Future directions for drug transporter modelling

Discovery of potential SARS-CoV 3CL protease inhibitors from approved antiviral drugs using: virtual screening, molecular docking, pharmacophore mapping evaluation and dynamics simulation

The spread of corona-virus disease 2019 (COVID-19) has been faster than any other corona-viruses that have succeeded in crossing the animal-human barrier. This disease, caused by the severe acute respiratory syndrome corona-virus 2 (SARS-CoV-2/2019-nCoV) posing a serious threat to global public health and local economies. There are three responsible for this disease; SARS-CoV-2, SARS-CoV and MERS-CoV. Whereas our goal is to test the affinity for a new class of compounds obtained from a hybridization of Chloroquine, Amodiaquine and Mefloquine with three targets SARS-CoV-2, SARS-CoV and MERS-CoV, in order to find new compounds as new inhibitors against Covid-19. In this work, we first used: the molecular docking/dynamics methods and ADME properties to study interaction and affinity between eight new compounds against three targets involved in the Covid-19. The results of the docking simulations and dynamics revealed that inhibitor of the malaria (Ligand 87) has an affinity to interact with SARS-CoV-2, SARS-CoV and MERS-CoV targets and they can be good inhibitors for treatment of Covid-19. Moreover, they give best affinity compared to the Remdesivir and Chloroquine and other clinical tests. The Pharmacokinetics was justified by means of lipophilicity and high coefficient of skin permeability. The in silico evaluation of ADME and drug-likeness revealed that L87 has higher absorption in the intestines with good bioavailability. However, an additional in vitro and/or in vivo experimental study should make it possible to verify the theoretical results obtained in silico.Communicated by Ramaswamy H. Sarma.

Modulation of the Blood-Brain Barrier for Drug Delivery to Brain

The blood-brain barrier (BBB) precisely controls brain microenvironment and neural activity by regulating substance transport into and out of the brain. However, it severely hinders drug entry into the brain, and the efficiency of various systemic therapies against brain diseases. Modulation of the BBB via opening tight junctions, inhibiting active efflux and/or enhancing transcytosis, possesses the potential to increase BBB permeability and improve intracranial drug concentrations and systemic therapeutic efficiency. Various strategies of BBB modulation have been reported and investigated preclinically and/or clinically. This review describes conventional and emerging BBB modulation strategies and related mechanisms, and safety issues according to BBB structures and functions, to try to give more promising directions for designing more reasonable preclinical and clinical studies.

Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies

The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand–P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand–P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (hP-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π–sigma, π–alkyl, and π–π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand–protein complex in the binding site. It was also observed that some interacting residues in hP-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.

In silico modelling, identification of crucial molecular fingerprints, and prediction of new possible substrates of human organic cationic transporters 1 and 2

The cation membrane transporters are crucial to regulate movement of foreign molecules within the body. The present study found out structural fingerprints within molecules to be recognized as substrate/non-substrate against these transporters.

Enantioselective Drug Recognition by Drug Transporters

Drug transporters mediate the absorption, tissue distribution, and excretion of drugs. The cDNAs of P-glycoprotein, multidrug resistance proteins (MRPs/ABCC), breast cancer resistance protein (BCRP/ABCG2), peptide transporters (PEPTs/SLC15), proton-coupled folate transporters (PCFT/SLC46A1), organic anion transporting polypeptides (OATPs/SLCO), organic anion transporters (OATs/SLC22), organic cation transporters (OCTs/SLC22), and multidrug and toxin extrusions (MATEs/SLC47) have been isolated, and their functions have been elucidated. Enantioselectivity has been demonstrated in the pharmacokinetics and efficacy of drugs, and is important for elucidating the relationship with recognition of drugs by drug transporters from a chiral aspect. Enantioselectivity in the transport of drugs by drug transporters and the inhibitory effects of drugs on drug transporters has been summarized in this review.

