BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines.

In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.

MDM-Pred: a freely available web application for predicting the metabolism of drug-like compounds by the gut microbiota.

The human gut microbiota (HGM) comprises a complex population of microorganisms that significantly affect human health, including their influence on xenobiotics metabolism. Many pharmaceuticals are taken orally and thus come into contact with HGM, which can metabolize them. Therefore, it is necessary to evaluate the effect of HGM on the fate of pharmaceuticals in the organism. We have collected information about over 600 compounds from more than eighty publications. At least half of them (329 compounds) are known to be metabolized by HGM. We have used PASS (Prediction of Activity Spectra for Substances) software to build three classification SAR models for HGM-mediated drug metabolism prediction. The first model with an accuracy of prediction 0.85 estimates whether compounds will be metabolized by HGM. The second model with an average accuracy of prediction 0.92 estimates which bacterial genera are responsible for the drug metabolism. The third model with an average accuracy of prediction 0.92 estimates the biotransformation reactions during HGM-mediated drug metabolism. The created models were used to develop the freely available web application MDM-Pred (http://www.way2drug.com/mdm-pred/).

Spontaneous immortalization of chicken fibroblasts generates stable, high-yield cell lines for serum-free production of cultured meat.

Cellular agriculture could meet growing demand for animal products, but yields are typically low and regulatory bodies restrict genetic modification for cultured meat production. Here we demonstrate the spontaneous immortalization and genetic stability of fibroblasts derived from several chicken breeds. Cell lines were adapted to grow as single-cell suspensions using serum-free culture medium, reaching densities of 108 × 106 cells per ml in continuous culture, corresponding to yields of 36% w/v. We show that lecithin activates peroxisome proliferator-activated receptor gamma (PPARγ), inducing adipogenesis in immortalized fibroblasts. Blending cultured adipocyte-like cells with extruded soy protein, formed chicken strips in which texture was supported by animal and plant proteins while aroma and flavour were driven by cultured animal fat. Visual and sensory analysis graded the product 4.5/5.0, with 85% of participants extremely likely to replace their food choice with this cultured meat product. Immortalization without genetic modification and high-yield manufacturing are critical for the market realization of cultured meat.

Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach.

MOTIVATION: Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical-chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. METHODS AND RESULTS: We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. CONCLUSION: The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry.

[Predict of metabolic stability of xenobiotics by the PASS and GUSAR programs]. / Prognoz metabolicheskoi stabil'nosti ksenobiotikov programmami PASS i GUSAR.

Metabolic stability refers to the susceptibility of compounds to the biotransformation; it is characterized by such pharmacokinetic parameters as half-life (T1/2) and clearance (CL). Generally, these parameters are estimated by in vitro assays, which are based on cells or subcellular fractions (mainly liver microsomal enzymes) and serve as models of the processes occurring in living organisms. Data obtained from the experiments are used to build QSAR (Quantitative Structure-Activity Relationship) models. More than 8000 compounds with known CL and/or T1/2 values obtained in vitro using human liver microsomes were selected from the freely available ChEMBL v.27 database. GUSAR (General Unrestricted Structure-Activity Relationships) and PASS (Prediction of Activity Spectra for Substances) softwares were used to make quantitative and classification models. The quality of the models was evaluated using 5-fold cross-validation. Compounds were subdivided into "stable" and "unstable" by means of the following threshold parameters: T1/2 = 30 minutes, CL = 20 ml/min/kg. The accuracy of the models ranged from 0.5 (calculated in 5-fold CV on the test set for the half-life prediction quantitative model) to 0.96 (calculated in 5-fold CV on the test set for the clearance prediction classification model).

PASS-based prediction of metabolites detection in biological systems.

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield ('major', 'minor', "trace" and "negligible") depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways ( http://www.way2drug.com/mg ).

Drug-drug interaction prediction using PASS.

