Development and validation of a pharmacophore-based QSAR model for the prediction of CNS activity.
ABSTRACT A QSAR model aimed at predicting central nervous system (CNS) activity was developed based on the structure-activity relationships of compounds from an in-house database of "drug-like" molecules. These compounds were initially identified as "CNS-active" or "CNS-inactive", and pharmacophore 3D descriptors were calculated from multiple conformations for each structure. A linear discriminant analysis (LDA) was performed on this structure-activity matrix, and this QSAR model was able to correctly classify approximately 80 % in both a learning set and a validation set. For validation purposes, the LDA model was applied to compounds for which the blood-brain barrier (BBB) penetration was known, and all of them were correctly predicted. The model was also applied to 960 other in-house compounds for which in vitro binding tests were performed on 20 receptors known to be present at the CNS level, and a high correlation was observed between compounds predicted as CNS-active and experimental hits. Finally, the model was also applied to a set of 700 structures with known CNS activity or inactivity randomly chosen from public sources, and more than 70 % of the compounds were correctly classified, including novel CNS chemotypes. These results demonstrate the applicability of the model to novel chemical structures and its usefulness for designing original CNS-focused compound libraries.
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ABSTRACT: The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug-Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.Current topics in medicinal chemistry 09/2012; DOI:10.2174/156802612803989228 · 4.47 Impact Factor
Value in Health 11/2008; 11(6). DOI:10.1016/S1098-3015(10)66307-3 · 2.89 Impact Factor
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ABSTRACT: Introduction: Ability to cross the blood-brain barrier is one of the key ADME characteristics of all drug candidates regardless of their target location in the body. While good brain penetration is essential for CNS drugs, it may lead to serious side effects in case of peripherally-targeted molecules. Despite a high demand of computational methods for estimating brain transport early in drug discovery, achieving good prediction accuracy still remains a challenging task. Areas covered: This article reviews various measures employed to quantify brain delivery and recent advances in QSAR approaches for predicting these properties from the compound's structure. Additionally, the authors discuss the classification models attempting to distinguish between permeable and impermeable chemicals. Expert opinion: Recent research in the field of brain penetration modeling showed an increasing understanding of the processes involved in drug disposition, although most models of brain/plasma partitioning still rely on purely statistical considerations. Preferably, new models should incorporate mechanistic knowledge since it is the prerequisite for guiding drug design efforts in the desired direction. To increase the efficiency of computational tools, a broader view is necessary, involving rate and extent of brain penetration, as well as plasma and brain tissue binding strength, instead of relying on any single property.Expert Opinion on Drug Metabolism & Toxicology 01/2013; DOI:10.1517/17425255.2013.754423 · 2.94 Impact Factor