Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

In Silico prediction of inhibitors for multiple transporters via machine learning methods

Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transporter inhibitors. However, these methods often focus on isolated endpoints, overlooking the interactions between transporters and lacking good interpretation. In this study, we integrated a comprehensive dataset and constructed models to assess the inhibitory effects on seven transporters. Both conventional machine learning and multi-task deep learning methods were employed. The results demonstrated that the MLT-GAT model achieved superior performance with an average AUC value of 0.882. It is noteworthy that our model excels not only in prediction performance but also in achieving robust interpretability, aided by GNN-Explainer. It provided valuable insights into transporter inhibition. The reliability of our model's predictions positioned it as a promising and valuable tool in the field of transporter inhibition research. Related data and code are available at https://gitee.com/wutiantian99/transporter_code.git.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

The Role of Adopted Orphan Nuclear Receptors in the Regulation of an Organic Anion Transporting Polypeptide 1B1 (OATP1B1) under the Action of Sex Hormones

Organic anion transporting polypeptide 1B1 (OATP1B1) is an influx transporter protein of the SLC superfamily, expressed mainly in the liver and some tumor cells. The mechanisms of its regulation are being actively studied. In the present study, the effect of sex hormones (estradiol, progesterone and testosterone) on OATP1B1 expression in HepG2 cells was examined. The role of adopted orphan receptors, farnasoid X receptor (FXR), constitutive androstane receptor (CAR), pregnane X receptor (PXR) and liver X receptor subtype alpha (LXRa), was also evaluated. Hormones were used in concentrations of 1, 10 and 100 μM, with incubation for 24 h. The protein expression of OATP1B1, FXR, CAR, PXR and LXRa was analyzed by Western blot. It was shown that estradiol (10 and 100 μM) increased the expression of OATP1B1, acting through CAR. Testosterone (1, 10 and 100 μM) increased the expression of OATP1B1, acting through FXR, PXR and LXRa. Progesterone (10 and 100 μM) decreased the expression of OATP1B1 (10 and 100 μM) and adopted orphan receptors are not involved in this process. The obtained results have important practical significance and determine ways for targeted regulation of the transporter, in particular in cancer.

Machine Learning Techniques Applied to the Study of Drug Transporters

With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug-drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.

Identification of the Stapled α-Helical Peptide ATSP-7041 as a Substrate and Strong Inhibitor of OATP1B1 In Vitro

ATSP-7041, a stapled α-helical peptide that inhibits murine double minute-2 (MDM2) and MDMX activities, is a promising modality targeting protein-protein interactions. As peptides of molecular weights over 1000 Da are not usually evaluated, data on the drug-drug interaction (DDI) potential of stapled α-helical peptides remain scarce. Here, we evaluate the interaction of ATSP-7041 with hepatic cytochrome P450s (CYPs; CYP1A2, CYP2C9, CYP2C19, CYP3A4, and CYP2D6) and transporters (organic anion transporting polypeptides (OATPs; OATP1B1 and OATP1B3), P-glycoprotein (P-gp), and breast cancer resistance protein (BCRP)). ATSP-7041 demonstrated negligible metabolism in human liver S9 fraction and a limited inhibition of CYP activities in yeast microsomes or S9 fractions. On the contrary, a substantial uptake by OATPs in HEK 293 cells, a strong inhibition of OATP activities in the cells, and an inhibition of P-gp and BCRP activities in reversed membrane vesicles were observed for ATSP-7041. A recent report describes that ALRN-6924, an ATSP-7041 analog, inhibited OATP activities in vivo; therefore, we focused on the interaction between ATSP-7041 and OATP1B1 to demonstrate that ATSP-7041, as a higher molecular weight stapled peptide, is a substrate and strong inhibitor of OATP1B1 activity. Our findings demonstrated the possibility of transporter-mediated DDI potential by high molecular weight stapled peptides and the necessity of their evaluation for drug development.

Fluorescence-based methods for studying activity and drug-drug interactions of hepatic solute carrier and ATP binding cassette proteins involved in ADME-Tox

In humans, approximately 70% of drugs are eliminated through the liver. This process is governed by the concerted action of membrane transporters and metabolic enzymes. Transporters mediating hepatocellular uptake of drugs belong to the SLC (Solute carrier) superfamily of transporters. Drug efflux either toward the portal vein or into the bile is mainly mediated by active transporters of the ABC (ATP Binding Cassette) family. Alteration in the function and/or expression of liver transporters due to mutations, disease conditions, or co-administration of drugs or food components can result in altered pharmacokinetics. On the other hand, drugs or food components interacting with liver transporters may also interfere with liver function (e.g., bile acid homeostasis) and may even cause liver toxicity. Accordingly, certain transporters of the liver should be investigated already at an early stage of drug development. Most frequently radioactive probes are applied in these drug-transporter interaction tests. However, fluorescent probes are cost-effective and sensitive alternatives to radioligands, and are gaining wider application in drug-transporter interaction tests. In our review, we summarize our current understanding about hepatocyte ABC and SLC transporters affected by drug interactions. We provide an update of the available fluorescent and fluorogenic/activable probes applicable in in vitro or in vivo testing of these ABC and SLC transporters, including near-infrared transporter probes especially suitable for in vivo imaging. Furthermore, our review gives a comprehensive overview of the available fluorescence-based methods, not directly relying on the transport of the probe, suitable for the investigation of hepatic ABC or SLC-type drug transporters.

Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

The organic anion transporting polypeptide 1B1 (OATP1B1) is an important hepatic uptake transporter. Inhibition of its normal function could lead to drug-drug interactions. In silico prediction is an effective means to identify potential OATP1B1 inhibitors and quantitative structure-activity relationship (QSAR) modeling is extensively used. As the structures of OATP1B1 substrates/inhibitors are quite diverse, machine learning based methods should be a good option for their QSAR analysis. In the present study, deep neural networks (DNNs) were employed to develop QSAR models for the substrates/inhibitors of OATP1B1 with different molecular fingerprints. Our results showed that QSAR models based on 4-hidden layer DNNs and ECFP4/FCFP4 fingerprints had the best generalization performance. The correlation coefficients (R²) of test set for ECFP4 and FCFP4 models were 0.641 and 0.653, respectively. Model application domain (AD) was calculated with Euclidean distance-based method, and AD could improve the performance of ECFP4 model but has little effect on FCFP4 model. Finally, the prediction of additional 8 compounds that not included in the data set further demonstrated that our QSAR models had a good predictive ability (averaged prediction accuracy >92%). The developed QSAR models could be used to screen large data sets and discover novel inhibitors for OATP1B1.

Comparative Modelling of Organic Anion Transporting Polypeptides: Structural Insights and Comparison of Binding Modes

To better understand the functionality of organic anion transporting polypeptides (OATPs) and to design new ligands, reliable structural data of each OATP is needed. In this work, we used a combination of homology model with molecular dynamics simulations to generate a comprehensive structural dataset, that encompasses a diverse set of OATPs but also their relevant conformations. Our OATP models share a conserved transmembrane helix folding harbouring a druggable binding pocket in the shape of an inner pore. Our simulations suggest that the conserved salt bridges at the extracellular region between residues on TM1 and TM7 might influence the entrance of substrates. Interactions between residues on TM1 and TM4 within OATP1 family shown their importance in transport of substrates. Additionally, in transmembrane (TM) 1/2, a known conserved element, interact with two identified motifs in the TM7 and TM11. Our simulations suggest that TM1/2-TM7 interaction influence the inner pocket accessibility, while TM1/2-TM11 salt bridges control the substrate binding stability.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.