A quantitative structure-activity relationship study on some HIV-1 protease inhibitors using molecular connectivity index

A novel one-pot method for the stereoselective synthesis of tetrahydropyrimidinones in a low melting mixture

A direct and metal free one-pot method has been developed for the stereoselective synthesis of tetrahydropyrimidinone derivatives from a vinyl arene and formaldehyde using a tartaric acid-dimethylurea (TA : DMU) melt as a green reaction medium. The substrate scope of this method is very general and the tetrahydropyrimidinone (THPM) derivatives are synthesized in good yields with a high degree of diastereoselectivity. In this reaction, the melt plays a triple role as the solvent, catalyst and reagent.

Synthesis of ferrocene-containing six-membered cyclic ureas via α-ferrocenyl carbocations

A convenient method for the synthesis of ferrocene-containing six membered cyclic ureas was developed.

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction

BACKGROUND

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.

RESULTS

The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.

CONCLUSION

The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

3D-QSAR analysis of human immunodeficiency virus entry-1 inhibitors by CoMFA and CoMSIA

3D-QSAR studies namely CoMFA, CoMFA region focusing and CoMSIA have been carried out on a series (36 compounds) of HIV-1 entry inhibitors. An alignment rule for the compounds was defined using Distill in SYBYL 7.3. Models were validated using a data set obtained by dividing the data set into a training set and test set using hierarchical clustering, based on the CoMFA fields and biological activities (pIC(50)). The best predictions were obtained with a CoMFA region-focusing model (q(2) = 0.719, r(pred)(2) = 0.911), CoMFA standard model (q(2) = 0.660, r(pred)(2) = 0.890), and CoMSIA (steric and hydrophobic) model (q(2) = 0.521, r(pred)(2) = 0.794). The statistical parameters from the models indicate that the data are well fitted and have high predictive ability. Moreover, the resulting 3D CoMFA/CoMSIA contour maps provide useful guidance for designing highly active inhibitors.

Study on QSTR of benzoic acid compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD₅₀ (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD₅₀: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD₅₀ = 1.2399 × ⁰J^A +2.6911 × ¹J^A – 0.4445 × J^B (R² = 0.9860), where ⁰J^A is zero order connectivity index, ¹J^A is the first order connectivity index and J_B = ⁰J^A × ¹J^A is the cross factor. The model was shown to have a good forecasting ability.

3D-QSAR CoMFA/CoMSIA models based on theoretical active conformers of HOE/BAY-793 analogs derived from HIV-1 protease inhibitor complexes

The three-dimensional quantitative structure-activity relationships (3D-QSAR) of a series of HOE/BAY-793 analogs (C(2)-symmetric diol peptidomimetics), developed by Budt and co-workers [Bioorg. Med. Chem. 3 (1995) 559] as inhibitors of HIV-1 protease (HIV-PR), were studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Theoretical active conformers for these peptidomimetics were generated, derived from modeled protease inhibitor complexes, in order to orient the compounds superposition and to afford a consistent alignment. The best CoMFA model (N=27, q(2)=0.637, R(2)=0.991) showed contributions of the steric (45.7%) and electrostatic (54.3%) fields to the activity, while the best CoMSIA model (N=27, q(2)=0.511, R(2)=0.987) showed contributions of the electrostatic (68.5%) and hydrogen bond donor (37.5%) fields. The models were also external validated using four compounds (test set) not included in the model generation process. The statistical parameters from both models indicate that the data are well fitted and have high predictive ability. Moreover, the resulting 3D CoMFA/CoMSIA contour maps provide useful guidance for designing highly active ligands. The CoMFA/CoMSIA models were also compared with previous 4D-QSAR models [E.F.F. da Cunha, M.G. Albuquerque, O.A.C. Antunes, R.B. de Alencastro, QSAR Comb. Sci. 24 (2005), 240-253.].

