Use of biomimetic chromatography and in vitro assay to develop predictive GA-MLR model for use in drug-property prediction among anti-depressant drug candidates

Exploration and optimisation of structure-activity relationships of new triazole-based C-terminal Hsp90 inhibitors towards in vivo anticancer potency

The development of new anticancer agents is one of the most urgent topics in drug discovery. Inhibition of molecular chaperone Hsp90 stands out as an approach that affects various oncogenic proteins in different types of cancer. These proteins rely on Hsp90 to obtain their functional structure, and thus Hsp90 is indirectly involved in the pathophysiology of cancer. However, the most studied ATP-competitive inhibition of Hsp90 at the N-terminal domain has proven to be largely unsuccessful clinically. Therefore, research has shifted towards Hsp90 C-terminal domain (CTD) inhibitors, which are also the focus of this study. Our recent discovery of compound C has provided us with a starting point for exploring the structure-activity relationship and optimising this new class of triazole-based Hsp90 inhibitors. This investigation has ultimately led to a library of 33 analogues of C that have suitable physicochemical properties and several inhibit the growth of different cancer types in the low micromolar range. Inhibition of Hsp90 was confirmed by biophysical and cellular assays and the binding epitopes of selected inhibitors were studied by STD NMR. Furthermore, the most promising Hsp90 CTD inhibitor 5x was shown to induce apoptosis in breast cancer (MCF-7) and Ewing sarcoma (SK-N-MC) cells while inducing cause cell cycle arrest in MCF-7 cells. In MCF-7 cells, it caused a decrease in the levels of ERα and IGF1R, known Hsp90 client proteins. Finally, 5x was tested in zebrafish larvae xenografted with SK-N-MC tumour cells, where it limited tumour growth with no obvious adverse effects on normal zebrafish development.

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Modification of gradient HPLC method for determination of small molecules' affinity to human serum albumin under column safety conditions: Robustness and chemometrics study

In the early stages of drug discovery, beyond the biological activity screening, determining the physicochemical properties that affect the distribution of molecules in the human body is an essential step. Plasma protein binding (PPB) is one of the most important investigated endpoints. Nevertheless, the methodology for measuring %PPB is significantly less popular and standardized than other physicochemical properties, like lipophilicity. Here, we proposed how to modify protocols presented by Valko into column safety conditions and evaluated their robustness using fractional factorial design. For robustness testing, four factors were selected: column temperature, mobile phase flow rate, maximum isopropanol concentration in the mobile phase, and buffer pH. Elaborate methods have been applied for the analysis of HSA affinity for three groups of antibiotic-oriented substances that vary in chemical structure: fluoroquinolones, sulfonamides, and tetrazole derivatives. Furthermore, based on the reversed-phase chromatography the workflow of pilot studies was proposed to select molecules that have high affinity to HSA and can not be eluted from the HSA column using the concentration of organic modifier recommended by the column manufacturer.

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.

Quercetin Antagonizes the Sedative Effects of Linalool, Possibly through the GABAergic Interaction Pathway

Sedatives promote calmness or sleepiness during surgery or severely stressful events. In addition, depression is a mental health issue that negatively affects emotional well-being. A group of drugs called anti-depressants is used to treat major depressive illnesses. The aim of the present work was to evaluate the effects of quercetin (QUR) and linalool (LIN) on thiopental sodium (TS)-induced sleeping mice and to investigate the combined effects of these compounds using a conventional co-treatment strategy and in silico studies. For this, the TS-induced sleeping mice were monitored to compare the occurrence, latency, and duration of the sleep-in response to QUR (10, 25, 50 mg/kg), LIN (10, 25, 50 mg/kg), and diazepam (DZP, 3 mg/kg, i.p.). Moreover, an in silico investigation was undertaken to assess this study's putative modulatory sedation mechanism. For this, we observed the ability of test and standard medications to interact with various gamma-aminobutyric acid A receptor (GABA_A) subunits. Results revealed that QUR and LIN cause dose-dependent antidepressant-like and sedative-like effects in animals, respectively. In addition, QUR-50 mg/kg and LIN-50 mg/kg and/or DZP-3 mg/kg combined were associated with an increased latency period and reduced sleeping times in animals. Results of the in silico studies demonstrated that QUR has better binding interaction with GABA_A α3, β1, and γ2 subunits when compared with DZP, whereas LIN showed moderate affinity with the GABA_A receptor. Taken together, the sleep duration of LIN and DZP is opposed by QUR in TS-induced sleeping mice, suggesting that QUR may be responsible for providing sedation-antagonizing effects through the GABAergic interaction pathway.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log Kp) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log Kp model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log Kp data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

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.