Estudo QSPR sobre os coeficientes de partição: descritores mecânico-quânticos e análise multivariada

Quantum chemistry and multivariate analysis were used to estimate the partition coefficients between n-octanol and water for a serie of 188 compounds, with the values of the q 2 until 0.86 for crossvalidation test. The quantum-mechanical descriptors are obtained with ab initio calculation, using the solvation effects of the Polarizable Continuum Method. Two different Hartree-Fock bases were used, and two different ways for simulating solvent cavity formation. The results for each of the cases were analised, and each methodology proposed is indicated for particular case.

Interactions of chitosan/genipin hydrogels during drug delivery: a QSPR approach

A hydrogel comprised of chitosan crosslinked using the low-toxicity crosslinker genipin was prepared, and the absorption of glibenclamide by the hydrogel was investigated. Optimized structures and their molecular electrostatic potentials were calculated using the AM1 method, and the results were used to evaluate the molecular interactions between the three compounds. The quantitative structure-property relationship model was also used to estimate the activity of the chemicals on the basis their molecular structures. In addition, theoretical Fourier transform infrared spectra were calculated to analyze the intermolecular interactions in the proposed system. Finally, the hydrophilicity of the hydrogel and its influence on the absorption process were also estimated.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Solution enthalpies of hydroxylic compounds

Solution enthalpies of adamantan-1-ol, 2-methyl- butan-2-ol, and 3-methylbutan-1-ol have been measured at 298.15 K, in a set of 16 protogenic and non-protogenic solvents. The identification and quantification of solvent effects on the solution processes under study were performed using quantitative-structure property relationships. The results are discussed in terms of solute-solvent-solvent interactions and also in terms of the influence of compound's size and position of its hydroxyl group.

Solution enthalpies of 1,4-dioxane: Study of solvent effects through quantitative structure-property relationships

Solution enthalpies of 1,4-dioxane have been obtained in 15 protic and aprotic solvents at 298.15 K. Breaking the overall process through the use of Solomonov's methodology the cavity term was calculated and interaction enthalpies (Delta H-int) were determined. Main factors involved in the interaction enthalpy have been identified and quantified using a QSPR approach based on the TAKA model equation. The relevant descriptors were found to be pi* and beta, which showed, respectively, exothermic and endothermic contributions. The magnitude of pi* coefficient points toward non-specific solute-solvent interactions playing a major role in the solution process. The positive value of the beta coefficient reflects the endothermic character of the solvents' hydrogen bond acceptor (HBA) basicity contribution, indicating that solvent molecules engaged in hydrogen bonding preferentially interact with each other rather than with 1,4-dioxane. (C) 2013 Elsevier B.V. All rights reserved.

Solution enthalpies of hydroxylic compounds

Solution enthalpies of adamantan-1-ol, 2-methyl- butan-2-ol, and 3-methylbutan-1-ol have been measured at 298.15 K, in a set of 16 protogenic and non-protogenic solvents. The identification and quantification of solvent effects on the solution processes under study were performed using quantitative-structure property relationships. The results are discussed in terms of solute-solvent-solvent interactions and also in terms of the influence of compound's size and position of its hydroxyl group.

Use of quantitative structure-property relationships to study the solvation process of 18-crown-6

Solution enthalpies of 18-crown-6 have been obtained for a set of 14 protic and aprotic solvents at 298.15 K. The complementary use of Solomonov's methodology and a QSPR-based approach allowed the identification of the most significant solvent descriptors that model the interaction enthalpy contribution of the solution process (Delta H-int(A/S)). Results were compared with data previously obtained for 1,4-dioxane. Although the interaction enthalpies of 18-crown-6 correlate well with those of 1,4-dioxane, the magnitude of the most relevant parameters, pi* and beta, is almost three times higher for 18-crown-6. This is rationalized in terms of the impact of the solute's volume in the solution processes of both compounds. (C) 2015 Elsevier B.V. All rights reserved.

Quantum similarity topological indices definition, analysis and comparison with classical molecular topological indices

Es mostra que, gracies a una extensió en la definició dels Índexs Moleculars Topològics, s'arriba a la formulació d'índexs relacionats amb la teoria de la Semblança Molecular Quàntica. Es posa de manifest la connexió entre les dues metodologies: es revela que un marc de treball teòric sòlidament fonamentat sobre la teoria de la Mecànica Quàntica es pot connectar amb una de les tècniques més antigues relacionades amb els estudis de QSPR. Es mostren els resultats per a dos casos d'exemple d'aplicació d'ambdues metodologies

Descripció de propietats moleculars i activitats biològiques emprant l'energia de repulsió electró-electró

En aquest treball es descriu l'ús de les mesures de semblança molecular quàntica (MSMQ) per a caracteritzar propietats i activitats biològiques moleculars, i definir descriptors emprables per a construir models QSAR i QSPR. L'estudi que es presenta consisteix en la continuació d'un treball recent, on es descrivien relacions entre el paràmetre log P i MSMQ, donant així una alternativa a aquest parimetre hidrofòbic empíric. L'actual contribució presenta una nova mesura, capaç d'estendre l'ús de les MSMQ, que consisteix en l'energia de repulsió electró-electró (Vee). Aquest valor, disponible normalment a partir de programari de química quàntica, considera la molècula com una sola entitat, i no cal recórrer a l'ús decontribucions de fragments. La metodologia s'ha aplicat a cinc tipus diferents de compostos on diferents propietats moleculars i activitats biològiques s'han correlacionat amb Vee com a únic descriptor molecular. En tots els casos estudiats, s'han obtingut correlacions satisfactòries.

