355 research outputs found

    Synthesis and Computational Studies on Hiv-1 Integrase Inhibitors

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    HIV-1 integrase (IN) is essential for viral replication and offers a promising target for the development of anti-retroviral drugs. Two decades of extensive research has lead to the recent approval of raltegravir as the first IN inhibitor. Advancement of drug candidate elvitegravir, which is currently in Phase III clinical trial, has furthermore accelerated efforts against this potential target for combating HIV. However, the emergence of resistance against raltegravir and elvitegravir demands exploration of novel chemical scaffolds that could circumvent resistance against currently used HIV-1 IN inhibitors. With the goal of discovering new agents targeting HIV, a novel structural class of HIV-IN inhibitors have been designed and synthesized. Substantial computational studies were also performed that could aid the design and development of potent HIV IN inhibitors. A part of this dissertation research, covered in Chapter 3, details the design, synthesis, and biological evaluation of 3-keto salicylic acid chalcones as novel HIV-1 IN inhibitors. In the chalcone series, the most active compound, 5-bromo-2-hydroxy-3-[3-(2,3,6-trichloro-phenyl)- acryloyl]-2-hydroxybenzoic acid (96) was selectively active against IN strand transfer (ST) with IC50 of 3.7 µM. While most of the compounds exhibit ST selectivity, a few were nonselective, such as 5-bromo-3-[3-(4-bromo-phenyl)-acryloyl]-2-hydroxybenzoic acid (86), which was active against both 3!-processing (3!-P) and ST with IC50 values of 11 ± 4 and 5 ± 2 M, respectively. The compounds also inhibited HIV-1 replication with potencies comparable to their integrase inhibitory activities. Thus, compounds 96 and 86 inhibited HIV-1 replication with EC50 values of 7.3 and 8.7 M, respectively. Chapter 4 describes the synthesis of structurally related amide derivatives which were designed by modification of the chalcone moiety. In the amide series, the most active compound, 5-bromo-3-[(3-chloro-2,4-difluoro benzyl)- carbamoyl]-2-hydroxybenzoic acid (151), inhibited ST with an IC50 of 4 µM. Chapter 5 discloses the synthesis, and biological studies of halogenated phenanthrene #-diketo acids as novel HIV-1 IN inhibitors. The two most active compounds of the series, 4-(8-chlorophenanthren-3-yl)-2,4-dioxobutanoic acid (179) and 4-(6-chlorophenanthren-2-yl)-2,4-dioxobutanoic acid (177) had ST IC50 values of 1.2 and 1.3 M, respectively, and corresponding 3!-P values of 11.0 and 5.0 M. In the last section of the dissertation detailed in Chapter 6, computational studies were conducted with the aim of exploring the possible binding modes of potent IN inhibitors and evaluating the structural requirements for IN inhibition. To determine the physicochemical parameters important for ligand binding, in the first part of this chapter, a PHASE pharmacophore hypothesis was developed and used for molecular alignments in the initial comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) 3D-quantitative structure activity relationship (3D-QSAR) modeling of the chalcone derivatives. A recent breakthrough in the field of anti-HIV research was achieved with the crystallization and 3D structure determination of a complete foamy virus IN-DNA complex. To take advantage of the power of structure-based drug design, in the second part of the computational studies, homology models of HIV-1 IN-DNA were constructed based on the foamy virus IN-DNA complex X-ray crystal structure as template through collaboration with the Oak Ridge National Laboratory. The binding modes of raltegravir and elvitegravir in our homology models were in accordance with their binding modes in their complexes with the foamy virus structure. The homology model was then used for docking and 3D-QSAR studies on our synthesized inhibitors and other integrase inhibitors including the clinically available raltegravir and elvitegravir. Free energy calculations using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) methods were carried out to rescore and validate the binding modes of HIV-1 integrase inhibitors. 3D-QSAR models derived from this study provided detailed insights into the structural requirements for IN inhibition and established predictive tools to guide further inhibitor design. Linear interaction energy (LIE) calculations were also performed to derive energy parameters contributing to the binding free energies of the IN inhibitors in the data set. These energy parameters were also analyzed to gain insight into the binding modes of raltegravir and elvitegravir as well as to validate the conformations of our synthesized chalcone and amide derivatives. The energy terms were then used as descriptors to develop a linear interaction approximation (LIA) activity model for the inhibition of integration catalytic step. In the next section of the chapter, lead optimization was attempted using structure- and ligand-based drug design tools. RACHEL, a drug optimization software, was used to design an inhibitor with desired binding interactions with the IN active site residues. The hit obtained from RACHEL was used to design a structurally related compound (157), the synthesis and activity testing of which has been described in Chapter 4. Docking studies were also performed on the phenanthrene derivatives synthesized in Chapter 5. The docking studies predominantly revealed two binding poses that were distinct from the possible binding modes of clinically used raltegravir and advanced IN inhibitor elvitegravir and, moreover, do not interact significantly with some of the key amino acids (Q148 and N155) implicated in viral resistance. Therefore, this series of compounds can further be investigated as IN inhibitors to circumvent resistance associated with current clinically used HIV-1 IN inhibitors

    Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

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    We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

    Improved QSAR modeling of anti-HIV-1 acivities by means of the optimized correlation weights of local graph invariants

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    We report the results derived from the use of molecular descriptors calculated with the correlation weights (CWs) of local graph invariants for modeling of anti-HIV-1 potencies of two groups of reverse transcriptase inhibitors. The presence of different chemical elements in the molecular structure of the inhibitors and the Morgan extended connectivity values of zeroth-, first-, and second order have been examined as local graph invariants in the labeled hydrogen-filled graphs. We have computed via Monte Carlo optimization procedure the values of CWs which produce the largest possible correlation coefficient between the numerical data on the anti-HIV-1 potencies and those values of the descriptors on the training set. The model of the anti-HIV-1 activity obtained with compounds of training set by means of optimization of correlation weights of chemical elements present together with Morgan extended connectivity of first order makes up a sensible model for a satisfactory prediction of the endpoints of the compounds belonging to the test set.Facultad de Ciencias ExactasInstituto de Investigaciones Fisicoquímicas Teóricas y AplicadasCentro de Investigaciones del Medio Ambient

    Molecular Docking Study of Four Chromene Derivatives as Novel HIV-1 Integrase Inhibitors

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    : Four ligands based on Chromene derivatives have been docked into integrase of prototype foamy virus, which has high structural similarity with that of HIV-1 integrase. The Autodock Vina (Vina) software was used for this purpose. The docking scores for the derivatives are -7.3 kcal/mol, -7.5 kcal/mol, -6.9 kcal/mol, and -7.2 kcal/mol, respectively, which are comparable with that for Raltegravir (-10.7 kcal/mol). The docking results provide a detailed evidence for the interactions of four Chromene derivatives. The results may lead to the design and development of new drug candidates against AID

    Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

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    <p>Abstract</p> <p>Background</p> <p>HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.</p> <p>Results</p> <p>In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the <it>L</it>-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.</p> <p>Conclusions</p> <p>The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.</p

    Molecular modeling studies on HIV-1 inhibitors and their potential use as anticancer agents.

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    M. Pharm. University of KwaZulu-Natal, Durban 2014.Acquired Immunodeficiency Syndrome (AIDS), currently regarded as one of the deadliest diseases, is a disease of the human immune system caused by the Human Immunodeficiency Virus (HIV). This dissertation addresses two classes of HIV-1 inhibitors: (i) integrase and (ii) protease inhibitors. With the first class, a 2D-QSAR study was carried out on compounds from a variety of structural classes; 40 diketo acid and carboxamide derivatives; possessing integrase inhibitory activity. This study investigated the relationship between molecular properties and HIV-1 integrase inhibitor activities and established accurate QSAR predictive model using the Genetic Function Algorithm (GFA) statistical model. The logarithmic inverse values of IC50 (μM) and physicochemical parameters represent the dependent variable and independent variable, respectively. Results demonstrated that the radius of gyration, Zagreb index, Wiener index and minimized energy are statistically significant with the correlation coefficient value of 0.820 and play an important role in HIV-1 integrase inhibition. With the second class, the binding affinities of some FDA-approved HIV-1 protease inhibitors, which were reported to possess anticancer activities, were estimated. The findings proposed here may alter perceptions about how NFV binds to the human Hsp90; the protein responsible for the overexpression of HER2+ breast cancer; since it has only been reported to inhibit NSCLC and a collection of yeast strains. A human Hsp90 homologue was built due to the lack of a full X-ray crystal structure of the human Hsp90 on protein data bank. The Ramachandran plot showed the validity of the human Hsp90 homologue where 98% of all residues, including the active site residues, were in the favoured region and 99.8% were in the allowed region. The NTD active acid residues were found to be Leu43, Asn46, Lys53, Ile91, Asp97, Met93, Asn101, Ser108, Gly109, Phe133 and Thr179. The obtained active site residues for the human Hsp90 homologue CTD were Gln523, Val534, Ser535, Lys538, Thr595, Tyr596, Gly597, Trp598 and Met602. The system stability and overall convergence of simulations were evaluated. The RMSD of all nine PIs did not exceed 2Å and the system stabilised after 1000 ps and 1800 ps MD simulation at the NTD and CTD, respectively. The fluctuations of potential energies at the NTD were <2000 kcal/mol for 5 ns of MD simulation and CTD show that the fluctuations of the potential energy to be ≤8000 kcal/mol. The free binding energy of NFV was -83.03 kcal/mol at the NTD and -39.3 kcal/mol at the CTD. This value shows a significant difference (~43.73 kcal/mol) between the interaction energy at the NTD and CTD. Energy decomposition analysis at the NTD and CTD show that these two active sites have major energy contributions from their respective active site residues. This study is of great importance to medicine as it predicts the biological activity of some potent HIV-1 IN and investigates the potential use of the current HIV-1 PR drugs as anticancer agents

