Combinatorial pharmacophore modeling of organic cation transporter 2 (OCT2) inhibitors: insights into multiple inhibitory mechanisms

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

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.

A New Reaction Mode of 3-Halooxindoles: Acting as C-C-O Three-Atom Components for (3+3) Cycloaddition to Access Indolenine-Fused 2H-1,4-Oxathiines

Herein, we report an unprecedented implementation of 3-halooxindoles as C-C-O three-atom components for (3+3) cycloaddition with pyridinium 1,4-zwitterionic thiolates, affording structurally diverse indolenine-fused 2H-1,4-oxathiines in moderate to high yields. A combined experimental and computational mechanistic study suggests that the reaction proceeds through addition of a S conjugate to the o-azaxylylene intermediate, followed by O-Michael addition and a sequential retro-Michael addition/pyridine extrusion pathway.

Update on drug-drug interaction at organic cation transporters: mechanisms, clinical impact, and proposal for advanced in vitro testing

Introduction: Organic cation transporters collectively called OCTs belong to three gene families (SLC22A1 OCT1, SLC22A2 OCT2, SLC22A3 OCT3, SLC22A4 OCTN1, SLC22A5 OCTN2, SLC29A4 PMAT, SLC47A1 MATE1, and SLC47A1 MATE2-K). OCTs transport structurally diverse drugs with overlapping selectivity. Some OCTs were shown to be critically involved in pharmacokinetics and therapeutic efficacy of cationic drugs. Drug-drug interactions at individual OCTs were shown to result in clinical effects. Procedures for in vitro testing of drugs for interaction with OCT1, OCT2, MATE1, and MATE2-K have been recommended.Areas covered: An overview of functional properties, cation selectivity, location, and clinical impact of OCTs is provided. In addition, clinically relevant drug-drug interactions in OCTs are compiled. Because it was observed that the half maximal concentration of drugs to inhibit transport by OCTs (IC₅₀) is dependent on the transported cation and its concentration, an advanced protocol for in vitro testing of drugs for interaction with OCTs is proposed. In addition, it is suggested to include OCT3 and PMAT for in vitro testing.Expert opinion: Research on clinical roles of OCTs should be reinforced including more transporters and drugs. An improvement of the in vitro testing protocol considering recent data is imperative for the benefit of patients.

Organic Cation Transporters in Health and Disease

The organic cation transporters (OCTs) OCT1, OCT2, OCT3, novel OCT (OCTN)1, OCTN2, multidrug and toxin exclusion (MATE)1, and MATE kidney-specific 2 are polyspecific transporters exhibiting broadly overlapping substrate selectivities. They transport organic cations, zwitterions, and some uncharged compounds and operate as facilitated diffusion systems and/or antiporters. OCTs are critically involved in intestinal absorption, hepatic uptake, and renal excretion of hydrophilic drugs. They modulate the distribution of endogenous compounds such as thiamine, L-carnitine, and neurotransmitters. Sites of expression and functions of OCTs have important impact on energy metabolism, pharmacokinetics, and toxicity of drugs, and on drug-drug interactions. In this work, an overview about the human OCTs is presented. Functional properties of human OCTs, including identified substrates and inhibitors of the individual transporters, are described. Sites of expression are compiled, and data on regulation of OCTs are presented. In addition, genetic variations of OCTs are listed, and data on their impact on transport, drug treatment, and diseases are reported. Moreover, recent data are summarized that indicate complex drug-drug interaction at OCTs, such as allosteric high-affinity inhibition of transport and substrate dependence of inhibitor efficacies. A hypothesis about the molecular mechanism of polyspecific substrate recognition by OCTs is presented that is based on functional studies and mutagenesis experiments in OCT1 and OCT2. This hypothesis provides a framework to imagine how observed complex drug-drug interactions at OCTs arise. Finally, preclinical in vitro tests that are performed by pharmaceutical companies to identify interaction of novel drugs with OCTs are discussed. Optimized experimental procedures are proposed that allow a gapless detection of inhibitory and transported drugs.

