Human Leucocyte Antigen Genetics in Idiosyncratic Drug-Induced Liver Injury with Evidence Based on the Roussel Uclaf Causality Assessment Method
Abstract
:1. Introduction
2. Search Terms and Strategy
3. Drugs Causing RUCAM-Based iDILI Cases with HLA Association
4. Drugs Causing iDILI Cases with Unverified Diagnosis and Suspected HLA Association
5. Characteristics of the RUCAM versus the DILIN Method
6. Drugs, iDILI, and Lack of HLA Association
7. HLA Genetic Association with RUCAM-Based iDILI by Some Drugs
8. Molecular Considerations of the Liver Injury
8.1. Amoxicillin and Amoxicillin–Clavulanate
8.2. Antituberculotics + Antiretrovirals
8.3. Carbamazepine
8.4. Dapsone
8.5. Enalapril
8.6. Erythromycin
8.7. Fenofibrate
8.8. Flucloxacillin
8.9. Flupirtine
8.10. Infliximab
8.11. Isoxazolyl Penicillins
8.12. Methimazole
8.13. Methyldopa
8.14. Minocycline
8.15. Nitrofurantoin
8.16. Sertaline
8.17. Terbinafine
8.18. Ticlopidine
8.19. Trimethoprim–Sulfamethoxazole
9. Specific Molecular Aspects of HLA in iDILI
10. Proposals for Future Studies
11. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Drug | HLA Allele | RUCAM-Based iDILI Cases (n) | RUCAM-Based Causality | First Author |
---|---|---|---|---|
Amoxicillin | A*01:01 C*03:02 B*58:01 DPB1*01:01 | 15 | Not specified | Nicoletti, 2019 [43] |
Amoxicillin–Clavulanate | A*02:01 DQB1*06:02 | 201 | A total of 14/201 patients had a possible causality, and 187 a probable or highly probable causality grading | Lucena, 2011 [44] |
Amoxicillin– Clavulanate | A*30:02 B*18:01 DRB1*15:01 DQB1*06:02 | 75 | Possible causality and higher | Stephens, 2013 [45] |
Amoxicillin–Clavulanate | DRB1*15:01 | 14 | Not specified | O’Donohue, 2000 [46] |
Antituberculotics + Antiretrovirals | B*57:02 B*57:03 | 46 | A total of 4/46 patients had a possible causality grading, 12 a probable, and 30 a highly probable causality | Petros, 2017 [47] |
Carbamazepine | A*31:01 | 29 | All patients had a possible causality and higher | Nicoletti, 2019 [48] |
Dapsone | B*13:01 | 4 | Highly probable causality | Devarbhavi, 2022 [49] |
Enalapril | A*33:01 | 4 | Not specified | Nicoletti, 2017 [50] |
Erythromycin | A*33:01 | 10 | Not specified | Nicoletti, 2017 [50] |
Fenofibrate | A*33:01 | 7 | Not specified | Nicoletti, 2017 [50] |
Flucloxacillin | B*5701 | 51 | A total of 4/51 patients had a possible causality, 18 a probable causality, and 29 a highly probable causality grading | Daly, 2009 [51] |
Flucloxacillin | B*57:01 | 6 | A total of 2/6 patients had a possible causality, 2 a probable, and 2 a highly probable causality | Monshi, 2013 [52] |
Flucloxacillin | B*57:01 | 197 | A total of 22/197 patients had a possible causality, 90 a probable, and 85 a highly probable causality grading | Nicoletti, 2019 [43] |
Flucloxacillin | B*57:01 | 1 | Score 8, probable causality | Teixera, 2020 [53] |
Flupirtine | DRB1*16:01-DQB*05:02 | 11 | A total of 1/11 patients had an unlikely causality grading, 5 a possible, and 5 a probable causality grading | Nicoletti, 2016 [54] |
Infliximab | B*39:01 | 18 | Not specified | Bruno, 2020 [55] |
Isoxazolyl Penicillins | C*07:04 DQB1*06:09 | 6 | Not specified | Nicoletti, 2019 [43] |
Methimazole | C*03:02 | 40 | A total of 1/40 patients had a possible causality grading, 37 a probable, and 2 a highly probable causality grading | Li, 2019 [56] |
Methyldopa | A*33:01 | 4 | Not specified | Nicoletti, 2017 [50] |
Minocycline | B*35:02 | 25 | Not specified | Urban, 2017 [57] |
Nitrofurantoin | A*33:01 DQB1*02:02 A*30:02 DQA1*02:01 DRB1*07:01 DPB1*16:01 C*06:02 | 26 | A total of 18/26 patients had a score of above 6, in line with a probable or highly probable causality | Daly, 2023 [58] |
Sertaline | A*33:01 | 5 | Not specified | Nicoletti, 2017 [50] |
Terbinafine | A*33:01 | 14 | Not specified | Nicoletti, 2017 [50] |
Ticlopidine | A*33:01 | 5 | Not specified | Nicoletti, 2017 [50] |
Trimethoprim–Sulfamethoxazole | B*14:01 B*14:02 B*35:01 | 86 | Not specified | Li, 2021 [59] |
Drug | HLA Allele | iDILI Cases (n) | Causality Assessment Method | First Author |
---|---|---|---|---|
