In silico strategies for modeling membrane transporter function

KV11.1, NaV1.5, and CaV1.2 Transporter Proteins as Antitarget for Drug Cardiotoxicity

Safety assessment of pharmaceuticals is a rapidly developing area of pharmacy and medicine. The new advanced guidelines for testing the toxicity of compounds require specialized tools that provide information on the tested drug in a quick and reliable way. Ion channels represent the third-largest target. As mentioned in the literature, ion channels are an indispensable part of the heart's work. In this paper the most important information concerning the guidelines for cardiotoxicity testing and the way the tests are conducted has been collected. Attention has been focused on the role of selected ion channels in this process.

Targeting thermoTRP ion channels: in silico preclinical approaches and opportunities

INTRODUCTION

A myriad of cellular pathophysiological responses are mediated by polymodal ion channels that respond to chemical and physical stimuli such as thermoTRP channels. Intriguingly, these channels are pivotal therapeutic targets with limited clinical pharmacology. In silico methods offer an unprecedented opportunity for discovering new lead compounds targeting thermoTRP channels with improved pharmacological activity and therapeutic index.

AREAS COVERED

This article reviews the progress on thermoTRP channel pharmacology because of (i) advances in solving their atomic structure using cryo-electron microscopy and, (ii) progress on computational techniques including homology modeling, molecular docking, virtual screening, molecular dynamics, ADME/Tox and artificial intelligence. Together, they have increased the number of lead compounds with clinical potential to treat a variety of pathologies. We used original and review articles from Pubmed (1997-2020), as well as the clinicaltrials.gov database, containing the terms thermoTRP, artificial intelligence, docking, and molecular dynamics.

EXPERT OPINION

The atomic structure of thermoTRP channels along with computational methods constitute a realistic first line strategy for designing drug candidates with improved pharmacology and clinical translation. In silico approaches can also help predict potential side-effects that can limit clinical development of drug candidates. Together, they should provide drug candidates with upgraded therapeutic properties.

Ion Channels as Therapeutic Targets for Type 1 Diabetes Mellitus

Ion channels are integral proteins expressed in almost all living cells and are involved in muscle contraction and nutrient transport. They play a critical role in the normal functioning of the excitable tissues of the nervous system and regulate the action potential and contraction events. Dysfunction of genes encodes ion channel proteins, which disrupt the channel function and lead to a number of diseases, among which is type 1 diabetes mellitus (T1DM). Therefore, understanding the complex mechanism of ion channel receptors is necessary to facilitate the diagnosis and management of treatment. In this review, we summarize the mechanism of important ion channels and their potential role in the regulation of insulin secretion along with the limitations of ion channels as therapeutic targets. Furthermore, we discuss the recent investigations of the mechanism regulating the ion channels in pancreatic beta cells, which suggest that ion channels are active participants in the regulation of insulin secretion.

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase

Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available.

Molecular Modelling, Dynamics, and Docking of Membrane Proteins

Computational tools and techniques are now most popular and promising to progress the research at rapid rate. Molecular modelling studies contribute their maximum role in wide variety of disciplines especially in proteomics and drug discovery strategies. Molecular dynamics and molecular docking algorithms are now became an essential part in daily research activities of every laboratory throughout the world. These strategies are now well established and standardised to study any specific protein of interest and drug molecule. But still there exist considerable drawbacks in a special concern with membrane proteins as the presently available tools and methods cannot be applied directly to them. Modelling, dynamics and docking studies of membrane proteins need a special care and attention as several challenges are to be crossed with an intensive care to produce a reliable result. This chapter is aimed to discuss such challenges and solutions to handle membrane proteins.

QSAR Studies on Bacterial Efflux Pump Inhibitors

Antimicrobial drug resistance occurs when bacteria undergo certain modifications to eliminate the effectiveness of drugs, chemicals, or other agents designed to cure infections. To date, the burden of resistance has remained one of the major clinical concerns as it renders prolonged and complicated treatments, thereby increasing the medical costs with lengthier hospital stays. Of complex causes for bacterial resistance, there has been increasing evidence that proved the significant role of efflux pumps in antibiotic resistance. Coadministration of Efflux Pump Inhibitors (EPIs) with antibiotics has been considered one of the promising ways not only to improve the efficacy but also to extend the clinical utility of existing antibiotics. This chapter begins with outlining current knowledge about bacterial efflux pumps and drug designs applied in identification of their modulating compounds. Following, the chapter addresses and provides a discussion on Quantitative Structure-Activity Relationship (QSAR) analyses in search of novel and potent efflux pump inhibitors.

QSAR Studies on Bacterial Efflux Pump Inhibitors

Antimicrobial drug resistance occurs when bacteria undergo certain modifications to eliminate the effectiveness of drugs, chemicals, or other agents designed to cure infections. To date, the burden of resistance has remained one of the major clinical concerns as it renders prolonged and complicated treatments, thereby increasing the medical costs with lengthier hospital stays. Of complex causes for bacterial resistance, there has been increasing evidence that proved the significant role of efflux pumps in antibiotic resistance. Coadministration of Efflux Pump Inhibitors (EPIs) with antibiotics has been considered one of the promising ways not only to improve the efficacy but also to extend the clinical utility of existing antibiotics. This chapter begins with outlining current knowledge about bacterial efflux pumps and drug designs applied in identification of their modulating compounds. Following, the chapter addresses and provides a discussion on Quantitative Structure-Activity Relationship (QSAR) analyses in search of novel and potent efflux pump inhibitors.