The Next Era: Deep Learning in Pharmaceutical Research

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer

MDR1, a protein commonly involved in drug transport, has been linked to multi drug resistance and disease progression in cancers such as non-small cell lung cancer. Hence, targeting this protein is essential for improving drug design and preventing adverse drug-drug interactions. The aim of the study was to examine chemotherapeutic drug binding to MDR1 and the interactions therein. We have used Schrödinger suite 2014, to perform homology modelling of human MDR1 based on Mouse MDR1, followed by Induced Fit Docking with Paclitaxel, Docetaxel, Gemcitabine, Carboplatin and Cisplatin drugs. Finally, we evaluated drug binding affinities using Prime/MMGBSA and using these scores we compared the affinities of combination therapies against MDR1. Analysis of the docking results showed Paclitaxel>Docetaxel>Gemcitabine>Carboplatin>Cisplatin as the order of binding affinities, with Paclitaxel having the best docking score. The combination drug binding affinity analysis showed Paclitaxel+Gemcitabine to have the best docking score and hence, efficacy. Through our investigation we have identified the residues Gln 195 and Gln 946 to be more frequently involved in drug binding interactions with MDR1. Our results suggest that, Paclitaxel or combination of Paclitaxel+Gemcitabine could serve as a suitable therapy against MDR1 in NSCLC patients. Thus, our study provides new insight into the possible repurposing of chemotherapeutic drugs in targeting elevated MDR1 levels in NSCLC patients, thereby ensuring better overall outcome. Further our study highlights the use of in silico methodologies in understanding drug binding to protein targets and its relevance to advancing lung cancer therapy.

Modulation of P-glycoprotein efflux pump: induction and activation as a therapeutic strategy

P-glycoprotein (P-gp) is an ATP-dependent efflux pump encoded by the MDR1 gene in humans, known to mediate multidrug resistance of neoplastic cells to cancer therapy. For several decades, P-gp inhibition has drawn many significant research efforts in an attempt to overcome this phenomenon. However, P-gp is also constitutively expressed in normal human epithelial tissues and, due to its broad substrate specificity, to its cellular polarized expression in many excretory and barrier tissues, and to its great efflux capacity, it can play a crucial role in limiting the absorption and distribution of harmful xenobiotics, by decreasing their intracellular accumulation. Such a defense mechanism can be of particular relevance at the intestinal level, by significantly reducing the intestinal absorption of the xenobiotic and, consequently, avoiding its access to the target organs. In this review, the current knowledge on this important efflux pump is summarized, and a new focus is brought on the therapeutic interest of inducing and/or activating P-gp for limiting the toxicity caused by its substrates. Several in vivo and in vitro studies validating the use of such a therapeutic strategy are discussed. An extensive literature search for reported P-gp inducers/activators and for the experimental models used in their characterization was conducted. Those studies demonstrate that effective antidotal pathways can be achieved by efficiently promoting the P-gp-mediated efflux of deleterious xenobiotics, resulting in a significant reduction in their intracellular levels and, consequently, in a significant reduction of their toxicity.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

Fluorescent probes of the isoxazole-dihydropyridine scaffold: MDR-1 binding and homology model

Isoxazole-1,4-dihydropyridines (IDHPs) were tethered to fluorescent moieties using double activation via a lanthanide assisted Weinreb amidation. IDHP-fluorophore conjugate 3c exhibits the highest binding to date for IDHPs at the multidrug-resistance transporter (MDR-1), and IDHP-fluorophore conjugates 3c and 7 distribute selectively in SH-SY5Y cells. A homology model for IDHP binding at MDR-1 is presented which represents our current working hypothesis.

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling

Active processes involved in drug metabolization and distribution mediated by enzymes, transporters, or binding partners mostly occur simultaneously in various organs. However, a quantitative description of active processes is difficult because of limited experimental accessibility of tissue-specific protein activity in vivo. In this work, we present a novel approach to estimate in vivo activity of such enzymes or transporters that have an influence on drug pharmacokinetics. Tissue-specific mRNA expression is used as a surrogate for protein abundance and activity and is integrated into physiologically based pharmacokinetic (PBPK) models that already represent detailed anatomical and physiological information. The new approach was evaluated using three publicly available databases: whole-genome expression microarrays from ArrayExpress, reverse transcription-polymerase chain reaction-derived gene expression estimates collected from the literature, and expressed sequence tags from UniGene. Expression data were preprocessed and stored in a customized database that was then used to build PBPK models for pravastatin in humans. These models represented drug uptake by organic anion-transporting polypeptide 1B1 and organic anion transporter 3, active efflux by multidrug resistance protein 2, and metabolization by sulfotransferases in liver, kidney, and/or intestine. Benchmarking of PBPK models based on gene expression data against alternative models with either a less complex model structure or randomly assigned gene expression values clearly demonstrated the superior model performance of the former. Besides accurate prediction of drug pharmacokinetics, integration of relative gene expression data in PBPK models offers the unique possibility to simultaneously investigate drug-drug interactions in all relevant organs because of the physiological representation of protein-mediated processes.

