Alternative methods for estimating common descriptors for QSAR studies of dyes and fluorescent probes using molecular modeling software: 1. Concepts and procedures

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Betacyanins are plant-based dyes with potential as histological stains

Interest is increasing in certain parts of the world in replacing synthetic dyes with dyes from natural sources, particularly from plants. Although textile dyers have used various groups of natural dyes, microscopists generally have restricted their use to anthocyanins. Recently, however, another class of plant-based dyes has found some favor, the betacyanins. Betacyanins are a group of red and violet betalain dyes found only in certain plants of the order Caryophyalles and in Basidiomycetes mushrooms. Although the chemical structures of betacyanins are known, little use has been made of that information to understand or predict their behavior with biomedical specimens. We investigated two common, widely distributed betacyanin-containing plants, edible beets (Beta vulgaris) and wild pokeweed (Phytolacca americana). Aqueous alcoholic extracts were made from beet root and pokeweed berries, adjusted to pH 4.1 or 5.3 and used together with Harris' hematoxylin to stain histological sections. We used a methanolic extract of pokeweed berries, pH 3.0, to stain cultured mycological specimens. Both extracts produced satisfactory staining that was equivalent to that of eosin Y, although the colors were more muted with the beet root extract. Epithelial cytoplasm, muscle, collagen and erythrocytes were well demonstrated. Betanin is the predominant component of beet root extract; it possesses one delocalized positive charge and three carboxylic acid substituents. The dyes are weak acids and the carboxylate anions are more diffuse than for eosin Y; this produces weaker bonding to tissue cations. The principal colored component of pokeweed berries, prebetanin, possesses a sulfonic acid group as well as carboxylic acids, which favors acid dyeing and more intense coloration. Both dyes show potential for hydrogen bonding and to a much lesser extent for some types of van der Waals forces. Complex formation with metals such as aluminum to create a nuclear stain is not likely with beet root dyes nor is it possible with pokeweed dyes. Betacyanins are suitable for staining microscopy preparations in place of other red acid dyes such as eosin. Of the two dyes tested here, prebetanin from pokeweed berries was superior to betanin from red beet roots. These berries are widely distributed and readily collected; the extraction procedure is simple and does not require expensive solvents.

Theoretical study on charge distribution in cetylpyridinium cationic surfactant

Here, we report the electrostatic potential maxima and partial atomic charges evaluated on cetylpyridinium cation. The Hartree-Fock method and six DFT functionals (namely, PBE, TPSS, B3LYP, PBE0, M06, and wB97) were used to calculate partial atomic charges via CHELPG, Mulliken, Löwdin, Hirshfeld, and natural population schemes. Calculations were performed for the gas phase and for the CPCM water solvated cation, resulting in a set of 70 types of partial atomic charges. The main tendencies in charge deviations were discussed. In comparison with the electrostatic potential-based CHELPG partial charges, Hirshfeld, Mulliken, Löwdin, and NPA partitioning schema lead to an almost steady decrease in the partial charges in the cetyl chain. This agreed closely with the redistribution of electrostatic potential mapped onto the 0.002 e/Bohr³ isodensity surface.

Benchmark of different charges for prediction of the partitioning coefficient through the hydrophilic/lipophilic index

A few different theoretical methods for assigning the partial atomic charges were benchmarked for calculation of the hydrophilic/lipophilic index (HLI). The coefficients were selected to produce the best correlation of the HLI values with the experimental octanol-water partition. Different parameters were checked in calculations of partial charges to get the best performance of the HLI values obtained. Thus, four partitioning schemes (Coulson, Mulliken, Merz-Kollman, Ford-Wang) were benchmarked for calculations of atomic charges with six semiempirical methods (AM1, PM3, RM1, PM6, PM6-D3H4, PM7). Moreover, five distinct types of partial atomic charges (Mulliken, Hirshfeld, Löwdin, CHELPG, NPA), obtained at the Hartree-Fock and DFT levels of theory with three basis sets, were tested for their ability to produce the HLI values with the best correlation to experimental logP coefficients of 50 mono-charged organic anions. In the case of the semiempirical methods, the best correlation between the HLI and logP values (the correlation coefficient r = 0.9216) was obtained with the AM1 Ford-Wang parametric electrostatic potential charges. The Mulliken and Coulson charges calculated with the PM7 method can be used as an alternative to AM1, with the r values of 0.9107 and 0.8984, respectively. In the case of the DFT, the PBE/def2-TZVP natural population analysis charges produce the best correlation (r = 0.9220). Nevertheless, in spite of a marginally lower performance (r = 0.9159), the NPA charges computed at the PBE/def2-SVP level are more robust and can be regarded as the optimum choice for calculating the HLI values. Graphical abstract The hydrophilic/lipophilic index (HLI).

Accomplishments of the Trustees and laboratory staff of the Biological Stain Commission, 2002-2013

During the 12 years from 2002 to 2013, the Trustees and laboratory personnel of the Biological Stain Commission (BSC) can claim many accomplishments. These accomplishments are itemized under 11 categories: continuous publication of the official journal, Biotechnic & Histochemistry; production of four special issues of Biotechnic & Histochemistry devoted to specific dyes or stains; standardization of staining and dye purity; mechanisms of staining and prediction of dye behavior; publication of books or book chapters; effects of fixation and processing on staining; cancer research; immunohistochemistry; BSC Laboratory activities; miscellaneous publications; and administrative accomplishments.