In Silico ADME Modeling 3: Computational Models to Predict Human Intestinal Absorption Using Sphere Exclusion and kNN QSAR Methods

Searching for Potential Novel BCR-ABL Tyrosine Kinase Inhibitors Through G-QSAR and Docking Studies of Some Novel 2-Phenazinamine Derivatives

BACKGROUND

The computational studies on 2-phenazinamines with their protein targets have been carried out to design compounds with potential anticancer activity and selectivity over specific BCR-ABL Tyrosine kinase.

METHODS

This has been achieved through G-QSAR and molecular docking studies. Computational chemistry was done by using VLife MDS 4.3 and Autodock 4.2. 2D and structures of ligands were drawn by using Chemdraw 2D Ultra 8.0 and were converted into 3D. These were optimized by using semi-empirical method called MOPAC. The protein structure was downloaded as PDB file from RCSC protein data bank. PYMOL was used for studying the binding interactions. The G-QSAR models generated were found to possess training (r2=0.8074), cross-validation (q2=0.6521), and external validation (pred_r2=0.5892) which proved their statistical significance. Accordingly, the newly designed series of 2-phenazinamines viz., 3-chloro-4-aryl-1-(phenazin-7-yl) azetidin-2-ones (4a-4e) were subjected to wet lab synthesis. Alternatively, docking studies were also conducted which showed binding interactions of some derivatives with > 30% higher binding energy values than the standard anticancer drug imatinib. The lower energy values obtained for these derivatives indicate energetically favorable interaction with protein binding site as compared to standard imatinib.

RESULTS

G-QSAR and molecular docking studies predicted better anticancer activity for the synthesized azitidine derivatives of 2-phenazinamines (4a-4e) as compared to standard drug.

CONCLUSION

It is therefore surmised that the molecular manipulations at appropriate sites of these derivatives suggested by structure activity relationship data will prove to be beneficial in raising anticancer potential.

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 1352 Drug Compounds

We report a trend analysis of human intravenous pharmacokinetic data on a data set of 1352 drugs. The aim in building this data set and its detailed analysis was to provide, as in the previous case published in 2008, an extended, robust, and accurate resource that could be applied by drug metabolism, clinical pharmacology, and medicinal chemistry scientists to a variety of scaling approaches. All in vivo data were obtained or derived from original references, either through the literature or regulatory agency reports, exclusively from studies utilizing intravenous administration. Plasma protein binding data were collected from other available sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of physicochemical properties, and resultant trends and patterns within the data are presented. In addition, the date of first disclosure of each molecule was reported and the potential "temporal" impact on data trends was analyzed. The findings reported here are consistent with earlier described trends between pharmacokinetic behavior and physicochemical properties. Furthermore, the availability of a large data set of pharmacokinetic data in humans will be important to further pursue analyses of physicochemical properties, trends, and modeling efforts and should propel our deeper understanding (especially in terms of clearance) of the absorption, distribution, metabolism, and excretion behavior of drug compounds.

Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues

A relatively larger dataset consisting of 970 compounds was collected. Classification RF models were established based on different training sets and different descriptors. model validation and evaluation.

A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest

Experimental pEC₅₀s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.

Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods

Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.

QSAR Studies on andrographolide derivatives as α-glucosidase inhibitors

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r² (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r² (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives.

In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set

The prediction of human pharmacokinetics early in the drug discovery cycle has become of paramount importance, aiding candidate selection and benefit-risk assessment. We present herein computational models to predict human volume of distribution at steady state (VD(ss)) entirely from in silico structural descriptors. Using both linear and nonlinear statistical techniques, partial least-squares (PLS), and random forest (RF) modeling, a data set of human VD(ss) values for 669 drug compounds recently published ( Drug Metab. Disp. 2008 , 36 , 1385 - 1405 ) was explored. Descriptors covering 2D and 3D molecular topology, electronics, and physical properties were calculated using MOE and Volsurf+. Model evaluation was accomplished using a leave-class-out approach on nine therapeutic or structural classes. The models were assessed using an external test set of 29 additional compounds. Our analysis generated models, both via a single method or consensus which were able to predict human VD(ss) within geometric mean 2-fold error, a predictive accuracy considered good even for more resource-intensive approaches such as those requiring data generated from studies in multiple animal species.