Search Results (61)

This research paper focused on the synthesis of 1-[2-hydroxy-3-(phenylcarbamoyloxy)propyl]-4-(R¹, R²-substituted phenyl)piperazin-1-ium chlorides (I)–(III), containing R¹, R² = H, Cl and/or OCH₃, and the evaluation of some of their physicochemical parameters. The in vitro biological investigation of these N-arylpiperazine (NAP) derivatives consisted in assessing their impact on purinergic P2X7-associated signaling, that is, the evaluation of antioxidant, anti-inflammatory and immunomodulatory characteristics. The ultraviolet type C (UVC) irradiation (λ = 254 nm, 0.954 kJ/m²) induced a pronounced stress response in human leukocytes without marked cytotoxicity while maintaining high cell viability (≥90%), as evidenced by increased interleukin (IL)-1β production (94%), elevated IL-1β mRNA expression, enhanced lipid peroxidation (66%), and increased intracellular adenosine 5′-triphosphate (ATP; 97%), respectively. Under basal conditions, these lipophilic NAPs, defined with logarithmic values of retention (capacity) factors corresponding to 100% water in isocratic elution RP-HPLC, i.e., k_w descriptors (varying from 2.3829 to 4.3689), and isocratic chromatographic hydrophobicity index (φ₀) parameters (ranging from 0.7578 to 0.8842), reduced IL-1β production (by 26–63%) and enhanced superoxide dismutase (SOD) activity (up to 64%) without inducing oxidative damage. Under UVC-induced stress, all evaluated compounds decreased lipid peroxidation (up to 45%) and significantly increased antioxidant enzyme activities, including SOD (up to 223%) as well as catalase (up to 145%). The observed effects were associated with changes in intracellular ATP levels and redox-related parameters. In the experiments described in this paper, intracellular ATP was measured so that no direct conclusions could be drawn regarding the extracellular ATP-dependent activation of purinergic receptors, including P2X7. Overall, the results demonstrated that variations within the structure of these NAPs significantly affected compounds’ biological activity, highlighting their potential for further optimization as cytoprotective and anti-inflammatory agents. Full article

Cereal lipids influence both the nutritional value and technological properties of flours; however, their composition remains poorly characterised, particularly in ancient wheat cultivars. This study investigated the lipid fraction of flours from three ancient wheat cultivars: Risciola and Carosella (soft wheat) and Saragolla (durum wheat). Fatty acid and phytosterol profiles were analysed by GC-FID, while ATR-FTIR spectroscopy provided complementary spectral information. Antiradical activity was assessed by DPPH and ABTS assays. In all samples, polyunsaturated fatty acids (PUFAs) predominated (60.23–64.04% of total identified fatty acids), with linoleic acid as the major component. Risciola showed the highest PUFA percentage and the most favourable PUFA/SFA ratio (SFA, saturated fatty acids). β-Sitosterol was the predominant phytosterol in all cultivars, while Saragolla showed a higher percentage of phytostanols (campestanol and sitostanol). Exploratory multivariate analysis provided a visual overview of compositional patterns among cultivars, consistent with differences in lipid profiles within the analysed sample set. ATR–FTIR analysis supported the chromatographic findings, while antiradical assays indicated differences in radical-scavenging capacity. Overall, the combined chromatographic, spectroscopic, and antiradical approach highlights the lipid fraction as an informative descriptor of nutritional quality, cultivar-related compositional diversity, and potential functional relevance, supporting the targeted use of ancient wheat flours in cereal-based applications. Full article

Thiocarbohydrazone derivatives represent a highly significant class in medicinal chemistry, characterized by a versatile scaffold defined with a thiocarbonyl (C=S) core and one or two imine (–C=N–) functionalities, allowing for precise modulation of their physicochemical and biological properties. The biological potential of a series of novel bis-substituted thiocarbohydrazone derivatives was predicted and evaluated through comprehensive in silico analysis. All investigated compounds complied with Lipinski’s Rule of 5, with most also satisfying the Rule of 3 while simultaneously exhibiting favorable pharmacokinetic properties and low predicted ecotoxicity. To substantiate these findings and elucidate the influence of para-substituents, chromatographic behavior of the studied derivatives was evaluated using reversed-phase thin-layer chromatography (RP-TLC). Initial linear regression analysis revealed statistically significant correlations between chromatographic parameters and in silico-derived descriptors of lipophilicity, pharmacokinetics, and ecotoxicity. Furthermore, cluster analysis and principal component analysis provided a robust and unambiguous interpretation of the structure–property relationships, highlighting substituent polarity as the leading factor controlling the bioactivity of bis-substituted thiocarbohydrazones, although the contribution of electronic effects cannot be neglected. Moreover, R_M⁰ correlates with lipophilicity and pharmacokinetics, whereas m reflects ecotoxicity. Collectively, these findings emphasize the critical role of subtle structural variations in shaping the overall properties of these novel derivatives. Full article