In silico predictive model to determine vector-mediated transport properties for the blood-brain barrier choline transporter

The blood–brain barrier choline transporter (BBB-ChT) may have utility as a drug delivery vector to the central nervous system (CNS). We therefore initiated molecular docking studies with the AutoDock and AutoDock Vina (ADVina) algorithms to develop predictive models for compound screening and to identify structural features important for binding to this transporter. The binding energy predictions were highly correlated with r²=0.88, F=692.4, standard error of estimate =0.775, and P-value<0.0001 for selected BBB-ChT-active/inactive compounds (n=93). Both programs were able to cluster active (Gibbs free energy of binding <−6.0 kcal*mol⁻¹) and inactive (Gibbs free energy of binding >−6.0 kcal*mol⁻¹) molecules and dock them significantly better than at random with an area under the curve value of 0.86 and 0.84, respectively. In ranking smaller molecules with few torsional bonds, a size-related bias in scoring producing false-negative outcomes was detected. Finally, important blood–brain barrier parameters, such as the logBB_passive and logBB_active values, were assessed to predict compound transport to the CNS accurately. Knowledge gained from this study is useful to better understand the binding requirements in BBB-ChT, and until such time as its crystal structure becomes available, it may have significant utility in developing a highly predictive model for the rational design of drug-like compounds targeted to the brain.

Exploiting nutrient transporters at the blood-brain barrier to improve brain distribution of small molecules

The blood-brain barrier (BBB) is a major physiological barrier for drugs that target CNS receptors or enzymes. Several methods exist by which permeability to the CNS can be increased, one of which is using native nutrient transporters to carry these drugs through the endothelial cells of the BBB. In this review, we focus on work that characterizes the use of nutrient transporters of the BBB in delivering drugs to the CNS.

Ionic channels as targets for drug design: a review on computational methods

Ion channels are involved in a broad range of physiological and pathological processes. The implications of ion channels in a variety of diseases, including diabetes, epilepsy, hypertension, cancer and even chronic pain, have signaled them as pivotal drug targets. Thus far, drugs targeting ion channels were developed without detailed knowledge of the molecular interactions between the lead compounds and the target channels. In recent years, however, the emergence of high-resolution structures for a plethora of ion channels paves the way for computer-assisted drug design. Currently, available functional and structural data provide an attractive platform to generate models that combine substrate-based and protein-based approaches. In silico approaches include homology modeling, quantitative structure-activity relationships, virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools. These strategies have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. In this review we summarize recent applications of in silico methods that are being used for the development of ion channel drugs.

Coexistence of passive and carrier-mediated processes in drug transport

The permeability of biological membranes is one of the most important determinants of the pharmacokinetic processes of a drug. Although it is often accepted that many drug substances are transported across biological membranes by passive transcellular diffusion, a recent hypothesis speculated that carrier-mediated mechanisms might account for the majority of membrane drug transport processes in biological systems. Based on evidence of the physicochemical characteristics and of in vitro and in vivo findings for marketed drugs, as well as results from real-life discovery and development projects, we present the view that both passive transcellular processes and carrier-mediated processes coexist and contribute to drug transport activities across biological membranes.

Predictive screening model for potential vector-mediated transport of cationic substrates at the blood-brain barrier choline transporter

A set of semi-rigid cyclic and acyclic bis-quaternary ammonium analogs, which were part of a drug discovery program aimed at identifying antagonists at neuronal nicotinic acetylcholine receptors, were investigated to determine structural requirements for affinity at the blood-brain barrier choline transporter (BBB CHT). This transporter may have utility as a drug delivery vector for cationic molecules to access the central nervous system. In the current study, a virtual screening model was developed to aid in rational drug design/ADME of cationic nicotinic antagonists as BBB CHT ligands. Four 3D-QSAR comparative molecular field analysis (CoMFA) models were built which could predict the BBB CHT affinity for a test set with an r(2) <0.5 and cross-validated q(2) of 0.60, suggesting good predictive capability for these models. These models will allow the rapid in silico screening of binding affinity at the BBB CHT of both known nicotinic receptor antagonists and virtual compound libraries with the goal of informing the design of brain bioavailable quaternary ammonium analogs that are high affinity selective nicotinic receptor antagonists.

