Lipofilnost salicilamida

Molecular lipophilicity was studied using salicylamide as a model drug. Log P value for the target compound was experimentally determined by the "shake-flask" method and calculated using nine different computer programs based on atom/fragment contributions, structural parameters, atom-type electrotopological-state indices and neural network modeling, and on topological structure descriptors. Our analysis demonstrates good agreement between the experimentally observed log P value of salicylamide and the value calculated by the CSLogP program, based on topological structure descriptors and electrotopological indices.U radu je dan pregled istraživanja molekularne lipofilnosti na primjeru salicamida. Log P vrijednost određena je eksperimentalnom («shake-flash») metodom i izračunata je pomoću devet različitih računalnih programa koji se temelje na atom/fragmentarnoj metodi, strukturnim parametrima, atom elektrotopologijskim indeksima uz modeliranje putem neuronskih mreža i topologijskim deskriptorima. Statistička obrada dobivenih rezultata pokazala je najbolju korelaciju eksperimentalno dobivene vrijednosti s log P vrijednošću dobivenom računalnim programoma CslogP, koji se temelji na topologijskim deskriptorima i elektrotopologijskim indeksima

Lipofilnost salicilamida

Molecular lipophilicity was studied using salicylamide as a model drug. Log P value for the target compound was experimentally determined by the "shake-flask" method and calculated using nine different computer programs based on atom/fragment contributions, structural parameters, atom-type electrotopological-state indices and neural network modeling, and on topological structure descriptors. Our analysis demonstrates good agreement between the experimentally observed log P value of salicylamide and the value calculated by the CSLogP program, based on topological structure descriptors and electrotopological indices.U radu je dan pregled istraživanja molekularne lipofilnosti na primjeru salicamida. Log P vrijednost određena je eksperimentalnom («shake-flash») metodom i izračunata je pomoću devet različitih računalnih programa koji se temelje na atom/fragmentarnoj metodi, strukturnim parametrima, atom elektrotopologijskim indeksima uz modeliranje putem neuronskih mreža i topologijskim deskriptorima. Statistička obrada dobivenih rezultata pokazala je najbolju korelaciju eksperimentalno dobivene vrijednosti s log P vrijednošću dobivenom računalnim programoma CslogP, koji se temelji na topologijskim deskriptorima i elektrotopologijskim indeksima

Machine learning approach in pharmacokinetics and toxicity prediction

Ph.DDOCTOR OF PHILOSOPH

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

Ph.DDOCTOR OF PHILOSOPH

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

With the aim of solubility estimation in water, polyethylene glycol 400 (PEG) and their binary mixtures, quantitative structure-property relationships (QSPRs) were investigated to relate the solubility of a large number of compounds to the descriptors of the molecular structures. The relationships were quantified using linear regression analysis (with descriptors selected by stepwise regression) and formal inference-based recursive modeling (FIRM). The models were compared in terms of the solubility prediction accuracy for the validation set. The resulting regression and FIRM models employed a diverse set of molecular descriptors explaining crystal lattice energy, molecular size, and solute-solvent interactions. Significance of molecular shape in compound's solubility was evident from several shape descriptors being selected by FIRM and stepwise regression analysis. Some of these influential structural features, e.g. connectivity indexes and Balaban topological index, were found to be related to the crystal lattice energy. The results showed that regression models outperformed most FIRM models and produced higher prediction accuracy. However, the most accurate estimation was achieved by the use of a combination of FIRM and regression models. The results also showed that the use of melting point in regression models improves the estimation accuracy especially for solubility in higher concentrations of PEG. Aqueous or PEG/water solubilities can be estimated by these models with root mean square error of below 0.70. © 2010 Elsevier B.V

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable