Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review

Abstract

Drug-induced liver injury (DILI) causes one in three market withdrawals due to adverse drug reactions, causing preventable human suffering and massive financial loss. We applied evidence-based methods to investigate the role of preclinical studies in predicting human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market). Evidence Stream 1: A systematic literature review of in vivo studies on rosiglitazone or troglitazone was conducted (PROSPERO registration CRD42018112353). Evidence Stream 2: in vitro data on troglitazone and rosiglitazone were retrieved from the US EPA ToxCast database. Evidence Stream 3: troglitazone- and rosiglitazone-related DILI cases were retrieved from WHO Vigibase. All three evidence stream analyses were conducted according to evidence-based methodologies and performed according to pre-registered protocols. Evidence Stream 1: 9288 references were identified, with 42 studies included in analysis. No reported biomarker for either drug indicated a strong hazard signal in either preclinical animal or human studies. All included studies had substantial limitations, resulting in “low” or “very low” certainty in findings. Evidence Stream 2: Troglitazone was active in twice as many in vitro assays (129) as rosiglitazone (60), indicating a strong signal for more off-target effects. Evidence Stream 3: We observed a fivefold difference in both all adverse events and liver-related adverse events reported, and an eightfold difference in fatalities for troglitazone, compared to rosiglitazone. In summary, published animal and human trials failed to predict troglitazone’s potential to cause severe liver injury in a wider patient population, while in vitro data showed marked differences in the two drugs’ off-target activities, offering a new paradigm for reducing drug attrition in late development and in the market. This investigation concludes that death and disability due to adverse drug reactions may be prevented if mechanistic information is deployed at early stages of drug development by pharmaceutical companies and is considered by regulators as a part of regulatory submissions.

A Multiple Logistic Regression Model Based on Gamma‐Glutamyl Transferase as a Biomarker for Early Prediction of Drug‐Induced Liver Injury in Vietnamese Patients

“…28 Similarly, preclinical models have been used to predict DILI in humans using 2 antidiabetic drugs, including troglitazone (withdrawn from the US market due to DILI) and rosiglitazone (remains on the US market). 29 In terms of clinical practice, risk stratification tools have been developed based on the identification of baseline risk, including environment-drug and patient-related risk factors for some drugs. A novel human leukocyte antigen class II association for DILI related to flupirtine in 6 cases among 614 European DILI cases and 10 588 controls was identified.…”

Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review

“…Bozada is a sysrev.com employee who has created many projects (public and private) for managed reviews. There is a chain of reviewer/sysrev membership links that lead to sysrev.com/p/3588 and sysrev.com/p/100 which are projects completed by toxicology and cancer research groups, the latter of which represents a hepatotoxicity project by the Evidence-Based Toxicology Collaboration led by Dr. Tsaioun at Johns Hopkins which provided support for a report on “Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review” ( Dirven et al, 2021 ).…”

Sysrev: A FAIR platform for Data Curation and Systematic Evidence Review

“…Bozada is a sysrev.com employee who has created many projects (public and private) for managed reviews. There is a chain of reviewer / sysrev membership links that lead to sysrev.com/p/3588 and sysrev.com/p/100 which are projects completed by toxicology and cancer research groups, the latter of which represents a hepatotoxicity project by the Evidence-Based Toxicology Collaboration led by Dr. Tsaioun at Johns Hopkins which provided support for a report on "Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review" [35]. learning is to accelerate machine learning, so as to build accurate models as quickly as possible and minimize the use of reviewer times on easily modeled documents.…”