Determination of fasiglifam-induced liver toxicity: Insights from the data monitoring committee of the fasiglifam clinical trials program

2019 ◽  
Vol 16 (3) ◽  
pp. 253-262 ◽  
Author(s):  
Jay S Shavadia ◽  
Abhinav Sharma ◽  
Xiangqiong Gu ◽  
James Neaton ◽  
Laurie DeLeve ◽  
...  

Background Different approaches to safety event collection influence the determination of liver toxicity within drug development programs. Herein, a description of how fasiglifam-induced liver injury was detected is provided. Methods This eight-trial drug development program was intended to evaluate fasiglifam (25 mg, 50 mg) against placebo or active comparators (glimepiride, sitagliptin) in approximately 11,000 suboptimally controlled patients with type 2 diabetes (terminated Dec 2013 due to liver toxicity). Liver safety had been pre-identified as a concern, and within the phase 3 trials, was measured through (1) adverse event reporting, (2) central predefined liver monitoring schedule with various thresholds for potential drug-induced liver injury, and (3) blinded adjudication of serious liver toxicity by a panel of experts in drug-induced liver injury. A single data monitoring committee provided safety oversight across all trials within the program. Findings Prior to program termination, 7595 of 7602 (99.9%) randomized participants across the eight trials received at least one dose of the study drug (fasiglifam, placebo, or active control). No concerning trends were noted in adverse or serious adverse event frequency, suspected unexpected serious adverse reaction, alanine or aspartate transaminase elevations, or hepatobiliary or gastrointestinal adverse events as reported by local site investigators. However, the predefined central liver safety measurements revealed a greater frequency of possible Hy’s Law cases (5 vs 2) and a 3- to 7-fold greater relative risk in alanine or aspartate transaminase elevation (with respect to upper limit of normal) within fasiglifam recipients compared with placebo/active control: alanine or aspartate transaminase > 3×: relative risk 3.34 (95% confidence interval 2.29–4.90), alanine or aspartate transaminase > 5×: relative risk 6.60 (95% confidence interval 3.03–14.38), alanine or aspartate transaminase > 8×: relative risk 6.14 (95% confidence interval 2.18–17.27), and alanine or aspartate transaminase > 10×: relative risk 6.74 (95% confidence interval 2.05, 22.14). All elevations resolved on study drug discontinuation. Drug-induced liver injury was adjudicated as highly likely or probably related in 0.64% of fasiglifam-treated versus 0.06% placebo or active control-treated patients. Conclusion In spite of clear liver toxicity detected with a systematic surveillance program, liver safety signals were not identified from investigator adverse event reporting alone. By integrating key safety monitoring processes within the randomized design of adequately sized clinical trials, the rare but serious liver toxicity signal became clear, leading to timely program termination.

Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.


2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.


2021 ◽  
Author(s):  
Huilin Tang ◽  
Liyuan Zhou ◽  
Xiaotong Li ◽  
Alan C Kinlaw ◽  
Jeff Y Yang ◽  
...  

Abstract Background Liver injury has been documented independently in novel coronavirus disease 2019 (COVID-19) patients and patients treated with lopinavir-ritonavir. Objective to investigate the drug-induced liver injury associated with lopinavir-ritonavir among the patients with COVID-19. Methods We conducted a disproportionality analysis of US Food and Drug Administration Adverse Event Reporting System (FAERS) between 2020Q1 and 2020Q3 to evaluate the association between lopinavir-ritonavir and risk of drug-induced liver injury (or severe drug-induced liver injury) and calculated their reporting odds ratios (RORs) with 95% confidence intervals (CIs). Results A total of 1,754 reports of drug-induced liver injury in patients with COVID-19. The ROR for drug-induced liver injury was 1.4 (95% CI, 1.1–1.7), 3.6 (95% CI, 2.7–4.7), and 0.8 (95% CI, 0.7-1.0) when comparing lopinavir-ritonavir with all other drugs, hydroxychloroquine/chloroquine only, and remdesivir, respectively. For severe drug-induced liver injury, RORs for lopinavir-ritonavir provided evidence of an association compared with all other drugs (ROR, 4.9; 95% CI, 3.7–6.5), compared with hydroxychloroquine/chloroquine only (ROR, 4.3; 95% CI, 3.0-6.2), and compared with remdesivir (ROR, 10.4; 95% CI, 7.2–15.0). Conclusions In the FAERS, we observed a disproportional signal for severe drug-induced liver injury associated with lopinavir-ritonavir in patients with COVID-19.


2019 ◽  
Vol 33 (1) ◽  
pp. 223-238 ◽  
Author(s):  
Michael D. Aleo ◽  
Falgun Shah ◽  
Scott Allen ◽  
Hugh A. Barton ◽  
Chester Costales ◽  
...  

2020 ◽  
Vol 53 (02) ◽  
pp. 60-64 ◽  
Author(s):  
Bianca Ueberberg ◽  
Ulrich Frommberger ◽  
Thomas Messer ◽  
Peter Zwanzger ◽  
Jens Kuhn ◽  
...  

Abstract Introduction Drug-induced liver injury (DILI) is the 4th most common cause of liver damage in Western countries and can be caused by antidepressants. Methods Against the background of increasing antidepressant prescriptions and increasing use of polypharmacy, we analyzed administered antidepressants and other pharmacological substances, liver toxicity, comorbid somatic secondary diseases together with the occurrence of DILI in a patient population of 6 centers throughout Germany. Results The majority of the enrolled 329 patients received polypharmacological treatment in an inpatient setting. During antidepressant treatment 5.1% of the patients had elevated serum transaminase levels, whereby exactly and not more than 1 criterion proposed to be indicative for DILI, was fulfilled by 3 patients (0.9%). Discussion During patient characterization it becomes clear that a sensitization for relevant risk constellations causing liver injury in MDD patients is relevant to prevent further serious adverse events.


2019 ◽  
Vol 21 (3) ◽  
pp. 220-223
Author(s):  
Anuj K.C. ◽  
S. Jha ◽  
S. Thapa

Drug induced liver injury (DILI) is one of the common cause of liver toxicity. Most of the drugs used today are hepatotoxic. DILI accounts for approximately one-half of the cases of acute liver failure and mimics all forms of acute and chronic liver disease. It is the single most common adverse drug reaction leading to a halt in the development of new medication by pharmaceutical company, failure of new drug to obtain regulatory approval, and withdrawal or restriction of existing drug from the market. The aim of this study is to evaluate common causes and patterns of DILI in our setting. Twenty-seven patients were enrolled in the study. Ant tubercular drugs were most common cause of DILI, accounting for 48.2%. Other common causes of DILI were paracetamol (14.8%) and NSAID’s (11.1%). The most common pattern of liver injury seen was mixed pattern which was present in63%, followed by cholestatic and hepatocellular pattern. Hence, we should be very careful while prescribing these frequently used drugs.


Drug Safety ◽  
2013 ◽  
Vol 36 (12) ◽  
pp. 1169-1178 ◽  
Author(s):  
Allen D. Brinker ◽  
Jenna Lyndly ◽  
Joseph Tonning ◽  
David Moeny ◽  
Jonathan G. Levine ◽  
...  

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