Synthesis, biological activity, and crystal structure of potent nonnucleoside inhibitors of HIV-1 reverse transcriptase that retain activity against mutant forms of the enzyme.

In an ongoing effort to develop novel and potent nonnucleoside HIV-1 reverse transcriptase (RT) inhibitors that are effective against the wild type (WT) virus and clinically observed mutants, 1,2-bis-substituted benzimidazoles were synthesized and tested. Optimization of the N1 and C2 positions of benzimidazole led to the development of 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)-4-methylbenzimidazole (1) (IC50 = 0.2 microM, EC50 = 0.

Molecular modeling calculations of HIV-1 reverse transcriptase nonnucleoside inhibitors: correlation of binding energy with biological activity for novel 2-aryl-substituted benzimidazole analogues.

The energies and physical descriptors for the binding of 20 novel 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)benzimidazole analogues (BPBIs) to HIV-1 reverse transcriptase (RT) have been determined using Monte Carlo (MC) simulations. The crystallographic structure of the lead compound, 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)-4-methylbenzimidazole, was used as a starting point to model the inhibitors in both the bound and the unbound states. The energy terms and physical descriptors obtained from the calculations were correlated with their respective experimental EC(50) values, resulting in an r(2) value of 0.

Prediction of activity for nonnucleoside inhibitors with HIV-1 reverse transcriptase based on Monte Carlo simulations.

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.