On some aspects of validation of predictive quantitative structure-activity relationship models

Descriptor analysis of estrogen receptor β-selective ligands using 2-phenylquinoline, tetrahydrofluorenone and 3-hydroxy 6H-benzo[c]chromen-6-one scaffolds

Estrogen receptor beta (ERβ) selective ligands have attracted much attention recently in the design of anti-cancer drugs that are devoid of the common side effects of estrogen. Structural studies of estrogen receptor alpha (ERα) and β revealed that there were considerable differences in their ligand-binding cavity and in their volume. Hence, the present study has hypothesized that size and shape descriptors can influence the affinity/selectivity of the ligands towards ERβ. To prove the same, quantitative structure-activity relationship (QSAR) analyses were carried out using multiple regression analysis on 2-phenylquinoline, tetrahydrofluorenone and 3-hydroxy-6H-benzo[c]chromen-6-one series. Results indicate that increased lipophilicity, decrease in ellipsoidal volume and width of substituents, presence of halogen atoms was essential for the ligands to have high affinity/selectivity towards ERβ. QSAR models obtained were both internally and externally validated. The study delineates that the size and shape descriptors are best modulators of ERβ affinity/selectivity. Docking studies were performed to support our QSAR results.

Molecular docking and QSAR studies of aromatase inhibitor androstenedione derivatives

Objectives

Aromatase (CYP19) inhibitors have emerged as promising candidates for the treatment of estrogen-dependent breast cancer. In this study, a series of androstenedione derivatives with CYP19 inhibitory activity was subjected to a molecular docking study followed by quantitative structure–activity relationship (QSAR) analyses in search of ideal physicochemical characteristics of potential aromatase inhibitors.

Methods

The QSAR studies were carried out using both two-dimensional (topological, and structural) and three-dimesional (spatial) descriptors. We also used thermodynamic parameters along with 2D and 3D descriptors. Genetic function approximation (GFA) and genetic partial least squares (G/PLS) were used as chemometric tools for QSAR modelling.

Key findings

The docking study indicated that the important interacting amino acids in the active site were Met374, Arg115, Ile133, Ala306, Thr310, Asp309, Val370, Leu477 and Ser478. The 17-keto oxygen of the ligands is responsible for the formation of a hydrogen bond with Met374 and the remaining parts of the molecules are stabilized by the hydrophobic interactions with the non-polar amino acids. The C2 and C19 positions in the ligands are important for maintaining the appropriate orientation of the molecules in the active site. The results of docking experiments and QSAR studies supported each other.

Conclusions

The developed QSAR models indicated the importance of some Jurs parameters, structural parameters, topological branching index and E-state indices of different fragments. All the developed QSAR models were statistically significant according to the internal and external validation parameters.

First report on interspecies quantitative correlation of ecotoxicity of pharmaceuticals

Pharmaceuticals being extensively and progressively used in human and veterinary medicine are emerging as significant environmental contaminants. Pharmaceuticals are designed to have a specific mode of action and many of them are persistent in the body. These features among others make pharmaceuticals to be evaluated for potential effects on aquatic flora and fauna. Low levels of pharmaceuticals have been detected in many countries in sewage treatment plant effluents, surface waters, groundwater and drinking waters. In contrast, there is a general scarcity of publicly available ecotoxicological data concerning pharmaceuticals. Interspecies toxicity correlations provide a tool for estimating contaminant sensitivity with known levels of uncertainty for a diversity of wildlife species. In this context, we have developed interspecies toxicity correlation between Daphnia magna (zooplankton) and fish (species according to OECD guidelines) assessing the ecotoxicological hazard potential of diverse 77 pharmaceuticals. The developed models are validated and consensus models are presented to predict toxicity of the individual compounds for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments which are responsible for the greater toxicity of the diverse pharmaceuticals are identified by the developed models. Developed models are also used to predict fish toxicities of 59 pharmaceuticals (for which Daphnia toxicities are present) and Daphnia toxicities of 30 pharmaceuticals (for which fish toxicities are present). This study will allow a better and comprehensive risk assessment of pharmaceuticals for which toxicity data is missing for a particular endpoint.

Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors

In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors.

QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors

One of the major economic alternatives to experimental toxicity testing is the use of quantitative structure-activity relationships (QSARs) which are used in formulating regulatory decisions of environmental protection agencies. In this background, we have modeled a large diverse group of 297 chemicals for their toxicity to Daphnia magna using mechanistically interpretable descriptors. Three-dimensional (3D) (electronic and spatial) and two-dimensional (2D) (topological and information content indices) descriptors along with physicochemical parameter logK(o/w) (n-octanol/water partition coefficient) and structural descriptors were used as predictor variables. The QSAR models were developed by stepwise multiple linear regression (MLR), partial least squares (PLS), genetic function approximation (GFA), and genetic PLS (G/PLS). All the models were validated internally and externally. Among several models developed using different chemometric tools, the best model based on both internal and external validation characteristics was a PLS equation with 7 descriptors and three latent variables explaining 67.8% leave-one-out predicted variance and 74.1% external predicted variance. The PLS model suggests that higher lipophilicity and electrophilicity, less negative charge surface area and presence of ether linkage, hydrogen bond donor groups and acetylenic carbons are responsible for greater toxicity of chemicals. The developed model may be used for prediction of toxicity, safety and risk assessment of chemicals to achieve better ecotoxicological management and prevent adverse health consequences.

