Discovery of HIV-1 integrase inhibitors by pharmacophore searching

A mini-review on integrase inhibitors: The cornerstone of next-generation HIV treatment

Integrase inhibitors represent one of the most remarkable and effective advances in the treatment of HIV-1 infection. Their lack of human cellular equivalence has established integrase as a unique and ideal target for HIV-1 treatment. Over the last two decades, a variety of drugs and small molecule inhibitors have been developed to control or treat HIV infection. Many of these FDA-approved drugs are considered first-line options for AIDS patients. Unfortunately, resistance to these drugs has dictated the development of novel and more efficacious antiretroviral drugs. In this review article, we illustrate the key classes of antiretroviral integrase inhibitors available. We provide a comprehensive analysis of recent advancements in the development of integrase inhibitors, focusing on novel compounds and their distinct mechanisms of action. Our literature review highlights emerging allosteric integrase inhibitors that offer improved efficacy, resistance profiles, and pharmacokinetics. By integrating these recent advancements and clinical insights, this review aims to provide a thorough and updated understanding of integrase inhibitors, emphasizing their evolving role in HIV treatment.

Analogue and structure based approaches for modelling HIV-1 integrase inhibitors

A set of 220 inhibitors belonging to different structure classes and having HIV-1 integrase activity were collected along with their experimental pIC50 values. Geometries of all the inhibitors were fully optimized using B3LYP/6-31 + G(d) level of theory. These ligands were docked against 4 different HIV-1 integrase receptors (PDB IDs: 4LH5, 5KRS, 3ZSQ and 3ZSV). 30 docked poses were generated for all 220 inhibitors and ligand interaction of the first docked pose and the docked pose with the highest score were analysed. Residue GLU170 of 4LH5 receptor shows the highest number of interactions followed by ALA169, GLN168, HIS171 and ASP167 residues. Hydrogen bonding and stacking are mainly responsible for the interactions of these inhibitors with the receptor. We performed Molecular Dynamics (MD) simulation to observe the root-mean-square deviation (RMSD), for measure the average change of displacement between the atoms for a particular frame with respect to a reference and The Root Mean Square Fluctuation (RMSF) for characterization of local changes along the protein chain of the docked complexes. Analogue based models were generated to predict the pIC50 values for integrase inhibitors using various types of descriptors such as constitutional, geometrical, topological, quantum chemical and docking based descriptors. The best models were selected on the basis of statistical parameters and were validated by training and test set division. A few new inhibitors were designed on the basis of structure activity relationship and their pIC50 values were predicted using the generated models. All the designed new inhibitors a very high potential and may be used as potent inhibitors of HIV integrase. These models may be useful for further design and development of new and potent HIV integrase inhibitors.Communicated by Ramaswamy H. Sarma.

Design, Synthesis, Molecular Modeling Study and Biological Evaluation of New N'-Arylidene-pyrido [2,3-d]pyrimidine-5-carbohydrazide Derivatives as Anti-HIV-1 Agents

In an attempt to identify potential new agents that are active against HIV-1, a series of novel pyridopyrimidine-5-carbohydrazide derivatives featuring a substituted benzylidene fragment were designed and synthesized based on the general pharmacophore of HIV-1 integrase inhibitors. The cytotoxicity profiles of these compounds showed no significant toxicity to human cells and they exhibited anti-HIV-1 activity with EC50 values ranging from 90 to 155 µM. Compound 5j bearing 4-methylbenzylidene group was found to be the most active compound with EC50 = 90 µM and selectivity index, CC50/EC50 = 6.4. Molecular modeling studies indicated the capacity of compound 5j to interact with two Mg2+ cations and several residues that are important in HIV-1 integrase inhibition. These findings suggested that pyridopyrimidine-5-carbohydrazide scaffold might become a promising template for development of novel anti-HIV-1 agents.

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.

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.

