Combined topomer CoMFA and hologram QSAR studies of a series of pyrrole derivatives as potential HIV fusion inhibitors

The Discovery and Development of Oxalamide and Pyrrole Small Molecule Inhibitors of gp120 and HIV Entry - A Review

Human immunodeficiency virus type-1 (HIV-1) is the causative agent responsible for the acquired immunodeficiency syndrome (AIDS) pandemic. More than 60 million infections and 25 million deaths have occurred since AIDS was first identified in the early 1980s. Advances in available therapeutics, in particular combination antiretroviral therapy, have significantly improved the treatment of HIV infection and have facilitated the shift from high mortality and morbidity to that of a manageable chronic disease. Unfortunately, none of the currently available drugs are curative of HIV. To deal with the rapid emergence of drug resistance, off-target effects, and the overall difficulty of eradicating the virus, an urgent need exists to develop new drugs, especially against targets critically important for the HIV-1 life cycle. Viral entry, which involves the interaction of the surface envelope glycoprotein, gp120, with the cellular receptor, CD4, is the first step of HIV-1 infection. Gp120 has been validated as an attractive target for anti-HIV-1 drug design or novel HIV detection tools. Several small molecule gp120 antagonists are currently under investigation as potential entry inhibitors. Pyrrole, piperazine, triazole, pyrazolinone, oxalamide, and piperidine derivatives, among others, have been investigated as gp120 antagonist candidates. Herein, we discuss the current state of research with respect to the design, synthesis and biological evaluation of oxalamide derivatives and five-membered heterocycles, namely, the pyrrole-containing small molecule as inhibitors of gp120 and HIV entry.

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods

BACKGROUND

HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application.

OBJECTIVE

We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities.

METHODS

We collected experimental values of k_off/K_i and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN).

RESULTS

For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r² of 0.688, 0.768 and 0.787, respectively, and r²_pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r² of 0.952, 0.869 and 0.960, respectively, and the r²_pred of 0.852, 0.628 and 0.814, respectively.

CONCLUSION

Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.