Identification of HIV-1 integrase inhibitors based on a four-point pharmacophore

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials

Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Asymmetric synthesis of N-N axially chiral compounds via organocatalytic atroposelective N-acylation

Compared with the well-developed C-C and C-N axial chirality, the asymmetric synthesis of N-N axial chirality remains elusive and challenging. Herein we report the first atroposelective N-acylation reaction of quinazolinone type benzamides with cinnamic anhydrides for the direct catalytic synthesis of optically active atropisomeric quinazolinone derivatives. This reaction features mild conditions and a broad substrate scope and produces N-N axially chiral compounds with high yields and very good enantioselectivities. Besides, the synthetic utility of the protocol was proved by a large scale reaction, transformation of the product and the utilization of the product as an acylation kinetic resolution reagent. Moreover, DFT calculations provide convincing evidence for the interpretation of stereoselection.

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents

The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.

Molecular dynamics simulations and applications in computational toxicology and nanotoxicology

Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

QSAR Models at the US FDA/NCTR

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds

Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.

Identification of new inhibitors for low molecular weight protein tyrosine phosphatase isoform B

The National Cancer Institute Diversity Set II (1356 compounds) and Diversity Set III (1597 compounds) were screened via in silico methods as potential inhibitors of low molecular weight protein tyrosine phosphatase (LWM-PTP) isoform B (EC 3.1.3.48). Those candidates that demonstrated comparable or better docking scores than that of pyridoxal 5'-phosphate (PLP), one of the most potent known inhibitors of LMW-PTP with a competitive inhibitor dissociation constant (Kis) of 7.6μM (pH 5.0), were analyzed via in vitro kinetic assays against LMW-PTP isoform B. While none of the compounds tested in vitro was significantly better that PLP, five compounds showed comparable inhibition. These five compounds are very diverse in structure and represent new therapeutic leads for inhibition of this isozyme.

Diverse small molecule inhibitors of human apurinic/apyrimidinic endonuclease APE1 identified from a screen of a large public collection

The major human apurinic/apyrimidinic endonuclease APE1 plays a pivotal role in the repair of base damage via participation in the DNA base excision repair (BER) pathway. Increased activity of APE1, often observed in tumor cells, is thought to contribute to resistance to various anticancer drugs, whereas down-regulation of APE1 sensitizes cells to DNA damaging agents. Thus, inhibiting APE1 repair endonuclease function in cancer cells is considered a promising strategy to overcome therapeutic agent resistance. Despite ongoing efforts, inhibitors of APE1 with adequate drug-like properties have yet to be discovered. Using a kinetic fluorescence assay, we conducted a fully-automated high-throughput screen (HTS) of the NIH Molecular Libraries Small Molecule Repository (MLSMR), as well as additional public collections, with each compound tested as a 7-concentration series in a 4 µL reaction volume. Actives identified from the screen were subjected to a panel of confirmatory and counterscreen tests. Several active molecules were identified that inhibited APE1 in two independent assay formats and exhibited potentiation of the genotoxic effect of methyl methanesulfonate with a concomitant increase in AP sites, a hallmark of intracellular APE1 inhibition; a number of these chemotypes could be good starting points for further medicinal chemistry optimization. To our knowledge, this represents the largest-scale HTS to identify inhibitors of APE1, and provides a key first step in the development of novel agents targeting BER for cancer treatment.

Novel neuraminidase inhibitors: identification, biological evaluation and investigations of the binding mode