A substrate pharmacophore for the human organic cation/carnitine transporter identifies compounds associated with rhabdomyolysis

The human organic cation/carnitine transporter (hOCTN2) is a high affinity cation/carnitine transporter expressed widely in human tissues and is physiologically important for the homeostasis of L-carnitine. The objective of this study was to elucidate the substrate requirements of this transporter via computational modeling based on published in vitro data. Nine published substrates of hOCTN2 were used to create a common feature pharmacophore that was validated by mapping other known OCTN2 substrates. The pharmacophore was used to search a drug database and retrieved molecules that were then used as search queries in PubMed for instances of a side effect (rhabdomyolysis) associated with interference with L-carnitine transport. The substrate pharmacophore was composed of two hydrogen bond acceptors, a positive ionizable feature and ten excluded volumes. The substrate pharmacophore also mapped 6 out of 7 known substrate molecules used as a test set. After searching a database of ~800 known drugs, thirty drugs were predicted to map to the substrate pharmacophore with L-carnitine shape restriction. At least 16 of these molecules had case reports documenting an association with rhabdomyolysis and represent a set for prioritizing for future testing as OCTN2 substrates or inhibitors. This computational OCTN2 substrate pharmacophore derived from published data partially overlaps a previous OCTN2 inhibitor pharmacophore and is also able to select compounds that demonstrate rhabdomyolysis, further confirming the possible linkage between this side effect and hOCTN2.

Applications and limitations of in silico models in drug discovery

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

Role of computational methods in pharmaceutical sciences

Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.

Nucleoside/nucleobase transporters: drug targets of the future?

BACKGROUND

Nucleoside/nucleobase transporters have been investigated since the 1960s. In particular, equilibrative nucleoside transporters were thought to be valuable drug targets, since they are involved in various kinds of viral and parasitic diseases as well as cancers.

DISCUSSION

In the postgenomic era multiple transporters, including different subtypes, have been cloned and characterized on the molecular level. In this article we summarize recent advances regarding structure, function and localization of nucleoside/nucleobase transporters as well as the pharmacological profile of selected drugs.

CONCLUSION

Knowledge of the different kinetic properties and structural features of nucleoside transporters can either be used for the rational design of therapeutics directly targeting the transporter itself or for the delivery of drugs using the transporter as a port of entry into the target cell. Equilibrative nucleoside transporters are of considerable pharmacological interest as drug targets for the development of drugs tailored to each patient's need for the treatment of cardiac disease, cancer and viral infections.

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Exploring the P-glycoprotein binding cavity with polyoxyethylene alkyl ethers

P-glycoprotein (ABCB1) moves allocrits from the cytosolic to the extracellular membrane leaflet, preventing their intrusion into the cytosol. It is generally accepted that allocrit binding from water to the cavity lined by the transmembrane domains occurs in two steps, a lipid-water partitioning step, and a cavity-binding step in the lipid membrane, whereby hydrogen-bond (i.e., weak electrostatic) interactions play a crucial role. The remaining key question was whether hydrophobic interactions also play a role for allocrit binding to the cavity. To answer this question, we chose polyoxyethylene alkyl ethers, C(m)EO(n), varying in the number of methylene and ethoxyl residues as model allocrits. Using isothermal titration calorimetry, we showed that the lipid-water partitioning step was purely hydrophobic, increasing linearly with the number of methylene, and decreasing with the number of ethoxyl residues, respectively. Using, in addition, ATPase activity measurements, we demonstrated that allocrit binding to the cavity required minimally two ethoxyl residues and increased linearly with the number of ethoxyl residues. The analysis provides the first direct evidence, to our knowledge, that allocrit binding to the cavity is purely electrostatic, apparently without any hydrophobic contribution. While the polar part of allocrits forms weak electrostatic interactions with the cavity, the hydrophobic part seems to remain associated with the lipid membrane. The interplay between the two types of interactions is most likely essential for allocrit flipping.

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Targeting drug transporters - combining in silico and in vitro approaches to predict in vivo

Transporter proteins are expressed throughout the human body in different vital organs. They play an important role to various extents in determining absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of therapeutic molecules. Over the past decade, numerous drug transporters have been cloned and considerable progress has been made toward understanding the molecular characteristics of individual transporters. In this chapter several in vitro and in silico techniques are described with applications to understand transporter behavior. These include employing new techniques to rapidly identify novel ligands for transporters. Ultimately these methods should lead to a greater overall appreciation of the role of transporters in vivo.

