Quantitative structure–activity relationship studies of diarylpyrimidine derivatives as anti-HIV drugs using new three-dimensional structure descriptors

A novel three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) was used to describe the chemical structures of 34 wild-type DAPYs, 33 mutant form L100I, 30 mutant form Y181C and 29 mutant form Y188L as anti-HIV drugs. Here four quantitative structure activity relationship models were built by partial least square regression. The estimation stability and prediction ability of models were strictly analyzed by both internal and external validations. The correlation coefficient (R _cum ² ), leave-one-out cross-validation correlation coefficient (Q _CV ² ) and predicted values versus experimental ones of external samples (Q _ext ² ) were 0.925, 0.769 and 0.949 for 34 diarylpyrimidines; 0.899, 0.788 and 0.889 for 33 mutant form L100I; 0.844, 0.761 and 0.935 for 30 mutant form Y181C; 0.890, 0.757 and 0.912 for 29 mutant form Y188L. These values indicated that the built PLS models had both favorable estimation stability and good prediction capabilities. Furthermore, the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of DAPY derivatives.     

Molecular modeling studies on structural requirement of diarylpropionitrile for selectivity to estrogen receptor subtypes

Molecular modeling techniques are widely used to discover drug candidates for selective disease. In the present study, ligand-based drug design techniques have been explored to find the structural requirement of diarylpropionitrile derivatives, a group of non-steroidal estrogen receptor (ER) modulators for selective binding to receptor subtypes. 2D/3D quantitative structure activity relationship (QSAR) and pharmacophore space modeling studies have been explored for this purpose. The classical QSAR models (R _α ² ?=?0.870, Q _α ² ?=?0.813, R _α-pred ² ?=?0.636; R _β ² ?=?0.853, Q _β ² ?=?0.745, R _β-pred ² ?=?0.565) show the importance of molecular refractivity, electronic contribution of atoms C₃, C₇, C₁₃ and C₁₄, and R₂ and R₄ substituents (Fig.?1) for specificity. The 3D QSAR, molecular fields (CoMFA, R _α ² ?=?0.999, Q _α ² ?=?0.679, R _α-pred ² ?=?0.678 and R _β ² ?=?0.999, Q _β ² ?=?0.611, R _β-pred ² ?=?0.691) and similarity (CoMSIA, R _α ² ?=?0.999, Q _α ² ?=?0.670, R _α-pred ² ?=?0.686 and R _β ² ?=?0.999, Q _β ² ?=?0.671, R _β-pred ² ?=?0.590) analyses show contour maps of steric, hydrophobic along with hydrogen bond (HB) donor and acceptor are important factors for binding affinity to both α- and β-subtypes. In addition, electronic contribution is crucial for α-subtype binding. Pharmacophore models derive the importance of HB acceptor and donor, aromatic ring, molecular steric, and hydrophobic interactions for selective binding to receptor subtypes. The derived models are correlated with structure-based molecular docking study, explaining the significant interactions between receptor and ligand for selective subtypes binding.

QSAR studies of imidazo[4,5-b]pyridine derivatives as anticancer drugs using RASMS method

A method—RASMS (random sampling analysis on molecular surface)—was used to describe the chemical structures of 65 imidazo[4,5-b]pyridine derivatives as anticancer drugs. Here a quantitative structure activity relationship model was built by multiple linear regression (MLR). The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations. The correlation coefficients of established MLR model, leave-one-out cross-validation, and predicted values versus experimental ones of external samples were r ² = 0.782, Q _CV ²  = 0.737, and r ²(test) = 0.775, respectively. These values indicated that the built MLR model had both favorable estimation stability and good prediction capabilities. Furthermore, satisfactory results showed that RASMS could preferably express the information related to the biological activity of imidazo[4,5-b]pyridine derivatives.     