Possible allosteric interactions of monoindazole-substituted P2 cyclic urea analogues with wild-type and mutant HIV-1 protease

Our ongoing efforts to understand the difference in the binding pattern of HIV-1 protease inhibitor (HIVPI) with the wild-type and mutant HIV-1 protease (HIVPR) and to provide mechanistic insight are continued further. We report here the results of a recent quantitative structure-activity relationship (QSAR) study on monoindazole-substituted P2 analogues of cyclic urea HIVPIs. The QSAR models revealed an inverted parabolic relationship between biological activity and calculated molar refractivity (CMR). That is, biological activity first decreases with increase in CMR and at a certain minimum point (inversion point) it suddenly changes and increases with further increase in CMR. CMR is a measure of volume-dependent-polarizability and is an indication of the polar interactions between ligand and receptor. The results seem to be best rationalized by larger molecules inducing a change in a receptor unit that allows for a new mode of interaction. Similar QSAR models were also observed for the biological activity of these molecules tested against a panel of mutant viruses including mutant strains with single amino acid substitution (I84V), double amino acid substitutions (I84V/V82F), and multiple amino acid changes corresponding to mutations observed in clinical isolates of patients treated with Ritonavir((R)). Interestingly the inversion points for these mutant strains were found larger than for wild-type. The subtle but significant difference in the inversion point indicates change in the shape and size of the binding pocket. Earlier QSAR studies have shown that the correlation of biological activity with an inverted parabola is an indicative of the 'allosteric interaction' of the ligands with the receptor. This report presents a detail analysis of these observations.

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

Hydrophobicity in the design of P2/P2′ tetrahydropyrimidinone HIV protease inhibitors

As part of an ongoing effort in understanding the role of hydrophobicity in the design of nonpeptidic HIV protease inhibitors, the QSAR study on P2/P2' tetrahydropyrimidinone is presented in this report. Our results suggest that the balance of hydrophobicity and a volume- dependent polarizability term plays a key role in the inhibition of the viral protease by these inhibitors. The size of the substituent of ligands at particular positions that induce steric fit is crucial. The role of hydrophobicity in the design of tetrahydropyrimidinone is discussed. It has been found that a sufficient spread in the data is required to observe the optimum value of ClogP for these inhibitors.

From SAR to comparative QSAR: role of hydrophobicity in the design of 4-hydroxy-5,6-dihydropyran-2-ones HIV-1 protease inhibitors

Role of hydrophobicity in the design of 4-hydroxy-5,6-dihydropyran-2-ones-a new class of emerging HIV-1 protease inhibitors (HIV-PI) was investigated by using comparative QSAR. These studies show that most of the data points in the individual dataset studied fall either on positive or negative side of the optimum value of ClogP. This is why, we observe either a positive or negative ClogP term in the QSAR. To observe the optimum value of ClogP for these inhibitors, a sufficient spread in the data is required. It is hoped that the results of this study would help in optimizing substituents for better binding at enzyme pockets and guide in the design of more effective HIV-PI of this class.

A mechanistic study of 3-aminoindazole cyclic urea HIV-1 protease inhibitors using comparative QSAR

Comparative QSAR studies on P2/P2' and P1/P1' substituted symmetrical and nonsymmetrical 3-aminoindazole cyclic urea HIV-1 protease inhibitors were performed. The protease inhibitory activity of these compounds was found to decrease with larger and more hydrophobic molecules, whereas the antiviral potency and translation across the cell membrane increases with increase in hydrophobicity and size. These results provide mechanistic insight about the mode of interaction of these compounds with HIV-1 protease receptor and would help in further improving the biological activity.

Six-membered cyclic ureas as HIV-1 protease inhibitors: a QSAR study based on CODESSA PRO approach. Quantitative structure-activity relationships

Quantitative structure-activity relationships (QSAR) for HIV-1 protease inhibitory activity of substituted tetrahydropyrimidinones have been produced using CODESSA PRO methodology and software. The best four-parameter equation (R(2)(cv)=0.847) allowed us to reveal two main structural factors which are strongly correlated with the title activity: molecular hydrophobicity and ability to form hydrogen bonds with the target enzyme.