Consensus hologram QSAR modeling for the prediction of human intestinal absorption

Consistent in silico models for ADME properties are useful tools in early drug discovery. Here, we report the hologram QSAR modeling of human intestinal absorption using a dataset of 638 compounds with experimental data associated. The final validated models are consistent and robust for the consensus prediction of this important pharmacokinetic property and are suitable for virtual screening applications. (C) 2012 Elsevier Ltd. All rights reserved.

Targeting TRAF6 for Cancer Therapeutical Development

Tumor necrosis factor (TNF)-Receptor Associated Factors (TRAFs) are a family of signal transducer proteins. TRAF6 is a unique member of this family in that it is involved in not only the TNF superfamily, but the toll-like receptor (TLR)/IL-1R (TIR) superfamily. The formation of the complex consisting of Receptor Activator of Nuclear Factor κ B (RANK), with its ligand (RANKL) results in the recruitment of TRAF6, which activates NF-κB, JNK and MAP kinase pathways. TRAF6 is critical in signaling with leading to release of various growth factors in bone, and promotes osteoclastogenesis. TRAF6 has also been implicated as an oncogene in lung cancer and as a target in multiple myeloma. In the hopes of developing small molecule inhibitors of the TRAF6-RANK interaction, multiple steps were carried out. Computational prediction of hot spot residues on the protein-protein interaction of TRAF6 and RANK were examined. Three methods were used: Robetta, KFC2, and HotPoint, each of which uses a different methodology to determine if a residue is a hot spot. These hot spot predictions were considered the basis for resolving the binding site for in silico high-throughput screening using GOLD and the MyriaScreen database of drug/lead-like compounds. Computationally intensive molecular dynamics simulations highlighted the binding mechanism and TRAF6 structural changes upon hit binding. Compounds identified as hits were verified using a GST-pull down assay, comparing inhibition to a RANK decoy peptide. Since many drugs fail due to lack of efficacy and toxicity, predictive models for the evaluation of the LD50 and bioavailability of our TRAF6 hits, and these models can be used towards other drugs and small molecule therapeutics as well. Datasets of compounds and their corresponding bioavailability and LD50 values were curated based, and QSAR models were built using molecular descriptors of these compounds using the k-nearest neighbor (k-NN) method, and quality of these models were cross-validated.

P2Y(1) antagonists: Combining receptor-based modeling and QSAR for a quantitative prediction of the biological activity based on consensus scoring

P2Y(1) is an ADP-activated G protein-coupled receptor (GPCR). Its antagonists impede platelet aggregation in vivo and are potential antithrombotic agents. Combining ligand and structure-based modeling we generated a consensus model (LIST-CM) correlating antagonist structures with their potencies. We docked 45 antagonists into our rhodopsin-based human P2Y(1) homology model and calculated docking scores and free binding energies with the Linear Interaction Energy (LIE) method in continuum-solvent. The resulting alignment was also used to build QSAR based on CoMFA, CoMSIA, and molecular descriptors. To benefit from the strength of each technique and compensate for their limitations, we generated our LIST-CM with a PLS regression based on the predictions of each methodology. A test set featuring untested substituents was synthesized and assayed in inhibition of 2-MeSADP-stimulated PLC activity and in radioligand binding. LIST-CM outperformed internal and external predictivity of any individual model to predict accurately the potency of 75% of the test set.

Fragment-Based QSAR and Molecular Modeling Studies on a Series of Discodermolide Analogs as Microtubule-Stabilizing Anticancer Agents

Inhibition of microtubule function is an attractive rational approach to anticancer therapy. Although taxanes are the most prominent among the microtubule-stabilizers, their clinical toxicity, poor pharmacokinetic properties, and resistance have stimulated the search for new antitumor agents having the same mechanism of action. Discodermolide is an example of nontaxane natural product that has the same mechanism of action, demonstrating superior antitumor efficacy and therapeutic index. The extraordinary chemical and biological properties have qualified discodermolide as a lead structure for the design of novel anticancer agents with optimized therapeutic properties. In the present work, we have employed a specialized fragment-based method to develop robust quantitative structure - activity relationship models for a series of synthetic discodermolide analogs. The generated molecular recognition patterns were combined with three-dimensional molecular modeling studies as a fundamental step on the path to understanding the molecular basis of drug-receptor interactions within this important series of potent antitumoral agents.