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination. We used OpeRational ClassificAtion (ORCA) system for categorizing DDIs. ORCA divides DDIs into five classes: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). We collected a training set consisting of several thousands of drug pairs. Algorithm of PASS program was used for the first, second and third classes DDI prediction. Chemical descriptors called PoSMNA (Pairs of Substances Multilevel Neighbourhoods of Atoms) were developed and implemented in PASS software to describe in a machine-readable format drug substances pairs instead of the single molecules. The average accuracy of DDI class prediction is about 0.84. A freely available web resource for DDI prediction was developed (http://way2drug.com/ddi/).

[Xenobiotic toxicity prediction combined with xenobiotic metabolism prediction in the human body]. / Komp'iuternaia otsenka toksichnosti ksenobiotikov s uchetom ikh metabolizma v organizme cheloveka.

The majority of xenobiotics undergo a number of chemical reactions known as biotransformation in human body. The biological activity, toxicity, and other properties of the metabolites may significantly differ from those of the parent compound. Not only xenobiotic itself and its final metabolites produced in large quantities, but the intermediate and final metabolites that are formed in trace quantities, can cause undesirable effects. We have developed a freely available web resource MetaTox (http://www.way2drug.com/mg/) for integral assessment of xenobiotics toxicity taking into account their metabolism in the humans. The generation of the metabolite structures is based on the reaction fragments. The estimates of the probability of the reaction of a certain class and the probability of site of biotransformation are used at the generation of the xenobiotic metabolism pathways. The web resource MetaTox allows researchers to assess the metabolism of compounds in the humans and to obtain assessment of their acute, chronic toxicity, and adverse effects.

Prediction of metabolites of epoxidation reaction in MetaTox.

Biotransformation is a process of the chemical modifications which may lead to the reactive metabolites, in particular the epoxides. Epoxide reactive metabolites may cause the toxic effects. The prediction of such metabolites is important for drug development and ecotoxicology studies. Epoxides are formed by some oxidation reactions, usually catalysed by cytochromes P450, and represent a large class of three-membered cyclic ethers. Identification of molecules, which may be epoxidized, and indication of the specific location of epoxide functional group (which is called SOE - site of epoxidation) are important for prediction of epoxide metabolites. Datasets from 355 molecules and 615 reactions were created for training and validation. The prediction of SOE is based on a combination of LMNA (Labelled Multilevel Neighbourhood of Atom) descriptors and Bayesian-like algorithm implemented in PASS software and MetaTox web-service. The average invariant accuracy of prediction (AUC) calculated in leave-one-out and 20-fold cross-validation procedures is 0.9. Prediction of epoxide formation based on the created SAR model is included as the component of MetaTox web-service ( http://www.way2drug.com/mg ).

[Computer evaluation of hidden potential of phytochemicals of medicinal plants of the traditional Indian ayurvedic medicine].

Applicability of our computer programs PASS and PharmaExpert to prediction of biological activity spectra of rather complex and structurally diverse phytocomponents of medicinal plants, both separately and in combinations has been evaluated. The web-resource on phytochemicals of 50 medicinal plants used in Ayurveda was created for the study of hidden therapeutic potential of Traditional Indian Medicine (TIM) (http://ayurveda.pharmaexpert.ru). It contains information on 50 medicinal plants, their using in TIM and their pharmacology activities, also as 1906 phytocomponents. PASS training set was updated by addition of information about 946 natural compounds; then the training procedure and validation were performed, to estimate the quality of PASS prediction. It was shown that the difference between the average accuracy of prediction obtained in leave-5%-out cross-validation (94,467%) and in leave-one-out cross-validation (94,605%) is very small. These results showed high predictive ability of the program. Results of biological activity spectra prediction for all phytocomponents included in our database are in good correspondence with the experimental data. Additional kinds of biological activity predicted with high probability provide the information about most promising directions of further studies. The analysis of prediction results of sets of phytocomponents in each of 50 medicinal plants was made by PharmaExpert software. Based on this analysis, we found that the combination of phytocomponents from Passiflora incarnata may exhibit nootropic, anticonvulsant and antidepressant effects. Experiments carried out in mice models confirmed the predicted effects of Passiflora incarnata extracts.

[A comparative assessment of the life quality of patients after various surgeries because of complicated duodenal ulcer]. / Sravnite'naia otsenka kachestva zhizni bol'nykh posle razlichnykh vidov operativnogo lecheniia po povodu oslozhnennoi duodenal'noi iazvy.

