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Overview of Causality Assessment for Drug-Induced Liver Injury (DILI) in Clinical Trials.

ABSTRACT: Causality assessment for suspected drug-induced liver injury (DILI) during drug development and following approval is challenging. The IQ DILI Causality Working Group (CWG), in collaboration with academic and regulatory subject matter experts (SMEs), developed this manuscript with the following objectives: (1) understand and describe current practices; (2) evaluate the utility of new tools/methods/practice guidelines; (3) propose a minimal data set needed to assess causality; (4) define best practices; and (5) promote a more structured and universal approach to DILI causality assessment for clinical development. To better understand current practices, the CWG performed a literature review, took a survey of member companies, and collaborated with SMEs. Areas of focus included best practices for causality assessment during clinical development, utility of adjudication committees, and proposals for potential new avenues to improve causality assessment. The survey and literature review provided renewed understanding of the complexity and challenges of DILI causality assessment as well as the use of non-standardized approaches. Potential areas identified for consistency and standardization included role and membership of adjudication committees, standardized minimum dataset, updated assessment tools, and best practices for liver biopsy and rechallenge in the setting of DILI. Adjudication committees comprised of SMEs (i.e., utilizing expert opinion) remain the standard for DILI causality assessment. A variety of working groups continue to make progress in pursuing new tools to assist with DILI causality assessment. The minimum dataset deemed adequate for causality assessment provides a path forward for standardization of data collection in the setting of DILI. Continued progress is necessary to optimize and advance innovative tools necessary for the scientific, pharmaceutical, and regulatory community.

SUBMITTER: Hey-Hadavi J

PROVIDER: S-EPMC8184702 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Drug-induced liver injury (DILI) represents an increasing morbidity in the general population, but more so in the elderly cohort of patients. Despite this, the concept of its prevention through prospective analysis has largely remained unexamined. We evaluated the utility of recently validated adverse drug reactions (ADR) avoidability tool in a cohort of elderly patients with DILI. METHODS:We examined 38 DILI-drug pairs from n=38 patients in a prospective cohort of patients presenting with adverse drug reactions to a Weill Cornell-affiliated tertiary hospital between February 2019 and January 2020. DILI outcomes were adjudicated by the updated Roussel Uclaf Causality Assessment Method (RUCAM). Two clinical pharmacologists and two general physicians utilized the Liverpool adverse drug reactions avoidability tool (LAAT) and the modified Hallas tools to rate the preventability of DILI-drug pairs. Inter-rater, exact agreement proportions, as well as intraclass correlation coefficients were generated and expressed as ordinal outcomes. RESULTS:The cases examined for the determination of DILI avoidability had probability likelihood of "probable" or "highly probable" by the updated RUCAM scale. Examination of the 38 DILI-drug pairs (n= 38 patients) resulted in a total of 152 ordinal outcome decisions. We found about 32.3% (50/152) and 34.2% (52/152) of DILI-drug pairs were rated as "avoidable" ("probable" or "definite") by the LAAT and the modified Hallas tools respectively. The overall median Krippendorf's kappa with the LAAT was 0.61 (SE 0.12, CI 0.36, 0.85) and for modified Hallas tool was 0.53 (SE 0.18; CI 0.16, 0.89). The inter-rater correlation coefficient (ICC) for the LAAT and modified Hallas were 0.50 [0.32, 0.65] and 0.63 [0.48, 0.76] respectively. Exact pairwise agreement was present in 30/38 (IQR 29.5, 34.5), and 28/38 (IQR 27.5-35.5) of DILI-ADR pairs using the LAAT and modified Hallas tools respectively. CONCLUSION:We found a significant proportion of drug-induced liver injury adjudicated by the updated RUCAM scale in elderly hospitalized cohort of patients were avoidable with significant implication for therapeutic commissioning as well as cost effectiveness interventions in this cohort of patients.

Project description:BackgroundDrug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.ResultsAs part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759?±?0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.ConclusionUsing chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

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Overview of Causality Assessment for Drug-Induced Liver Injury (DILI) in Clinical Trials.

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