In vitro and pharmacophore-based discovery of novel hPEPT1 inhibitors

Repurposable Drugs for Immunotherapy and Strategies to Find Candidate Drugs

Conventional drug discovery involves significant steps, time, and expenses; therefore, novel methods for drug discovery remain unmet, particularly for patients with intractable diseases. For this purpose, the drug repurposing method has been recently used to search for new therapeutic agents. Repurposed drugs are mostly previously approved drugs, which were carefully tested for their efficacy for other diseases and had their safety for the human body confirmed following careful pre-clinical trials, clinical trials, and post-marketing surveillance. Therefore, using these approved drugs for other diseases that cannot be treated using conventional therapeutic methods could save time and economic costs for testing their clinical applicability. In this review, we have summarized the methods for identifying repurposable drugs focusing on immunotherapy.

The novel small molecular BH3 mimetics SM3 and its regulation of cell apoptosis and autophagy

Bcl-2 family proteins play an important role in regulation of the cell survival and death. The inhibition of the anti-apoptotic proteins of Bcl-2 family leads to the apoptosis of cancer. BH3 mimetics have been developed targeting anti-apoptotic proteins of Bcl-2 family as small molecular drugs. It has been proved that BH3 mimetics has effect on apoptosis and proliferation in leukemia and some of them has been used in phase one or two clinical trials. Besides, with the development of the research on autophagic cell death, the antagonism and the synergism of autophagy and apoptosis is significant in cell death. As a hub of these two pathways of cell death, Bcl-2 protein is a potential target in basic research and clinical applications. In our studies, we found 32 potential BH3 mimetics compounds from 140,000 small molecular compounds via pharmacophore-based virtual screening. Furthermore, we demonstrated SM3, one of the 32 potential BH3 mimetics, induced autophagy and apoptosis simultaneously in dose-time dependence in A549 cell. SM3 induced apoptosis by intrinsic apoptosis pathway and induced autophagy by weakening the interaction between Beclin-1 and Bcl-2 complex. We wish to provide evidences and clues for the structural optimizing and further study of new compounds in the future.

Chemical Modulation of the Human Oligopeptide Transporter 1, hPepT1

In humans, peptides derived from dietary proteins and peptide-like drugs are transported via the proton-dependent oligopeptide transporter hPepT1 (SLC15A1). hPepT1 is located across the apical membranes of the small intestine and kidney, where it serves as a high-capacity low-affinity transporter of a broad range of di- and tripeptides. hPepT1 is also overexpressed in the colon of inflammatory bowel disease (IBD) patients, where it mediates the transport of harmful peptides of bacterial origin. Therefore, hPepT1 is a drug target for prodrug substrates interacting with intracellular proteins or inhibitors blocking the transport of toxic bacterial products. In this study, we construct multiple structural models of hPepT1 representing different conformational states that occur during transport and inhibition. We then identify and characterize five ligands of hPepT1 using computational methods, such as virtual screening and QM-polarized ligand docking (QPLD), and experimental testing with uptake kinetic measurements and electrophysiological assays. Our results improve our understanding of the substrate and inhibitor specificity of hPepT1. Furthermore, the newly discovered ligands exhibit unique chemotypes, providing a framework for developing tool compounds with optimal intestinal absorption as well as future IBD therapeutics against this emerging drug target.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.

Structure-based model profiles affinity constant of drugs with hPEPT1 for rapid virtual screening of hPEPT1's substrate

The human proton-coupled peptide transporter (hPEPT1) with broad substrates is an important route for improving the pharmacokinetic performance of drugs. Thus, it is essential to predict the affinity constant between drug molecule and hPEPT1 for rapid virtual screening of hPEPT1's substrate during lead optimization, candidate selection and hPEPT1 prodrug design. Here, a structure-based in silico model for 114 compounds was constructed based on eight structural parameters. This model was built by the multiple linear regression method and satisfied all the prerequisites of the regression models. For the entire data set, the r(2) and adjusted r(2) values were 0.74 and 0.72, respectively. Then, this model was used to perform substrate/non-substrate classification. For 29 drugs from DrugBank database, all were correctly classified as substrates of hPEPT1. This model was also used to perform substrate/non-substrate classification for 18 drugs and their prodrugs; this QSAR model also can distinguish between the substrate and non-substrate. In conclusion, the QSAR model in this paper was validated by a large external data set, and all results indicated that the developed model was robust, stable, and can be used for rapid virtual screening of hPEPT1's substrate in the early stage of drug discovery.

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Targeting Mycobacterium tuberculosis topoisomerase I by small-molecule inhibitors

We describe inhibition of Mycobacterium tuberculosis topoisomerase I (MttopoI), an essential mycobacterial enzyme, by two related compounds, imipramine and norclomipramine, of which imipramine is clinically used as an antidepressant. These molecules showed growth inhibition of both Mycobacterium smegmatis and M. tuberculosis cells. The mechanism of action of these two molecules was investigated by analyzing the individual steps of the topoisomerase I (topoI) reaction cycle. The compounds stimulated cleavage, thereby perturbing the cleavage-religation equilibrium. Consequently, these molecules inhibited the growth of the cells overexpressing topoI at a low MIC. Docking of the molecules on the MttopoI model suggested that they bind near the metal binding site of the enzyme. The DNA relaxation activity of the metal binding mutants harboring mutations in the DxDxE motif was differentially affected by the molecules, suggesting that the metal coordinating residues contribute to the interaction of the enzyme with the drug. Taken together, the results highlight the potential of these small molecules, which poison the M. tuberculosis and M. smegmatis topoisomerase I, as leads for the development of improved molecules to combat mycobacterial infections. Moreover, targeting metal coordination in topoisomerases might be a general strategy to develop new lead molecules.

