Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

In the present paper, the antifungal activity of a series of benzoxazole and oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by using quantitative structure-activity relationships chemometric methodology with artificial neural network (ANN) regression approach. In vitro antifungal activity of the tested compounds was presented by minimum inhibitory concentration expressed as log(1/cMIC). In silico pharmacokinetic parameters related to absorption, distribution, metabolism and excretion (ADME) were calculated for all studied compounds by using PreADMET software. A feedforward back-propagation ANN with gradient descent learning algorithm was applied for modelling of the relationship between ADME descriptors (blood-brain barrier penetration, plasma protein binding, Madin-Darby cell permeability and Caco-2 cell permeability) and experimental log(1/cMIC) values. A 4-6-1 ANN was developed with the optimum momentum and learning rates of 0.3 and 0.05, respectively. An excellent correlation between experimental antifungal activity and values predicted by the ANN was obtained with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Environmental risk assessment of volatile organic contaminants in the Sava river aquifer, Belgrade, Serbia

The aim of this study was to investigate the environmental risk from the gasoline range volatile organic contaminants in the Sava river aquifer. The investigated area is located in New Belgrade, in the vicinity of the largest heating plant in Belgrade, the capital of Serbia. Our previous research on the oil pollutants in the groundwater at this locality was focused on the origin and spatial distribution of these contaminants, and estimation of potential human health risks from exposure to these compounds. [1] The purpose of our present study is a Tier I Environmental risk assessment in this part of the aquifer. Groundwater samples were collected from 28 hydrogeological boreholes. Preliminary analyses of the organic compounds extracted from the groundwater samples were conducted by gas chromatography with flame ionization detection (GC-FID), and by gas chromatography – mass spectrometry (GC-MS). Volatile organic compounds (VOCs) were analyzed and identified by headspace gas chromatography – mass spectrometry. Chemicals of concern were quantified by headspace gas chromatography with flame ionization detection (HS-GC-FID). In the groundwater samples analyzed, the most frequently detected VOCs were from the group of the gasoline range organics. Concentrations of the individual VOCs ranged from below detection limits to 5.2 mg/L. For each of the compounds quantified, the Risk Quotient (RQ) was calculated as the ratio of the measured concentration of that compound in the groundwater sample and the lowest Predicted Non-Effect Concentration for freshwater aquatic organisms (PNEC). The PNEC values were adopted from the European chemicals agency’s (ECHA) Registration Dossier database. [2] At some of the sampling points, the detected concentrations of VOCs were higher than that of the PNEC values with resulting RQ > 1. Considering the fact that the VOC compounds analyzed were present as mixtures, the mixture RQ was calculated (as a sum of the individual RQs) for each sampling point. Out of 28 sampling points, at 7 of them the mixture RQs were higher than 1 which indicates a potentially medium to high ecological risk from these compounds in this part of the aquifer. In addition to the conclusion from our previous study on the human risk assessment from exposure to the volatile organic compounds in the groundwater at this location, [1] this research emphasizes a necessity for a continuous monitoring of the water quality in the investigated area

Validation of an adsorption kinetic model for lindane removal by a porous polymer

Lindane was one of the most commonly used organochlorine pesticides for controlling a wide range of horticultural, agricultural, and public health pests during the second half of the 20th century. [1] Owing to its toxic nature, bioaccumulation capability, and long transportable nature, it belongs to a group of persistent organic pollutants (POPs). [2] Although lindane use has been restricted or even banned, its residues persist and represent a serious environmental problem since lindane residues are still found in water, sediments, soil, plants, and animals. [3] Therefore, there is a growing interest in lindane removal or degradation by various methods. In this context, sorption has received considerable attention as one of the most effective and simplest technological approaches for the removal of lindane. In the present study, we investigated the kinetics of the lindane sorption process onto a porous functionalized copolymer based on glycidyl methacrylate. Five widely used isotherm kinetic models (pseudo-first-order, pseudo-second-order, Elovich, Avrami, and fractional power models) were employed via non-linear and linear fitting. The extent of kinetic model compatibility was evaluated through seven error functions. According to the obtained results, the pseudo-second-order model was the best-fitting kinetic model for describing the kinetics of lindane sorption by the investigated sorbent

Microbial Secondary Metabolites and Biotechnology

Many research teams are working to demonstrate that microorganisms can be our daily partners, due to the great diversity of biochemical transformations and molecules they are able to produce. This Special Issue highlights several facets of the production of microbial metabolites of interest. From the discovery of new strains or new bioactive molecules issued from novel environments, to the increase in their synthesis by traditional or innovative methods, different levels of biotechnological processes are addressed. Combining the new dimensions of "Omics" sciences, such as genomics, transcriptomics or metabolomics, microbial biotechnologies are opening up incredible opportunities for discovering and improving microorganisms and their production