Allopurinol | A*34:02 B*53:01 B*58:01 | 11 | No RUCAM but DILIN method | Fontana, 2021 [60] |
Allopurinol | B*58:01 | 3 | None | Kim, 2017 [61] |
Amoxicillin– Clavulanate | DRB1*1501 DQB1*0602 | 35 | None | Hautekeete, 1999 [62] |
Halothane | DR2 | 14 | None | Otsuka, 1985 [63] |
Lapatinib | DRB1*07:01 | 65 | None | Tangamornsuksan, 2020 [64] |
Lumiracoxib | DRB1*15:01 | 139 | None | Singer, 2010 [65] |
Nitrofurantoin | DRB1*11:04 | 78 | No RUCAM but DILIN method | Chalasani, 2023 [66] |
Pazopanib | B*57:01 C*04:01 C*06:02 | 2190 | None | Xu, 2016 [67] |
Terbinafine | A*33:01 | 15 | No RUCAM but DILIN method | Fontana, 2018 [68] |
Ticlopidine | A*33:03 | 22 | None | Hirata, 2008 [69] |
Ximelagatran | DRB1*07 DQA1*02 | 74 | None | Kindmark, 2008 [70] |
RUCAM with Its Basic Features and Specifics |
---|
● Fully validated method based on cases with positive re-exposure test results (gold standard), thereby providing a robust CAM [5,14] |
● External validation by inter-rater reliability in 3 studies [71,72,73] |
● Worldwide use, with 81,856 DILI cases assessed by the RUCAM published up to mid-2020, thereby outperforming any other CAM in terms of the number of cases published [4] |
● Valid and reproducible assessment of DILI and HILI cases [15] |
● A typical intelligent diagnostic algorithm in line with concepts of artificial intelligence (AI) to solve complex processes by scored items [74] |
● A diagnostic algorithm for objective, standardized, and quantitative causality assessment [3,5,13,14,15,16,75]. Summing up the individual scores derived from each key element provides the final causality gradings: score ≤ 0, excluded causality; 1–2, unlikely; 3–5, possible; 6–8, probable; and ≥9, highly probable [15]. |
● Assessment is user-friendly and cost-effective, with results available in time and without the need for rounds to provide arbitrary opinions [5,7,15] |
● Transparency of case data and clear result presentation [5,7,15] |
● Suitable for re-evaluation by peers [5] and regional registries, national or international regulatory agencies, and pharma firms [5,15] |
● Encourages prospective case data collection to obtain the best results; however, the RUCAM is also prepared for studies with a retrospective study protocol [15] |
● Real-time evaluation of the DILI case at the bed side [15] |
Clearly defined and scored key elements [15] |
● Time frame of the latency period |
● Time frame of the dechallenge |
● Recurrent ALT or ALP increase after drug cessation |
● Risk factors |
● Individual comedications |
● Exclusion of alternative competing causes |
● Markers of HAV, HBV, HCV, and HEV |
● Markers of CMV, EBV, HSV, and VZV |
● Cardiac hepatopathy and other alternative causes |
● Liver and biliary tract imaging |
● Doppler sonography of liver vessels |
● Prior known hepatotoxicity of drug or herb |
● Unintentional re-exposure |
Other important specifics [15] |
● Laboratory-based liver injury criteria |
● Laboratory-based liver injury pattern |
● Liver injury-specific method |
● Structured, liver-related method |
● Quantitative method, based on scored key elements |
Challenges and Limitations of the RUCAM |
---|
● The quality of published RUCAM-based case data strongly depends on the qualification and experience of the submitting physician |
● The RUCAM cannot compensate for inadequate-quality data and case providers not familiar with liver diseases; quality problems also remain on the side the reviewers and journal management [76,77,78,79] |
● Intentional upgrading of causality levels from possible to probable in cases initially assessed by the objective updated RUCAM and subsequently re-assessed by the global introspection in a report with Western co-authors remains debatable [76], as substantiated in three Letters to the Editor presented by authors from India and Iceland [77], and China [78,79]. |
● Fraudulent upgrading from possible to probable RUCAM gradings of published cases with the intention to provide more power to risky liver injury, uncovered in court, is outside of any ethical standard [80] |