Using cheminformatics to predict cross reactivity of "designer drugs" to their currently available immunoassays

Background

A challenge for drug of abuse testing is presented by ‘designer drugs’, compounds typically discovered by modifications of existing clinical drug classes such as amphetamines and cannabinoids. Drug of abuse screening immunoassays directed at amphetamine or methamphetamine only detect a small subset of designer amphetamine-like drugs, and those immunoassays designed for tetrahydrocannabinol metabolites generally do not cross-react with synthetic cannabinoids lacking the classic cannabinoid chemical backbone. This suggests complexity in understanding how to detect and identify whether a patient has taken a molecule of one class or another, impacting clinical care.

Methods

Cross-reactivity data from immunoassays specifically targeting designer amphetamine-like and synthetic cannabinoid drugs was collected from multiple published sources, and virtual chemical libraries for molecular similarity analysis were built. The virtual library for synthetic cannabinoid analysis contained a total of 169 structures, while the virtual library for amphetamine-type stimulants contained 288 compounds. Two-dimensional (2D) similarity for each test compound was compared to the target molecule of the immunoassay undergoing analysis.

Results

2D similarity differentiated between cross-reactive and non-cross-reactive compounds for immunoassays targeting mephedrone/methcathinone, 3,4-methylenedioxypyrovalerone, benzylpiperazine, mephentermine, and synthetic cannabinoids.

Conclusions

In this study, we applied 2D molecular similarity analysis to the designer amphetamine-type stimulants and synthetic cannabinoids. Similarity calculations can be used to more efficiently decide which drugs and metabolites should be tested in cross-reactivity studies, as well as to design experiments and potentially predict antigens that would lead to immunoassays with cross reactivity for a broader array of designer drugs.

Human immunodeficiency virus protease inhibitors interact with ATP binding cassette transporter 4/multidrug resistance protein 4: a basis for unanticipated enhanced cytotoxicity

Human immunodeficiency virus (HIV) pharmacotherapy, by combining different drug classes such as nucleoside analogs and HIV protease inhibitors (PIs), has increased HIV-patient life expectancy. Consequently, among these patients, an increase in non-HIV-associated cancers has produced a patient cohort requiring both HIV and cancer chemotherapy. We hypothesized that multidrug resistance protein 4/ATP binding cassette transporter 4 (MRP4/ABCC4), a widely expressed transporter of nucleoside-based antiviral medications as well as cancer therapeutics might interact with PIs. Among the PIs evaluated (nelfinavir, ritonavir, amprenavir, saquinavir, and indinavir), only nelfinavir both effectively stimulated MRP4 ATPase activity and inhibited substrate-stimulated ATPase activity. Saos2 and human embryonic kidney 293 cells engineered to overexpress MRP4 were then used to assess transport and cytotoxicity. MRP4 expression reduced intracellular accumulation of nelfinavir and consequently conferred survival advantage to nelfinavir cytotoxicity. Nelfinavir blocked Mrp4-mediated export, which is consistent with its ability to increase the sensitivity of MRP4-expressing cells to methotrexate. In contrast, targeted inactivation of Abcc4/Mrp4 in mouse cells specifically enhanced nelfinavir and 9-(2-phosphonylmethoxyethyl) adenine cytotoxicity. These results suggest that nelfinavir is both an inhibitor and substrate of MRP4. Because nelfinavir is a new MRP4/ABCC4 substrate, we developed a MRP4/ABCC4 pharmacophore model, which showed that the nelfinavir binding site is shared with chemotherapeutic substrates such as adefovir and methotrexate. Our studies reveal, for the first time, that nelfinavir, a potent and cytotoxic PI, is both a substrate and inhibitor of MRP4. These findings suggest that HIV-infected cancer patients receiving nelfinavir might experience both enhanced antitumor efficacy and unexpected adverse toxicity given the role of MRP4/ABCC4 in exporting nucleoside-based antiretroviral medications and cancer chemotherapeutics.