Background: The pathogenicity of influenza A and B viruses depends on the function of influenza neuraminidase (NA). Emerging resistant influenza A viruses of subtype H1N1 increasingly challenge the effectiveness of established NA inhibitors. Recent computational studies have indicated several weak points of NA that can be exploited for rational inhibitor design to conquer this imminent threat, such as the opening of the binding pocket due to the flexibility of the 150-, 245- and 430-loops. Methods: We employed shape-focused virtual screening based on a recently discovered lead compound, katsumadain A, to identify novel promising compounds with significant inhibitory efficacy on NA and resistance-breaking capacity on oseltamivir-resistant strains. A potential binding mode of these compounds was derived employing ligand-based techniques and protein–ligand docking using representative protein conformations selected from molecular dynamics simulations. Results: Five novel compounds were identified by virtual screening. Their IC50 values, determined in chemiluminescence-based NA inhibition assays, are in the range of 0.18–17 µM. In particular, artocarpin exhibits high affinity toward three H1N1 oseltamivir-sensitive influenza A viruses. It also inhibits the NA of an oseltamivir-resistant H1N1 isolate.

Computer tools in the discovery of HIV-1 integrase inhibitors

Computer-aided drug design (CADD) methodologies have made great advances and contributed significantly to the discovery and/or optimization of many clinically used drugs in recent years. CADD tools have likewise been applied to the discovery of inhibitors of HIV-1 integrase, a difficult and worthwhile target for the development of efficient anti-HIV drugs. This article reviews the application of CADD tools, including pharmacophore search, quantitative structure-activity relationships, model building of integrase complexed with viral DNA and quantum-chemical studies in the discovery of HIV-1 integrase inhibitors. Different structurally diverse integrase inhibitors have been identified by, or with significant help from, various CADD tools.

Toward novel HIV-1 integrase binding inhibitors: Molecular modeling, synthesis, and biological studies

The identification of a novel hit compound as integrase binding inhibitor has been accomplished by means of virtual screening techniques. A small family of structurally related molecules has been synthesized and biologically evaluated with one of the compounds showing an IC(50)=12 microM.

Novel virtual screening protocol based on the combined use of molecular modeling and electron-ion interaction potential techniques to design HIV-1 integrase inhibitors

HIV-1 integrase (IN) is an essential enzyme for viral replication and represents an intriguing target for the development of new drugs. Although a large number of compounds have been reported to inhibit IN in biochemical assays, no drug active against this enzyme has been approved by the FDA so far. In this study, we report, for the first time, the use of the electron-ion interaction potential (EIIP) technique in combination with molecular modeling approaches for the identification of new IN inhibitors. An innovative virtual screening approach, based on the determination of both short- and long-range interactions between interacting molecules, was employed with the aim of identifying molecules able to inhibit the binding of IN to viral DNA. Moreover, results from a database screening on the commercial Asinex Gold Collection led to the selection of several compounds. One of them showed a significant inhibitory potency toward IN in the overall integration assay. Biological investigations also showed, in agreement with modeling studies, that these compounds prevent recognition of DNA by IN in a fluorescence fluctuation assay, probably by interacting with the DNA binding domain of IN.

Dynamic Pharmacophore Model Optimization: Identification of Novel HIV-1 Integrase Inhibitors

We extended the previously described dynamic pharmacophore model studies of HIV-1 integrase (IN) by considering more key residues in the active site, including Mg2+. First, we applied a Monte Carlo sampling method to map the complementary features of the IN binding surface. Two types of dynamic pharmacophore models were generated. One considers Mg2+ as part of the IN and therefore as an excluded volume, and the other treats Mg2+ as a positively charged feature, representing a new type of pharmacophore model aimed to identify compounds potentially preventing Mg2+ binding. Second, we validated the models with 385 known active (IC50 < 20 microM) and 235 (IC50 > 100 microM) inactive IN inhibitors. Third, we used the derived models to screen our small molecule database. Twenty-two structurally novel compounds were tested in an in vitro assay specific for IN, and two of them showed IC50 < or = 10 microM for strand transfer reaction.