Thermodynamic and structure-property study of liquid-vapor equilibrium for aroma compounds

Thermodynamic parameters (T, DeltaH degrees , DeltaS degrees , K) were collected from the literature and/or calculated for five esters, four ketones, two aldehydes, and three alcohols, pure compounds and compounds in aqueous solution. Examination of correlations between these parameters and the range values of DeltaH degrees and DeltaS degrees puts forward the key roles of enthalpy for vaporization of pure compounds and of entropy in liquid-vapor equilibrium of compounds in aqueous solution. A structure-property relationship (SPR) study was performed using molecular descriptors on aroma compounds to better understand their vaporization behavior. In addition to the role of polarity for vapor-liquid equilibrium of compounds in aqueous solution, the structure-property study points out the role of chain length and branching, illustrated by the correlation between the connectivity index CHI-V-1 and the difference between T and log K for vaporization of pure compounds and compounds in aqueous solution. Moreover, examination of the esters' enthalpy values allowed a probable conformation adopted by ethyl octanoate in aqueous solution to be proposed.

QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

Extensive production and utilization of aromatic aldehydes and their derivatives without proper certification is alarming with regard to environmental safety. This concern motivated our construction of predictive quantitative structure-activity relationship (QSAR) models for the toxicity of aldehydes to the ecologically important species Tetrahymena pyriformis. Quantum topological molecular similarity (QTMS) descriptors, along with the lipid-water partition coefficient (log K(o/w)), were used as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31 + G(d,p), B3LYP/6-311 + G(2d,p) and MP2/6-311+G(2d,p). The data set of 77 aromatic aldehydes was divided into a training set (n = 58) and a test (n = 19) set, and 58 models were developed using partial least squares (PLS) and genetic partial least squares (G/PLS). We evaluated the overall predictive capacity of the models based on leave-one-out predictions for the training set compounds and model derived predictions for the test set compounds. For both PLS and G/PLS, the models built at the HF/6-31G(d) level show better predictivity (based on overall prediction) than the models developed at any of the other five levels. Further validation was also performed utilizing (process and model) randomization tests. We show that improved predictive QSAR models for aldehydic toxicity to Tetrahymena pyriformis can be generated using QTMS descriptors along with log K(o/w).

QSAR of cytochrome inhibitors

Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure-activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clinically important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure-activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug-drug interactions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.

Docking and 3D-QSAR studies of acetohydroxy acid synthase inhibitor sulfonylurea derivatives

Docking and three dimensional quantitative-structure activity relationship (3D-QSAR) studies were performed on acetohydroxy acid synthase (AHAS) inhibitor sulfonylurea analogues with potential herbicidal activity. The 3D-QSAR studies were carried out using shape, spatial and electronic descriptors along with a few structural parameters. Genetic function approximation (GFA) was used as the chemometric tool for this analysis. The whole data set (n = 45) was divided into a training set (75% of the data set) and a test set (remaining 25%) on the basis of the K-means clustering technique on a standardised topological, physicochemical and structural descriptor matrix. Models developed from the training set were used to predict the activity of the test set compounds. All models were validated internally, externally and using the Y-randomisation technique. Docking studies suggested that the molecules bind within a pocket of the enzyme formed by some important amino acid residues (Met351, Asp375, Arg377, Gly509, Met570 and Val571). In QSAR studies, molecular shape analysis showed that bulky substitution at the R(1) position may enhance AHAS inhibitory activity. Charged surface area descriptors suggested that negative charge distributed over a large surface area may enhance this activity. The hydrogen bond acceptor parameter supported the charged surface area descriptors and suggested that, for better activity, the number of electronegative atoms present in the molecule should be high. The spatial descriptors show that, for better activity, the molecules should possess a bulky substituent and a small substitution at the R(2) and R(3) positions, respectively.

On two novel parameters for validation of predictive QSAR models

Validation is a crucial aspect of quantitative structure–activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q² for internal validation and predictive R² for external validation) may be supplemented with two novel parameters r_m² and R_p² for a stricter test of validation. The parameter r_m²_(overall) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter R_p² penalizes model R² for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of r_m² parameter, r_m²_(LOO) and r_m²_(test), penalize a model more strictly than Q² and R²_pred respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q² and R²_pred) but fail to achieve the required values for the novel parameters r_m² and R_p². Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

Comparative QSAR studies of CYP1A2 inhibitor flavonoids using 2D and 3D descriptors

Comparative Quantitative Structure Activity Relationship (QSAR) analyses have been performed with 21 naturally occurring flavonoids for their inhibitory effects on cytochrome P450 1A2 enzyme using two-dimensional (topological, structural, and thermodynamic) and three-dimensional (spatial) descriptors. The chemometric tools used for the analyses are stepwise multiple linear regression, partial least squares, genetic function approximation, and genetic partial least squares. The data set was divided into a training set (n = 15) and test set (n = 6), based on K-means clustering technique applied on standardized two-dimensional descriptor matrix, and models were developed from the training set compounds. The best model (genetic partial least squares model using two-dimensional descriptors) was selected based on the highest external predictive R(2) (R(2)(pred)) value (0.840) and the lowest root mean square error of prediction value (0.351). The developed QSAR equations suggest the importance of the double bond present at 2 and 3 positions and requirement of absence of hydroxyl substituent or glycosidic linkage at 3 position of the 1,4-benzopyrone nucleus. Furthermore, the phenyl ring present at 2 position of the 1,4-benzopyrone ring should not be substituted with hydroxyl group. Moreover, hydroxyl groups present at 5 and 7 positions of the benzopyran nucleus should not be glycosylated for good cytochrome P450 1A2 enzyme inhibitory activity.