Molecular and cellular physiology of organic cation transporter 2

Organic cation transporters play a critical role in mediating the distribution of cationic pharmaceuticals. Indeed, organic cation transporter (OCT)2 is the initial step in the renal secretion of organic cations and consequently plays a defining role in establishing the pharmacokinetics of many cationic drugs. Although a hallmark of OCTs is their broad selectivity, this characteristic also makes them targets for unwanted, adverse drug-drug interactions (DDIs), making them a focus for efforts to develop models of ligand interaction that could predict and preempt these adverse interactions. This review discusses the molecular characteristics of these transporters as well as the evidence that established the OCTs as key players in the distribution of organic cations. However, the primary focus is the present understanding of the complexity of ligand interaction with OCTs, particularly OCT2, including evidence for the presence of multiple ligand-binding sites and the influence of substrate structure on the affinity of the transporter for inhibitory ligands. This leads to a discussion of the complexities associated with the development of protocols for assessing the inhibitory potential of new molecular entities to perpetrate unwanted DDIs, the criteria that should be considered in the interpretation of the results of such protocols, and the challenges associated with development of models capable of predicting unwanted DDIs.

Multiple binding sites in organic cation transporters require sophisticated procedures to identify interactions of novel drugs

In vitro evaluation of drugs for interaction with transporters is essential during drug development. As polyspecific organic cation transporters (OCTs) are critical for pharmacokinetics of many cationic drugs, in vitro testing of human OCT1 and human OCT2 is recommended. In the currently applied tests it is determined whether uptake of one model cation in stably transfected epithelial cells is inhibited using a substrate concentration in the micromolar range. In this review experimental evidence for the existence of low- and high-affinity cation binding sites in OCTs that may interact with drugs is compiled. Most data were obtained from studies performed with rat Oct1. Whereas overlapping low-affinity cation binding sites are directly involved in transport, the high-affinity cation binding sites may induce allosteric inhibition of transport. Remarkably, high-affinity inhibition is only observed when uptake is measured using nanomolar substrate concentrations far below the respective Km values. Affinities of inhibitors are dependent on molecular structure and concentration of the employed substrate. Because the currently applied in vitro tests for identification of interaction of novel drugs with OCTs do not consider the influence of substrate structure and are not capable of identifying high-affinity inhibition, more sophisticated testing protocols are proposed.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

Assessment of Substrate-Dependent Ligand Interactions at the Organic Cation Transporter OCT2 Using Six Model Substrates

Organic cation transporter (OCT) 2 mediates the entry step for organic cation secretion by renal proximal tubule cells and is a site of unwanted drug-drug interactions (DDIs). But reliance on decision tree-based predictions of DDIs at OCT2 that depend on IC₅₀ values can be suspect because they can be influenced by choice of transported substrate; for example, IC₅₀ values for the inhibition of metformin versus MPP transport can vary by 5- to 10-fold. However, it is not clear whether the substrate dependence of a ligand interaction is common among OCT2 substrates. To address this question, we screened the inhibitory effectiveness of 20 µM concentrations of several hundred compounds against OCT2-mediated uptake of six structurally distinct substrates: MPP, metformin, N,N,N-trimethyl-2-[methyl(7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)amino]ethanaminium (NBD-MTMA), TEA, cimetidine, and 4-4-dimethylaminostyryl-N-methylpyridinium (ASP). Of these, MPP transport was least sensitive to inhibition. IC₅₀ values for 20 structurally diverse compounds confirmed this profile, with IC₅₀ values for MPP averaging 6-fold larger than those for the other substrates. Bayesian machine-learning models of ligand-induced inhibition displayed generally good statistics after cross-validation and external testing. Applying our ASP model to a previously published large-scale screening study for inhibition of OCT2-mediated ASP transport resulted in comparable statistics, with approximately 75% of "active" inhibitors predicted correctly. The differential sensitivity of MPP transport to inhibition suggests that multiple ligands can interact simultaneously with OCT2 and supports the recommendation that MPP not be used as a test substrate for OCT2 screening. Instead, metformin appears to be a comparatively representative OCT2 substrate for both in vitro and in vivo (clinical) use.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line