Diketo acid pharmacophore. 2. Discovery of structurally diverse inhibitors of HIV-1 integrase

Because of its unique role in the viral replication process, HIV-1 integrase (IN) is an important antiretroviral drug target. The beta-diketo acid class of IN inhibitors has played a major role in validating IN as a legitimate target for antiretroviral drug design. S-1360 (1) and L-870,810 (2) are examples of beta-diketo acid related compounds to enter clinical trials. With an aim to discover novel lead compounds with diverse structural scaffolds, we employed common feature pharmacophore models using four known beta-diketo acid analogues including S-1360 (J. Med. Chem. 2005, 1, 111-120). The best-ranked pharmacophore model (Hypo1) contained a hydrophobic (HYA), an H-bond acceptor (HBA), and two H-bond donor (HBD) features. A search of a 3D database containing approximately 150,000 small molecules using Hypo1 found 1700 compounds that satisfied all the features of the pharmacophore query. Of the 1700 compounds, 110 were selected for in vitro screening studies on the basis of their docking scores, predicted binding location inside the active site of IN, and their druglike properties. Forty-eight compounds inhibited IN catalytic activities with an IC50 value less than 100 microM. Twenty-seven structurally diverse inhibitors are reported here. Out of the 27 compounds, 13 compounds inhibited strand transfer activity of IN with an IC50 value less than 30 microM. These compounds are novel, druglike, and readily amenable for synthetic optimization.

Pharmacophore-Based Design of HIV-1 Integrase Strand-Transfer Inhibitors

Using a training set of diketo-like acid HIV-1 integrase (IN) strand-transfer inhibitors, a 3D pharmacophore model was derived having quantitative predictive ability in terms of activity. The best statistical hypothesis consisted of four features (one hydrophobic aromatic region, two hydrogen-bond acceptors, and one hydrogen-bond donor) with r of 0.96. The resulting pharmacophore model guided the rational design of benzylindoles as new potent IN inhibitors, whose microwave-assisted synthesis and biological evaluation are reported.

Rhodium-catalyzed tandem cyclization-cycloaddition reactions of enynebenzaldehydes: construction of polycyclic ring systems

o-(1,6-Enynyl)benzaldehydes underwent a novel mode of cycloaddition using Rh(I)-precatalyst, via[3+2] cycloaddition of presumed dipolar carbonyl ylide intermediate generated by Rh-catalyst and the utility of this mechanistically intriguing enyne cyclization can be found in a number of polycyclic natural product skeletons.

Generation of predictive pharmacophore model for SARS-coronavirus main proteinase

Pharmacophore-based virtual screening is an effective, inexpensive and fast approach to discovering useful starting points for drug discovery. In this study, we developed a pharmacophore model for the main proteinase of severe acute respiratory syndrome coronavirus (SARS-CoV). Then we used this pharmacophore model to search NCI 3D database including 250, 251 compounds and identified 30 existing drugs containing the pharmacophore query. Among them are six compounds that already exhibited anti-SARS-CoV activity experimentally. This means that our pharmacophore model can lead to the discovery of potent anti-SARS-CoV inhibitors or promising lead compounds for further SARS-CoV main proteinase inhibitor development.

Design, synthesis and biological evaluation of heteroaryl diketohexenoic and diketobutanoic acids as HIV-1 integrase inhibitors endowed with antiretroviral activity

Highly active anti-retroviral therapy (HAART) using reverse transcriptase (RT) and protease (PR) inhibitors and, more recently, inhibitors of the fusion is currently the best clinical approach in combating acquired immunodeficiency syndrome (AIDS), caused by infection from human immunodeficiency virus type 1 (HIV-1). However, this therapy does not completely eradicate the virus, so that resistant strains easily emerge. The above problem calls urgently for research on inhibitors of further viral targets such as integrase (IN), the third enzyme produced by HIV. Recently, our research group was engaged in studies on conformationally restrained cinnamoyl compounds related to curcumin as anti-IN agents. Compounds containing both a 3,4,5-trihydroxyphenyl group and a carboxylic acid function were potent IN inhibitors active against viral replication. More recently, a promising new class of inhibitors synthesized by Merck Company has emerged, which contain aryldiketoacid (ADK) functionality. The ADKs selectively inhibited the stand transfer (ST) step of integration and were proven to be effective IN inhibitors in vivo. Our interest in the field of IN inhibitors led us to design pyrrole and indole derivatives containing both a cinnamoyl moiety and a diketoacid group. A number of the cited derivatives were proven potent IN inhibitors, which selectively inhibited the ST step at submicromolar concentrations and were effective against virus replication in HIV-1 infected cells.

