In silico assessment of risks associated with pesticides exposure during pregnancy.

Novel Quantitative Structure-Activity Relationship (QSAR) models of compounds' placenta (PL) permeability expressed as their log FM (fetus-to-mother blood concentration) values or binary PL1/0 (crossing/non-crossing) score were generated using a number of statistical tools: Multiple Linear Regression, Boosted Trees, Principal Component Analysis and Artificial Neural Networks, on the basis of molecular descriptors calculated by Mordred software and selected using Partial Least Squares (PLS) analysis. It was established that the most important predictor of both log FM and the binary PL1/0 score is Lipinski - a binary variable reflecting the compounds' ability to satisfy the criteria of drug-likeness according to the Lipinski's "Rule of 5". The quantitative (log FM) and qualitative (PL1/0) models of PL permeability were applied to 345 pesticides from different chemical families (triazines, carbamates, pyrethroids, organochlorine, organophosphorus and miscellaneous compounds). The ability of studied pesticides to cross the placenta was assessed; the basic physico-chemical parameters responsible for good or poor placenta transport of pesticides were identified and the relationships between the pesticides' PL permeability, blood-brain barrier (BBB) transfer and gastro-intestinal (GI) absorption were investigated. It was found (on the basis of logistic regression analysis) that the probability of a compound crossing the placenta (PL1) is inversely correlated with its lipophilicity and molar refractivity and positively correlated with the total count of oxygen and nitrogen atoms.

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds.

Chromatographic retention factor log kIAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight - log MW; molar volume - VM; polar surface area - PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds - FRB; H-bond donor count - HD; H-bond acceptor count - HA; energy of the highest occupied molecular orbital - EHOMO; energy of the lowest unoccupied orbital - ELUMO; dipole moment - DM; polarizability - α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes' soil-water partition coefficient normalized to organic carbon log Koc. It was established that log kIAM obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log kIAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log Koc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R2 ≥ 0.80, n = 50) which proves the models' reliability.

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.

RP-18 TLC Chromatographic and Computational Study of Skin Permeability of Steroids.

The skin permeability of steroids, as investigated in this study, is important because some of these compounds are, or could, be used in preparations applied topically. Several models of skin permeability, involving thin layer chromatographic and calculated descriptors, were generated and validated using Kp reference values obtained in silico and then tested on a group of solutes whose experimental Kp values could be found (log Kpexp). The study established that the most applicable log Kp model is based on RP-18 thin layer chromatographic data (RM) and the calculated descriptors VM (molar volume) and PSA (polar surface area). Two less efficient, yet simple, equations based on PSA or VM combined with HD (H-donor count) can be used with caution for rapid, rough estimations of compounds' skin permeability prior to their chemical synthesis.

Evaluation of drug-likeness and ADME properties of sunscreens and preservatives using reversed-phase thin layer chromatographic retention data and calculated descriptors.

RP-18 TLC chromatography was used to evaluate the pharmacokinetic properties (volume of distribution, VD; plasma protein binding, %PPB; the ability to cross the blood-brain barrier expressed as log PS and log BB) of several cosmetic raw materials - sunscreen and preservatives. The majority of these compounds are intended for topical use on skin and their drug-likeness and the ability to cross biological barriers are undesired. The retention parameters RM0, S, PC1 and RM75 % obtained for mobile phases containing six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physicochemical properties (PSA, MW, VM) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' pharmacokinetic properties VD, %PPB and log PS. RM75 % and the novel parameters derived from it (RM75 %/MW and RM75 %/VM) can be considered time- and cost-effective alternatives to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some pharmacokinetic properties investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

RP-18 TLC retention data and calculated physico-chemical parameters as predictors of soil-water partition and bioconcentration of organic sunscreens.

RP-18 TLC chromatography was used to evaluate the impact on the environment (mobility in soil expressed as soil-water partition coefficient, log Koc; bioconcentration factor in aquatic organisms, log BCF) of several cosmetic raw materials - sunscreens, preservatives and vitamins. The retention parameters RM0 (RM extrapolated to zero concentration of an organic modifier in a mobile phase), S (slope), PC1 (1st principal component) and RM75% (single TLC run parameter for mobile phases containing 75% (v/v) of an organic modifier) obtained for six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physico-chemical properties (PSA - polar surface area; MW - molecular weight; VM - molar volume) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' mobility in soil or the affinity for aquatic organisms. The parameters RM0, S and RM75% obtained for THF, dioxane and acetone may be used to investigate even very lipophilic compounds. RM75% is of a little bit limited use but it should be considered a time- and cost-effective alternative to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some environmental parameters investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs.