● Challenging are reports titled as DILI but, in fact, several cohorts were lumped together with non-drugs like herbs or so-called dietary supplements as causatives of HILI, providing biased results for drugs and the other causatives due to cohort heterogeneity |
● Publications occasionally report on RUCAM-based DILI cohorts that include cases with a possible causality grading, which confounds good data with a probable or highly probable causality level [76]. This problem must be solved prior to submission by deleting all cases with a possible or lower causality grading from the analysis to be published |
● Challenging for the RUCAM are mixed cohorts of DILI caused by multiple medicinal products without providing individual RUCAM scores for each product or giving causality gradings as means ± SEM or ± SD for drug groups [3,4] |
● Misuses of the RUCAM are reports on DILI without values of the ALT and ALP, preventing both verification of criteria characterizing the liver injury as well as calculation of the R (ratio) and selection of the appropriate RUCAM subtype for correct causality assessment [15] |
● Misuses of the RUCAM are attempts including the results of positive unintentional re-exposure without adherence to the specific criteria [15] |
Reports on Validation of the RUCAM |
---|
● The RUCAM was internally validated using published DILI reports with positive test results for re-exposure, also named positive rechallenge, which demonstrated without incorporation of the rechallenge test into the score a sensitivity of 86%, specificity of 89%, positive predictive value of 93% and negative predictive value of 78% [14]. Such results were commonly appreciated [5] and underlined the value of the original RUCAM as a robust diagnostic algorithm [13]. Positive unintentional re-exposure tests are considered the gold standard among DILI experts [5,14], as erroneous re-exposure of a suspected drug provides in retrospect the strongest evidence of DILI [5] if strict criteria are fulfilled [15]. The good validation data were confirmed by subsequent studies [71,72,73] |
● A good reliability based on interrater agreement by using the original RUCAM for DILI cases was reported as a first external study [71] |
● A second external study reported that there were no discrepancies in the assessments by the two hepatologists who used the original RUCAM in suspected iDILI cases due to sevoflurane and desflurane [73]. This was a prospective incidence study of 15 patients that provided RUCAM-based causality gradings of highly probable in 3 cases, probable gradings in 5 cases, and possible gradings in 7 patients |
● A third external validation study used the updated RUCAM for the determination of causality described in 72 patients with COVID-19 and suspected DILI [72]. Two independent rating pairs (consisting of two clinical pharmacologists plus two general physicians), who had received a short training program for pilot testing just prior to the actual RUCAM use, determined the likelihood of DILI using the RUCAM scale in these DILI patients. As a result, the overall Krippendorf kappa was 0.52, with an intraclass correlation coefficient (ICC) of 0.79, viewed as excellent reliability for using the updated RUCAM [72]. Whether this was achieved through the prior training remains to be verified by assessors without prior training. Confirming previous reports [14,71], this good reliability result was remarkable as based on a retrospective study design [72] |
Published Experiences and Weaknesses of the US DILI Network Method |
---|
● Cases were enrolled in the registry within 6 months of DILI onset and underwent global introspection syn so called expert opinion |
● Causality assessment in real time for clinicians’ use was not feasible |
● There was no accepted definition provided for an expert in DILI |
● For each case, consensus must be achieved, excluding minority votes |
● Consensus is still a subjective opinion |
● The network process restricts the naming of offending agents to 3 |
● Strong opinions or biases of single experts were reported |
● Lengthy and lively conversations often occurred during the processes |
