152 research outputs found
Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies
The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies
QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.
Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents
NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN
Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening
Molecular Modeling Studies of Curcumin Analogs as Anti-Angiogenic Agents
Angiogenesis plays a pivotal role in the metastasis of cancer: curcumin showed excellent anti-angiogenesis activity on metastatic tumors. Several curcumin analogues have been synthesized and studied, and their biological activity was reported in the literature. One class of potent analogues are aromatic enones. In Dr Bowen's laboratory sixty three compounds were synthesized and in the laboratory of Dr Jack Arbizer (Emory University, Atlanta, GA) they were tested for their anti-angiogenic activity with an SVR endothelial cell growth assay developed by Dr Arbizer. The precise mechanism or the specific biological target on which these analogs exert their inhibition potential as anti-angiogenic agents is unknown. Therefore, structure-based molecular modeling is not a possibility. However, ligand based molecular modeling methods are available for studying and predicting which compounds among the sixty three can be further optimized for selectivity and desired property.
Computational studies were carried out to identify which structural features within the series of analogues are significantly important for activity. Initially, pharmacophore modeling was carried out in Molecular Operating Environment (MOE) software to identify the Interaction Pharmacophore Elements (IPE) and their relative geometry in three-dimensional space. Two different three dimensional quantitative structural Activity Relationship (3D-QSAR) studies, Comparative Molecular Field Analysis (CoMFA), and Comparative Molecular Similarity Indices Analysis (CoMSIA) were carried out with this dataset. SYBYL (versions 7.2 and 7.3) were used for the development of the models. Forty six compounds were used as the calibration or the training set. The model yielded a cross validated q2 of 0.289 for CoMFA and 0.146 for CoMSIA analyses. Eleven compounds were used as the test set (or the prediction) set to externally validate the QSAR models and their robustness. The predictions of the model are acceptable with a few outliers
Pharmacophore derivation using discotech and comparison of semi-emperical, AB initio and density functional CoMFA studies for sigma 1 and sigma 2 receptor-ligands
This study describes the development of pharmacophore and CoMFA models for sigma receptor ligands. CoMFA studies were performed for 48 bioactive sigma 1 receptorligands using [H3 ](+) pentazocine as the radioligand, for 30 PCP derivatives for sigma 1 receptor-ligands using [3H](+)SK-F 10047 as the radioligand and for 24 bioactive sigma 2 receptor-ligands using the radioligand [H3](+)DTG in the presence of pentazocine. Distance Comparisons (DISCOtech) was used as the starting point for CoMFA studies. The conformers, derived by DISCOtech were optimized using AMi, or HF/3-21G* in Gaussian 98. The optimized geometries were aligned with the pharmacophore, derived using DISCOtech. Atomic charges were calculated using AMl, HF/3-21G*, B3LYP/3-21G*, MP2/3-21G* methods in Gaussian 98. The CoMFA Maps that were developed using Sybyl 6.9 were compared on steric and electrostatic field differences. With leaveone-out cross validation the numbers of optimal components were decided. Using these numbers of optimal components no cross validation was performed in a training set. After a test set, it was known that CoMFA models derived from HF/3-21G* optimized geometries were more reliable in predicting bioactivities than CoMFA models derived from AMi optimized geometries
Rational Drug Design for Neglected Diseases: Implementation of Computational Methods to Construct Predictive Devices and Examine Mechanisms
Over a billion individuals worldwide suffer from neglected diseases. This equates to approximately one-sixth of the human population. These infections are often endemic in remote tropical regions of impoverished populations where vectors can flourish and infected individuals cannot be effectively treated due to a lack of hospitals, medical equipment, drugs, and trained personnel. The few drugs that have been approved for the treatments of such illnesses are not widely used because they are riddled with inadequate implications of cost, safety, drug availability, administration, and resistance. Hence, there exists an eminent need for the design and development of improved new therapeutics. Influential world-renowned scientists in the Consortium for Parasitic Drug Development (CPDD) have preformed extensive biological testing for compounds active against parasites that cause neglected diseases. These data were acquired through several collaborations and found applicable to computational studies that examine quantitative structure-activity relationships through the development of predictive models and explore structural relationships through docking. Both of these in silico tools can contribute to an understanding of compound structural importance for specific targets. The compilation of manuscripts presented in this dissertation focus on three neglected diseases: trypanosomiasis, Chagas disease, and leishmaniasis. These diseases are caused by kinetoplastid parasites Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp., respectively. Statistically significant predictive devices were developed for the inhibition of the: (1) T. brucei P2 nucleoside transporter, (2) T. cruzi parasite at two temperatures, and (3) two species of Leishmania. From these studies compound structural importance was assessed for the targeting of each parasitic system. Since these three parasites are all from the Order Kinetoplastida and the kinetoplast DNA has been determined a viable target, compound interactions with DNA were explored to gain insight into binding modes of known and novel compounds
Review of QSAR Models and Software Tools for predicting Biokinetic Properties
In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools.
While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog
Recent Trends in In-silico Drug Discovery
A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed
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