Design of novel bioisosteres of beta-diketo acid inhibitors of HIV-1 integrase

HIV-1 integrase (IN) is an attractive and validated target for the development of novel therapeutics against AIDS. Significant efforts have been devoted to the identification of IN inhibitors using various methods. In this context, through virtual screening of the NCI database and structure-based drug design strategies, we identified several pharmacophoric fragments and incorporated them on various aromatic or heteroaromatic rings. In addition, we designed and synthesized a series of 5-aryl(heteroaryl)-isoxazole-3-carboxylic acids as biological isosteric analogues of beta-diketo acid containing inhibitors of HIV-1 IN and their derivatives. Further computational docking studies were performed to investigate the mode of interactions of the most active ligands with the IN active site. Results suggested that some of the tested compounds could be considered as lead compounds and suitable for further optimization.

Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors

We present here a dynamic receptor-based pharmacophore model representing the complementary features of the active site region of HIV-1 integrase (IN), which was developed from a series of representative conformations of IN. Conformations of IN were sampled through a molecular dynamics study of the catalytic domain of an IN monomer, and an ensemble of representative IN structures were collected via a probability-based representative conformer sampling method that considers both the potential energy and the structural similarity of the protein conformations. The dynamic pharmacophore model was validated by a set of 128 known inhibitors, and the results showed that over 72% of the active inhibitors (IC(50) lower than 20 microM) could be successfully identified by the dynamic model. Therefore, we screened our in-house database of commercially available compounds against this model and successfully identified a set of structurally novel IN inhibitors. Compounds 7 and 18 with IC(50)s of 8 microM and 15 microM, respectively, against the strand transfer reaction were the most potent. Moreover, 7, 8 and 20 showed a 5-fold selectivity for the strand transfer reaction over 3'-processing.

β-Diketo Acid Pharmacophore Hypothesis. 1. Discovery of a Novel Class of HIV-1 Integrase Inhibitors

HIV-1 Integrase (IN) is an essential enzyme for viral replication. The discovery of beta-diketo acids was crucial in the validation of IN as a legitimate target in drug discovery against HIV infection. In this study, we discovered a novel class of IN inhibitors using a 3D pharmacophore guided database search. We used S-1360 (1), the first IN inhibitor to undergo clinical trials, and three other analogues to develop a common feature pharmacophore hypothesis. Testing this four-featured pharmacophore against a multiconformational database of 150,000 structurally diverse small molecules yielded 1,700 compounds that satisfied the 3D query. Subsequently, all 1,700 compounds were docked into the active site of IN. On the basis of docking scores, Lipinski's rule-of-five, and structural novelty, 110 compounds were selected for biological screening. We found that compounds that contain both salicylic acid and a 2-thioxo-4-thiazolidinone (rhodanine) group (e.g. 5-13) showed significant inhibitory potency against IN, while the presence of either salicylic acid or a rhodanine group alone did not. Although some of the compounds containing only a salicylic acid showed inhibitory potency against IN, none of the compounds containing only rhodanine exhibited considerable potency. Of the 52 compounds reported in this study, 11 compounds (5, 6, 8, 10-13, 32-33, 51, and 53) inhibited 3'-processing or strand transfer activities of IN with IC(50) < or = 25 microM. This is the first reported use of S-1360 and its analogues as leads in developing a pharmacophore hypothesis for IN inhibition and for identification of new compounds with potent inhibition of this enzyme.

