Adam Buciński

Hydrophobicity parameter from high-performance liquid chromatography on an immobilized artificial membrane column and its relationship to bioactivity

Abstract There are numerous measures of hydrophobicity employed in the quantitative structure-bioactivity relationship studies. Individual bioactivity parameters may appear best predicted by the specific hydrophobicity parameters. Introduction of an original reversed-phase material, the immobilized artificial membranes (IAMs) opened new perspectives for bioactivity predictions in the case of the hydrophobicity-driven processes. Comparative studies of performance of the HPLC-derived hydrophobicity measures demonstrate the advantages of the IAM-derived hydrophobicity parameters in predicting the human skin permeation by non-ionizable agents as exemplified by steroid hormones. On the other hand, the skin permeation data of agents ionized at physiological pH appeared less dependent of the retention on IAMs. The IAM columns increase the means of characterization of various aspects of hydrophobicity. Their advantages over the slow equilibrium methods of hydrophobicity determination are the simplicity of operation and the suitability of the generated retention measures for predicting specific bioactivity parameters.

Chromatographic hydrophobicity parameter determined on an immobilized artificial membrane column: relationships to standard measures of hydrophobicity and bioactivity

A dynamic chromatographic hydrophobicity parameter, log k′IAM was determined for 3 test series of drug solutes (β-adrenolytics, imidazoline α-adrenomimetics and phenothiazine neuroleptics) employing a reversed-phase high-performance liquid chromatographic (RP HPLC) system comprising an immobilized artificial membrane (IAM) column. The log k′IAM parameter determined with buffers of physiological pH correlated well with the ionization-corrected reference hydrophobicity parameters from the n-octanol/water system in the case of β-adrenolytics (r = 0.962) but a poorer correlation (r = 0.839) was noted in the case of phenothiazines. Regression analysis of several pharmacokinetic data reported for the agents under study in terms of hydrophobicity parameters demonstrated the performance of log k′IAM to be as good as that of the reference hydrophobicity parameters in predicting bioactivity. There is an important advantage of the log k′IAM parameter over n-octanol/water partition data, which are tedious to determine: log k′IAM is derived in a simple, fast and reproducible manner.

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

Quantitative structure-retention relationship (QSRR) is a technique capable of improving the identification of analytes by predicting their retention time on a liquid chromatography column (LC) and/or their properties. This approach is particularly useful when LC is coupled with a high-resolution mass spectrometry (HRMS) platform. The main aim of the present study was to develop and describe appropriate QSRR models that provide usable predictive capability, allowing false positive identification to be removed during the interpretation of metabolomics data, while additionally increasing confidence of experimental results in doping control area. For this purpose, a dataset consisting of 146 drugs, metabolites and banned compounds from World Anti-Doping Agency (WADA) lists, was used. A QSRR study was carried out separately on high quality retention data determined by reversed-phase (RP-LC-HRMS) and hydrophilic interaction chromatography (HILIC-LC-HRMS) systems, employing a single protocol for each system. Multiple linear regression (MLR) was applied to construct the linear QSRR models based on a variety of theoretical molecular descriptors. The regression equations included a set of three descriptors for each model: ALogP, BELe6, R2p and ALogP(2), FDI, BLTA96, were used in the analysis of reversed-phase and HILIC column models, respectively. Statistically significant QSRR models (squared correlation coefficient for model fitting, R(2)=0.95 for RP and R(2)=0.84 for HILIC) indicate a strong correlation between retention time and the molecular descriptors. An evaluation of the best correlation models, performed by validation of each model using three tests (leave-one-out, leave-many-out, external tests), demonstrated the reliability of the models. This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.

Polish validation of the TEMPS-A: the profile of affective temperaments in a college student population.

BACKGROUND AND AIMS The TEMPS-A scale is a self-evaluation measure to assess five affective temperaments: depressive, cyclothymic, hyperthymic, irritable and anxious. The scale has already been validated in over 10 languages. In this paper, the first report on the validation of the Polish version of TEMPS-A is presented. METHODS The TEMPS-A questionnaire version that includes 110 questions has been adapted following the translation-back translation methodology from English to Polish, checked by the originators of the five scales (H.S.A., K.K.A.). In the next step, the Polish version of TEMPS-A was administered to 521 Polish undergraduate students. Internal consistency of temperamental scales was measured with Cronbach-alpha coefficients. Correlation among the temperaments was examined using Pearsons bivariate correlation. Differences between sexes were tested with ANOVA. RESULTS The Cronbach-alpha and the Kuder-Richardson 20 reliability coefficients for the depressive, cyclothymic, hyperthymic, irritable and anxious temperaments were between 0.69 and 0.83. The percentage of subjects whose Z-scores were above 2 SD, was the highest among depressive (4%) and anxious (3.5%) temperaments, followed by the cyclothymic (2.9%), hyperthymic (1%), and irritable (0.6%). The strongest positive correlations between the temperamental scales were found between depressive and anxious, as well as between cyclothymic and irritable ones (correlation coefficients 0.63 and 0.57, respectively). Male subjects attained significantly higher scores for hyperthymic temperament, compared to females, while females scored significantly higher than males on cyclothymic and anxious temperaments. LIMITATIONS Our healthy young subjects are not representative of the Polish population. As external validation has been achieved in other language versions, it was not repeated in the present Polish version. CONCLUSIONS The Polish version of TEMPS-A has a good internal consistency. The findings generally cohere with those from previously validated versions in other languages.

Isolation of xanthone and benzophenone derivatives from Cyclopia genistoides (L.) Vent. (honeybush) and their pro-apoptotic activity on synoviocytes from patients with rheumatoid arthritis.

A fast and efficient method for the isolation of the C-glucosidated xanthones mangiferin and isomangiferin from the South-African plant Cyclopia genistoides was developed for the first time. The procedure involved extraction, liquid-liquid partitioning with ethyl acetate and subsequent precipitation of mangiferin and isomangiferin from methanol and acetonitrile-water fractions, respectively. Additionally, two benzophenone derivatives: 3-C-β-glucosides of maclurin and iriflophenone, were isolated from C. genistoides extracts using semi-preparative HPLC. Apart from the above, the isolation procedure also yielded hesperidin and small amounts of luteolin. The structures of the compounds were determined by 1D and 2D NMR experiments and/or LC-DAD-ESI-MS. The selected Cyclopia constituents were screened for pro-apoptotic activity on TNF-α-stimulated synovial cells isolated from rheumatoid arthritis patients. The strongest effect, measured as percent of apoptotic cells, was recorded for isomangiferin (75%), followed by iriflophenone 3-C-β-glucoside (71%), hesperidin (67%) and mangiferin (65%). The results are encouraging for further studies on the use of the above compounds in the treatment of rheumatoid arthritis.

Magnetic beads method for determination of binding of drugs to melanin

Binding to melanin is considered to be a reason for several adverse effects of drugs and should be known to reduce the failure rate due to inappropriate pharmacokinetics in search for better pharmaceuticals. A new, reliable and convenient method of determination of affinity of drugs and drug candidates to melanin has been proposed employing magnetic beads. For that aim the reaction conditions to effectively covalently immobilize melanin on surface of superparamagnetic beads have been determined. Binding efficiency of melanin towards antipsychotic and other basic drugs has been determined and compared to that obtained in the affinity HPLC systems employing aminopropylsilica stationary phases with immobilized melanin. The magnetic beads method provided melanin binding data correlating well with the ability of agents to evoke extrapyramidal symptoms. Quantitative structure-property relationships have been derived describing the melanin binding efficiency in terms of structural descriptors of drugs from calculation chemistry. Thus, an approach has been proposed to evaluate a priori melanin binding potency of drug candidates based solely on their chemical formula.

A protein-coated magnetic beads as a tool for the rapid drug-protein binding study.

A simple and fast method for the determination of the association constant (K(a)) of ligand to human serum albumin (HSA) has been developed by using human serum albumin-coated magnetic beads (HSA-MB). To date, magnetic beads (MB) have been increasingly used as a bioseparation tool, especially for DNA, RNA, protein, enzyme and cell isolation or purification. In this study, HSA-MB were used as a new tool to determine the affinity of known ligands to HSA. The K(a) for l-tryptofan, fenoprofen, ketoprofen, tolbutamide and warfarin obtained from Schathard analysis are consistent with previously reported values. The different K(a) values for ketoprofen after the acetylation of HSA-MB by preincubation with acetylosalicylic acid indicate that these beads can be successfully adapted in combined experiment. In addition, the HSA-MB experiment with phenytoin and valproic acid proved to be a simple method to examine drug displacement effect.

Artificial neural networks in prediction of antifungal activity of a series of pyridine derivatives against Candida albicans.

Quantitative structure-activity relationships (QSAR) studies of antifungal activity against Candida albicans of a large series of new pyridine derivatives were conducted with the use of artificial neural networks (ANNs). The application of ANNs has been provided with respect to the prediction of antimicrobial potency of new pyridine derivatives based on their structural descriptors generated by calculation chemistry. Antifungal activity against C. albicans has been related to a number of physicochemical and structural parameters of the pyridine derivatives investigated. The activity was expressed as logarithm of the reciprocal of the minimal inhibitory concentrations, log 1/MIC. Molecular descriptors of agents were obtained from structure fragment reference databases and by quantum-chemical calculations combined with molecular modeling. A high correlation resulted between the ANN predicted antifungal activity, log 1/MIC(pred), and that one from biological experiments, log 1/MIC(exp), for the data used in the testing set of pyridine was obtained with correlation coefficient, R, on the level of 0.9112.

Artificial neural networks analysis used to evaluate the molecular interactions between selected drugs and human α1-acid glycoprotein

Quantitative structure-retention relationships (QSRR) were proposed for alpha(1)-acid glycoprotein (AGP) column using physicochemical molecular descriptors of the selected drugs and interacting with that column. The set of 52 structurally diverse drug compounds, with experimentally derived logarithms of retention factors (log k) values was considered. Thirty-six physicochemical property descriptors were calculated by standard molecular modeling and used to establish QSRR and predict the retention data by artificial neural network (ANN). The QSRR indicated that heat of formation (HF), Moriguchi n-octanol-water partition coefficient (M log P) and the energy of the highest occupied molecular orbital (HOMO) are the most important for interactions between drugs and AGP. The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between log k observed and computed. The final model possessed a 36-5-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.975, 0.950 and 0.972, respectively.

Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy

Summary Background Exploitation of the several types of information on patient, disease and treatment variables ranging from sociological to genetic ones by means of chemometric analysis was considered and evaluated. Aim Performance of modern data processing methods, namely principal component analysis (PCA) and artificial neural network (ANN) analysis, is demonstrated for predictions of the recurrence of breast cancer in patients treated previously with mastectomy. Materials/Methods The data on 718 patients were retrospectively evaluated. 11 subject and treatment variables were determined for each patient. A matrix of 718×11 data points was subjected to PCA and ANN processing. The properly trained ANN was used to predict the patients with recurrence and without recurrence within a 10-year period after mastectomy. Results It was found that the prognostic potency of the trained and validated ANN was reasonably high. Additionally, using the principal component analysis (PCA) method two principal components, PC1 and PC2, were extracted from the input data. They accounted cumulatively for 37.5% of the variance of the data analyzed. An apparent clustering of the variables and patients was observed – these have been interpreted in terms of their similarities and dissimilarities. Conclusions It has been concluded that ANN analysis offers a promising implementation to established methods of statistical analysis of multivariable data on cancer patients. On the other hand, PCA has been recommended as an alternative to classical regression analysis of multivariable clinical data. By means of ANN and PCA practically useful systematic information may be extracted from large sets of data, which can be of value for prognosis and appropriate adjustment of the treatment of breast cancer.