Cluster analysis and three-dimensional QSAR studies of HIV-1 integrase inhibitors

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

Computational design of a full-length model of HIV-1 integrase: modeling of new inhibitors and comparison of their calculated binding energies with those previously studied

A full-length model of integrase (IN) of the human immunodeficiency virus type 1 (HIV-1) was constructed based on the distinctly resolved X-ray crystal structures of its three domains, named N-terminal, catalytic core and C-terminal. Thirty-one already known inhibitors with varieties of structural differences as well as nine newly tested ones were docked into the catalytic core. The molecular dynamic (MD) and binding properties of these complexes were obtained by MD calculations. The binding energies calculated by molecular mechanic/Poisson Boltzmann solvation area were significantly correlationed with available IC50. Four inhibitors including two newly designed were also docked into the full-length model and their MD behaviors and binding properties were calculated. It was found that one of the newly designed compounds forms a better complex with HIV-1 IN compared to the rest including raltegravir. MD calculations were performed with AMBER suite of programs using ff99SB force field for the proteins and the general Amber force field for the ligands. In conclusion, the results have produced a promising standpoint not only in the construction of the full-length model but also in development of new drugs against it. However, the role of multimer formation and the involvement of DNAs, and their subsequent effect on the complexation and inhibition, are required to arrive at a conclusive decision.

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.

Quantitative structure-activity relationship studies on 1-aryl-tetrahydroisoquinoline analogs as active anti-HIV agents

Predictive quantitative structure-activity relationship analysis was developed for a diverse series of recently synthesized 1-aryl-tetrahydroisoquinoline analogs with anti-HIV activities in this study. The conventional 2D-QSAR models were developed by genetic function approximation (GFA) and stepwise multiple linear regression (MLR) with acceptable explanation of 94.9% and 95.5% and good predicted power of 91.7% and 91.7%, respectively. The results of the 2D-QSAR models were further compared with 3D-QSAR model generated by molecular field analysis (MFA), investigating the substitutional requirements for the favorable receptor-drug interaction and quantitatively indicating the important regions of molecules for their activities. The results obtained by combining these methodologies give insights into the key features for designing more potent analogs against HIV.

Structural basis for ligand recognition at the benzodiazepine binding site of GABAA alpha 3 receptor, and pharmacophore-based virtual screening approach

Given the heterogeneity of GABA(A) receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA(A) alpha(3) receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure-activity relationship) studies on a series of novel GABA(A) alpha(3) subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to "Message-Address" concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on "Drug likeness" and "Similarity-based" methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.

Three-dimensional quantitative structure–activity relationship studies on diverse structural classes of HIV-1 integrase inhibitors using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques, were applied to a set of 89 HIV-1 integrase (IN) inhibitors (training set=61, test set=28), belonging to 11 structurally different classes. The biological data for 3' processing mechanism were used. For CoMFA calculations, three different fitting methods for alignment process were investigated. The best CoMFA model yielded the cross-validated r(2) r(2)(cv) =0.698 and the non-cross-validated r(2) (r(2))=0.947. The derived model indicated the importance of steric (60.8%) as well as electrostatic (39.2%) contributions. For CoMSIA calculations, different combinations of the fields were tested. The best CoMSIA model gave r(2)(cv) =0.724 and r(2)=0.864. This model showed that steric (30.3%), hydrogen bond donor (43.4%) and hydrogen bond acceptor (26.3%) properties played major roles in HIV-1 IN inhibition. The mapping of hydrogen bond interaction fields with the HIV-1 IN active site gave details on hydrogen bond forming between ligands and enzyme. These obtained results agree well with the experimental observations that there should be hydrogen bond interactions between ligands and Glu152, Lys156 and Lys159 residues. The results not only lead to a better understanding of structural requirements of HIV-1 IN inhibitors but also can help in the design of new IN inhibitors.

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.