The development of new HIV-1 protease inhibitors addressing these issues is therefore of high importance

The development of new HIV-1 protease inhibitors addressing these issues is therefore of high importance. the human immunodeficiency virus type 1 (HIV-1) encodes for the aspartic protease AZ 23 which mediates proteolytic processing of the and the viral gene products liberating functional enzymes and structural proteins which are essential for the formation of the mature, infectious virus. The entire processing of and precursors is finely coordinated and regulated by the activity of retroviral protease [4], [5]. Inactivation of the aspartic protease leads to the formation of noninfectious virions. Protease inhibitors represent a valid option in first line therapy of HIV-infected patients [6] and even their monotherapy has been shown to be effective in maintaining long-term viral suppression in a majority of patients [7]. Recently, many different classes of HIV-1 protease inhibitors have been developed, showing excellent antiviral profiles [8]C[13]. Two different approaches have been taken in the design of protease inhibitors, one involving targets which are peptidic in nature and another one employs non-peptidal character. However, peptidal protease inhibitors have shown low bioavailability and poor pharmacokinetics and normally possess multiple stereocentres [14]. Some have also reported artherogenic dyslipidemia [15] peripheral lipodystropy [16]. Hence, efforts have increasingly focused upon identifying non-peptidic HIV-1 protease inhibitors. Currently, licensed non-peptidal protease inhibitors include indinavir, ritonavir, saquinavir, and neflinavir. Some newer inhibitors with nonpeptide structure have also been developed, such as lopinavir, the AZ 23 cyclic urea mozinavir, atazanavir, tipranavir and the C2-symmetric protease inhibitor L-mannaric acid. In spite of having such a diversity of drugs available for treatment of HIV infections, millions of dollars are being spent on AIDS research for developing new AZ 23 drugs. Drug-related side effects, toxicity, and the development of AZ 23 drug-resistant HIV strains is a compelling reason for more efforts to develop newer inhibitors [17]. Resistance arises from mutations in the viral genome, specifically in the regions that encode the molecular targets of therapy, i.e. HIV-1 protease enzymes. These mutations alter the viral enzymes in such a way that the drug no longer inhibits the enzyme functions and the virus restores its free replication power. Moreover, the rate at which the virus reproduces and the high number of errors made in the viral replication process creates a large amount of mutated viral strains [18]. Thus, resistance toward the marketed HIV-1 protease inhibitors is a serious threat to efficient HIV treatment. AZ 23 Moreover, many of the HIV-1 protease inhibitors in the market suffer from poor pharmacokinetic properties due to poor aqueous solubility, low metabolic stability, high protein binding, and poor membrane permeability. The development of new HIV-1 protease inhibitors addressing these issues is therefore of high importance. Hence, a computational analysis that includes ligand and target based drug design approach has been used to identify new lead compounds with high potency. A pharmacophore represents the 3D arrangements of structural or chemical features of a drug (small organic compounds, peptides, peptidomimetics, etc.) that may be essential for interaction with the target/optimum binding. These pharmacophores can be used in different ways in drug design programs: (1) as a 3D query tool in virtual screening to identify potential new compounds from 3D databases of drug-like molecules with patentable structures different from those already discovered; (2) to predict the activities of a set of new compounds yet to be Rabbit polyclonal to PHF10 synthesized; (3) to understand the possible mechanism of action [19], [20]. The aim of the reported endeavor was to generate pharmacophore models for HIV-1 protease inhibitors through analog-based pharmacophore generation process (HypoGen algorithm) which employed a set of cyclic cyanoguanidines and cyclic urea ligands that have been experimentally observed to interact with a HIV-1 protease enzyme and also to compare these models with those obtained in a structure-based approach to identify novel structural characteristics and scaffolds for HIV-1 protease. The aspired aim was achieved by development of validated, robust and highly predictive pharmacophore models from both ligand and structure based approaches. The validity of the pharmacophore models was established by Fischers randomization test, internal and external test set predictions. The complementary nature of ligand and structure-based model has augmented the statistical findings of both the pharmacophores. The significance of the present study is clearly reflected by the identification of four highly potent lead compounds as protease inhibitors. Materials and Methods Ligand Based 3D Pharmacophore Generation All molecular modeling calculations were performed on recent software package Catalyst [21] which has an in-build pharmacophore generation facility. Catalyst is an.

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