Structure-activity relationship for FDA approved drugs as inhibitors of the human sodium taurocholate cotransporting polypeptide (NTCP)

The hepatic bile acid uptake transporter sodium taurocholate cotransporting polypeptide (NTCP) is less well characterized than its ileal paralog, the apical sodium dependent bile acid transporter (ASBT), in terms of drug inhibition requirements. The objectives of this study were (a) to identify FDA approved drugs that inhibit human NTCP, (b) to develop pharmacophore and Bayesian computational models for NTCP inhibition, and (c) to compare NTCP and ASBT transport inhibition requirements. A series of NTCP inhibition studies were performed using FDA approved drugs, in concert with iterative computational model development. Screening studies identified 27 drugs as novel NTCP inhibitors, including irbesartan (Ki = 11.9 μM) and ezetimibe (Ki = 25.0 μM). The common feature pharmacophore indicated that two hydrophobes and one hydrogen bond acceptor were important for inhibition of NTCP. From 72 drugs screened in vitro, a total of 31 drugs inhibited NTCP, while 51 drugs (i.e., more than half) inhibited ASBT. Hence, while there was inhibitor overlap, ASBT unexpectedly was more permissive to drug inhibition than was NTCP, and this may be related to NTCP possessing fewer pharmacophore features. Findings reflected that a combination of computational and in vitro approaches enriched the understanding of these poorly characterized transporters and yielded additional chemical probes for possible drug-transporter interaction determinations.

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.

Transporters, Trojan horses and therapeutics: suitability of bile acid and peptide transporters for drug delivery

Membrane transporters are major determinants for the pharmacokinetic, safety and efficacy behavior of drugs. Available technologies to study function and structure of transport proteins has strongly stimulated research in transporter biology and uncovered their importance for the drug discovery and development process, especially for drug absorption and disposition. Physiological transport systems are investigated as potential ferries to improve drug absorption and membrane permeation and to achieve organ-specific drug action. In particular, the bile acid transport systems in the liver and the small intestine and the oligopeptide transporters are of significant importance for molecular drug delivery.

In silico repositioning of approved drugs for rare and neglected diseases

One approach to speed up drug discovery is to examine new uses for existing approved drugs, so-called 'drug repositioning' or 'drug repurposing', which has become increasingly popular in recent years. Analysis of the literature reveals many examples of US Food and Drug Administration-approved drugs that are active against multiple targets (also termed promiscuity) that can also be used to therapeutic advantage for repositioning for other neglected and rare diseases. Using proof-of-principle examples, we suggest here that with current in silico technologies and databases of the structures and biological activities of chemical compounds (drugs) and related data, as well as close integration with in vitro screening data, improved opportunities for drug repurposing will emerge for neglected or rare/orphan diseases.

Essential metabolites of Mycobacterium tuberculosis and their mimics

An organism requires a range of biomolecules for its growth. By definition, these are essential molecules which constitute the basic metabolic requirements of an organism. A small organic molecule with chemical similarity to that of an essential metabolite may bind to the enzyme that catalyzes its production and inhibit it, likely resulting in the stasis or death of the organism. Here, we report a high-throughput approach for identifying essential metabolites of an organism using genetic and biochemical approaches and then implement computational approaches to identify metabolite mimics. We generated and genotyped 5,126 Mycobacterium tuberculosis mutants and performed a statistical analysis to determine putative essential genes. The essential molecules of M. tuberculosis were classified as products of enzymes that are encoded by genes in this list. Although incomplete, as many enzymes of M. tuberculosis have yet to be identified and characterized, this is the first report of a large number of essential molecules of the organism. We identified essential metabolites of three distinct metabolic pathways in M. tuberculosis and selected molecules with chemical similarity using cheminformatics strategies that illustrate a variety of different pharmacophores. Our approach is aimed at systematic identification of essential molecules and their mimics as a blueprint for development of effective chemical probes of M. tuberculosis metabolism, with the ultimate goal of seeking drugs that can kill this pathogen. As an illustration of this approach, we report that compounds JFD01307SC and l-methionine-S-sulfoximine, which share chemical similarity with an essential molecule of M. tuberculosis, inhibited the growth of this organism at micromolar concentrations.

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.

Why we should be vigilant: drug cytotoxicity observed with in vitro transporter inhibition studies

From routine in vitro drug-transporter inhibition assays, observed inhibition is typically assumed from direct interaction with the transporter. Other mechanisms that possibly reduce substrate uptake are not frequently fully examined. The objective of this study was to investigate the association of transporter inhibition with drug cytotoxicity. From a pool of drugs that were identified as known ASBT or OCTN2 inhibitors, 21 drugs were selected to screen inhibitory potency of their prototypical substrate and cytotoxicity against three human sodium-dependent solute carrier (SLC) transporters: apical sodium-dependent bile acid transporter (ASBT), organic cation/carnitine transporter (OCTN2), and the excitatory amino acid transporter 4 (EAAT4) in stable cell lines. Twenty drugs showed apparent inhibition in OCTN2-MDCK and ASBT-MDCK. Four dihydropyridine calcium channel blockers were cytotoxic to MDCK cells, and the observed cytotoxicity of three of them accounted for their apparent OCTN2 inhibition, and consequently were classified as non-OCTN2 inhibitors. Meanwhile, since their cytotoxicity only moderately contributed to ASBT inhibition, these three were still considered ASBT inhibitors. Four other drugs showed apparent inhibition in EAAT4-HEK cells, and cytotoxicity of three drugs corresponded with their inhibition of this transporter. Therefore, cytotoxicity significantly affected EAAT4 observations. Results showed the potential of cytotoxicity as a mechanism that can account for apparent in vitro transporter inhibition. Drug cytotoxicity varied in different cell lines, which could increase false positives for pharmacophore development.

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.

