Prediction of Activity for Nonnucleoside Inhibitors with HIV-1 Reverse Transcriptase Based on Monte Carlo Simulations

Abstract

Results of Monte Carlo (MC) simulations for more than 200 nonnucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) representing eight diverse chemotypes have been correlated with their anti-HIV activities in an effort to establish simulation protocols and methods that can be used in the development of more effective drugs. Each inhibitor was modeled in a complex with the protein and by itself in water, and potentially useful descriptors of binding affinity were collected during the MC simulations. A viable regression equation was obtained for each data set using an extended linear response approach, which yielded r(2) values between 0.54 and 0.85 and an average unsigned error of only 0.50 kcal/mol. The most common descriptors confirm that a good geometrical match between the inhibitor and the protein is important and that the net loss of hydrogen bonds with the inhibitor upon binding is unfavorable. Other physically reasonable descriptors of binding are needed on a chemotype case-by-case basis. By including descriptors in common from the individual fits, combination regressions that include multiple data sets were also developed. This procedure led to a refined "master" regression for 210 NNRTIs with an r(2) of 0.60 and a cross-validated q(2) of 0.55. The computed activities show an rms error of 0.86 kcal/mol in comparison with experiment and an average unsigned error of 0.69 kcal/mol. Encouraging results were obtained for the predictions of 27 NNRTIs, representing a new chemotype not included in the development of the regression model. Predictions for this test set using the master regression yielded a q(2) value of 0.51 and an average unsigned error of 0.67 kcal/mol. Finally, additional regression analysis reveals that use of ligand-only descriptors leads to models with much diminished predictive ability.

Optimization of Azoles as Anti-Human Immunodeficiency Virus Agents Guided by Free-Energy Calculations

“…However, good correlations have been observed consistently for NNRTI series in assays for inhibition of reverse transcription and in cell-based anti-HIV analyses. 19 For lead optimization, the critical element is that the computations need to be qualitatively correct in providing guidance toward enhanced activity. The message from the FEP results was that it should be most beneficial to pursue chlorination at C4, C2′, C6′, and possibly C3.…”

Modeling of Activity of Efavirenz with the Mutant of HIV Reverse Transcriptase using Variable Connectivity Indices

“…Recently, Rizzo et al [6] proposed the following correlation for the estimation of anti-HIV activities for more than 200 NNRTIs:…”

“…The structure, activity and synthesis of efavirenz analogues as HIV-1 RT have been studied extensively, such as the works of Ren et al [2], Lindberg et al [3] and Cocuzza et al [4]. Rizzo et al [5,6], on the other hand, have developed models for estimation of activity for non-nucleoside inhibitors with HIV-1 RT based on Monte Carlo simulations. More recently, Udier-Blagovic et al [1] used a regression equation developed by Rizzo et al [6] to predict the activities for 47 efavirenz analogues with the K 103N mutant, and they also proposed two new correlations by fitting the experimental activities to improve predictive accuracy, but no significantly improved results were obtained.…”

Synthesis and antibacterial properties of new N4-acetylated hexahydro-2,7-dioxopyrido[2,3-f]quinoxaline-8-carboxylic acids

“…[1][2][3][4][5] Some derivatives such as 7-(4-arylpiperazin-1-yl)-6-fluoro-8-trifluoromethylquinolones, for example 1b, were reported to act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1 reverse transcriptase. [6][7][8][9] 1a (R 1 = c-C 3 H 5 ; R 2 On the other hand, simple or heterocyclic-fused quinoxalinones have become interesting compounds for study as antiviral agents [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] exemplified by the 3,4-dihydroquinoxalin-2-one core (2, Y=O), a second generation of NNRTIs, that is opaviraline (2a) [22][23][24][25][26][27] and the related 2-thione talveraline (2b), [14][15][16][17][18][19] are in clinical development. …”