Physico-geographical mesoregions of Poland : verification and adjustment of boundaries on the basis of contemporary spatial data

The programme of identification, cataloguing and evaluation of Polish landscapes, part of the implementation of the European Landscape Convention, has caused an increase in interest in physico-geographical regionalisation over recent years. The commonly accepted regionalisation of Poland developed by J. Kondracki (Kondracki & Richling 1994) is sufficient for work at an overview scale (e.g. 1:500,000), whereas its spatial accuracy is too low to make use of it for the purpose of Polish landscape cataloguing. The aim of this article is to present a more up-to-date and detailed division of Poland into mesoregions, adjusted to the 1:50,000 scale. In comparison with older work, the number of mesoregions has increased from 316 to 344. In many cases, some far-reaching changes in meso- and macroregions were made. Nevertheless, in most cases the previous system of units was maintained, with more detailed adjustment of boundaries based on the latest geological and geomorphological data and the use of GIS tools for the DEM analysis. The division presented here is a creatively developing new work aligning the proposals of the majority of Polish researchers. At the same time, it is a regionalisation maintaining the idea of the work developed by J. Kondracki as well as his theoretical assumptions and the criteria used to distinguish units, which makes it a logical continuation of his regional division

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts. June 4-7, 2019, Szczyrk, Polan

In silico prediction of blood-brain barrier penetration of drugs

Blood-brain barrier (BBB) is a complex cellular system, which separates the brain and central nervous system (CNS) from the bloodstream. BBB permeability (BBBp) is one of the most important pharmacokinetic properties not only for CNS-active drugs. The brain penetration of CNS-nonactive drugs should be very low to minimize the unwanted CNS side effects. Determination of BBBp of therapeutic compounds is an important component in the design of drugs. Usually the blood-brain partition coefficient (log BB) is used to determine BBB permeability of chemical compounds. Quantitative structure-activity relationship (QSAR) models offer predicting log BB from the molecular structure of a compound. Experimental determination of log BB of the compound is difficult, labour-consuming and time-consuming. It is desirable to predict the blood-brain partition coefficient of compounds from their molecular structures or from physicochemical properties. Various descriptors have been revealed in many studies to be important for predicting BBBp of small molecules via passive diffusion. The most important descriptors usually used to build QSAR models and the QSAR modeling methods were presented in this work. The in silico models based on QSAR are frequently used, but are limited by the restricted accessibility of in vivo data during the early drug discovery phase.Bariera krew-mózg (ang. Blood-brain barrier - BBB) jest złożonym systemem, który oddziela ośrodkowy układ nerwowy (OUN) od krwioobiegu. Zdolność przenikania bariery krew-mózg (ang. Blood-brain barrier permeability – BBBp) stanowi jedną z najważniejszych właściwości farmakokinetycznych dla leków działających ośrodkowo. Równocześnie, poziom przenikania do mózgu leków działających poza OUN powinien być niski, dla uniknięcia ośrodkowych działań niepożądanych. Ustalenie BBBp substancji leczniczej jest ważnym elementem projektowania leków. Najczęściej używanym wskaźnikiem poziomu przenikania jest współczynnik rozdziału pomiędzy mózg i krew (log BB). Modele matematyczne ilościowej zależności pomiędzy strukturą i aktywnością (ang. quantitative structure-activity relationship - QSAR) dają możliwość przewidywania parametru log BB na podstawie badania struktury związku chemicznego. Doświadczalne ustalanie wartości log BB jest trudne, czasochłonne i pracochłonne. Bardzo przydatna jest więc możliwość przewidywania współczynnika rozdziału związku pomiędzy mózg i krew, na podstawie właściwości fizykochemicznych lub ich struktury. Znacząca rola różnych deskryptorów molekularnych w przewidywaniu log BB została udowodniona w wielu doświadczeniach. W niniejszej pracy opisano najważniejsze z parametrów, często używanych do tworzenia modeli QSAR oraz popularne metody modelowania QSAR. Stosowanie modeli in silico, opartych na metodach QSAR, jest bardzo rozpowszechnione. We wstępnej fazie poszukiwania leku użyteczność tych metod jest ograniczona brakiem dostępu do danych z badań in vivo

Development and Validation of Quantitative Structure-Activity Relationship Models for Compounds Acting on Serotoninergic Receptors

A quantitative structure-activity relationship (QSAR) study has been made on 20 compounds with serotonin (5-HT) receptor affinity. Thin-layer chromatographic (TLC) data and physicochemical parameters were applied in this study. RP2 TLC 60F254 plates (silanized) impregnated with solutions of propionic acid, ethylbenzene, 4-ethylphenol, and propionamide (used as analogues of the key receptor amino acids) and their mixtures (denoted as S1–S7 biochromatographic models) were used in two developing phases as a model of drug-5-HT receptor interaction. The semiempirical method AM1 (HyperChem v. 7.0 program) and ACD/Labs v. 8.0 program were employed to calculate a set of physicochemical parameters for the investigated compounds. Correlation and multiple linear regression analysis were used to search for the best QSAR equations. The correlations obtained for the compounds studied represent their interactions with the proposed biochromatographic models. The good multivariate relationships (R2=0.78–0.84) obtained by means of regression analysis can be used for predicting the quantitative effect of biological activity of different compounds with 5-HT receptor affinity. “Leave-one-out” (LOO) and “leave-N-out” (LNO) cross-validation methods were used to judge the predictive power of final regression equations

Statistical Methods in the Study of Protein Binding and Its Relationship to Drug Bioavailability in Breast Milk

Protein binding (PB) is indicated as the factor most severely limiting distribution in the organism, reducing the bioavailability of the drug, but also minimizing the penetration of xenobiotics into the fetus or the body of a breastfed child. Therefore, PB is an important aspect to be analyzed and monitored in the design of new drug substances. In this paper, several statistical analyses have been introduced to find the relationship between protein binding and the amount of drug in breast milk and to select molecular descriptors responsible for both pharmacokinetic phenomena. Along with descriptors related to the physicochemical properties of drugs, chromatographic descriptors from TLC and HPLC experiments were also used. Both methods used modification of the stationary phase, using bovine serum albumin (BSA) in TLC and human serum albumin (HSA) in HPLC. The use of the chromatographic data in the protein binding study was found to be positive —the most effective application of normal-phase TLC and HPLCHSA data was found. Statistical analyses also confirmed the prognostic value of affinity chromatography data and protein binding itself as the most important parameters in predicting drug excretion into breast milk

IAM Chromatographic Models of Skin Permeation

Chromatographic retention factor log kIAM obtained from IAM HPLC chromatography with buffered aqueous mobile phases and calculated molecular descriptors (surface area—Sa; molar volume—VM; polar surface area—PSA; count of freely rotable bonds—FRB; H-bond acceptor count—HA; energy of the highest occupied molecular orbital—EHOMO; energy of the lowest unoccupied orbital—ELUMO; and polarizability—α) obtained for a group of 160 structurally unrelated compounds were tested in order to generate useful models of solutes’ skin permeability coefficient log Kp. It was established that log kIAM obtained in the conditions described in this study is not sufficient as a sole predictor of the skin permeability coefficient. Simple put, potentially useful models based on log kIAM and readily available calculated descriptors, accounting for 85 to 91% of the total variability, were generated using Multiple Linear Regression (MLR).The models proposed in the study were tested on a group of 20 compounds with known experimental log Kp values

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

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model