Sobre o uso de métodos quimiométricos em química combinatória

Combinatorial chemistry has emerged as a tool to circumvent a major problem of pharmaceutical industries to discover new lead compounds. A rapid and massive evaluation of a myriad of newly synthesised compounds can be carried out. Combinatorial synthesis leads to high throughput screening en masse towards another myriad of biological targets. The design of a set of compounds based upon combinatorial chemistry may be envisaged by using of QSPR-SIMCA and QSAR-SIMCA as tools for classification purposes. This work deals with the definition and establishment of a spanned substituent space (SSS) that reduces the analogue numbers with no exclusion of global content. The chemical diversity may be set properly within a specified pharmacological field. This allows a better use of its potentiality without loosing information.

Estudo do coeficiente de partição octanol-água de bifenilas policloradas (PCBs) utilizando parâmetros topológicos

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

Prédiction de la toxicocinétique des composés organiques volatils à partir de leur structure moléculaire

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Développement de modèles prédictifs de la toxicocinétique de substances organiques

Les modèles pharmacocinétiques à base physiologique (PBPK) permettent de simuler la dose interne de substances chimiques sur la base de paramètres spécifiques à l’espèce et à la substance. Les modèles de relation quantitative structure-propriété (QSPR) existants permettent d’estimer les paramètres spécifiques au produit (coefficients de partage (PC) et constantes de métabolisme) mais leur domaine d’application est limité par leur manque de considération de la variabilité de leurs paramètres d’entrée ainsi que par leur domaine d’application restreint (c. à d., substances contenant CH3, CH2, CH, C, C=C, H, Cl, F, Br, cycle benzénique et H sur le cycle benzénique). L’objectif de cette étude est de développer de nouvelles connaissances et des outils afin d’élargir le domaine d’application des modèles QSPR-PBPK pour prédire la toxicocinétique de substances organiques inhalées chez l’humain. D’abord, un algorithme mécaniste unifié a été développé à partir de modèles existants pour prédire les PC de 142 médicaments et polluants environnementaux aux niveaux macro (tissu et sang) et micro (cellule et fluides biologiques) à partir de la composition du tissu et du sang et de propriétés physicochimiques. L’algorithme résultant a été appliqué pour prédire les PC tissu:sang, tissu:plasma et tissu:air du muscle (n = 174), du foie (n = 139) et du tissu adipeux (n = 141) du rat pour des médicaments acides, basiques et neutres ainsi que pour des cétones, esters d’acétate, éthers, alcools, hydrocarbures aliphatiques et aromatiques. Un modèle de relation quantitative propriété-propriété (QPPR) a été développé pour la clairance intrinsèque (CLint) in vivo (calculée comme le ratio du Vmax (μmol/h/kg poids de rat) sur le Km (μM)), de substrats du CYP2E1 (n = 26) en fonction du PC n octanol:eau, du PC sang:eau et du potentiel d’ionisation). Les prédictions du QPPR, représentées par les limites inférieures et supérieures de l’intervalle de confiance à 95% à la moyenne, furent ensuite intégrées dans un modèle PBPK humain. Subséquemment, l’algorithme de PC et le QPPR pour la CLint furent intégrés avec des modèles QSPR pour les PC hémoglobine:eau et huile:air pour simuler la pharmacocinétique et la dosimétrie cellulaire d’inhalation de composés organiques volatiles (COV) (benzène, 1,2-dichloroéthane, dichlorométhane, m-xylène, toluène, styrène, 1,1,1 trichloroéthane et 1,2,4 trimethylbenzène) avec un modèle PBPK chez le rat. Finalement, la variabilité de paramètres de composition des tissus et du sang de l’algorithme pour les PC tissu:air chez le rat et sang:air chez l’humain a été caractérisée par des simulations Monte Carlo par chaîne de Markov (MCMC). Les distributions résultantes ont été utilisées pour conduire des simulations Monte Carlo pour prédire des PC tissu:sang et sang:air. Les distributions de PC, avec celles des paramètres physiologiques et du contenu en cytochrome P450 CYP2E1, ont été incorporées dans un modèle PBPK pour caractériser la variabilité de la toxicocinétique sanguine de quatre COV (benzène, chloroforme, styrène et trichloroéthylène) par simulation Monte Carlo. Globalement, les approches quantitatives mises en œuvre pour les PC et la CLint dans cette étude ont permis l’utilisation de descripteurs moléculaires génériques plutôt que de fragments moléculaires spécifiques pour prédire la pharmacocinétique de substances organiques chez l’humain. La présente étude a, pour la première fois, caractérisé la variabilité des paramètres biologiques des algorithmes de PC pour étendre l’aptitude des modèles PBPK à prédire les distributions, pour la population, de doses internes de substances organiques avant de faire des tests chez l’animal ou l’humain.

Descripció de propietats moleculars i activitats biològiques emprant l'energia de repulsió electró-electró

En aquest treball es descriu l'ús de les mesures de semblança molecular quàntica (MSMQ) per a caracteritzar propietats i activitats biològiques moleculars, i definir descriptors emprables per a construir models QSAR i QSPR. L'estudi que es presenta consisteix en la continuació d'un treball recent, on es descrivien relacions entre el paràmetre log P i MSMQ, donant així una alternativa a aquest parimetre hidrofòbic empíric. L'actual contribució presenta una nova mesura, capaç d'estendre l'ús de les MSMQ, que consisteix en l'energia de repulsió electró-electró (Vee). Aquest valor, disponible normalment a partir de programari de química quàntica, considera la molècula com una sola entitat, i no cal recórrer a l'ús de contribucions de fragments. La metodologia s'ha aplicat a cinc tipus diferents de compostos on diferents propietats moleculars i activitats biològiques s'han correlacionat amb Vee com a únic descriptor molecular. En tots els casos estudiats, s'han obtingut correlacions satisfactòries.