Troubleshooting computational methods in drug discovery

Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen as an efficient approach for lead discovery as well as providing insights on absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). What is perhaps less well known and widely described are the limitations of the different technologies. We have therefore presented a troubleshooting approach to QSAR, homology modeling, docking as well as hybrid methods. If such computational or cheminformatics methods are to become more widely used by non-experts it is critical that such limitations are brought to the user's attention and addressed during their workflows. This could improve the quality of the models and results that are obtained.

Pharmaceutical and pharmacological importance of peptide transporters

Peptide transport is currently a prominent topic in membrane research. The transport proteins involved are under intense investigation because of their physiological importance in protein absorption and also because peptide transporters are possible vehicles for drug delivery. Moreover, in many tissues peptide carriers transduce peptidic signals across membranes that are relevant in information processing. The focus of this review is on the pharmaceutical relevance of the human peptide transporters PEPT1 and PEPT2. In addition to their physiological substrates, both carriers transport many β-lactam antibiotics, valaciclovir and other drugs and prodrugs because of their sterical resemblance to di- and tripeptides. The primary structure, tissue distribution and substrate specificity of PEPT1 and PEPT2 have been well characterized. However, there is a dearth of knowledge on the substrate binding sites and the three-dimensional structure of these proteins. Until this pivotal information becomes available by X-ray crystallography, the development of new drug substrates relies on classical transport studies combined with molecular modelling. In more than thirty years of research, data on the interaction of well over 700 di- and tripeptides, amino acid and peptide derivatives, drugs and prodrugs with peptide transporters have been gathered. The aim of this review is to put the reports on peptide transporter-mediated drug uptake into perspective. We also review the current knowledge on pharmacogenomics and clinical relevance of human peptide transporters. Finally, the reader's attention is drawn to other known or proposed human peptide-transporting proteins.

A collaborative database and computational models for tuberculosis drug discovery

The search for molecules with activity against Mycobacterium tuberculosis (Mtb) is employing many approaches in parallel including high throughput screening and computational methods. We have developed a database (CDD TB) to capture public and private Mtb data while enabling data mining and collaborations with other researchers. We have used the public data along with several cheminformatics approaches to produce models that describe active and inactive compounds. We have compared these datasets to those for known FDA approved drugs and between Mtb active and inactive compounds. The distribution of polar surface area and pK(a) of active compounds was found to be a statistically significant determinant of activity against Mtb. Hydrophobicity was not always statistically significant. Bayesian classification models for 220, 463 molecules were generated and tested with external molecules, and enabled the discrimination of active or inactive substructures from other datasets in the CDD TB. Computational pharmacophores based on known Mtb drugs were able to map to and retrieve a small subset of some of the Mtb datasets, including a high percentage of Mtb actives. The combination of the database, dataset analysis, Bayesian and pharmacophore models provides new insights into molecular properties and features that are determinants of activity in whole cells. This study provides novel insights into the key 1D molecular descriptors, 2D chemical substructures and 3D pharmacophores which can be used to mine the chemistry space, prioritizing those molecules with a higher probability of activity against Mtb.

Discovery of ligands for the human intestinal Di-/Tripeptide transporter (hPEPT1) using a QSAR-assisted virtual screening strategy

The discovery of novel ligands for the hPEPT1 transporter is reported. By exploiting a fast and rigorously validated QSAR model in combination with the distance in activity-centered chemical space (DACCS) approach, a database of commercially available compounds (Sigma-Aldrich) was screened for virtual hits. Twelve compounds were then purchased and characterized in an apical [14C]Gly-Sar uptake competition assay. Four compounds displayed affinity in the medium-to-high range. A simple benzophenone derivative displayed high affinity with a sub-millimolar binding constant (Ki=0.24 mM). The results of this study will serve as starting points for future projects, including the design and synthesis of compound libraries that seek to systematically explore the fundamental requirements for binding and transport by hPEPT1.

Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter

The human apical sodium-dependent bile acid transporter (ASBT; SLC10A2) is the primary mechanism for intestinal bile acid reabsorption. In the colon, secondary bile acids increase the risk of cancer. Therefore, drugs that inhibit ASBT have the potential to increase the risk of colon cancer. The objectives of this study were to identify FDA-approved drugs that inhibit ASBT and to derive computational models for ASBT inhibition. Inhibition was evaluated using ASBT-MDCK monolayers and taurocholate as the model substrate. Computational modeling employed a HipHop qualitative approach, a Hypogen quantitative approach, and a modified Laplacian Bayesian modeling method using 2D descriptors. Initially, 30 compounds were screened for ASBT inhibition. A qualitative pharmacophore was developed using the most potent 11 compounds and applied to search a drug database, yielding 58 hits. Additional compounds were tested, and their K(i) values were measured. A 3D-QSAR and a Bayesian model were developed using 38 molecules. The quantitative pharmacophore consisted of one hydrogen bond acceptor, three hydrophobic features, and five excluded volumes. Each model was further validated with two external test sets of 30 and 19 molecules. Validation analysis showed both models exhibited good predictability in determining whether a drug is a potent or nonpotent ASBT inhibitor. The Bayesian model correctly ranked the most active compounds. In summary, using a combined in vitro and computational approach, we found that many FDA-approved drugs from diverse classes, such as the dihydropyridine calcium channel blockers and HMG CoA-reductase inhibitors, are ASBT inhibitors.