This study examines the compositional differentiation of two Cucurbita species, C. maxima Duchesne and C. moschata Duchesne, to identify chemical markers relevant for their nutritional and functional potential. Multivariate statistical analysis, including principal component analysis (PCA), was applied to chromatographic, chemical, and antioxidant descriptors to visualize patterns of variability among samples. Classification artificial neural network (cANN) models were used to explore the potential of machine learning for sample differentiation, using integrated lipidomic, carotenoid, phenolic, and liquid chromatographic datasets, providing a multidimensional biochemical characterization of Cucurbita samples, achieving good classification within the analyzed dataset, reflecting the model’s capacity to describe the available data. The integration of chemometric and ANN approaches provides a framework for the compositional profiling and quality assessment of Cucurbita species, offering insights into their sustainable valorization as sources of bioactive compounds for food and nutraceutical applications while acknowledging the need for further validation on larger datasets. Full article

Modern pesticide risk assessment relies on data on bioaccumulation and acute aquatic toxicity, yet generating such data is labour-intensive and animal-demanding. This study evaluated whether phospholipid affinity of pesticides, quantified by the chromatographic hydrophobicity index CHI_IAM obtained from high-throughput gradient biomimetic chromatography, can serve as a surrogate descriptor of these endpoints. Nineteen pesticides representing different chemical and functional classes were analyzed on IAM.PC.DD2 columns, and CHI_IAM values were determined. Bioconcentration factors (BCF) in fish and acute toxicity data (96 h LC₅₀ for fish, 48 h EC₅₀ for Daphnia magna) were retrieved from the Pesticide Properties DataBase. CHI_IAM ranged from −12.1 to 54.8 and correlated strongly with log₁₀BCF (r = 0.84) and log₁₀LC₅₀ in fish (r = −0.84), and moderately with log₁₀EC₅₀ for Daphnia (r = 0.76). Highly lipophilic pesticides with high CHI_IAM showed elevated BCF and low LC₅₀/EC₅₀ values, whereas polar compounds with low CHI_IAM exhibited negligible bioconcentration and low acute toxicity. Deviations from these trends, for compounds with specific modes of action, highlighted the contribution of mechanisms beyond membrane toxicity. Overall, CHI_IAM measured under high-throughput conditions retains prognostic value for ecotoxicological assessment and may serve as a rapid experimental descriptor to support preliminary screening. Full article

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using machine learning and deep learning. Usually, model predictions do not contain any indication of the possible error (uncertainty) or only one criterion is used for this purpose. The spread of predictions of several models included in the ensemble, and the molecular similarity of the considered molecule and the most “similar” molecule from the training set, are values that allow us to estimate the uncertainty. The Euclidean distance between vectors, calculated based on real-valued molecular descriptors, can be used for the assessment of molecular similarity. Another factor indicating uncertainty is the molecule’s belonging to one of the clusters (data set clustering). Together, all three factors can be used as features for the uncertainty assessment model. Classification models that predict whether a prediction belongs to the worst 15% were obtained. The area under the receiver operating curve value is in the range of 0.73–0.82 for the considered tasks: the prediction of retention indices in gas chromatography, retention times in liquid chromatography, and collision cross-sections in ion mobility spectroscopy. Full article

Lipophilicity is one of the principal parameters that describe the pharmacokinetic behavior of a drug, including its absorption, distribution, metabolism, elimination, and toxicity. In this study, the lipophilicity and other physicochemical, pharmacokinetic, and toxicity properties that affect the bioavailability of newly synthesized dialkylaminoalkyldiquinothiazine hybrids as potential drug candidates are presented. The lipophilicity, as R_M0, was determined experimentally by the RP-TLC method using RP18 plates and acetone–TRIS buffer (pH 7.4) as the mobile phase. The chromatographic parameters of lipophilicity were compared to computationally calculated partition coefficients obtained by various types of programs such as iLOGP, XLOGP3, WLOGP, MLOGP, SILCOS-IT, LogP, logP, and milogP. In addition, the selected ADMET parameters were determined in silico using the SwissADME and pkCSM platforms and correlated with the experimental lipophilicity descriptors. The results of the lipophilicity study confirm that the applied algorithms can be useful for the rapid prediction of logP values during the first stage of study of the examined drug candidates. Of all the algorithms used, the biggest similarity to the chromatographic value (R_M0) for certain compounds was seen with iLogP. It was found that both the SwissADME and pkCSM web tools are good sources of a wide range of ADMET parameters that describe the pharmacokinetic profiles of the studied compounds and can be fast and low-cost tools in the evaluation of examined drug candidates during the early stages of the development process. Full article

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure–Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively. Full article

