Abstract
Causality assessment in liver injury induced by drugs and herbs remains a debated issue, requiring innovation and thorough understanding based on detailed information. Artificial intelligence (AI) principles recommend the use of algorithms for solving complex processes and are included in the diagnostic algorithm of Roussel Uclaf Causality Assessment Method (RUCAM) to help assess causality in suspected cases of idiosyncratic drug-induced liver injury (DILI) and herb-induced liver injury (HILI). From 1993 until the middle of 2020, a total of 95,865 DILI and HILI cases were assessed by RUCAM, outperforming by case numbers any other causality assessment method. The success of RUCAM can be traced back to its quantitative features with specific data elements that are individually scored leading to a final causality grading. RUCAM is objective, user friendly, transparent, and liver injury specific, with an updated version that should be used in future DILI and HILI cases. Support of RUCAM was also provided by scientists from China, not affiliated to any network, in the results of a scientometric evaluation of the global knowledge base of DILI. They highlighted the original RUCAM of 1993 and their authors as a publication quoted the greatest number of times and ranked first in the category of the top 10 references related to DILI. In conclusion, for stakeholders involved in DILI and HILI, RUCAM seems to be an effective diagnostic algorithm in line with AI principles.
Highlights
Roussel Uclaf Causality Assessment Method (RUCAM) was viewed as a valuable diagnostic algorithm based on the principles of artificial intelligence (AI) [8] and appreciated by providing additional cases of drug-induced liver injury (DILI) assessed for causality by RUCAM [9,10]
A new list of top drugs implicated in DILI assessed by RUCAM clarified issues [11] raised by the disputable list provided by the LiverTox database, which did not use RUCAM to assess causality for its DILI cases but a diagnostic approach based on a subjective assessment [12,13]
Related to RUCAM, other new aspects focused on the scientometric evaluation of the global knowledge base of DILI, its utility in the liver injury of patients with COVID-19 infections treated with drugs or herbs, and its mandatory use in DILI and herb-induced liver injury (HILI) cases to establish new risk factors or mechanistic steps
Summary
Idiosyncratic drug-induced liver injury (DILI) and herb-induced liver injury (HILI) require innovation, they continuously attract international interest [1,2,3,4,5,6,7,8,9,10,11,12], with a focus on various topics such as mechanistic steps in DILI [1] including unresolved issues [2], the scientometric evaluation of the global knowledge base of DILI highlighting Roussel Uclaf Causality Assessment Method (RUCAM) [3] and its authors [4], and diagnostic aspects of DILI and HILI cases using RUCAM [5,6,7]. It is critical that the common practice within the scientific DILI and HILI community is to use poorly documented cases of the LiverTox database to attempt feature descriptions such as of the risk factors of drugs or herbs causing liver injury. Under these critical conditions, LiverTox requires substantial revision to become an appraised tool of liver injury case details. Related to RUCAM, other new aspects focused on the scientometric evaluation of the global knowledge base of DILI, its utility in the liver injury of patients with COVID-19 infections treated with drugs or herbs, and its mandatory use in DILI and HILI cases to establish new risk factors or mechanistic steps. In line with RUCAM, AI-based algorithms are being applied in many other complex diseases for diagnostic and therapeutic reasons
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