QSAR study of mGlu5 inhibitors by genetic algorithm-multiple linear regressions

In this study, the quantitative structure–activity relationship (QSAR) model for some pyrazole/imidazole amide derivatives as mGlu5 inhibitors was developed. The data set was split into the training and test subsets, randomly. The most relevant variables were selected using the genetic algorithm (GA) variable selection method. Multiple linear regression (MLR) method was used as a linear model to predict the activity of mGlu5 inhibitors based on compounds in training set. The external set of nine compounds selected out of 47 compounds, and used to evaluate the predictive ability of QSAR model. The built model could give high statistical quantities (R _train ²  = 0.837, Q ² = 0.759, R _test ²  = 0.919) in which proved that the GA-MLR model was a useful tool to predict the inhibitory activity of pyrazole/imidazole amide derivatives. The results suggested that the atomic masses, atomic van der Waals volumes, atomic electronegativities, and the number of imines (aromatic) are the most important independent factors that contribute to the mGlu5 inhibition activity of pyrazole/imidazole amides derivatives.     

QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes

A large series of pyrrolidine amides derivatives as DPP-IV inhibitors was subjected to quantitative structure–activity relationship (QSAR) analysis. These 248 geometrical structures were constructed and optimized at the HF/6-31G* level of theory by the Gaussian program. The 2D–QSAR model was developed from a training set consisting of 186 compounds by the minimum redundancy maximum relevance–sequential floating back–support vector regression method with a good determination coefficient: the squared correlation coefficient (R _train ²  = 0.867) and the tenfold cross-validation squared correlation coefficient (q _train-CV ²  = 0.669). The QSAR model was then tested using an external test set consisting of 62 compounds and provided a satisfactory external predictive ability (R _test ²  = 0.666). 2D–QSAR model is robust and reliable when compared with 3D–QSAR techniques for the analogous compounds. According to the QSAR analysis, the electronic effect plays an important role for the substituents of the pyrrolidine and carbon rings. The study would serve as a guideline in designing more potent and selective drugs against type 2 diabetes.     

Binding site analysis, 3D-QSAR studies, and molecular design of flavonoids derivatives as potent neuraminidase inhibitors

Binding site analysis of flavonoids derivatives indicated that Arg152, Trp178, Ile222, Glu227, and Ala246 were the key residues in the active pocket of 1nnc. Main influencing factors of interactions between flavonoids derivatives and neuraminidase (NA) were hydrogen bond and electrostatic. Meanwhile, 3D-QSAR models of flavonoids derivatives were constructed to understand chemical–biological interactions governing their activities toward NA. The developed 3D-QSAR models were robust and had good predictive capabilities. R ², Q ², R _test ² , and Q _ext ² of the CoMFA and CoMSIA models were 0.816 and 0.929, 0.607 and 0.750, 0.507 and 0.642, and 0.478 and 0.568, respectively. Moreover, hydrogen bonds and electrostatic factors highly contributed to inhibitory activity, which were unanimous in the docking results. In addition, based on the most active sample ID33, seven new compounds with high inhibitory activity and docking score were obtained.     

Improved superaugmented eccentric connectivity indices

Topochemical versions of all the four superaugmented eccentric connectivity indices (denoted by: ^SAc ξ₄ ^c, ^SAc ξ₅ ^c, ^SAc ξ ₆ ^c , and ^SAc ξ ₇ ^c ) were utilized for the development of models for prediction of hiCE and hCE1 inhibitory activities. The values of these topochemical indices were computed for each of the 65 analogs constituting the data set using an in-house computer program. Resulting data was analyzed and suitable models were developed after identification of the active ranges by maximization of moving average with regard to active derivatives. Subsequently, two biological activities were assigned to each analog using proposed models, which were then compared with the reported hiCE and hCE1 inhibitory activities. Statistical significance of topological indices/models was investigated through sensitivity, specificity, and Matthews correlation coefficient (MCC). The overall accuracy of prediction varied from a minimum of 81% for a model based upon ^SAc ξ ₄ ^c to a maximum of 92% in case of a model based upon ^SAc ξ ₅ ^c with regard to hiCE inhibitory activity and from a minimum of 85% for a model based upon ^SAc ξ ₄ ^c to a maximum of 94% in case of a model based upon ^SAc ξ ₇ ^c with regard to hCE1 inhibitory activity. An excellent relationship between new generation superaugmented eccentric connectivity topochemical indices ( ^SAc ξ ₄ ^c , ^SAc ξ ₅ ^c , ^SAc ξ ₆ ^c , and ^SAc ξ ₇ ^c ) and hiCE and hCE1 inhibitory activities can be attributed to the sensitivity of the proposed topological indices toward nature, number, and relative position of heteroatom. High predictability amalgamated with high potency of the active ranges offer proposed models a vast potential for providing lead structures for development of potent and selective hiCE and hCE1 inhibitors.     