Several chromatographic parameters (RM0 and S obtained from RP-18 TLC with methanol-pH 7.4 phosphate buffer mobile phases by extrapolation to zero concentration of methanol; Rf and RM obtained from RP-18 TLC with acetonitrile-pH 7.4 phosphate buffer 70:30 v/v as a mobile phase) and calculated molecular descriptors (molecular weight-MW; molar volume-VM; polar surface area-PSA; total count of nitrogen and oxygen atoms-(N+O); H-bond donor count-HD; H-bond acceptor count-HA; distribution coefficient-log D; total energy-ET; binding energy-Eb; hydration energy-Eh; energy of the highest occupied molecular orbital-EHOMO; energy of the lowest unoccupied orbital-ELUMO; electronic energy-Ee; surface area-Sa; octanol-water partition coefficient-log P; dipole moment-DM; refractivity-R, polarizability-α) and their combinations (Rf/PSA, RM/MW, RM/VM) were tested in order to generate useful models of solutes' skin permeability coefficient log Kp. It was established that neither RM0 nor S obtained in the conditions used in this study is a good predictor of the skin permeability coefficient. The chromatographic parameters Rf and Rf/PSA were also unsuitable for this purpose. A simple and potentially useful, purely computational model based on (N+O), log D and HD as independent variables and accounting for ca. 83% of total variability was obtained. The evaluation of parameters derived from RM (RM, RM/MW, RM/VM) as independent variables in log Kp models proved that RM/VM is the most suitable descriptor belonging to this group. In a search for a reliable log Kp model based on this descriptor two possibilities were considered: a relatively simple model based on 5 independent variables: (N+O), log D, RM/VM, ET and Eh and a more complex one, involving also Eb, MW and PSA.

Contribution of repeated infections in asthma persistence from preschool to school age: Design and characteristics of the PreDicta cohort.

BACKGROUND: The PreDicta cohort was designed to prospectively evaluate wheeze/asthma persistence in preschoolers in association with viral/microbial exposures and immunological responses. We present the cohort design and demographic/disease characteristics and evaluate unsupervised and predefined phenotypic subgroups at inclusion. METHODS: PreDicta is a 2-year prospective study conducted in five European regions, including children 4-6 years with a diagnosis of asthma as cases and healthy age-matched controls. At baseline, detailed information on demographics, asthma and allergy-related disease activity, exposures, and lifestyle were recorded. Lung function, airway inflammation, and immune responses were also assessed. Power analysis confirmed that the cohort is adequate to answer the initial hypothesis. RESULTS: A total of 167 asthmatic children (102 males) and 66 healthy controls (30 males) were included. Groups were homogeneous in respect to most baseline characteristics, with the exception of male gender in cases (61%) and exposure to tobacco smoke. Comorbidities and number and duration of infections were significantly higher in asthmatics than controls. 55.7% of asthmatic children had at least one positive skin prick test to aeroallergens (controls: 33.3%, P = .002). Spirometric and exhaled nitric oxide values were within normal limits; only baseline FEV0.5 and FEV1 reversibility values were significantly different between groups. Viral infections were the most common triggers (89.2%) independent of severity, control, or atopy; however, overlapping phenotypes were also common. Severity and control clustered together in an unsupervised analysis, separating moderate from mild disease. CONCLUSIONS: The PreDicta cohort presented no differences in non-asthma related measures; however, it is well balanced regarding key phenotypic characteristics representative of "preschool asthma".

Molecular spectrum of the SPAST, ATL1 and REEP1 gene mutations associated with the most common hereditary spastic paraplegias in a group of Polish patients.

Hereditary spastic paraplegias (HSPs) consist of a heterogeneous group of genetically determined neurodegenerative disorders. Progressive lower extremity weakness and spasticity are the prominent features of HSPs resulting from retrograde axonal degeneration of the corticospinal tracts. Three genetic types, SPG3 (ATL1), SPG4 (SPAST) and SPG31 (REEP1), appear predominantly and may account for up to 50% of autosomal dominant hereditary spastic paraplegias (AD-HSPs). Here, we present the results of genetic testing of the three mentioned SPG genetic types in a group of 216 unrelated Polish patients affected with spastic paraplegia. Molecular evaluation was performed by multiplex ligation-dependent probe amplification (MLPA) and DNA sequencing. Nineteen novel mutations: 13 in SPAST, 4 in ATL1 and 2 in REEP1, were identified among overall 50 different mutations detected in 57 families. Genetic analysis resulted in the identification of molecular defects in 54% of familial and 8.4% of isolated cases. Our research expanded the causative mutations spectrum of the three most common genetic forms of HSPs found in a large cohort of probands originating from the Central Europe.