Dataset Information

A compound attributes-based predictive model for drug induced liver injury in humans.

ABSTRACT: Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7159228 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A compound attributes-based predictive model for drug induced liver injury in humans.

Liu Yang Y Gao Hua H He Yudong D YD

PloS one 20200415 4

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and st ...[more]

PMID: 32294131

Similar Datasets

Project description: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human DILI using two anti-diabetic drugs from the same class, but with different toxicological profiles: troglitazone (withdrawn from US market due to DILI) and rosiglitazone (remains on US market).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.

Project description:To establish an inducible liver injury mouse model and transplant human hepatocytes to obtain liver-humanized mice.We crossed three mouse strains, including albumin (Alb)-cre transgenic mice, inducible diphtheria toxin receptor (DTR) transgenic mice and severe combined immune deficient (SCID)-beige mice, to create Alb-cre/DTR/SCID-beige (ADSB) mice, which coincidentally harbor Alb-cre and DTR transgenes and are immunodeficient. As the Cre expression is driven by the liver-specific promoter Alb (encoding ALB), the DTR stop signal flanked by two loxP sites can be deleted in the ADSB mice, resulting in DTR expression in the liver. ADSB mice aged 8-10 wk were injected intraperitoneally (i.p.) with diphtheria toxin (DT) and liver damage was assessed by serum alanine aminotransferase (ALT) level. Two days later, mouse livers were sampled for histological analysis, and human hepatocytes were transplanted into the livers on the same day. A human ALB enzyme-linked immunosorbent assay was performed 7, 14, 21 and 28 d after transplantation. Human CD68 immunohistochemistry was performed 30 and 90 d after transplantation.We crossed Alb-cre with DTR and SCID-beige mice to obtain ADSB mice. These mice were found to have liver damage 4 d after i.p. injection of 2.5 ng/g bodyweight DT. Bodyweight began to decrease on day 2, increased on day 7, and was lowest on day 4 (range, 10.5%-13.4%). Serum ALT activity began to increase on day 2 and reached a peak value of 289.7 ± 16.2 IU/mL on day 4, then returned to background values on day 7. After transplantation of human liver cells, peripheral blood human ALB level was 1580 ± 454.8 ng/mL (range, 750.2-3064.9 ng/mL) after 28 d and Kupffer cells were present in the liver at 30 d in ADSB mice.Human hepatocytes were successfully repopulated in the livers of ADSB mice. The inducible mouse model of humanized liver in ADSB mice may have functional applications, such as hepatocyte transplantation, hepatic regeneration and drug metabolism.

Dataset Information

A compound attributes-based predictive model for drug induced liver injury in humans.

Publications

A compound attributes-based predictive model for drug induced liver injury in humans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets