Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes

QSPR study on Hydrophobicity of Pt(II) complexes with surface electrostatic potential-based descriptors

Pt(II) complexes play an important role in bioinorganic chemistry due to their antitumor activities. In the present study, we focused on building predictive models for the hydrophobicity of Pt(II) complexes. A five-parameter model, integrating frontier orbital energies (E_HOMO, E_LUMO) and descriptors derived from electrostatic potentials on molecular surface, was firstly constructed by using multiple linear regression (MLR) method. Mechanistic interpretations of the introduced descriptors were elucidated in terms of intermolecular interactions in the n-octanol/water partition system. Then, four up-to-date modeling methods, including support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), were utilized to build the nonlinear models. Systematical validations including leave-one-out cross-validation, the validation for test set, as well as a very rigorous Monte Carlo cross-validation (MCCV) were performed to verify the reliability of the constructed models. The peak, median and integralR_ext² values of the best GP model are 0.88, 0.86 and 0.84, respectively. The root mean squared errors for the test set (RMSEP) of the MLR, SVM, LSSVM and GP models fall in the range of 0.62-0.71. Although they are not superior to prior models built with the use of a number of descriptors, the results are satisfactory. Applicability domain of the model was evaluated.

Mechanism of the Intestinal Absorption of Six Flavonoids from Zizyphi Spinosi Semen Across Caco-2 Cell Monolayer Model

BACKGROUND

Flavonoid compounds are one kind of active ingredients isolated from a traditional Chinese herb Zizyphi spinosae semen (ZSS). Studies have shown that ZSS flavonoids have significant antioxidant effects.

METHODS

In this study, the Caco-2 cell monolayer model was constructed to investigate the intestinal absorption characteristics and mechanism of Isovitexin (IV), Swertisin (ST), Isovitexin-2''-O-β-D-glucopyranoside (IVG), Spinosin (S), 6'''-p-coumaroylspinosin (6-CS) and 6'''-feruloylspinosin (6-FS).

RESULTS

The results of the bidirectional transport assay showed that the six flavonoids have good intestinal absorption in a near-neutral and 37°C environment, and the absorbability in descending order was 6-FS>6- CS>IVG>S>IV>ST. The results of carrier inhibition experiments and transport kinetics indicated that the absorption mechanism of six flavonoids was energy-dependent monocarboxylate transporter (MCT)-mediated active transport. In particular, the para-cellular pathway also participated in the transport of IV, ST, IVG and S. Furthermore, the efflux process of six flavonoids was mediated by P-glycoprotein (P-gp) and multidrug resistance protein (MRP), which may result in a decrease of bioavailability.

CONCLUSION

Our findings provide significant information for revealing the relationship between the intestinal absorption mechanism of flavonoids and its structure as well as laying a basis for the research of flavonoid preparations.

Prediction of the hydrophobicity of platinum(IV) complexes based on molecular surface properties

A quantitative structure-property relationship (QSPR) study was performed for predicting the hydrophobicity of Pt(IV) complexes. Two four-parameter equations, one based solely on structural descriptors derived from electrostatic potentials (ESPs) on molecular surface, and the other integrated ESP descriptors with molecular surface area (A_S), were firstly constructed. Mechanistic interpretations of the structural descriptors introduced were elucidated in terms of solute-solvent intermolecular interactions. Subsequently, several up-to-date modeling techniques, including support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), were utilized to build the nonlinear models. Systematical validations including leave-one-out cross-validation, the validation for test set, as well as a more rigorous Monte Carlo cross-validation were performed to verify the reliability of the constructed models. The predictive performances of the four different nonlinear modeling methods follow the order of LSSVM≈GP > RF > SVM. The pure-ESP-based models are generally inferior to the A_S-integrated ones. Comparisons with previous results were made.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Background:

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods:

Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results:

The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion:

The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

Use of the index of ideality of correlation to improve models of eco-toxicity

Persistent organic pollutants are compounds used for various everyday purposes, such as personal care products, food, pesticides, and pharmaceuticals. Decomposition of considerable part of the above pollutants is a long-time process. Under such circumstances, estimation of toxicity for large arrays of organic substances corresponding to the above category of pollutants is a necessary component of theoretical chemistry. The CORAL software is a tool to establish quantitative structure-activity relationships (QSARs). The index of ideality of correlation (IIC) was suggested as a criterion of predictive potential of QSAR. The statistical quality of models for eco-toxicity of organic pollutants, which are built up, with use of the IIC is better than statistical quality of models, which are built up without use of data on the IIC.