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Quantitative Structure-activity Relationships (quantitative + structure-activity_relationships)
Selected Abstracts15th European Symposium on Quantitative Structure-Activity Relationships and Molecular Modelling (Euro-QSAR 2004)MOLECULAR INFORMATICS, Issue 4 2005Esin AKI-SENER No abstract is available for this article. [source] Quantitative Structure-Activity Relationships of Streptococcus pneumoniae MurD Transition State Analogue InhibitorsMOLECULAR INFORMATICS, Issue 6 2004Miha Kotnik Abstract Quantitative structure-activity relationship (QSAR) studies on a set of Streptococcus pneumoniae MurD transition-state inhibitors were performed, using a comprehensive set of molecular descriptors calculated by CODESSA software. Multiple and best multiple linear regressions were applied to generate models for predicting their inhibitory activity. The results (the best model had r2 = 0.8818, s2 = 0.0749, F = 87.04 and r=0.8488) demonstrate the importance of hydrogen bonding and that a matching conformation of ligands for interaction with the enzyme active site is required. [source] Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessmentsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Mike H. I. Comber Abstract The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed. [source] A QSAR analysis of toxicity of Aconitum alkaloidsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 6 2004Angélica M. Bello-Ramírez Abstract Biological activity of Aconitum alkaloids may be related to their toxicity rather than to a specific pharmacological action. A Quantitative structure-activity relationships (QSAR) analysis was performed on the following two groups of alkaloids: compounds with an aroyl/aroyloxy group at R14 position (yunaconitine, bulleyaconitine, aconitine, beiwutine, nagarine, 3-acetyl aconitine, and penduline), and compounds with the aroyloxy group at R4 position (N -deacetyllappaconitine, lappaconitine, ranaconitine, N -deacetylfinaconitine, N -deacetylranaconitine). The LD50 (,mol/kg) of the 12 alkaloids were obtained from the literature. LD50 was significantly lower in group 1 than in group 2. The steric and core,core repulsion energies were significantly higher in group 1. The total energy and heat of formation and electronic energies were significantly lower in group 1. The reactivity index of N, C1,, C4, and C6, were similar between groups. The reactivity index of C2, was significantly higher and the reactivity index of C3, and C5, were significantly lower in group 1. Log P and pKa were similar between groups. Molecular weight was significantly higher in group 1. A significant linear relationship was observed between log LD50 and either analgesic log ED50 or local anesthetic log ED50. The LD50/analgesic ED50 obtained from average values was 5.9 for group 1 and 5.0 for group 2. However, the LD50/local anesthetic ED50 was 40.4 and 318, respectively. The study supports that the analgesic effects of these alkaloids are secondary to their toxic effects whereas alkaloids from group 2 are susceptible to be further studied as local anesthetic agents. [source] Computational modeling of tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone derivatives: An atomistic drug design approach using Kier-Hall electrotopological state (E-state) indicesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 11 2008Nitin S. Sapre Abstract Quantitative structure-activity relationships (QSAR), based on E-state indices have been developed for a series of tetrahydroimidazo-[4,5,1-jk]-benzodiazepinone derivatives against HIV-1 reverse transcriptase (HIV-1 RT). Statistical modeling using multiple linear regression technique in predicting the anti-HIV activity yielded a good correlation for the training set (R2 = 0.913, R = 0.897, Q2 = 0.849, MSE = 0.190, F -ratio = 59.97, PRESS = 18.05, SSE = 0.926, and p value = 0.00). Leave-one-out cross-validation also reaffirmed the predictions (R2 = 0.850, R = 0.824, Q2 = 0.849, MSE = 0.328, and PRESS = 18.05). The predictive ability of the training set was also cross-validated by a test set (R2 = 0.812, R = 0.799, Q2 = 0.765, MSE = 0.347, F -ratio = 64.69, PRESS = 7.37, SSE = 0.975, and p value = 0.00), which ascertained a satisfactory quality of fit. The results reflect the substitution pattern and suggest that the presence of a bulky and electropositive group in the five-member ring and electron withdrawing groups in the seven-member ring will have a positive impact on the antiviral activity of the derivatives. Bulky groups in the six-member ring do not show an activity-enhancing impact. Outlier analysis too reconfirms our findings. The E-state descriptors indicate their importance in quantifying the electronic characteristics of a molecule and thus can be used in chemical interpretation of electronic and steric factors affecting the biological activity of compounds. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [source] Guidelines for developing and using quantitative structure-activity relationshipsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003John D. Walker Abstract Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects. [source] An overview of the use of quantitative structure-activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessmentsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Christine L. Russom Abstract Ecological risk assessments for chemical stressors are used to establish linkages between likely exposure concentrations and adverse effects to ecological receptors. At times, it is useful to conduct screening risk assessments to assist in prioritizing or ranking chemicals on the basis of potential hazard and exposure assessment parameters. Ranking of large chemical inventories can provide evidence for focusing research and/or cleanup efforts on specific chemicals of concern. Because of financial and time constraints, data gaps exist, and the risk assessor is left with decisions on which models to use to estimate the parameter of concern. In this review, several methods are presented for using quantitative structure-activity relationships (QSARs) in conducting hazard screening or screening-level risk assessments. The ranking methods described include those related to current regulatory issues associated with chemical inventories from Canada, Europe, and the United States and an example of a screening-level risk assessment conducted on chemicals associated with a watershed in the midwest region of the United States. [source] Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessmentsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 8 2003Mike H. I. Comber Abstract The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed. [source] Animal use replacement, reduction, and refinement: Development of an integrated testing strategy for bioconcentration of chemicals in fish,INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, Issue 1 2007Watze de Wolf Abstract When addressing the use of fish for the environmental safety of chemicals and effluents, there are many opportunities for applying the principles of the 3Rs: Reduce, Refine, and Replace. The current environmental regulatory testing strategy for bioconcentration and secondary poisoning has been reviewed, and alternative approaches that provide useful information are described. Several approaches can be used to reduce the number of fish used in the Organization for Economic Cooperation and Development (OECD) Test Guideline 305, including alternative in vivo test methods such as the dietary accumulation test and the static exposure approach. The best replacement approach would seem to use read-across, chemical grouping, and quantitative structure-activity relationships with an assessment of the key processes in bioconcentration: Adsorption, distribution, metabolism, and excretion. Biomimetic extraction has particular usefulness in addressing bioavailable chemicals and is in some circumstances capable of predicting uptake. Use of alternative organisms such as invertebrates should also be considered. A single cut-off value for molecular weight and size beyond which no absorption will take place cannot be identified. Recommendations for their use in bioaccumulative (B) categorization schemes are provided. Assessment of biotransformation with in vitro assays and in silico approaches holds significant promise. Further research is needed to identify their variability and confidence limits and the ways to use this as a basis to estimate bioconcentration factors. A tiered bioconcentration testing strategy has been developed taking account of the alternatives discussed. [source] A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR studyJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 10 2010Jingheng Wu Abstract Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q2) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] The Use of Atomic Charges and Orbital Energies as Hydrogen-bonding-donor Parameters for QSAR Studies: Comparison of MNDO, AM1 and PM3 MethodsJOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 6 2000TARAVAT GHAFOURIAN Hydrogen-bonding, important in drug-receptor interactions, also determines the solubility and partitioning of drugs between phases. It is, therefore, important to incorporate the effects of hydrogen-bonding in studies of quantitative structure-activity relationships (QSAR). In this study the atomic charge on the most positively charged hydrogen atom in a molecule and the energy of the lowest unoccupied molecular orbital (LUMO) have been used as a measure of hydrogen-bond-donor capacity. For several hydrogen-bonding acids the Mulliken atomic charges and the energy of the LUMO produced by use of three semi-empirical methods, AM1, PM3 and MNDO, and MNDO electrostatic-potential-derived atomic charges, have been compared in correlations with solvatochromic hydrogen-bonding acidity (,,2H). Atomic charges and LUMO energies, particularly those calculated by use of the AM1 and MNDO methods, were found to correlate well with ,,2H. They were also found to be good models of hydrogen-bonding in QSAR correlations. [source] Current trends in QSAR on NO donors and inhibitors of nitric oxide synthase (NOS),,MEDICINAL RESEARCH REVIEWS, Issue 4 2002Christos A. Kontogiorgis Abstract This article evaluates the quantitative structure-activity relationships (QSAR) of nitric oxide (NO) radical donors and nitric oxide synthases (NOS) inhibitors, using the C-QSAR program of Biobyte. Furoxans, triazines, amidoximes, tetrazoles, imidazoles and N,,2-nitroarylamino acid analogues were included in this survey. In nine out of seventeen cases, the clog P plays a significant part in the QSAR of the NO radical donors and of the NOS inhibition. Many of the compounds must be interacting with a hydrophobic space in a non-specific way. In some cases molecular refractivity CMR/MR as well as sterimol parameters (B1 and L) are important. Electronic effects, with the exception of the Hammett's constant , and the Swain,Lupton parameter F, are not found to govern the biological activity. Stereochemical and electronic features are also found to be important. Indicator variables were used after the best model was found to account for the usual structural features. © 2002 Wiley Periodicals, Inc. Med Res Rev, 22, No. 4, 385,418, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/med.10012 [source] Are Mechanistic and Statistical QSAR Approaches Really Different?MOLECULAR INFORMATICS, Issue 6-7 2010MLR Studies on 158 Cycloalkyl-Pyranones Abstract Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. [source] Structure-Activity Relationships for the Toxicity of Substituted Poly-hydroxylated Benzenes to Tetrahymena pyriformis: Influence of Free Radical FormationMOLECULAR INFORMATICS, Issue 6 2003Tatiana Abstract The aim of this study was to develop quantitative structure-activity relationships for the toxicity to Tetrahymena pyriformis of 30 substituted poly-hydroxylated benzenes. Physico-chemical descriptors for the expression of free radical formation, associated with the OH moiety on the aromatic ring, were calculated. These included one-electron equilibrium constants that did and did not account for the oxidation of an OH-group, homolytic bond dissociation energy (BDE), electronegativity (EN) and absolute hardness (AH), in addition to the distribution coefficient (log D) as a measure of hydrophobicity. The reactivity descriptors were calculated using the semi-empirical AM1 Hamiltonian in the MOPAC molecular orbital software. Statistically significant two-parameter QSARs for toxicity were obtained by combination of log D with either BDE or AH. The QSARs suggested that toxicity is associated with hydrophobicity and the probability of radical formation. [source] | |||||||||||||