CP-MLR Directed QSAR Studies on the Antimycobacterial Activity of Functionalised Alkenols - Topological Descriptors in Modeling the Activity

The antimycobacterial activity of nitro/ acetamido alkenol derivatives and chloro/ amino alkenol derivatives has been analyzed through combinatorial protocol in multiple linear regression (CP-MLR) using different topological descriptors obtained from Dragon software. Among the topological descriptor classes considered in the study, the activity is correlated with simple topological descriptors (TOPO) and more complex 2D autocorrelation descriptors (2DAUTO). In model building the descriptors from other classes, that is, empirical, constitutional, molecular walk counts, modified Burden eigenvalues and Galvez topological charge indices have made secondary contribution in association with TOPO and / or 2DAUTO classes. The structure-activity correlations obtained with the TOPO descriptors suggest that less branched and saturated structural templates would be better for the activity. For both the series of compounds, in 2DAUTO the activity has been correlated to the descriptors having mass, volume and/ or polarizability as weighting component. In these two series of compounds, however, the regression coefficients of the descriptors have opposite arithmetic signs with respect to one another. Outwardly these two series of compounds appear very similar. But in terms of activity they belong to different segments of descriptor-activity profiles. This difference in the activity of these two series of compounds may be mainly due to the spacing difference between the C1 (also C6) substituents and rest of the functional groups in them.

Comparison of steroid substrates and inhibitors of P-glycoprotein by 3D-QSAR analysis

Steroid derivatives show a complex interaction with P-glycoprotein (Pgp). To determine the essential structural requirements of a series of structurally related and functionally diverse steroids for Pgp-mediated transport or inhibition, a three-dimensional quantitative structure activity relationship study was performed by comparative similarity index analysis modeling. Twelve models have been explored to well correlate the physiochemical features with their biological functions with Pgp on basis of substrate and inhibitor datasets, in which the best predictive model for substrate gave cross-validated q(2) = 0.720, non-cross-validated r(2) = 0.998, standard error of estimate SEE = 0.012, F = 257.955, and the best predictive model for inhibitor gave q(2) = 0.536, r(2) = 0.950, SEE = 1.761 and F = 45.800. The predictive ability of all models was validated by a set of compounds that were not included in the training set. The physiochemical similarities and differences of steroids as Pgp substrate and inhibitor, respectively, were analyzed to be helpful in developing new steroid-like compounds. (C) 2004 Elsevier B.V. All rights reserved.

3D-QSAR studies on 4-Hydroxyphenylpyruvate Dioxygenase inhibitors by comparative molecular field analysis (CoMFA)

A comparative molecular field analysis (CoMFA) of alkanoic acid 3-oxo-cyclohex-1-enyl ester and 2-acylcyclohexane-1,3-dione derivatives of 4-hydroxyphenylpyruvate dioxygenase inhibitors has been performed to determine the factors required for the activity of these compounds. The substrate's conformation abstracted from dynamic modeling of the enzyme-substrate complex was used to build the initial structures of the inhibitors. Satisfactory results were obtained after an all-space searching procedure, performing a leave-one out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.779 and 0.989, respectively. The results provide the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of new HPPD ligands with predetermined affinities.

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.

Machine learning methods for quantitative structure-property relationship modeling

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

Quantitative structure-activity relationships for a series of inhibitors of cruzain from Trypanosoma cruzi: Molecular modeling, CoMFA and CoMSIA studies

Human parasitic diseases are the foremost threat to human health and welfare around the world. Trypanosomiasis is a very serious infectious disease against which the currently available drugs are limited and not effective. Therefore, there is an urgent need for new chemotherapeutic agents. One attractive drug target is the major cysteine protease from Trypanosoma cruzi, cruzain. In the present work, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted on a series of thiosemicarbazone and semicarbazone derivatives as inhibitors of cruzain. Molecular modeling studies were performed in order to identify the preferred binding mode of the inhibitors into the enzyme active site, and to generate structural alignments for the three-dimensional quantitative structure-activity relationship (3D QSAR) investigations. Statistically significant models were obtained (CoMFA. r(2) = 0.96 and q(2) = 0.78; CoMSIA, r(2) = 0.91 and q(2) = 0.73), indicating their predictive ability for untested compounds. The models were externally validated employing a test set, and the predicted values were in good agreement with the experimental results. The final QSAR models and the information gathered from the 3D CoMFA and CoMSIA contour maps provided important insights into the chemical and structural basis involved in the molecular recognition process of this family of cruzain inhibitors, and should be useful for the design of new structurally related analogs with improved potency. (C) 2009 Elsevier Inc. All rights reserved.

Structural basis for selective inhibition of trypanosomatid glyceraldehyde-3-phosphate dehydrogenase: Molecular docking and 3D QSAR studies

The glycolytic enzyme glyceraldehyde-3 -phosphate dehydrogenase (GAPDH) is as an attractive target for the development of novel antitrypanosomatid agents. In the present work, comparative molecular field analysis and comparative molecular similarity index analysis were conducted on a large series of selective inhibitors of trypanosomatid GAPDH. Four statistically significant models were obtained (r(2) > 0.90 and q(2) > 0.70), indicating their predictive ability for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values were in good agreement with the experimental results. Molecular modeling studies provided further insight into the structural basis for selective inhibition of trypanosomatid GAPDH.

Advanced QSAR Studies on PPAR delta Ligands Related to Metabolic Diseases

PPAR delta is a nuclear receptor that, when activated, regulates the metabolism of carbohydrates and lipids and is related to metabolic syndrome and type 2 diabetes. To understand the main interactions between ligands and PPAR delta, we have constructed 2D and 3D QSAR models and compared them with HOMO, LUMO and electrostatic potential maps of the compounds studied, as well as docking results. All QSAR models showed good statistical parameters and prediction outcomes. The QSAR models were used to predict the biological activity of an external test set, and the predicted values are in good agreement with the experimental results. Furthermore, we employed all maps to evaluate the possible interactions between the ligands and PPAR delta. These predictive QSAR models, along with the HOMO, LUMO and MEP maps, can provide insights into the structural and chemical properties that are needed in the design of new PPAR delta ligands that have improved biological activity and can be employed to treat metabolic diseases.

Pharmacokinetic properties and in silico ADME modeling in drug discovery

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME - absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.

Molecular Surface Features in Modeling the HIV-1 RT Inhibitory Activity of 2-(2,6-Disubstituted phenyl)-3-(substituted pyrimidin-2-yl)-thiazolidin-4-ones

The human immunodeficiency virus-1 reverse transcriptase inhibitory activity of 2-(2,6-disubstituted phenyl)-3-(substituted pyrimidin-2-yl)-thiazolidin-4-ones have been analyzed using combinatorial protocol in multiple linear regression (CP-MLR) with several electronic and molecular surface area features of the compounds obtained from Molecular Operating Environment (MOE) software. The study has indicated the role of different charged molecular surface areas in modeling the inhibitory activity of the compounds. The derived models collectively suggested that the compounds should be compact without bulky substitutions on its peripheries for better HIV-1 RT inhibitory activity. It also emphasized the necessity of hydrophobicity and compact structural features for their activity. The scope of the descriptors identified for these analogues have been verified by extending the dataset with different 2-(disubstituted phenyl)-3-(substituted pyridin-2-yl)-thiazolidin-4-ones. The joint analysis of extended dataset highlighted the information content of identified descriptors in modeling the HIV-1 RT inhibitory activity of the compounds.