Synthesis of Novel Fatty Substituted 4-methyl-2HChromen-2-one via Cross Metathesis: Potential Antioxidants and Chemotherapeutic Agents

A series of novel fatty substituted 4-methyl-2H-chromen-2-one (coumarins) were synthesized by employing cross metathesis, a key step in the synthesis. The antioxidant activities of the title compounds were compared with the commercial antioxidants, namely butylated hydroxy toluene (BHT) and α-tocopherol, glycosidic and other substituted 4-methyl-2H-chromen-2-ones. Among the different 4-methyl-2H-chromen-2-ones, the glycosidic substituted 4-methyl-2H-chromen-2-ones was excellent, while those with aliphatic fatty acid chain and hydroxyl substitutents were good. Among the substituted 4-methyl-2H-chromen-2-ones, glycosidic, hydroxyl and cyano containing 4-methyl-2H-chromen-2-ones exhibited good, while fatty substituted exhibited moderate anticancer activities against the four different cancer cell lines tested, namely DU145 (Prostate carcinoma cancer cell), HepG2 (Hepato cellular carcinoma cancer cell), SKOV3 (Ovarian cancer cell) and MDA-MB 231 (Human breast cancer cell). The study reveals that these substituted coumarins can be potential candidates in a number of food and pharmaceutical formulations.

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.

Computational models on quantitative prediction of bioactivity of HIV-1 integrase 3' processing inhibitors

In this study, four computational quantitative structure-activity relationship (QSAR) models were built to predict the bioactivity of 3' processing (3'P) inhibitors of HIV-1 integrase. Some 453 inhibitors whose bioactivity values were detected by the radiolabelling method were collected. The molecular structures were represented with MOE descriptors. In total, 21 descriptors were selected for modelling. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the training/test set divided by SOM, the correlation coefficients (r) were over 0.84, and for the training/test set split randomly, the r values were over 0.86. Some molecular properties such as hydrogen bond donor capacity, atomic partial charge properties, molecular refractivity, the number of aromatic bonds and molecular surface area, volume and shape properties played important roles for inhibiting 3' processing step of HIV-1 integrase.

Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine

In this study, four computational quantitative structure-activity relationship models were built to predict the biological activity of HIV-1 integrase strand transfer (ST) inhibitors. 551 Inhibitors whose bioactivities were detected by radiolabeling method were collected. The molecules were represented with 20 selected MOE descriptors. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the test set divided by SOM, the correlation coefficients (rs) were over 0.91, and for the test set split randomly, the rs were over 0.86.

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.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.

Elucidation of the molecular mechanisms of a salicylhydrazide class of compounds by proteomic analysis

Previously, we described a series of salicylhydrazide compounds with potent anti-cancer activities against a panel of human cancer cell lines derived from different origins. Preclinical evaluation showing efficacy both in vitro and in vivo in human cancer models indicated that these agents may represent a promising class of anticancer drugs. In the present study, we performed an in-depth investigation on the underlying molecular mechanisms of the most potent compounds, SC21 and SC23, using a proteomic method and bioinformatics tools. We demonstrated that SC23 induced apoptosis through multiple signaling pathways. In particular, SC23 regulated the expression of Bcl-2, p21, acetylated histone H3 and beta-tubulin and the combined modulation of these proteins may result in the induction of apoptosis. We also examined the effect of SC21 and SC23 on cell cycle progression and found that both compounds arrested cells in S-phase in most cell lines tested. To better understand the signaling networks involved, we analyzed the SC21- and SC23-treated cell lysates by the Kinexus 628 antibody microarray. The results were interpreted with the aid of Ingenuity Pathway Analysis (IPA) software. It was found that SC21 interfered with JAK/STAT signaling and elicited apoptosis through Fas and caspases pathways. Unlike SC21, SC23 induced RAR activation and caused cell cycle arrest. The signaling networks identified by this work may provide the basis for future mechanistic studies. The validation of the proposed pathways and the elucidation of the signaling cross-talk are currently under way.

2,3-dihydro-6,7-dihydroxy-1H-isoindol-1-one-based HIV-1 integrase inhibitors

The bis-salicylhydrazides class of HIV-1 integrase (IN) inhibitors has been postulated to function by metal chelation. However, members of this series exhibit potent inhibition only when Mn2+ is used as cofactor. The current study found that bis-aroylhydrazides could acquire inhibitory potency in Mg2+ using dihydroxybenzoyl substituents as both the right and left components of the hydrazide moiety. Employing a 2,3-dihydro-6,7-dihydroxy-1 H-isoindol-1-one ring system as a conformationally constrained 2,3-dihydroxybenzoyl equivalent provided good selectivity for IN-catalyzed strand transfer versus the 3'-processing reactions as well as antiviral efficacy in cells using HIV-1 based vectors.

Organocatalytic asymmetric robinson annulation of alpha,beta-unsaturated aldehydes: applications to the total synthesis of (+)-palitantin

The highly enantioselective organocatalytic Robinson annulation of alpha,beta-unsaturated aldehydes was achieved, catalyzed by l-proline and trialkylamines and providing the formal [4 + 2] cycloaddition adducts. Additionally, in some examples in the catalysis with diarylpyrrolinol silyl ethers, the reactions afforded the [4 + 2] adducts with high enantioselectivity (>99.5% ee). The structure of the adduct, obtained from the reaction of 3-methylbut-2-enal and (E)-3-(2-nitrophenyl)acrylaldehyde, was confirmed by X-ray analysis. The absolute configurations of some [4 + 2] cycloadducts were investigated, and the methodology was applied in the synthesis of (+)-palitantin.

Biologically active coumarins as inhibitors of HIV-1

Considerable progress has been made in recent years in the field of drug development against HIV. Many different kinds of natural products, including coumarins, have been found to be active in anti-HIV models and are thus undergoing further investigation. This review demonstrates the variety of coumarins with unique mechanisms of action in the different stages of HIV replication. The discovery and development of coumarins as anti-HIV agents has expanded in the past two decades. Most of the studies have been focused on the inhibitory activity of reverse transcriptase, but anti-integrase and antiprotease activities were also described. The objective of this review is to evaluate data on coumarins’ potent activity with respect to the inhibition of HIV-reverse transcriptase, HIV-integrase or HIV-protease. Recent requirements for potential anti-HIV agents increasingly require adequate definition of the mechanism of action as well as definition of toxic effects and this also applies to natural as well as synthetic coumarins. Structural modification is a powerful tool to increase the potential of bioactive principles. By applying scientific expertise and modern scientific technology, new single compounds will assuredly be developed as potent anti-HIV candidates for world-class new drug development.

High-throughput ligand screening via preclustering and evolved neural networks

The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.

Discovery and structure-activity relationship studies of a unique class of HIV-1 integrase inhibitors

HIV-1 integrase (IN) is an essential enzyme for viral replication and a validated target for the development of drugs against AIDS. Currently there are no approved drugs that target IN. However, new IN inhibitors are under clinical investigation. As more IN inhibitors enter human drug trials, there is a growing need for the design of novel lead compounds with diverse structural scaffolds and promising pharmacokinetic properties to counteract the difficulties observed with first-generation IN inhibitors. We have identified a novel class of IN inhibitors through the systematic exploration of structure-activity relationships in a series of linomide analogues. The predicted bound conformation of the most active analogues inside the IN active site also supports the observed structure-activity correlation in this new compound class.

Synthesis, structure, toxicological and pharmacological investigations of 4-hydroxycoumarin derivatives

Twenty 4-hydroxycoumarin derivatives were synthesized. Five of them are described for the first time. The X-ray crystal structure analysis of 3,3'-(2,3,4-trimethoxyphenylmethylene)bis-(4-hydroxy-2H-1-benzopyran-2-one) (7) and 3,3'-(3,5-dimethoxy-4-hydroxyphenylmethylene)bis-(4-hydroxy-2H-1-benzopyran-2-one) (9) confirmed the structure of these compounds. A comparative pharmacological study of the anticoagulant effect with respect to Warfarin showed that the synthesized compounds have different anticoagulant activities. The most prospective compound is 3,3'-(4-chlorophenylmethylene)bis-(4-hydroxy-2H-1-benzopyran-2-one) (12) with low toxicity, very good index of absorption and dose dependent anticoagulant activity.