Efficient 3D Database Screening for Novel HIV-1 IN Inhibitors

We describe the use of pharmacophore modeling as an efficient tool in the discovery of novel HIV-1 integrase (IN) inhibitors. A three-dimensional hypothetical model for the binding of diketo acid analogues to the enzyme was built by means of the Catalyst program. Using these models as a query for virtual screening, we found several compounds that contain the specified 3D patterns of chemical functions. Biological testing shows that our strategy was successful in searching for new structural leads as HIV-1 IN inhibitors.

HIV-1 integrase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model has been generated for HIV-1 integrase (HIV-1 IN) from known inhibitors. A dataset consisting of 26 inhibitors was selected on the basis of the information content of the structures and activity data as required by the catalyst/HypoGen program. Our model was able to predict the activity of other known HIV-1 IN inhibitors not included in the model generation, and can be further used to identify structurally diverse compounds with desired biological activity by virtual screening.

Structure-based HIV-1 integrase inhibitor design: a future perspective

The pol gene of HIV-1 encodes for three essential enzymes, protease (PR), reverse transcriptase (RT) and integrase (IN). More than 16 drugs, targeting two of these enzymes, PR and RT have been approved by the FDA. At present, there are no clinically useful agents that inhibit the third enzyme, IN. Combination chemotherapy consisting of PR and RT inhibitors has shown remarkable success in the clinic and has benefited many patients. It is thought that a combination of drugs targeting all three enzymes should further incapacitate the virus. Discovery of highly selective PR inhibitors owe their success to the recent development in structure-guided drug design. During the past several years a plethora of structures of HIV-1 PR in complex with an inhibitor have been solved by x-ray crystallography. This incredible wealth of information provided opportunities for the discovery of second and third generation inhibitors. Due to the inherent nature of IN and insufficient structural information, structure-based inhibitor design selective for IN has not kept pace. However, because of recent developments in the field such information could soon become available. In this review, emphasis is placed on inhibitors with identified or proposed drug binding sites on IN.

Identification of HIV-1 integrase inhibitors via three-dimensional database searching using ASV and HIV-1 integrases as targets

Integration of viral DNA into the host cell genome is a critical step in the life cycle of HIV. This essential reaction is catalyzed by integrase (IN) through two steps, 3'-processing and DNA strand transfer. Integrase is an attractive target for drug design because there is no known cellular analogue and integration is essential for successful replication of HIV. A computational three-dimensional (3-D) database search was used to identify novel HIV-1 integrase inhibitors. Starting from the previously identified Y3 (4-acetylamino-5-hydroxynaphthalene-2,7-disulfonic acid) binding site on the avian sarcoma virus integrase (ASV IN), a preliminary search of all compounds in the nonproprietary, open part of the National Cancer Institute 3-D database yielded a collection of 3100 compounds. A more rigorous scoring method was used to rescreen the 3100 compounds against both ASV IN and HIV-1 IN. Twenty-two of those compounds were selected for inhibition assays against HIV-1 IN. Thirteen of the 22 showed inhibitory activity against HIV-1 IN at concentrations less than 200 microM and three of them showed antiviral activities in HIV-1 infected CEM cells with effective concentrations (EC50) ranging from 0.8 to 200 microM. Analysis of the computer-generated binding modes of the active compounds to HIV-1 IN showed that simultaneous interaction with the Y3 site and the catalytic site is possible. In addition, interactions between the active compounds and the flexible loop involved in the binding of DNA by IN are indicated to occur. The structural details and the unique binding motif between the HIV-1 IN and its inhibitors identified in the present work may contribute to the future development of IN inhibitors.