Molecular similarity methods for predicting cross-reactivity with therapeutic drug monitoring immunoassays

Immunoassays are used for therapeutic drug monitoring (TDM), yet may suffer from cross-reacting compounds able to bind the assay antibodies in a manner similar to the target molecule. To our knowledge, there has been no investigation using computational tools to predict cross-reactivity with TDM immunoassays. The authors used molecular similarity methods to enable calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of TDM immunoassays. Utilizing different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient, the authors compared cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. Thus, 2-dimensional shape similarity of cross-reacting molecules and the target molecules of TDM immunoassays provides a fast chemoinformatics methods for a priori prediction of potential of cross-reactivity that might be otherwise undetected. These methods could be used to reliably focus cross-reactivity testing on compounds with high similarity to the target molecule and limit testing of compounds with low similarity and ultimately with a very low probability of cross-reacting with the assay in vitro.

Mutagenesis and cysteine scanning of transmembrane domain 10 of the human dipeptide transporter

PURPOSE

The human dipeptide transporter (hPEPT1) facilitates transport of dipeptides and drugs from the intestine into the circulation. The role of transmembrane domain 10 (TMD10) of hPEPT1 in substrate translocation was investigated using cysteine-scanning mutagenesis with 2-Trimethylammonioethyl methanethiosulfonate (MTSET).

METHODS

Each amino acid in TMD10 was mutated individually to cysteine, and transport of [(3)H]Gly-Sar was evaluated with and without MTSET following transfection of each mutant in HEK293 cells. Similar localization and expression levels of wild type (WT) hPEPT1 and all mutants were confirmed by immunostaining and biotinylation followed by western blot analysis.

RESULTS

E595C- and G594C-hPEPT1 showed negligible Gly-Sar uptake. E595D-hPEPT1 showed similar uptake to WT-hPEPT1, but E595K- and E595R-hPEPT1 did not transport Gly-Sar. Double mutations E595K/R282E and E595R/R282E did not restore uptake. G594A-hPEPT1 showed similar uptake to WT-hPEPT1, but G594V-hPEPT1 eliminated uptake. Y588C-hPEPT1 showed uptake of 20% that of WT-hPEPT1. MTSET modification supported a model of TMD10 with an amphipathic helix from I585 to V600 and increased solvent accessibility from T601 to F605.

CONCLUSIONS

Our results suggest that G594 and E595 in TMD10 of hPEPT1 have key roles in substrate transport and that Y588 may have an important secondary mechanistic role.

Novel inhibitors of human organic cation/carnitine transporter (hOCTN2) via computational modeling and in vitro testing

PURPOSE

The objective was to elucidate the inhibition requirements of the human organic cation/carnitine transporter (hOCTN2).

METHODS

Twenty-seven drugs were screened initially for their potential to inhibit uptake of L-carnitine into a stably transfected hOCTN2-MDCK cell monolayer. A HipHop common features pharmacophore was developed and used to search a drug database. Fifty-three drugs, including some not predicted to be inhibitors, were selected and screened in vitro.

RESULTS

A common features pharmacophore was derived from initial screening data and consisted of three hydrophobic features and a positive ionizable feature. Among the 33 tested drugs that were predicted to map to the pharmacophore, 27 inhibited hOCTN2 in vitro (40% or less L-carnitine uptake from 2.5 microM L-carnitine solution in presence of 500 microM drug, compared to L-carnitine uptake without drug present). Hence, the pharmacophore accurately prioritized compounds for testing. K(i) measurements showed low micromolar inhibitors belonged to diverse therapeutic classes of drugs, including many not previously known to inhibit hOCTN2. Compounds were more likely to cause rhabdomyolysis if the C(max)/K(i) ratio was higher than 0.0025.

CONCLUSION

A combined pharmacophore and in vitro approach found new, structurally diverse inhibitors for hOCTN2 that may possibly cause clinical significant toxicity such as rhabdomyolysis.

Chemoinformatic methods for predicting interference in drug of abuse/toxicology immunoassays

BACKGROUND

Immunoassays used for routine drug of abuse (DOA) and toxicology screening may be limited by cross-reacting compounds able to bind to the antibodies in a manner similar to the target molecule(s). To date, there has been little systematic investigation using computational tools to predict cross-reactive compounds.

METHODS

Commonly used molecular similarity methods enabled calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of DOA/toxicology screening assays. We used various molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient. These data were then compared with cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Previously untested compounds that were predicted to have a high probability of cross-reactivity were tested.

RESULTS

Molecular similarity calculated using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. This result was validated experimentally by discovery of additional cross-reactive compounds based on computational predictions.

CONCLUSIONS

The computational methods employed are amenable toward rapid screening of databases of drugs, metabolites, and endogenous molecules and may be useful for identifying cross-reactive molecules that would be otherwise unsuspected. These methods may also have value in focusing cross-reactivity testing on compounds with high similarity to the target molecule(s) and limiting testing of compounds with low similarity and very low probability of cross-reacting with the assay.

High-affinity interaction of sartans with H+/peptide transporters

Sartans are very effective drugs for treatment of hypertension, heart failure, and other cardiovascular disorders. They antagonize the effects of angiotensin II at the AT(1) receptor and display p.o. bioavailability rates of 13 to 80%. Because some sartans sterically resemble dipeptide derivatives, we investigated whether they are transported by peptide transporters. We first assessed the effects of sartans on [(14)C]glycylsarcosine uptake into Caco-2 cells expressing H(+)/peptide transporter (PEPT) 1 and into SKPT cells expressing PEPT2. Losartan, irbesartan, valsartan, and eprosartan inhibited [glycine-1-(14)C]glycylsarcosine ([(14)C]Gly-Sar) uptake into Caco-2 cells in a competitive manner with K(i) values of 24, 230, 390, and >1000 microM. Losartan and valsartan also strongly inhibited the total transepithelial flux of [(14)C]Gly-Sar across Caco-2 cell monolayers. In SKPT cells, [(14)C]Gly-Sar uptake was inhibited with K(i) values of 2.2 microM (losartan), 65 microM (irbesartan), 260 microM (valsartan), and 490 microM (eprosartan). We determined by the two-electrode voltage-clamp technique whether the compounds elicited transport currents by PEPT1 or PEPT2 when expressed in Xenopus laevis oocytes. No currents were observed for any of the sartans, but the compounds strongly and reversibly inhibited peptide-induced currents. Uptake of valsartan, losartan, and cefadroxil was quantified in HeLa cells after heterologous expression of human PEPT1 (hPEPT1). In contrast to cefadroxil, no PEPT1-specific uptake of valsartan and losartan was found. We conclude that the sartans tested in this study display high-affinity interaction with PEPTs but are not transported themselves. However, they strongly inhibit hPEPT1-mediated uptake of dipeptides and cefadroxil.

Computational discovery of novel low micromolar human pregnane X receptor antagonists

Very few antagonists have been identified for the human pregnane X receptor (PXR). These molecules may be of use for modulating the effects of therapeutic drugs, which are potent agonists for this receptor (e.g., some anticancer compounds and macrolide antibiotics), with subsequent effects on transcriptional regulation of xenobiotic metabolism and transporter genes. A recent novel pharmacophore for PXR antagonists was developed using three azoles and consisted of two hydrogen bond acceptor regions and two hydrophobic features. This pharmacophore also suggested an overall small binding site that was identified on the outer surface of the receptor at the AF-2 site and validated by docking studies. Using computational approaches to search libraries of known drugs or commercially available molecules is preferred over random screening. We have now described several new smaller antagonists of PXR discovered with the antagonist pharmacophore with in vitro activity in the low micromolar range [S-p-tolyl 3',5-dimethyl-3,5'-biisoxazole-4'-carbothioate (SPB03255) (IC(50), 6.3 microM) and 4-(3-chlorophenyl)-5-(2,4-dichlorobenzylthio)-4H-1,2,4-triazol-3-ol (SPB00574) (IC(50), 24.8 microM)]. We have also used our computational pharmacophore and docking tools to suggest that most of the known PXR antagonists, such as coumestrol and sulforaphane, could also interact on the outer surface of PXR at the AF-2 domain. The involvement of this domain was also suggested by further site-directed mutagenesis work. We have additionally described an FDA approved prodrug, leflunomide (IC(50), 6.8 microM), that seems to be a PXR antagonist in vitro. These observations are important for predicting whether further molecules may interact with PXR as antagonists in vivo with potential therapeutic applications.

Machine learning methods and docking for predicting human pregnane X receptor activation

The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.

A comprehensive in vitro and in silico analysis of antibiotics that activate pregnane X receptor and induce CYP3A4 in liver and intestine

We have investigated several in silico and in vitro methods to improve our ability to predict potential drug interactions of antibiotics. Our focus was to identify those antibiotics that activate pregnane X receptor (PXR) and induce CYP3A4 in human hepatocytes and intestinal cells. Human PXR activation was screened using reporter assays in HepG2 cells, kinetic measurements of PXR activation were made in DPX-2 cells, and induction of CYP3A4 expression and activity was verified by quantitative polymerase chain reaction, immunoblotting, and testosterone 6beta-hydroxylation in primary human hepatocytes and LS180 cells. We found that in HepG2 cells CYP3A4 transcription was activated strongly (> 10-fold) by rifampin and troleandomycin; moderately (> or = 7-fold) by dicloxacillin, tetracycline, clindamycin, griseofulvin, and (> or = 4-fold) erythromycin; and weakly (> 2.4-fold) by nafcillin, cefaclor, sulfisoxazole, and (> 2-fold) cefadroxil and penicillin V. Similar although not identical results were obtained in DPX-2 cells. CYP3A4 mRNA and protein expression were induced by these antibiotics to differing extents in both liver and intestinal cells. CYP3A4 activity was significantly increased by rifampin (9.7-fold), nafcillin and dicloxacillin (5.9-fold), and weakly induced (2-fold) by tetracycline, sufisoxazole, troleandomycin, and clindamycin. Multiple pharmacophore models and docking indicated a good fit for dicloxacillin and nafcillin in PXR. These results suggest that in vitro and in silico methods can help to prioritize and identify antibiotics that are most likely to reduce exposures of medications (such as oral contraceptive agents) which interact with enzymes and transporters regulated by PXR. In summary, nafcillin, dicloxacillin, cephradine, tetracycline, sulfixoxazole, erythromycin, clindamycin, and griseofulvin exhibit a clear propensity to induce CYP3A4 and warrant further clinical investigation.