● The network process is described as cumbersome, time-consuming, and costly, needing data exchanges, monthly meetings, and logistics with administrative, organizational, and technological expertise |
● Each case received a final likelihood range as a percentage, arbitrarily assigned by the assessors, not based on individually scored elements |
● The total bilirubin was one of the inclusion criteria if >2.5 mg/dL without ruling out unconjugated hyperbilirubinemia due to, e.g., Gilbert syndrome |
● Network experts missed the diagnosis of HEV in wrongly diagnosed DILI cases needing a downgrading of the percentage DILI likelihood |
● Not using a gold standard, a good method reliability was assumed |
● External validation of the method with a different group of experts is explicitly discouraged as labor is considered intensive and expensive |
● The network method was only used in US centers |
● Despite the weaknesses, the network method is assumed to be best standard for the time being, but it was still imperfect in 2016, asking for mandatory improvements |
● Finally, the original RUCAM was surprisingly quoted and described with 11 plain words: “RUCAM requires decline in liver enzymes to get a high score”. |
Drugs with IDILI and No Detectable Significant Signal in HLA Region |
---|
Atorvastatin and other statins |
Fasiglifam (TAK-875) |
Azathioprine and other thiopurines |
Interferon beta |
Ciprofloxacin and other fluoroquinolones |
Isoniazid |
Diclofenac |
Nimesulide |
Role of HLA in the Development of iDILI |
---|
● There is a well-documented association of HLA with RUCAM-based iDILI caused by a limited number of drugs |
● Drugs implicated in RUCAM-based iDILI are largely metabolized by hepatic microsomal cytochrome P450 isoforms |
● In addition, a minority of drugs implicated in RUCAM-based iDILI are metabolized by non-CYPs like alcohol dehydrogenase, aldehyde oxidases, aldehyde dehydrogenase, flavin-containing monooxygenases, and xanthine oxidase |
● HLA represents a complex of genes that encode the major histocompatibility complex (MHC) to regulate immunity |
● The processes metabolizing the drugs lead not only to harmless metabolites but eventually also to reactive oxygen species (ROS), which in turn trigger the injury of intracellular organelles of the hepatocytes |
● The drug or its reactive metabolites function as haptens, bind to proteins, and then form neoantigens that present on specific HLA molecules with the risk of triggering an inappropriate immune response that contributes to the liver injury |
● Neoantigens derived from damaged liver cell organelles and toxic drug metabolites attack circulating immune cells, which enter the liver and function there outside of the hepatocytes as resident immune cells and will activate silent immune cells to active immune cells |
● The initiation of an immune response requires the activation of antigen-presenting cells (APCs) by molecules such as danger-associated molecular pattern molecules (DAMPs) |
● The mechanism by which DAMPs induce an immune response proceeds via the activation of inflammasomes. Although it appears that the liver damage is mediated by the adaptive immune system, an innate immune response is required for an adaptive immune response |
● The dominant immune response in the liver is immune tolerance, and it is only when immune tolerance fails that significant liver injury occurs |
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Teschke, R.; Danan, G. Human Leucocyte Antigen Genetics in Idiosyncratic Drug-Induced Liver Injury with Evidence Based on the Roussel Uclaf Causality Assessment Method. Medicines 2024, 11, 9. https://doi.org/10.3390/medicines11040009
Teschke R, Danan G. Human Leucocyte Antigen Genetics in Idiosyncratic Drug-Induced Liver Injury with Evidence Based on the Roussel Uclaf Causality Assessment Method. Medicines. 2024; 11(4):9. https://doi.org/10.3390/medicines11040009
Chicago/Turabian StyleTeschke, Rolf, and Gaby Danan. 2024. "Human Leucocyte Antigen Genetics in Idiosyncratic Drug-Induced Liver Injury with Evidence Based on the Roussel Uclaf Causality Assessment Method" Medicines 11, no. 4: 9. https://doi.org/10.3390/medicines11040009