Early Drug-Induced Liver Injury Risk Screening: "Free, " as Good as It Gets. (27th May 2022)

• Record Type: Journal Article
• Authors: Martin, Matthew T; Koza-Taylor, Petra; Di, Li; Watt, Eric D; Keefer, Christopher; Smaltz, Daniel; Cook, Jon; Jackson, Jonathan P
• Is Part Of: Toxicological sciences. Volume 188:Number 2(2022)
• Page Start: 208
• Page End: 218
• Publication Date: 2022-05-27
• Subjects: DILI -- physicochemical -- in vitro -- hepatotoxicity -- random forest -- hepatic partition coefficient; Toxicology -- Periodicals; Toxicology -- Periodicals; Toxicology; Periodicals; 615.9
• DOI: 10.1093/toxsci/kfac054
• Languages: English
• ISSNs: 1096-6080

Abstract

Abstract: For all the promise of and need for clinical drug-induced liver injury (DILI) risk screening systems, demonstrating the predictive value of these systems versus readily available physicochemical properties and inherent dosing information has not been thoroughly evaluated. Therefore, we utilized a systematic approach to evaluate the predictive value of in vitro safety assays including bile salt export pump transporter inhibition and cytotoxicity in HepG2 and transformed human liver epithelial along with physicochemical properties. We also evaluated the predictive value of in vitro ADME assays including hepatic partition coefficient (Kp) and its unbound counterpart because they provide insight on hepatic accumulation potential. The datasets comprised of 569 marketed drugs with FDA DILIrank annotation (most vs less/none), dose and physicochemical information, 384 drugs with Kp and plasma protein binding data, and 279 drugs with safety assay data. For each dataset and combination of input parameters, we developed random forest machine learning models and measured model performance using the receiver operator characteristic area under the curve (ROC AUC). The median ROC AUC across the various data and parameters sets ranged from 0.67 to 0.77 with little evidence of additive predictivity when including safety or ADME assay data. Subsequent machine learning models consistently demonstrated daily dose, fraction sp 3 or ionization, and cLogP/D inputs produced the best, simplest model for predicting clinical DILI risk with an ROC AUC of 0.75. This systematic framework should be used for future assay predictive value assessments and highlights the need for continued improvements to clinical DILI risk annotation.