Bacterial peptide recognition and immune activation facilitated by human peptide transporter PEPT2

Microbial detection requires the recognition of pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) that are distributed on the cell surface and within the cytosol. The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family functions as an intracellular PRR that triggers the innate immune response. The mechanism by which PAMPs enter the cytosol to interact with NLRs, particularly muropeptides derived from the bacterial proteoglycan cell wall, is poorly understood. PEPT2 is a proton-dependent transporter that mediates the active translocation of di- and tripeptides across epithelial tissues, including the lung. Using computational tools, we initially established that bacterial dipeptides, particularly gamma-D-glutamyl-meso-diaminopimelic acid (gamma-iE-DAP), are suitable substrates for PEPT2. We then determined in primary cultures of human upper airway epithelia and transiently transfected CHO-PEPT2 cell lines that gamma-iE-DAP uptake was mediated by PEPT2 with an affinity constant of approximately 193 microM, whereas muramyl dipeptide was not transported. Exposure to gamma-iE-DAP at the apical surface of differentiated, polarized cultures resulted in activation of the innate immune response in an NOD1- and RIP2-dependent manner, resulting in release of IL-6 and IL-8. Based on these findings we report that PEPT2 plays a vital role in microbial recognition by NLR proteins, particularly with regard to airborne pathogens, thereby participating in host defense in the lung.

Shape signatures: new descriptors for predicting cardiotoxicity in silico

Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors ( k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT(2B) receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT(2B) represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73-83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69-73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT(2B) receptor and hERG.

New predictive models for blood-brain barrier permeability of drug-like molecules

PURPOSE

The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling.

MATERIALS AND METHODS

The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.

RESULTS

The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs.

CONCLUSIONS

Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.

Design of high-affinity peptide conjugates with optimized fluorescence quantum yield as markers for small peptide transporter PEPT1 (SLC15A1)

We employed a computational approach to design and synthesize a series of fluorescently labeled hPEPT1 substrates. Five Alexa Fluor-350-labeled peptides were assessed for their in vitro inhibitory activity in hPEPT1-transfected CHO cells. At least four labeled peptides show potent inhibitory activity toward hPEPT1-mediated uptake of [(3)H]-GlySar and three compounds displayed a significant cellular uptake specifically mediated by hPEPT1.

Future directions for drug transporter modelling

Since the late 1980s computational methods such as quantitative structure-activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug-drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic-pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Human pregnane X receptor antagonists and agonists define molecular requirements for different binding sites

The pregnane X receptor (PXR) is an important transcriptional regulator of the expression of xenobiotic metabolism and transporter genes. The receptor is promiscuous, binding many structural classes of molecules that act as agonists at the ligand-binding domain, triggering up-regulation of genes, increasing the metabolism and excretion of therapeutic agents, and causing drug-drug interactions. It has been suggested that human PXR antagonists represent a means to counteract such interactions. Several azoles have been hypothesized to bind the activation function-2 (AF-2) surface on the exterior of PXR when agonists are concurrently bound in the ligand-binding domain. In the present study, we have derived novel computational models for PXR agonists using different series of imidazoles, steroids, and a set of diverse molecules with experimental PXR agonist binding data. We have additionally defined a novel pharmacophore for the steroidal agonist site. All agonist pharmacophores showed that hydrophobic features are predominant. In contrast, a qualitative comparison with the corresponding PXR antagonist pharmacophore models using azoles and biphenyls showed that they are smaller and hydrophobic with increased emphasis on hydrogen bonding features. Azole antagonists were docked into a proposed hydrophobic binding pocket on the outer surface at the AF-2 site and fitted comfortably, making interactions with key amino acids involved in charge clamping. Combining computational and experimental data for different classes of molecules provided strong evidence for agonists and antagonists binding distinct regions on PXR. These observations bear significant implications for future discovery of molecules that are more selective and potent antagonists.

Structural variation governs substrate specificity for organic anion transporter (OAT) homologs. Potential remote sensing by OAT family members