BACKGROUND AND PURPOSE

An influx drug/proton antiporter of unknown structure has been functionally demonstrated at the blood-brain barrier. This transporter, which handles some psychoactive drugs like diphenhydramine, clonidine, oxycodone, nicotine and cocaine, could represent a new pharmacological target in drug addiction therapy. However, at present there are no known drugs/inhibitors that effectively inhibit/modulate this transporter in vivo.

EXPERIMENTAL APPROACH

The FLAPpharm approach was used to establish a pharmacophore model for inhibitors of this transporter. The inhibitory potency of 44 selected compounds was determined against the specific substrate, [(3)H]-clonidine, in the human cerebral endothelial cell line hCMEC/D3 and ranked as good, medium, weak or non-inhibitor.

KEY RESULTS

The pharmacophore model obtained was used as a template to screen xenobiotic and endogenous compounds from databases [Specs, Recon2, Human Metabolome Database (HMDB), human intestinal transporter database], and hypothetical candidates were tested in vitro to determine their inhibitory capacity with [(3)H]-clonidine. According to the transporter database, 80% of the proton antiporter inhibitor candidates could inhibit P-glycoprotein/MDR1/ABCB1 and specificity is improved by reducing inhibitor size/shape and increasing water solubility. Virtual screening results using HMDB and Recon2 for endogenous compounds appropriately scored tryptamine as an inhibitor.

CONCLUSIONS AND IMPLICATIONS

The pharmacophore model for the proton-antiporter inhibitors was a good predictor of known inhibitors and allowed us to identify new good inhibitors. This model marks a new step towards the discovery of this drug/proton antiporter and will be of great use for the discovery and design of potent inhibitors that could potentially help to assess and validate its pharmacological role in drug addiction in vivo.

In silico ADME/T modelling for rational drug design

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Combinatorial Pharmacophore Modeling of Multidrug and Toxin Extrusion Transporter 1 Inhibitors: a Theoretical Perspective for Understanding Multiple Inhibitory Mechanisms

A combinatorial pharmacophore (CP) model for Multidrug and toxin extrusion 1 (MATE1/SLC47A1) inhibitors was developed based on a data set including 881 compounds. The CP model comprises four individual pharmacophore hypotheses, HHR1, DRR, HHR2 and AAAP, which can successfully identify the MATE1 inhibitors with an overall accuracy around 75%. The model emphasizes the importance of aromatic ring and hydrophobicity as two important structural determinants for MATE1 inhibition. Compared with the pharmacophore model of Organic Cation Transporter 2 (OCT2/ SLC22A2), a functional related transporter of MATE1, the hypotheses of AAAP and PRR5 are suggested to be responsible for their ligand selectivity, while HHR a common recognition pattern for their dual inhibition. A series of analysis including molecular sizes of inhibitors matching different hypotheses, matching of representative MATE1 inhibitors and molecular docking indicated that the small inhibitors matching HHR1 and DRR involve in competitive inhibition, while the relatively large inhibitors matching AAAP are responsible for the noncompetitive inhibition by locking the conformation changing of MATE1. In light of the results, a hypothetical model for inhibiting transporting mediated by MATE1 was proposed.