Showings of studying the life quality with the aid of the SF-36 questionnaire can be used for assessing long-term results of various types of surgeries at complicated duodenal ulcer. Unlike stomach resections, functional surgeries provide a higher life quality of patients. Such differences become more apparent as time after the surgery passes by.

A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches.

A new approach is described that is able to predict the most probable metabolic sites on the basis of a statistical analysis of various metabolic transformations reported in the literature. The approach is applied to the prediction of aromatic hydroxylation sites for diverse sets of substrates. Training is performed using the aromatic hydroxylation reactions from the Metabolism database (Accelrys). Validation is carried out on heterogeneous sets of aromatic compounds reported in the Metabolite database (MDL). The average accuracy of prediction of experimentally observed hydroxylation sites estimated for 1552 substrates from Metabolite is 84.5%. The proposed approach is compared with two electronic models for P450 mediated aromatic hydroxylation: the oxenoid model using the atomic oxygen and the model using the methoxy radical as a model for the heme active oxygen species. For benzene derivatives, the proposed method is inferior to the oxenoid model and as accurate as the methoxy-radical model. For hetero- and polycyclic compounds, the oxenoid model is not applicable, and the statistical method is the most accurate. Broad applicability and high speed of calculations provide the basis for using the proposed statistical approach for high-throughput metabolism prediction in the early stages of drug discovery.

[Comparative evaluation of the motor-evacuation function of the stomach after functional operations for complicated duodenal ulcers]. / Sravnitel'naia otsenka motorno-évakuatornoi funktsii zheludka posle funktsional'nykh operatsii pri olozhnennykh duodenal'nykh iazvakh.

Motor-evacuatory disorders (MED) of the stomach in 114 patients who have undergone functional operations for duodenal ulcers were analyzed. Clinical picture, barium contrast and ultrasonic examinations, fibrogastroduodenoscopy, pH-monitoring were used for evaluation. Many patients have MED of different degree in early postoperative period. Some patients have severe disorders that require drug therapy. US and endoscopy role in complex assessment and differential analysis of stomach evacuatory disorders is emphasized. Isolated elements of study methods are discussed.

[The use of donor blood plasma with a high titer of natural antiendotoxin antibodies, irradiated by ultraviolet light, in the combined detoxification therapy of peritonitis]. / Primenenie plazmy krovi donorov s vysokim titrom estestvennykh antiéndotoksinovykh antitel, obluchennoi ul'trafioletovym svetom, v kompleksnoi detoksikatsionnoi terapii peritonitov.

The ultraviolet (UV) irradiation of donor blood plasma with an elevated level of natural antibodies to Re-glycolipid with doses ranging from 100 to 200 J/m2 has been found to lead to a statistically significant rise in its protective activity. The use of such plasma in clinical practice has made it possible to decrease the frequency of lethal outcomes from 24.3% to 16.5% (p < 0.01), the occurrence of secondary infectious complications from 62% to 44.4% (p < 0.01) and the duration of patients' stay in a hospital by 6-8 days. These data make it possible to regard the use of UV-irradiated plasma with an elevated level of Re-antibodies as an effective method of the immunotherapy of septic complications caused by gram-negative bacteria.

[The use of blood plasma with elevated titers of antibodies to Re-chemotype glycolipid in peritonitis]. / Primenenie plazmy krovi s povyshennymi titrami antitel k glikolipidu Re-khemotipa pri peritonitakh.

The article analyzes the possibility of immunotherapy of septic complications in cases of peritonitis caused by gram-negative bacteria. A strictly inverse correlation between the severity of intoxication and the level of antibodies to glycolipid Re in blood serum has been established. About 5% of healthy nonimmunized donors have elevated Re-antibody titers (1:128 and higher) in their blood plasma. Screening of blood preparations from more than 1000 donors permitted the creation of the blood plasma bank used for the treatment of peritonitis patients. Immunotherapy of such patients has made it possible to decrease almost two-fold the death rate and to reduce the severity and duration of the intoxication syndrome in patients, as well as to improve the results of the treatment of peritonitis.