Novel compounds in preclinical/early clinical development for the treatment of HIV infections

Virtually all the compounds that are currently used, or under advanced clinical trial, for the treatment of HIV infections, belong to one of the following classes: (i) nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), (ii) non-nucleoside reverse transcriptase inhibitors (NNRTIs) and (iii) protease inhibitors (PIs). In addition to the reverse transcriptase and protease step, various other events in the HIV replicative cycle are potential targets for chemotherapeutic intervention: (i) viral adsorption, through binding to the viral envelope glycoprotein gp120 (polysulphates, polysulphonates, polyoxometalates, zintevir, negatively charged albumins); (ii) viral entry, through blockade of the viral coreceptors CXCR4 and CCR5 [bicyclams (AMD3100), polyphemusins (T22), TAK-779]; (iii) virus-cell fusion, through binding to the viral glycoprotein gp41 [T-20 (DP-178), siamycins, betulinic acid derivatives]; (iv) viral assembly and disassembly, through NCp7 zinc finger-targeted agents [2,2'-dithiobisbenzamides (DIBAs), azadicarbonamide (ADA)]; (v) proviral DNA integration, through integrase inhibitors such as L-chicoric acid; (vi) viral mRNA transcription, through inhibitors of the transcription (transactivation) process (peptoid CGP64222, fluoroquinolone K-12, Streptomyces product EM2487). Also, in recent years new NRTIs, NNRTIs and PIs have been developed that possess, respectively, improved metabolic characteristics (i.e. phosphoramidate and cyclosaligenyl pronucleotides of d4T), or increased activity against NNRTI-resistant HIV strains, or, in the case of PIs, a different, non-peptidic scaffold. Given the multitude of molecular targets with which anti-HIV agents can interact, one should be cautious in extrapolating from cell-free enzymatic assays to the mode of action of these agents in intact cells. A number of compounds (i.e. zintevir and L-chicoric acid, on the one hand; and CGP64222 on the other hand) have recently been found to interact with virus-cell binding and viral entry in contrast to their proposed modes of action targeted at the integrase and transactivation process, respectively.

Carbonyl J Derivatives: A New Class of HIV-1 Integrase Inhibitors

Integration of a DNA copy of the HIV-1 genome is required for viral replication and pathogenicity, and this highly specific molecular process is mediated by the virus-encoded integrase protein. The requirement for integration, combined with the lack of a known analogous process in mammalian cells, makes integrase an attractive target for therapeutic inhibitors of HIV-1 replication. While many reports of HIV-1 IN inhibitors exist, no such compounds have yet emerged to treat HIV-1 infection. As such, new classes of integrase inhibitors are needed. We have combined molecular modeling and combinatorial chemistry to identify and develop a new class of HIV-1 integrase inhibitors, the Carbonyl J [N,N'-bis(2-(5-hydroxy-7-naphthalenesulfonic acid)urea] derivatives. This new class includes a number of compounds with sub-micromolar IC(50) values for inhibiting purified HIV-1 integrase in vitro. Herein we describe the chemical characteristics that are important for integrase inhibition and cell toxicity within the Carbonyl J derivatives. Copyright 2000 Academic Press.

Developing a dynamic pharmacophore model for HIV-1 integrase

We present the first receptor-based pharmacophore model for HIV-1 integrase. The development of "dynamic" pharmacophore models is a new method that accounts for the inherent flexibility of the active site and aims to reduce the entropic penalties associated with binding a ligand. Furthermore, this new drug discovery method overcomes the limitation of an incomplete crystal structure of the target protein. A molecular dynamics (MD) simulation describes the flexibility of the uncomplexed protein. Many conformational models of the protein are saved from the MD simulations and used in a series of multi-unit search for interacting conformers (MUSIC) simulations. MUSIC is a multiple-copy minimization method, available in the BOSS program; it is used to determine binding regions for probe molecules containing functional groups that complement the active site. All protein conformations from the MD are overlaid, and conserved binding regions for the probe molecules are identified. Those conserved binding regions define the dynamic pharmacophore model. Here, the dynamic model is compared to known inhibitors of the integrase as well as a three-point, ligand-based pharmacophore model from the literature. Also, a "static" pharmacophore model was determined in the standard fashion, using a single crystal structure. Inhibitors thought to bind in the active site of HIV-1 integrase fit the dynamic model but not the static model. Finally, we have identified a set of compounds from the Available Chemicals Directory that fit the dynamic pharmacophore model, and experimental testing of the compounds has confirmed several new inhibitors.