Substrate-Dependent Inhibition of the Human Organic Cation Transporter OCT2: A Comparison of Metformin with Experimental Substrates

The importance of the organic cation transporter OCT2 in the renal excretion of cationic drugs raises the possibility of drug-drug interactions (DDIs) in which an inhibitor (perpetrator) drug decreases OCT2-dependent renal clearance of a victim (substrate) drug. In fact, there are clinically significant interactions for drugs that are known substrates of OCT2 such as metformin. To identify drugs as inhibitors for OCT2, individual drugs or entire drug libraries have been investigated in vitro by using experimental probe substrates such as 1-methyl-4-phenylpyridinium (MPP⁺) or 4–4-dimethylaminostyryl-N-methylpyridinium (ASP⁺). It has been questioned whether the inhibition data obtained with an experimental probe substrate such as MPP⁺ or ASP⁺ might be used to predict the inhibition against other, clinical relevant substrates such as metformin. Here we compared the OCT2 inhibition profile data for the substrates metformin, MPP⁺ and ASP⁺. We used human embryonic kidney (HEK 293) cells stably overexpressing human OCT2 as the test system to screen 125 frequently prescribed drugs as inhibitors of OCT2-mediated metformin and MPP⁺ uptake. Data on inhibition of OCT2-mediated ASP⁺ uptake were obtained from previous literature. A moderate correlation between the inhibition of OCT2-mediated MPP⁺, ASP⁺, and metformin uptake was observed (pairwise r_s between 0.27 and 0.48, all P < 0.05). Of note, the correlation in the inhibition profile between structurally similar substrates such as MPP⁺ and ASP⁺ (Tanimoto similarity T = 0.28) was even lower (r_s = 0.27) than the correlation between structurally distinct substrates, such as ASP⁺ and metformin (T = 0.01; r_s = 0.48) or MPP⁺ and metformin (T = 0.01; r_s = 0.40). We identified selective as well as universal OCT2 inhibitors, which inhibited transport by more than 50% of one substrate only or of all substrates, respectively. Our data suggest that the predictive value for drug-drug interactions using experimental substrates rather than the specific victim drug is limited.

Making Transporter Models for Drug-Drug Interaction Prediction Mobile

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models.

Regulation of human hepatic drug transporter activity and expression by diesel exhaust particle extract

Diesel exhaust particles (DEPs) are common environmental air pollutants primarily affecting the lung. DEPs or chemicals adsorbed on DEPs also exert extra-pulmonary effects, including alteration of hepatic drug detoxifying enzyme expression. The present study was designed to determine whether organic DEP extract (DEPe) may target hepatic drug transporters that contribute in a major way to drug detoxification. Using primary human hepatocytes and transporter-overexpressing cells, DEPe was first shown to strongly inhibit activities of the sinusoidal solute carrier (SLC) uptake transporters organic anion-transporting polypeptides (OATP) 1B1, 1B3 and 2B1 and of the canalicular ATP-binding cassette (ABC) efflux pump multidrug resistance-associated protein 2, with IC50 values ranging from approximately 1 to 20 μg/mL and relevant to environmental exposure situations. By contrast, 25 μg/mL DEPe failed to alter activities of the SLC transporter organic cation transporter (OCT) 1 and of the ABC efflux pumps P-glycoprotein and bile salt export pump (BSEP), whereas it only moderately inhibited those of sodium taurocholate co-transporting polypeptide and of breast cancer resistance protein (BCRP). Treatment by 25 μg/mL DEPe was next demonstrated to induce expression of BCRP at both mRNA and protein level in cultured human hepatic cells, whereas it concomitantly repressed mRNA expression of various transporters, including OATP1B3, OATP2B1, OCT1 and BSEP. Such changes in transporter expression were found to be highly correlated to those caused by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a reference activator of the aryl hydrocarbon receptor (AhR) pathway. This suggests that DEPe, which is enriched in known ligands of AhR like polycyclic aromatic hydrocarbons, alters drug transporter expression via activation of the AhR cascade. Taken together, these data established human hepatic transporters as targets of organic chemicals containing in DEPs, which may contribute to their systemic effects through impairing hepatic transport of endogenous compound or drug substrates of these transporters.