Lipophilicity is one of the most important properties of compounds required to estimate the absorption, distribution, and transport in biological systems, in addition to solubility, stability, and acid–base nature. It is crucial in predicting the ADME profile of bioactive compounds. The study assessed the usefulness of computational and chromatographic methods (thin-layer chromatography in a reversed-phase system, RP-TLC) for estimating the lipophilicity of 21 newly synthesized compounds belonging to diquinothiazines and quinonaphthiazines. In order to obtain reliable values of the relative lipophilicities of diquinothiazines and quinonaphthiazines, the partition coefficients obtained using different algorithms such as AlogPs, AClogP, AlogP, MLOGP, XLOGP2, XLOGP3, logP, and ClogP were compared with the chromatographic R_M0 values of all the tested compounds measured by the experimental RP-TLC method (logP_TLC). Additionally, logP_TLC values were also correlated with other descriptors, as well as the predicted ADME and drug safety profiling parameters. The linear correlations of logP_TLC values of the tested compounds with other calculated molecular descriptors such as molar refractivity, as well as ADME parameters (Caco-2 substrates, P-gp inhibitors, CYP2C19, and CYP3A4) generally show poor predictive power. Therefore, in silico ADME profiling can only be helpful at the initial step of designing these new candidates for drugs. The compliance of all discussed diquinothiazines and naphthoquinothiazines with the rules of Lipiński, Veber, and Egan suggests that the tested pentacyclic phenothiazine analogs have a chance to become therapeutic drugs, especially orally active drugs. Full article

Chromatographic methods, apart from in silico ones, are commonly used rapid techniques for the evaluation of certain properties of biologically active compounds used for their prediction of pharmacokinetic processes. Thiosemicarbazides are compounds possessing anticancer, antimicrobial, and other valuable biological activities. The aim of the investigation was to estimate the lipophilicity of 1-aryl-4-(phenoxy)acetylthiosemicarbazides, to predict their oral adsorption and the assessment of their % plasma–protein binding (%PPB). RP-HPLC chromatographic techniques with five diversified HPLC systems, including columns with surface-bonded octadecylsilanes (C-18), phosphatidylcholine (immobilized artificial membrane, IAM), cholesterol (Chol), and α1-acid glycoprotein (AGP) and human serum albumin (HSA), were applied. The measured lipophilicity of all investigated compounds was within the range recommended for potential drug candidates. However, some derivatives are strongly bonded to HSA (%PPB ≈ 100%), which may limit some pharmacokinetic processes. HPLC determined lipophilicity descriptors were compared with those obtained by various computational approaches. Full article

Blood–brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/− groups). A number of regression models were constructed in order to observe the connections between the APIs’ physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R² = 0.87. Models for Kp,uu,brain resulted in lower statistics: R² = 0.56 for the group of 23 APIs with the participation of k IAM. Full article

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log K_p) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log K_p model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log K_p 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. Full article

A comparative quantitative structure–retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods. Full article

Oxidative degradation of drugs is one of the major routes of drug substance and drug product instability. Among the diverse routes of oxidation, autoxidation is considered to be challenging to predict and control, potentially due to the multi-step mechanism involving free radicals. C–H bond dissociation energy (C–H BDE) is evidenced to be a calculated descriptor shown to predict drug autoxidation. While computational predictions for the autoxidation propensity of drugs are both swift and possible, no literature to date has highlighted the relationship between the computed C–H BDE and the experimentally-derived autoxidation propensities of solid drugs. The objective of this study is to investigate this missing relationship. The present work is an extension to the previously reported novel autoxidation approach that involves subjecting a physical mixture of pre-milled polyvinyl pyrrolidone (PVP) K-60 and a crystalline drug under high temperature and pressurized oxygen setup. The drug degradation was measured using chromatographic methods. An improved trend between the extent of solid autoxidation and C–H BDE could be observed after normalizing the effective surface area of drugs in the crystalline state, pointing to a positive relationship. Additional studies were conducted by dissolving the drug in N-methyl pyrrolidone (NMP) and exposing the solution under a pressurized oxygen setup at diverse elevated temperatures. Chromatographic results of these samples indicated a similarity in the formed degradation products to the solid-state experiments pointing to the utility of NMP, a PVP monomer surrogate, as a stressing agent for faster and relevant autoxidation screening of drugs in formulations. Full article

Abraham model solute descriptors are reported for the first time for 62 additional C₁₀ through C₁₃ methyl- and ethyl-branched alkanes. The numerical values were determined using published gas chromatographic retention Kováts retention indices for 157 alkane solutes eluted from a squalane stationary phase column. The 95 alkane solutes that have known descriptor values were used to construct the Abraham model KRI versus L-solute descriptor correlation needed in our calculations. The calculated solute descriptors can be used in conjunction with previously published Abraham model correlations to predict a wide range of important physico-chemical and biological properties. The predictive computations are illustrated by estimating the air-to-polydimethylsiloxane partition coefficient for each of the 157 alkane solutes. Full article