Organic anion transporters (OATs, SLC22) interact with a remarkably diverse array of endogenous and exogenous organic anions. However, little is known about the structural features that determine their substrate selectivity. We examined the substrate binding preferences and transport function of olfactory organic anion transporter, Oat6, in comparison with the more broadly expressed transporter, Oat1 (first identified as NKT). In analyzing interactions of both transporters with over 40 structurally diverse organic anions, we find a correlation between organic anion potency (pKi) and hydrophobicity (logP) suggesting a hydrophobicity-driven association with transporter-binding sites, which appears particularly prominent for Oat6. On the other hand, organic anion binding selectivity between Oat6 and Oat1 is influenced by the anion mass and net charge. Smaller mono-anions manifest greater potency for Oat6 and di-anions for Oat1. Comparative molecular field analysis confirms these mechanistic insights and provides a model for predicting new OAT substrates. By comparative molecular field analysis, both hydrophobic and charged interactions contribute to Oat1 binding, although it is predominantly the former that contributes to Oat6 binding. Together, the data suggest that, although the three-dimensional structures of these two transporters may be very similar, the binding pockets exhibit crucial differences. Furthermore, for six radiolabeled substrates, we assessed transport efficacy (Vmax) for Oat6 and Oat1. Binding potency and transport efficacy had little correlation, suggesting that different molecular interactions are involved in substrate binding to the transporter and translocation across the membrane. Substrate specificity for a particular transporter may enable design of drugs for targeting to specific tissues (e.g. olfactory mucosa). We also discuss how these data suggest a possible mechanism for remote sensing between OATs in different tissue compartments (e.g. kidney, olfactory mucosa) via organic anions.

Pharmacophore-based discovery of ligands for drug transporters

The ability to identify ligands for drug transporters is an important step in drug discovery and development. It can both improve accurate profiling of lead pharmacokinetic properties and assist in the discovery of new chemical entities targeting transporters. In silico approaches, especially pharmacophore-based database screening methods have great potential in improving the throughput of current transporter ligand identification assays, leading to a higher hit rate by focusing in vitro testing to the most promising hits. In this review, the potential of different in silico methods in transporter ligand identification studies are compared and summarized with an emphasis on pharmacophore modeling. Various implementations of pharmacophore model generation, database compilation and flexible screening algorithms are also introduced. Recent successful utilization of database searching with pharmacophores to identify novel ligands for the pharmaceutically significant transporters hPepT1, P-gp, BCRP, MRP1 and DAT are reviewed and the challenges encountered with current approaches are discussed.

Structural requirements for the substrates of the H+/peptide cotransporter PEPT2 determined by three-dimensional quantitative structure-activity relationship analysis

The renal type H(+)/peptide cotransporter PEPT2 has a substantial influence on the in vivo disposition of dipeptides and tripeptides as well as peptide-like drugs within the body, particularly in kidney, lung, and the brain. The comparative molecular similarity indices analysis (CoMSIA) method was applied to identify those regions in the substrate structures that are responsible for recognition and for differences in affinity. We have developed a comprehensive 3D quantitative structure-activity relationship (3D-QSAR) model based on 83 compounds that is able to explain and predict the binding affinities of new PEPT2 substrates. This 3D-QSAR model possesses a high predictive power (q(2) = 0.755; r(2) = 0.893). An additional 3D-QSAR model based on the same compounds was generated and correlated with affinity data of the intestinal H(+)/peptide cotransporter PEPT1. By comparing the CoMSIA contour plots, differences in selectivity between the intestinal and the renal type peptide carrier become evident.

Apical sodium dependent bile acid transporter (ASBT, SLC10A2): a potential prodrug target

A major hurdle impeding the successful clinical development of drug candidates can be poor intestinal permeability. Low intestinal permeability may be enhanced by a prodrug approach targeting membrane transporters in the small intestine. Transporter specificity, affinity, and capacity are three factors in targeted prodrug design. The human apical sodium dependent bile acid transporter (SLC10A2) belongs to the solute carrier family (SLC) of transporters and is an important carrier protein expressed in the small intestine. In spite of its appearing to be an excellent target for prodrug design, few studies have targeted human apical sodium dependent bile acid transporter (hASBT) to improve oral bioavailability. This review discusses bile acids including their chemistry and their absorptive disposition. Additionally, hASBT-mediated prodrug targeting is discussed, including QSAR, in vitro models for hASBT assay, and the current progress in utilizing hASBT as a drug delivery target.

Insights for Human Ether-a-Go-Go-Related Gene Potassium Channel Inhibition Using Recursive Partitioning and Kohonen and Sammon Mapping Techniques

The human ether-a-go-go related gene (hERG) can be inhibited by marketed drugs, and this inhibition may lead to QT prolongation and possibly fatal cardiac arrhythmia. We have collated literature data for 99 diverse hERG inhibitors to generate Kohonen maps, Sammon maps, and recursive partitioning models. Our aim was to investigate whether these computational models could be used either individually or together in a consensus approach to predict the binding of a prospectively selected test set of 35 diverse molecules and at the same time to offer further insights into hERG inhibition. The recursive partitioning model provided a quantitative prediction, which was markedly improved when Tanimoto similarity was included as a filter to remove molecules from the test set that were too dissimilar to the training set (r2 = 0.83, Spearman rho = 0.75, p = 0.0003 for the 18 remaining molecules, >0.77 similarity). This model was also used to screen and prioritize a database of drugs, recovering several hERG inhibitors not used in model building. The mapping approaches used molecular descriptors required for hERG inhibition that were not reported previously and in particular highlighted the importance of molecular shape. The Sammon map model provided the best qualitative classification of the test set (95% correct) compared with the Kohonen map model (81% correct), and this result was also superior to the consensus approach. This study illustrates that patch clamping data from various literature sources can be combined to generate valid models of hERG inhibition for prospective predictions.

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.