Multivariate assessment of anticancer oleanane triterpenoids lipophilicity

Application of Biomimetic Chromatography and QSRR Approach for Characterizing Organophosphate Pesticides

Biomimetic chromatography is a powerful tool used in the pharmaceutical industry to characterize the physicochemical properties of molecules during early drug discovery. Some studies have indicated that biomimetic chromatography may also be useful for the evaluation of toxicologically relevant molecules. In this study, we evaluated the usefulness of the biomimetic chromatography approach for determining the lipophilicity, affinity to phospholipids, and bind to plasma proteins of selected organophosphate pesticides. Quantitative structure-retention relationship (QSRR) models were proposed to understand the structural features that influence the experimentally determined properties. ACD/labs, Chemicalize, and alvaDesc software were used to calculate theoretical descriptors. Multilinear regression was used as the regression type, and feature selection was supported by a genetic algorithm. The obtained QSRR models were validated internally and externally, and they demonstrated satisfactory performance with key statistical parameters ranged from 0.844 to 0.914 for R2 and 0.696-0.898 for R2ext, respectively, indicating good predictive ability.

Modeling of small molecule's affinity to phospholipids using IAM-HPLC and QSRR approach enhanced by similarity-based machine algorithms

Immobilized artificial membrane chromatography (IAM) has been proposed as a more biosimilar alternative to classical lipophilicity measurement. Determination of small molecule's affinity to phospholipids can be supported for predicting their behavior in the human body. Therefore, a better understanding of the molecular interaction mechanism between small xenobiotics and phospholipids can accelerate drug discovery. Here, the quantitative structure-retention relationships (QSRR) approach was integrated with mechanistic descriptors calculated using Chemicalize software to propose an easy-to-interpretation QSRR model. Considering the heterogeneous character of the data set, locally weighted least squares kernel regression belonging to similarity-based machine learning methods have been applied. The results showed that lipophilicity, charge, and maximum projection area determine molecule binding to phospholipids. Full validation of the obtained model based on OECD recommendations has been performed and the applicability domain was defined using the probability-oriented distance-based approach. The high values of predictive squared correlation coefficient (Q2), and small root mean square error of prediction (RMSEP), 0.812 and 6.739, respectively, confirmed that the obtained QSRR model is not well-fitted to the training data but also showed prediction power. Additionally, only 1.5% of molecules from the training set and 2.8% from the validation test are outside the applicability domain, confirming great predictive abilities.

Assessment of Lipophilicity Parameters of Antimicrobial and Immunosuppressive Compounds

Lipophilicity in addition to the solubility, acid-base character and stability is one of the most important physicochemical parameters of a compound required to assess the ADMET properties (absorption, distribution, metabolism, excretion and toxicity) of a bioactive molecule. Therefore, the subject of this work was to determine the lipophilicity parameters of selected antimicrobial and immunosuppressive compounds such as delafloxacin, linezolid, sutezolid, ceftazidime, everolimus and zotarolimus using thin-layer chromatography in reversed phase system (RP-TLC). The chromatographic parameters of lipophilicity (R_MW) for tested compounds were determined on different stationary phases: RP18F₂₅₄, RP18WF₂₅₄ and RP2F₂₅₄ using ethanol, acetonitrile, and propan-2-ol as organic modifiers of mobile phases used. Chromatographically established R_MW values were compared with partition coefficients obtained by different computational methods (AlogPs, AClogP, AlogP, MlogP, XlogP2, XlogP3, logP_KOWWIN, ACD/logP, milogP). Both cluster and principal component analysis (CA and PCA) of the received results allowed us to compare the lipophilic nature of the studied compounds. The sum of ranking differences analysis (SRD) of all lipophilicity parameters was helpful to select the most effective method of determining the lipophilicity of the investigated compounds. The presented results demonstrate that RP-TLC method may be a good tool in determining the lipophilic properties of studied substances. Obtained lipophilic parameters of the compounds can be valuable in the design of their new derivatives as efficient antimicrobial and immunosuppressive agents.

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

Can Immobilized Artificial Membrane Chromatography Support the Characterization of Antimicrobial Peptide Origin Derivatives?

The emergence and spread of multiple drug-resistant bacteria strains caused the development of new antibiotics to be one of the most important challenges of medicinal chemistry. Despite many efforts, the commercial availability of peptide-based antimicrobials is still limited. The presented study aims to explain that immobilized artificial membrane chromatography can support the characterization of antimicrobial peptides. Consequently, the chromatographic experiments of three groups of related peptide substances: (i) short cationic lipopeptides, (ii) citropin analogs, and (iii) conjugates of ciprofloxacin and levofloxacin, with a cell-penetrating peptide were discussed. In light of the discussion of the mechanisms of action of these compounds, the obtained results were interpreted.