    Investigation on Quantitative Structure-Activity Relationships of 1,3,4 Oxadiazole Derivatives as Potential Telomerase Inhibitors

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    The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/164022/article, DOI : 10.2174/1570163815666180724113208. © 2018 Bentham ScienceA series of 1,3,4-oxadiazole derivatives with significant broad-spectrum anticancer activity against different cell lines, and demonstrated telomerase inhibition, was subjected to Quantitative Structure-Activity Relationships (QSAR) analysis. Validated models with high correlation coefficients were developed. The Multiple Linear Regression (MLR) models, by Ordinary Least Squares (OLS), showed good robustness and predictive capability, according to the Multi-Criteria Decision Making (MCDM = 0.8352), a technique that simultaneously enhances the performances of a certain number of criteria. The descriptors selected for the models, such as electrotopological state (E-state) descriptors, and extended topochemical atom (ETA) descriptors, showed the relevant chemical information contributing to the activity of these compounds. The results obtained in this study make sure about the identification of potential hits as prospective telomerase inhibitors.Peer reviewedFinal Accepted Versio

    Molecular modeling studies on HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90 as a potential anti-HIV-1 target.

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    Masters Degree. University of KwaZulu-Natal, Durban.Human immunodeficiency virus (HIV) infection is the leading cause of death globally. This dissertation addresses two HIV-1 target proteins namely, HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90. More specifically for HIV-1 RT, a case study for the identification of potential inhibitors as anti-HIV agents was carried out. A more refined virtual screening (VS) approach was implemented, which was an improvement on work previously published by our group- “target-bound pharmacophore modeling approach”. This study generated a pharmacophore library based only on highly contributing amino acid residues (HCAAR), instead of arbitrary pharmacophores, most commonly used in the conventional approaches in literature. HCAAR were distinguished based on free binding energy (FBE) contributions, obtained using calculation from molecular dynamics (MD) simulations. Previous approaches have relied on the docking score (DS) to generate energy-based pharmacophore models. However, DS are reportedly unreliable. Thus we present a model for a per-residue energy decomposition (PRED), constructed from MD simulation ensembles generating a more trustworthy pharmacophore model which can be applied in drug discovery workflow. This approach was employed in screening for potential HIV-1 RT inhibitors using the pharmacophoric features of the compound GSK952. The complex was subjected to docking and thereafter MD simulations confirmed the stability of the system. Experimentally determined inhibitors with known HIV-RT inhibitory activity were used to validate the proposed protocol. Two potential hits ZINC46849657 and ZINC54359621 showed a significant potential with regards to FBE. Reported results obtained from this work confirm that this new approach is favourable to the future of drug design process. Hsp90 was recently discovered to play a vital role in HIV-1 replication. Thus has emerged, as a promising target for anti-HIV-1 drugs. The molecular mechanism of Hsp90 is poorly understood, thus the second study was aimed to address this issue and provide a clear insight to the inhibition mechanism of Hsp90. Reasonable continuous MD simulations were employed for both unbound and bound Hsp90 conformations, to understand the dimerization and inhibition mechanisms. Results demonstrated that coumermycin A1 (C-A1), a newly discovered Hsp90 inhibitor, binds at the CTD dimer of Hsp90 and lead to a significant separation between orthogonally opposed residues, such as Arg591.B, Lys594.A, Ser663.A, Thr653.B, Ala665.A, Thr649.B, Leu646.B and Asn669A. A Large difference in magnitudes was observed in the radius of gyration (Rg), per-residue fluctuation, root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) confirming a completely more flexible state for the unbound conformation associated with dimerization. Whereas, a less globally correlated motion in the case of the bound conformer of Hsp90 approved a reduction of the dimeric process. This undoubtedly underlines the inhibition process due to ligand binding. The detailed dynamic analyses of Hsp90 presented herein are believed to give a greater insight and understanding to the function and mechanisms of inhibition of Hsp90. The report on the inhibitor-binding mode would also be of great assistance in the design of prospective inhibitors against Hsp90 as potential HIV target

    A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

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    BACKGROUND: Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients. RESULTS: In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46. CONCLUSION: The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses
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