Membrane transporters in drug development

Membrane transporters can be major determinants of the pharmacokinetic, safety and efficacy profiles of drugs. This presents several key questions for drug development, including which transporters are clinically important in drug absorption and disposition, and which in vitro methods are suitable for studying drug interactions with these transporters. In addition, what criteria should trigger follow-up clinical studies, and which clinical studies should be conducted if needed. In this article, we provide the recommendations of the International Transporter Consortium on these issues, and present decision trees that are intended to help guide clinical studies on the currently recognized most important drug transporter interactions. The recommendations are generally intended to support clinical development and filing of a new drug application. Overall, it is advised that the timing of transporter investigations should be driven by efficacy, safety and clinical trial enrolment questions (for example, exclusion and inclusion criteria), as well as a need for further understanding of the absorption, distribution, metabolism and excretion properties of the drug molecule, and information required for drug labelling.

Targeting the brain--surmounting or bypassing the blood-brain barrier

The constituents of the blood-brain barrier, including its efflux transporter system, can efficiently limit brain penetration of potential CNS therapeutics. Effective extrusion from the brain by transporters is a frequent reason for the pharmaceutical industry to exclude novel compounds from further development for CNS therapeutics. Moreover, high transporter expression levels that are present in individual patients or may be generally associated with the pathophysiology seem to be a major cause of therapeutic failure in a variety of CNS diseases including brain tumors, epilepsy, brain HIV infection, and psychiatric disorders. Increasing knowledge of the structure and function of the blood-brain barrier creates a basis for the development of strategies which aim to enhance brain uptake of beneficial pharmaceutical compounds. The different strategies discussed in this review aim to modulate blood-brain barrier function or to bypass constituents of the blood-brain barrier.

Targeting regulation of ABC efflux transporters in brain diseases: a novel therapeutic approach

Blood-brain barrier efflux transporters limit the brain penetration and efficacy of various central nervous system drugs. In several CNS diseases, therapy- or pathophysiology-associated transcriptional activation of efflux transporters further strengthens the barrier function. Targeting the regulatory pathways that drive efflux transporter expression in different diseases represents an intriguing approach for prevention of these events thereby promoting delivery to the brain and enhancing or restoring drug efficacy. In particular, the approach holds the promise to preserve basal transporter expression and activity, which is of specific relevance in view of the protective function of efflux transport. The elucidation of the signaling cascades involved in transporter regulation is a major presupposition for the development of preventive strategies. Orphan nuclear receptors as well as the Wnt/beta-catenin signaling pathway have been implicated in drug-induced changes in transporter expression. Targeting these xenobiotic sensors is therefore discussed as a means to optimize brain delivery and therapeutic outcome. Relevant progress has also been made with the identification of key signaling events that drive P-glycoprotein expression in response to pathophysiological mechanisms. In the epileptic brain, complex signaling events involving cyclooxygenase-2 activity trigger P-glycoprotein expression in response to glutamate release and activation of endothelial NMDA receptors. Moreover, reactive oxygen species and inflammatory cytokines have been identified as regulatory factors which might affect P-glycoprotein in several CNS diseases. Recent data substantiated several interesting targets in the respective signaling cascades thereby rendering a basis for the ongoing development of innovative approaches to optimize central nervous system drug brain penetration and efficacy.

Modeling of the intestinal peptide transporter hPepT1 and analysis of its transport capacities by docking and pharmacophore mapping

An early pharmacokinetic screen for peptidomimetic drugs should have the ability to predict molecules with high affinity for intestinal transporters, as peptide-like derivatives are seldom absorbed passively. Hence, the first objective of this study was to generate a reliable model for the structure of the hPepT1 protein, which is the main intestinal transporter involved in the absorption of both dietary peptides and peptidomimetics. The modeling was based on the resolved structure of the homologous bacterial lactose permease LacY using a fragmental strategy. The interaction capacities of the hPepT1 model were explored by docking a set of 50 known ligands. Despite the known predilection of hPepT1 for hydrophobic ligands, docking results unveiled the key role of the polar interactions stabilized by charged termini, especially concerning the ammonium head group. The docking results were further verified by developing a pharmacophore model that confirmed the key features required for optimal hPepT1 affinity. The consistency of the docking results and the agreement with the pharmacophore model afford an encouraging validation for the proposed model and suggest that it can be exploited to design peptide-like molecules with an improved affinity for such a transporter.