Computational screening of inhibitors for HIV-1 integrase using a receptor based pharmacophore model

The HIV (human immuno­deficiency virus) integrase has a crucial role in viral replication. Moreover, it has no cellular homologue in humans. Hence, it is considered as an attractive drug target. Many inhibitors against the integrase protein has been designed and discussed. The Y-3 inhibitor (4-acetyl amino-5-hydroxy naphthalene - 2, 7- disulfonic acid) is already known to inhibit HIV-1 integrase. However, it is not suitable as a drug like candidate due to its high cyto-toxicity. In this report, a pharmacophore model for HIV integrase is described using the already known Y-3 inhibitor binding site. Fourteen compounds chemically related to the Y-3 inhibitor were generated using the described pharmacophore model and reported. Subsequent computational analysis showed that these compounds have interactions with the Y3 binding site and their possible utility as an integrase inhibitor is discussed.

Discovery and preclinical evaluation of a novel class of small-molecule compounds in hormone-dependent and -independent cancer cell lines

We discovered a series of salicylhydrazide class of compounds with remarkable anticancer activity against a panel of hormone receptor-positive and -negative cell lines. In the present study, we evaluated the in vitro activity of SC21 and SC23 against a range of human tumor cell types and the in vivo efficacy of compound SC21 in a PC3 human prostate cancer xenograft model in mice. We also determined the effects of SC21 on cell cycle regulation and apoptosis. Our in vitro results show that salicylhydrazides are highly potent compounds effective in both hormone receptor-positive and -negative cancer cells. SC21 induced apoptosis and blocked the cell cycle in G(0)/G(1) or S phase, depending on the cell lines used and irrespective of p53, p21, pRb, and p16 status. SC21 effectively reduced the tumor growth in mice without apparent toxicity. Although the mechanism of action of SC21 is not completely elucidated, the effect on cell cycle, the induction of apoptosis and the activity against a panel of tumor cell lines of different origins prompted us to carry out an in-depth preclinical evaluation of SC21.

Development and application of a high-throughput screening assay for HIV-1 integrase enzyme activities

Integrase (IN) mediates the covalent insertion of the retroviral genome into its host chromosomal DNA. This enzymatic activity can be reconstituted in vitro with short DNA oligonucleotides, which mimic a single viral DNA end, and purified IN. Herein we report a highly efficient and sensitive high-throughput screen, HIV Integrase Target SRI Assay (HITS), for HIV-1 IN activity using 5' biotin-labeled DNA (5' BIO donor) and 3' digoxygenin-labeled DNA (3' DIG target). Following 3' processing of the 5' BIO donor, strand transfer proceeds with integration of the 5' BIO donor into the 3' DIG target. Products were captured on a streptavidin-coated microplate and the amount of DIG retained in the well was measured. The end point values, measured as absorbance, ranged from 0.9 to 1.5 for IN-mediated reactions as compared with background readings of 0.05 to 0.12. The Z factor for the assay ranged from 0.7 to 0.85. The assay was used to screen drugs in a high-throughput format, and furthermore, we adapted the assay to study mechanistic questions regarding the integration process. For example, using variations of the assay format, we showed high preference of E strand of the long terminal repeat (LTR) viral DNA as a target strand compared with its complementary A strand. The E strand is the strand processed by IN. Furthermore, we explored the reported inhibitory effect of reverse transcriptase on integration.

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.

Model of full-length HIV-1 integrase complexed with viral DNA as template for anti-HIV drug design

We report structural models of the full-length integrase enzyme (IN) of the human immunodeficiency virus type 1 (HIV-1) and its complex with viral and human DNA. These were developed by means of molecular modeling techniques using all available experimental evidence, including X-ray crystallographic and NMR structures of portions of the full-length protein. Special emphasis was placed on obtaining a model of the enzyme's active site with the viral DNA apposed to it, based on the hypothesis that such a model would allow structure-based design of inhibitors that retain activity in vivo. This was because bound DNA might be present in vivo after 3'-processing but before strand transfer. These structural models were used to study the potential binding modes of various diketo-acid HIV-1 IN inhibitors (many of them preferentially inhibiting strand transfer) for which no experimentally derived complexed structures are available. The results indicate that the diketo-acid IN inhibitors probably chelate the metal ion in the catalytic site and also prevent the exposure of the 3'-processed end of the viral DNA to human DNA.

