Mitigating the Inhibition of Human Bile Salt Export Pump by Drugs: Opportunities Provided by Physicochemical Property Modulation, In Silico Modeling, and Structural Modification

TLDR

BSEP is a hepatocyte canalicular transporter essential for bile flow, and its inhibition by drugs—often linked to cholestatic liver injury—suggests a key mechanism underlying drug‑induced liver injury. The study aimed to identify chemical features driving BSEP inhibition by assaying 624 compounds in a membrane‑vesicle BSEP inhibition assay. The authors quantified BSEP inhibition for 624 compounds, examined correlations with physicochemical properties, and used the data to construct QSAR models. The support vector machine model achieved 0.87 accuracy (κ = 0.74), demonstrating that combining computational and experimental approaches can reduce the risk of BSEP inhibition early in drug discovery.

Abstract

The human bile salt export pump (BSEP) is a membrane protein expressed on the canalicular plasma membrane domain of hepatocytes, which mediates active transport of unconjugated and conjugated bile salts from liver cells into bile. BSEP activity therefore plays an important role in bile flow. In humans, genetically inherited defects in BSEP expression or activity cause cholestatic liver injury, and many drugs that cause cholestatic drug-induced liver injury (DILI) in humans have been shown to inhibit BSEP activity in vitro and in vivo. These findings suggest that inhibition of BSEP activity by drugs could be one of the mechanisms that initiate human DILI. To gain insight into the chemical features responsible for BSEP inhibition, we have used a recently described in vitro membrane vesicle BSEP inhibition assay to quantify transporter inhibition for a set of 624 compounds. The relationship between BSEP inhibition and molecular physicochemical properties was investigated, and our results show that lipophilicity and molecular size are significantly correlated with BSEP inhibition. This data set was further used to build predictive BSEP classification models through multiple quantitative structure-activity relationship modeling approaches. The highest level of predictive accuracy was provided by a support vector machine model (accuracy = 0.87, κ = 0.74). These analyses highlight the potential value that can be gained by combining computational methods with experimental efforts in early stages of drug discovery projects to minimize the propensity of drug candidates to inhibit BSEP.