A 2D image-based method for modeling some c-Src tyrosine kinase inhibitors

The detailed application of multivariate image analysis method for evaluation of quantitative structure–activity relationship (MIA–QSAR) of some c-Src tyrosine kinase inhibitors is demonstrated here. Partial least squares (PLS) and genetic algorithm principal components general regression neural network (GA–PC–GRNN) methods were applied. The performance of PLS and GA–PC–GRNN were investigated by several validation methods. The resulted PLS model had a high statistical quality (R ² = 0.951 and R _CV ²  = 0.949) for predicting the activity of the compounds. For GA–PC–GRNN model, these values were R ² = 0.850 and R _CV ²  = 0.692. Applicability domain of developed models was considered using leverage and William plots were used to visualize it. PLS method was proved to be more predictive and accurate.     

Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques

β-Amyloid precursor protein cleavage enzyme (BACE) has been shown to be an attractive therapeutic target to control Alzheimer’s disease (AD). Inhibition of β-secretase enzyme can prevent the deposition of Aβ (β-amyloid) peptides, which is thought to be the major cause of AD. The present study has been considered to explore 3D-QSAR, HQSAR, and pharmacophore mapping studies of BACE inhibitors. Contour maps of 3D-QSAR studies (CoMFA: R ² = 0.998, se = 0.067, Q ² = 0.765, R _pred ²  = 0.772, r _m ²  = 0.739; CoMSIA: R ² = 0.992, se = 0.125, Q ² = 0.730, R _pred ²  = 0.713, r _m ²  = 0.687) explain the importance of steric and electrostatic, along with hydrogen-bond (HB) acceptor and donor for binding affinity to BACE. HQSAR study (R ² = 0.941, se = 0.326, Q ² = 0.792, R _pred ²  = 0.713, r _m ²  = 0.709) indicates the important fragments of the molecular fingerprints that might be crucial for binding affinity. Pharmacophore space modeling (R ² = 0.937, rmsd = 0.937, Q ² = 0.935, R _pred ²  = 0.709, r _m ²  = 0.837) describes that HB acceptor, donor, hydrophobic, and steric are the important features for interaction with receptor cavity. Finally, the models are adjudged through the docking study elucidating the interactions between the receptor and the ligand, indicating the structural requirements of potent BACE inhibitors.     

Bayesian Approach to Estimate AUC,Partition Coefficient and Drug Targeting Index for Studies with Serial Sacrifice Design

Purpose

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC ₀ ^∞ and any AUC ₀ ^∞ -based NCA parameter or derivation.

Methods

In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC ₀ ^∞ s and the tissue-to-plasma AUC ₀ ^∞ ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration.

Results

Bayesian NCA approach is accurate and precise in point estimation of the AUC ₀ ^∞ and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods.

Conclusions

This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC ₀ ^∞ -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.     

3D-QSAR analysis of anilinoquinoline inhibitors of colony stimulating factor-1 kinase (cFMS): implementation of field-based molecular alignment

Colony-stimulating factor-1 receptor (cFMS) serves as a binding site for colony-stimulating factor-1 and is primarily involved in the growth and differentiation of monocytes and macrophages. This crucial function of cFMS links it to various immune system-related disease conditions, such as rheumatoid arthritis, cancer, and immune nephritis. Hence, the potent inhibitors of cFMS may serve novel therapeutic benefits for the treatment of mentioned disease conditions. In the present study, a set of 46 anilinoquinoline derivatives was utilized to perform atom-based 3D-QSAR analysis. The best 3D-QSAR model was selected on the basis of the highest value of Q _test ² , i.e., 0.535. The selected model also displayed high values of R _train ² (0.974), Pearson-r (0.826), and the lowest value of SD (0.099). The contour plots generated for different properties helped to understand biological activity variation pattern with structural changes in molecule at appropriate sites. Therefore, the selected 3D-QSAR model and information revealed from it would provide beneficial guidance for the designing of new potent cFMS inhibitors that can further be explored as novel therapeutic agents for various immune system-related disease conditions.     

Molecular modeling studies on 3,4-dihydroquinazolines as trypanothione reductase inhibitors using 3D-QSAR and docking approaches

The trypanothione reductase (TryR) has been used as a key validated target to guide drug discovery for human African trypanosomiasis (HAT). 3D-QSAR and docking studies were performed on a series of 3,4-dihydroquinazolines as TryR inhibitors to establish a molecular model for new drug design. The CoMFA and CoMSIA models resulted from 53 molecules gave r _cv ² values of 0.591 and 0.574, r ² values of 0.968 and 0.943, respectively. The external validation indicated that CoMSIA model with a valid r _m ² value of 0.864 exhibited better predictive power than CoMFA model. 3D contour maps generated from CoMFA and CoMSIA along with the docking analyses have identified several key features responsible for the activity. A set of analogs were proposed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The results can be served as a useful guideline for designing novel 3,4-dihydroquinazoline derivatives with improved activity against human African trypanosomes.     

3D-QSAR study of tyrosine and propanoic acid derivatives as PPARα/γ dual agonists using CoMSIA

The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel therapeutics for Type 2 Diabetes. Three dimensional-quantitative structure–activity relationship approach has been applied to a series of α-substituted 3-phenylpropanoic acid and tyrosine derivatives, reported as PPARα/γ dual agonists. Comparative molecular similarity indices analysis has been employed in correlating pharmacological data available for single enantiomer at individual receptor subtype. Three models: PPARα, PPARγ and PPARdual-model, using sum of individual activities as dependent parameter, are developed with statistically significant r _cv ²  > 0.5 and r _ncv ²  > 0.9 and lower values of standard error of estimation. This information can be used to design and prediction of enantioselective novel PPAR agonists. Activities of two sets of designed new molecules have also been predicted using generated models.     

Clinical applicability of current pharmacokinetic models: Splanchnic elimination of 5-fluorouracil in cancer patients

What can be inferred from limited clinical data by using current models of hepatic elimination? We examined this question by analyzing previously published data on the steady-state uptake of the anticancer agent 5-fluorouracil (5-FU) in seven cancer patients in terms of the venous equilibration model, the undistributed and distributed forms of the sinusoidal perfusion model, and the convection-dispersion model. Because of appreciable extrasplanchnic removal of 5-FU, the value of the steady infusion rate was not used in our analysis. When the data from all patients were pooled by plotting the measured hepatic venous concentration against the measured hepatic arterial concentration, the high concentration data fell on a limiting straight line of slope 1, indicating that at high dose rates elimination of 5-FU in both the liver and gastrointestinal tract was close to saturation. The intercept of this line gave a model-independent estimate of V_max/Q= 48.0±11.6 (SD) μM for the pooled data set, where V_max is the maximum splanchnic elimination rate of 5-FU, and Q is the hepatic blood flow. The low concentration data points fell on a limiting straight line through the origin, from which model-dependent values of the Michaelis constant were determined. The venous equilibration model gave K _m=9.4μM,while the undistributed sinusoidal perfusion model gave K _m ^* =26,5μM. With these values of K _m,both models fit the pooled data equally well. These methods were applied to analyses of the five individual data sets which contained sufficiently high concentration data points. The resulting mean values were V_max/Q=41.0±5.1 (sem) μM,K _m=8.4±1.3μM and K _m ^* =23.2±3.2 μM. However, the splanchnic region is a highly heterogeneous organ system, for which an undistributed analysis provides no more than an upper bound on the Michaelis constant K _m ⁺ (K _m ⁺ ?K _m ^* ).A perfusion model distributed to represent total splanchnic elimination is developed in the Appendix. Using previous estimates of the degree of functional heterogeneity in the liver alone, this model yields K _m ⁺ values for individual patients which have a mean of 20.3±2.8 μM.     

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking of ATP-competitive triazine analogs of human mTOR inhibitors

The mTOR (mammalian target of rapamycin), a serine/threonine kinase has been identified as an important target for cancer. A 3D-QSAR analysis was carried out on 40 triazine based analogs of ATP-competitive mTOR kinase inhibitors. The study includes molecular field analysis (MFA) with G/PLS to predict the steric and electrostatic molecular field requirement for the activity of inhibitors. The QSAR model was developed using a training set of 33 compounds. The analyzed MFA model revealed a good fit, having r ² value of 0.897 and r _cv ² value of 0.718. The predictive power of the model generated was validated using a test set comprising 7 molecules with r _pred ² value of 0.826. The analysis of the best MFA model provided insights into the structure–activity correlation of mTOR kinase inhibitors. Molecular docking studies revealed that all inhibitors bind in the ATP pocket of the kinase domain. Our QSAR model and molecular docking results corroborate with each other and propose directions for the design of new inhibitors with better activity toward mTOR kinase.     

Theoretical studies on benzimidazole derivatives as E. coli biotin carboxylase inhibitors

Biotin carboxylase (AccC) protein plays an essential role in cell wall biosynthesis in majority of bacterial genera. Inhibition of cell wall biosynthesis might be an ideal way to control the bacterial multiplication in the host system. AccC is one of the promising targets for the antibacterial drugs production. The benzimidazole derivatives are hopeful biotin carboxylase inhibitors, which sensitizes to the Escherichia coli (E. coli) and many other bacterial species too. In steam of developing better benzimidazole derivatives, we describe a quantitative pharmacophore model of benzimidazole derivatives using Phase module of Schr?dinger LLC. This model suggested that the following features are essential for ligand binding, i.e., two aromatic rings, two hydrogen bond donors, one hydrogen bond acceptor, and one hydrophobic group. Further, atom-based 3D-QSAR model was constructed using training set of 37 inhibitors. The constructed QSAR model has cross validated co-efficient value of (Q ²) 0.736 and regression co-efficient value of (R ²) 0.937. The external validation indicated that our QSAR model possessed high predicted powers with $ r_{o}^{2} $ value of 0.933, $ r_{\text m}^{2} $ value of 0.876. The best active and least active compounds were docked into the active site of receptor using Glide and hotspots of the active site were analyzed. The QSAR elucidated here for benzimidazole derivatives combined with their binding information will provide an opportunity to explore the chemical space to promote the potency of AccC inhibitors.     

Pharmacophore-based in silico high-throughput screening to identify novel topoisomerase-I inhibitors

Topoisomerase-I (TOP-I) has emerged as a potential target for the design and development of anticancer compounds. TOP-I inhibitors have shown promise in the treatment of various cancers including renal cell cancer, whose exact cause is yet to be known. Recent studies indicate that indenoisoquinolines can provide greater stability to drug-topoisomerase-DNA cleavage complexes, which makes them a more appropriate anticancer class of compounds compared to camptothecin. In view of such significance, a three-dimensional pharmacophore model has been developed using a training set of 36 indenoisoquinoline-based topoisomerase inhibitors. The validated best model consists of three chemical features: one hydrophobic, one positive ionizable, and one ring aromatic with good correlation values of r _(training) ²  = 0.827 and r _(test) ²  = 0.702. Furthermore, 98 % validation by CatScramble method and a good r ² of 0.703 from 22 external test set compounds have testified the universal applicability of the generated model. Validated three feature pharmacophore model has been used to screen the chemical database from the National Cancer Institute (NCI) leading to the identification of 17 druggable TOP-I inhibitors which can be raised into drug candidates after further evaluation.