Anti-HIV drug discovery has been increasingly focusing on HIV-1-RT (reverse transcriptase) as a potential therapeutic target. Tetrahydroimidazobenzodiazepinone (TIBO) belongs to non-nucleoside group of reverse transcriptase inhibitors (NNRTIs). A computational chemistry study has been performed on a series of tetrahydroimidazo-benzodiazepinones as HIV-1-NNRT inhibitors. Problem statement: In order to search out new drug of desired activity from the lead compounds, there was need to know the interaction of drugs with their receptor i.e., type of force(s) that have predominant role. Approach: Log P and SASA have been used for measurement of hydrophobic interaction, energy of protonation for measurement of most favorable hydrogen bond acceptor site, bond length and bond strain for measurement of strength of hydrogen bond formed between drug and receptor, atomic charges, ionization potential, electronegativity, E ‡ n and E ‡ m and their difference ∆E ‡ nm for measurement of polar interaction. The 3D modeling and geometry optimization of the compounds and receptor amino acids have been done by semiempirical method with MOPAC2002 associated with CAChe software. Results: The study has shown that hydrophobic interaction is predominant and made major contribution, while hydrogen bonding and polar interactions help in proper orientation of the compound (or its functional groups) to make maximam interaction. Conclusion: In this study theoretical technique has been discussed by which new hypothetical HIV-1-NNRT inhibitors can be developed prior to their synthesis only by introducing effective hydrophobic substituents at specific sites.
ABSTRACT:Log P, solvent-accessible surface area (SASA), total energy, bond length, and bond strain of the most favorable H-bond formed between drug and receptor; and quantum chemical descriptor ⌬E ‡ nm -based quantitative structure-activity relationship (QSAR) study of tetrahydroimidazodiazepinone derivatives have been done. For QSAR study, the 3D modeling and geometry optimization of all the derivatives and receptor's amino acid have been carried out on CAChe software by applying semiempirical method using MOPAC 2002. Softness Calculator using semiempirical PM3 methods has done the atomic softness of every atom of the derivatives and receptor's amino acids. The biological activities of tetrahydroimidazodiazepinone derivatives have been taken from the literature. The predicted values of biological activity with the help of multiple linear regression analysis are close to observed activity. The cross-validation coefficient and correlation coefficient also indicate that the QSAR model is valuable. Regression analysis shows that hydrophobic interaction is predominant and made major contribution, whereas hydrogen bonding and polar interactions help in proper orientation of the compound (or its functional groups) to make maximum interaction. With the help of these descriptors, prediction of the biological activity of new derivative is possible.
Quantitative structure-activity relationship (QSAR) models for bioconcentration factor (BCF) prediction applicable to 131 organic compounds of different chemical structures were prepared. The study showed that the best QSAR model, p BCF = 0.00250227 M W -0.0723952 E T -0.21352 eHOMO -0.892481 eLUMO -2.58291, was made from four quantum chemical descriptors. The best model was selected on the basis of the value of the correlation coefficient (r 2 = 0.871), cross validation coefficient (r 2 CV = 0.856), standard error (Std. Err. = 0.978), standard error of estimation (Std. Err. Est. = 0.556), F statistic (F = 213.22) and p value (p = 0.009), that were calculated by Statistica software. The molecular weight followed a direct relationship with the observed bioconcentration factor ( o BCF) up to a M W of 361 Da, and thereafter it followed an inverse relationship. The total energy and HOMO-LUMO gap, showed inverse relationships with o BCF. With the help of the QSAR model, bioconcentration factors of several hypothetical molecules can be predicted prior to their synthesis.
Experimental determination of BCFs is expensive and demanding if performed correctly. Because of this, measuring the BCFs of many thousands of chemical substances that are potential regulatory interest is simply not possible. Hence, prediction of BCFs of the PCBs based on QSAR were made time to time to increase the probability of success and reduce the time and cost in exploring the toxicological and ecological characteristics of molecules. DFT methods are, in general, capable of generating a variety of isolated molecular descriptors as well as local reactivity descriptors quite accurately. In this work, prediction of BCFs of the fifty seven PCBs based on quantum chemical descriptors derived from DFT method using the B88-PW91 GGA energy function with the DZVP basis set have been made. The study concluded that dipole moment and ionization potential are reliable descriptors for correlation of bioconcentration factors of polychlorinated biphenyls with their electronic structures. The resulted QSAR model (r 2 = 0.9139, 2 CV r = 0.8986, k = 2, SE = 0.2668) can be useful for predicting the BCFs of compounds prior to their synthesis.
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