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 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.

Integrase inhibitors to treat HIV/AIDS

HIV integrase is a rational target for treating HIV infection and preventing AIDS. It took approximately 12 years to develop clinically usable inhibitors of integrase, and Phase I clinical trials of integrase inhibitors have just begun. This review focuses on the molecular basis and rationale for developing integrase inhibitors. The main classes of lead compounds are also described, as well as the concept of interfacial inhibitors of protein-nucleic-acid interactions that might apply to the clinically used strand-transfer inhibitors.

2,6-Bis(3,4,5-trihydroxybenzylydene) derivatives of cyclohexanone: novel potent HIV-1 integrase inhibitors that prevent HIV-1 multiplication in cell-based assays

A number of 2,6-bisbenzylidenecyclohexane-1-one derivatives have been synthesized and tested as HIV-1 integrase (IN) inhibitors with the aim of obtaining compounds capable to elicit antiviral activity at non-cytotoxic concentrations in cell-based assays. 3,5-Bis(3,4,5-trihydroxybenzylidene)-4-oxocyclohexaneacetic acid (20d) resulted one of the most potent and selective derivatives in acutely infected MT-4 cells (EC(50) and CC(50) values of 2 and 40 microM, respectively). In enzyme assays with recombinant HIV-1 integrase (rIN), this compound proved able to inhibit both 3'-processing and disintegration with IC(50) values of 0.2 and 0.5 microM, respectively. In order to develop a model capable to predict the anti HIV-IN activity and useful to design novel derivatives, we performed a comparative molecular field analysis (CoMFA) like 3-D-QSAR. In our model the ligands were described quantitatively in the GRID program, and the model was optimized by selecting only the most informative variables in the GOLPE program. We found the predictive ability of the model to increase significantly when the number of variables was reduced from 20,925 to 1327. A Q(2) of 0.73 was obtained with the final model, confirming the predictive ability of the model. By studying the PLS coefficients in informative 3-D contour plots, ideas for the synthesis of new compounds could be generated.

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.

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

Virtual screening of virtual libraries

Virtual screening of virtual libraries (VSVL) is a rapidly changing area of research. Great efforts are being made to produce better algorithms, selection methods and infrastructure. Yet, the number of successful examples in the literature is not impressive, although the quality of work certainly is high. Why is this? One reason is that these methods tend to be applied at the lead generation stage and therefore there is a large lead-time before successful examples appear in the literature. However, any computational chemist would confirm that these methods are successful and there exists a glut of start-up companies specialising in virtual screening. Moreover, the scientific community would not be focussing so much attention on this area if it were not yielding results. Even so, the paucity of literature data is certainly a hindrance to the development of better methods. The VSVL process is unique within the discovery process, in that it is the only method that can screen the > 10(30) genuinely novel molecules out there. Already, some VSVL methods are evaluating 10(13) compounds, a capacity that high throughput screening can only dream of. There is a huge potential advantage for the company that develops efficient and effective methods, for lead generation, lead hopping and optimization of both potency and ADME properties. To do this, it requires more than the software, it requires confidence to exploit the methodology, to commit synthesis on the basis of it, and to build this approach into the medicinal chemistry strategy. It is a fact that these tools remain quite daunting for the majority of scientists working at the bench. The routine use of these methods is not simply a matter of education and training. Integration of these methods into accessible and robust end user software, without dilution of the science, must be a priority. We have reached a coincidence, where several technologies have the required level of maturity predictive computational chemistry methods, algorithms that manage the combinatorial explosion, high throughput crystallography and ADME measurements and the massive increase in computational horsepower from distributed computing. The author is confident that the synergy of these technologies will bring great benefit to the industry, with more efficient production of higher quality clinical candidates. The future is bright. The future is virtual!