The eighth and ninth transmembrane domains in organic anion transporting polypeptide 1B1 affect the transport kinetics of estrone-3-sulfate and estradiol-17beta-D-glucuronide

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 are responsible for the hepatic uptake of organic anions. They share similar sequences and structures with 12 putative transmembrane domains (TMs). Their substrate specificities are very broad and overlap each other, whereas each transporter specifically recognizes certain substrates. Because the homology of the amino acid sequence in the latter part of OATP1B1 and OATP1B3 is relatively low, to determine which TMs in the latter part of OATP1B1 are important for its substrate recognition, we constructed several cell lines expressing chimeric transporters in which some TMs of OATP1B1 were substituted with those of OATP1B3, and we investigated the transport kinetics of estrone-3-sulfate (E-sul; a substrate preferentially accepted by OATP1B1) and estradiol-17beta-D-glucuronide (EG; a substrate accepted by both transporters). As the number of substituted TMs at the N terminus with those of OATP1B3 increased, the K(m) value of E-sul greatly increased and its uptake clearance decreased. The substitution of TM7 or TM9 of OATP1B1 with that of OATP1B3 (named 1B1-TM7 or 1B1-TM9) did not change the transport kinetics of EG, whereas the K(m) value of E-sul in 1B1-TM9 increased 7.4-fold. Conversely, the substitution of TM8 resulted in an 18-fold increase in the K(m) value of E-sul and abolished the transporter-mediated uptake of EG. These results suggest that TM8 in OATP1B1 is critical for the substrate recognition of both E-sul and EG and that TM9 is important for the recognition of E-sul, whereas it is interchangeable with that of OATP1B3 for EG transport.

Molecular modeling of the full-length human TRPV1 channel in closed and desensitized states

The transient receptor potential vanilloid subtype 1 (TRPV1) is a member of the TRP family gated by vanilloids, heat, and protons. Structurally, TRPV1 subunits have a modular architecture underlying different functionalities, namely stimuli recognition, channel gating, ion selectivity, subunit oligomerization, and regulation by intracellular signaling molecules. Considering modular organization and recent structural information in the ion channel field, we have modeled a full-length TRPV1 by assembly of its major modules: the cytosolic N-terminal, C-terminal, and membrane-spanning region. For N-terminal, we used the ankyrin repeat structure fused with the N-end segment. The membrane domain was modeled with the structure of the eukaryotic, voltage-gated Kv1.2 K+ channel. The C-terminus was cast using the coordinates of HCN channels. The extensive structure-function data available for TRPV1 was used to validate the models in terms of the location of molecular determinants of function in the structure. Additionally, the current information allowed the modeling of the vanilloid receptor in the closed and desensitized states. The closed state shows the N-terminal module highly exposed and accessible to adenosine triphosphate and the C-terminal accessible to phosphoinositides. In contrast, the desensitized state depicts the N-terminal and C-terminal modules close together, compatible with an interaction mediated by Ca2+ -calmodulin complex. These models identify potential previously unrecognized intra- and interdomain interactions that may play an important functional role. Although the molecular models should be taken with caution, they provide a helpful tool that yields testable hypothesis that further our understanding on ion channels work in terms of underlying protein structure.

Antibacterial drug discovery and structure-based design

Bacterial resistance continues to develop and pose a significant threat, both in hospitals and, more recently, in the community. A focus on other therapeutic areas by the larger pharmaceutical companies has left a shortfall in the pipeline of novel antibacterials. Recently, many new structures have been studied by structure-genomics initiatives, delivering a wealth of targets to consider. Using the tools of structure-based design, antibacterial discovery must exploit these targets to accelerate the process of drug discovery.

Molecular modeling of blood–brain barrier nutrient transporters: In silico basis for evaluation of potential drug delivery to the central nervous system

For drugs that act in the brain, the blood-brain barrier (BBB) is a considerable physical barrier which influences the distribution of drugs to the brain. The BBB is essentially impermeable for hydrophilic and/or charged compounds. Nutrient membrane transporters have an important physiological role in the transport of essential substances across the BBB required for normal brain function. We and others have shown that these transporters may have utility as drug delivery vectors, thereby increasing brain distribution of these compounds via these systems. In this review, we evaluate molecular (in silico) models of BBB transport proteins. Few BBB membrane transporters have been crystallized, but their crystal structures have a possibility for use in homology modeling. Other techniques commonly used are 2D quantitative structure-activity relationships (QSAR), as well as 3D-QSAR techniques including comparative molecular field analysis (CoMFA) and comparative similarity index analysis (CoMSIA). Each of these models provides valuable information for ascertaining their potential basis for BBB transport and brain drug delivery.

Computational approaches to modeling drug transporters

Computational modeling has advanced our understanding of drug absorption, tissue distribution, excretion and toxicity profiles by providing both direct and indirect knowledge of drug-transporter interactions that would otherwise be unavailable using experimental methods. Currently, two complementary approaches are available in modeling transporters: substrate-based and transporter-based methods. The transporter-based approach directly predicts the transporter's three-dimensional structure to assist in understanding the drug transport process, whereas substrate-based models infer such information by studying a group of substrates or inhibitors with measured activities. In this review, the available strategies in both transporter-based and substrate-based approaches are explained and illustrated with applications and case studies. With increasing computational power and continuously improving modeling algorithms, computational techniques can assist in further understanding transporter-substrate interactions as well as, the optimization of transporter-directed drug design.