Design, data analysis, and simulation of in vitro drug transport kinetic experiments using a mechanistic in vitro model

The use of in vitro data for quantitative predictions of transporter-mediated elimination in vivo requires an accurate estimation of the transporter Michaelis-Menten parameters, V(max) and K(m), as a first step. Therefore, the experimental conditions of in vitro studies used to assess hepatic uptake transport were optimized regarding active transport processes, nonspecific binding, and passive diffusion (P(dif)). A mechanistic model was developed to analyze and accurately describe these active and passive processes. This two-compartmental model was parameterized to account for nonspecific binding, bidirectional passive diffusion, and active uptake processes based on the physiology of the cells. The model was used to estimate kinetic parameters of in vitro transport data from organic anion-transporting peptide model substrates (e.g., cholecystokinin octapeptide deltorphin II, fexofenadine, and pitavastatin). Data analysis by this mechanistic model significantly improved the accuracy and precision in all derived parameters [mean coefficient of variations (CVs) for V(max) and K(m) were 19 and 23%, respectively] compared with the conventional kinetic method of transport data analysis (mean CVs were 58 and 115%, respectively, using this method). Furthermore, permeability was found to be highly temperature-dependent in Chinese hamster ovary (CHO) control cells and artificial membranes (parallel artificial membrane permeability assay). Whereas for some compounds (taurocholate, estrone-3-sulfate, and propranolol) the effect was moderate (1.5-6-fold higher permeability at 37 degrees C compared with that at 4 degrees C), for fexofenadine a 16-fold higher passive permeability was seen at 37 degrees C. Therefore, P(dif) was better predicted if it was evaluated under the same experimental conditions as V(max) and K(m), i.e., in a single incubation of CHO overexpressed cells or rat hepatocytes at 37 degrees C, instead of a parallel control evaluation at 4 degrees C.

Determination and modelling of stereoselective interactions of ligands with drug transporters: a key dimension in the understanding of drug disposition

1. Stereochemistry is an important dimension in pharmacology and plays a role in every aspect of the pharmacological fate of chiral xenobiotics. This includes small molecule-drug transporter binding. 2. This paper reviews the reported stereoselectivities of substrate and inhibitor interactions with P-glycoprotein and the organic cation transporter obtained using standard functional and binding studies, as well as data obtained from online cellular membrane affinity chromatography studies. 3. The use of stereochemical data in quantitative structure-activity relationship (QSAR) and pharmacophore modelling is also addressed as is the effect of ignoring the fact that small molecule-drug transporter interactions take place in three-dimensional and asymmetric space.

Structure-ADME relationship: still a long way to go?

BACKGROUND

Theoretical models for predicting absorption, distribution, metabolism and excretion (ADME) properties play increasingly important roles in support of the drug development process.

OBJECTIVE

We briefly review the in silico prediction models for three important ADME properties, namely, aqueous solubility, human intestinal absorption, and oral bioavailability.

METHODS

Rather than giving detailed descriptions of the ADME prediction models, we focus on the discussions of the prediction accuracies of the in silico models.

RESULTS/CONCLUSION

We find that the robustness and predictive capability of the ADME models are directly associated with the complexity of the ADME property. For the ADME properties involving complex phenomena, such as bioavailability, the in silico models usually cannot give satisfactory predictions. Moreover, the lack of large and high-quality data sets also greatly hinder the reliability of ADME predictions. While considerable progress has been achieved in ADME predictions, many challenges remain to be overcome.

Molecular characterization of CYP2B6 substrates

CYP2B6 has not been as fully characterized at the molecular level as other members of the human cytochrome P450 family. As more widely used in vitro probes for characterizing the involvement of this enzyme in the metabolism of xenobiotics have become available, the number of molecules identified as CYP2B6 substrates has increased. In this study we have analyzed the available kinetic data generated by multiple laboratories with human recombinant expressed CYP2B6 and along with calculated molecular properties derived from the ChemSpider database, we have determined the molecular features that appear to be important for CYP2B6 substrates. In addition we have applied 2D and 3D QSAR methods to generate predictive pharmacophore and 2D models. For 28 molecules with K(m) data, the molecular weight (mean +/- SD) is 253.78+/-74.03, ACD/logP is 2.68+/-1.51, LogD(pH 5.5) is 1.51+/-1.43, LogD(pH 7.4) is 2.02+/-1.25, hydrogen bond donor (HBD) count is 0.57 +/-0.57, hydrogen bond acceptor (HBA) count is 2.57+/-1.37, rotatable bonds is 3.50+/-2.71 and total polar surface area (TPSA) is 27.63+/-19.42. A second set of 15 molecules without K(m) data possessed similar mean molecular property values. These properties are comparable to those of a set of 21 molecules used in a previous pharmacophore modeling study (Ekins et al., J Pharmacol Exp Ther 288 (1), 21-29, 1999). Only the LogD and HBD values were statistically significantly different between these different datasets. We have shown that CYP2B6 substrates are generally small hydrophobic molecules that are frequently central nervous system active, which may be important for drug discovery research.