin could not be well differentiated. CONCLUSIONS: DILI-CAT is a data-driven, diagnostic tool built to define drug-specific phenotypes for DILI adjudication. The data provide proof of principle that a drug-specific, data-driven causality assessment tool can be developed for different drugs and raise the possibility that such a process could enhance causality assessment methods.

A novel phenotype-based drug-induced liver injury causality assessment tool (DILI-CAT) allows for signal confirmation in early drug development.

BACKGROUND: Drug-induced liver injury (DILI) requires accurate case adjudication, with expert opinion being the current best practice. AIM: We utilised a novel DILI causality assessment tool (DILI-CAT), which uses drug-specific liver injury phenotypes, to examine potential DILI in early phase ximelagatran clinical development. METHODS: We conducted a retrospective analysis of liver injury events from four Stroke Prevention using an ORal Thrombin Inhibitor in Atrial Fibrillation (SPORTIF) trials, in which patients were randomised to receive oral ximelagatran or adjusted-dose warfarin. A stepwise process was used with iterative adjustments. The DILI phenotype was characterised by latency, R-value, and AST/ALT ratio. A scoring algorithm was applied to liver events to assess how closely the liver events matched the Interquatile-Range for the working phenotype for each of the three parameters. FINDINGS: Data from 3115 patients included in the SPORTIF trials as above were available. The initial ximelagatran phenotype was developed based on five liver injury cases from the ximelagatran arm and was then validated against an additional eight cases (5 ximelagatran, 3 warfarin); in these eight cases, there was a statistically significant difference in the total DILI-CAT scores of the two drugs (p = 0.016) between ximelagatran and warfarin. Together, these ten ximelagatran cases generated a second, refined ximelagatran phenotype, which was validated against an additional 75 cases (53 ximelagatran/22 warfarin)-again with statistically significant different DILI-CAT ximelagatran vs. warfarin scores (p < 0.001). CONCLUSION: DILI-CAT, a clinically intuitive, data-driven, computer-assisted scoring algorithm, is a useful tool for early detection of drug's hepatotoxicity in clinical drug development.

Tools for causality assessment in drug-induced liver disease.

PURPOSE OF REVIEW: There are three liver-specific causality assessment tools currently available to guide clinical diagnosis of Drug-Induced Liver Injury (DILI): Roussel-Uclaf Causality Assessment Method (RUCAM), Digestive-Disease-Week Japan 2004 scale (DDW-J), and Clinical Diagnostic Scale (CDS). The purpose of this review is to assess these tools and discuss how to improve the causality assessment process as a whole. RECENT FINDINGS: Existing DILI-specific causality assessment tools are surprisingly similar and exhibit only minor differences in point allocation. But difference in threshold for likelihood of being DILI. We reviewed the literature on currently used causality assessment tools, identified areas for future improvement, and herein propose approaches for refinement. Opportunities to improve current models, as well as the assessment process, in general, include in particular provision of more precise clinical detail and to perhaps add new components to scoring systems. For example, the incorporation of drug-specific clinical signature patterns, accounting for a drug's inherent hepatotoxicity potential, and/or incorporation of other drug properties to scoring systems may allow enhancement. Further, more systemic exclusion of competing diagnoses is needed. Finally, causality assessment processes will likely benefit from a data-driven and computer-assisted approach. SUMMARY: Current tools used for DILI adjudication are imperfect. Avenues to improve these tools are described.