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Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer. [ABSTRACT FROM AUTHOR]

The increasing prominence of biologics in the pharmaceutical market requires more advanced delivery systems to deliver these delicate and complex drug molecules for better therapeutic outcomes. Fibre technology has emerged as a promising approach for creating controlled and targeted drug delivery systems. Fibre-based drug delivery systems offer unprecedented opportunities for improving drug administration, fine-tuning release profiles, and advancing the realm of personalized medicine. These applications range from localized delivery at specific tissue sites to systemic drug administration while safeguarding the stability and integrity of delicate therapeutic compounds. Notwithstanding the promise of fibre-based drug delivery, several challenges such as non-scalability impede cost-effectiveness in the mass production of fibre systems. Biocompatibility and toxicity concerns must also be addressed. Furthermore, issues relating to stability, in-vitro in-vivo correlations, degradation rates, and by-product safety present additional hurdles. Pharmacoinformatics shows the impact of technologies in pharmaceutical processes. Emerging technologies such as Artificial Intelligence (AI) are a transformative force, progressively being applied to enhance various facets of pharmacy, medication development, and clinical healthcare support. However, there is a dearth of studies about the integration of AI in facilitating the translation of predominantly lab-scale pharmaceutical technologies into real-world healthcare interventions. This article explores the application of AI in fibre technology, its potential, challenges, and practical applications within the pharmaceutical field. Through a comprehensive analysis, it presents how the immense capabilities of AI can be leveraged with existing fibre technologies to revolutionize drug delivery and shape the future of therapeutic interventions by enhancing scalability, material integrity, synthesis, and development. [ABSTRACT FROM AUTHOR]

Objective: to determine the molecular mechanisms of action of 7-hydroxymatairesinol (7(OH)MR), the main lignan of spruce extracts, characterized by oncoprotective and anti-inflammatory properties.Material and methods. The analysis of 7(OH)MR was carried out by chemoinformatic approach using the combinatorial theory of solvability and the topological theory of recognition. The postgenomic approach makes it possible to assess the effect of drugs on genome transcription (transcriptome) and on the proteome as a whole.Results. 7(OH)MP has anti-inflammatory (inhibition of 5-lipoxygenase, matrix metalloproteinase 2, mitogen-activated kinase p38-alpha, leukotriene В4 receptor, prostacyclin receptor), and oncoprotective (antioxidant effect due to inhibition of heme oxygenase-2, inhibition of cyclin-dependent kinases 3 and 4, epidermal growth factor, mTOR protein) pharmacological effects.Conclusion. By reducing the expression of proliferative genes and genes involved in chronic inflammation, the 7(OH)MP molecule inhibits the proliferation of tumor cells. Pharmacoinformatic modeling showed that the anti-inflammatory effects of 7(OH)MR may contribute to increased lifespan in animal models.

Objective: to conduct chemoreactomic, pharmacoinformatic and chemoneurocytological analyzes of the properties of racetams (piracetam, aniracetam, pramiracetam, levetiracetam, fonturacetam).Material and methods. Chemoreactomic, pharmacoinformatic and chemoneurocytological methods of molecule properties analyzis are based on chemoreactomic methodology – the latest direction in the application of machine learning systems in the field of postgenomic pharmacology. Analysis of pharmacological capabilities of molecules within the framework of chemoreactomic methodology is carried out by comparing the chemical structure of racetam molecules with the structures of molecules for which pharmacological properties were studied using training artificial intelligence algorithms based on big data information presented in PubChem, HMDB, STRING, PharmGKB databases. Based on the entire complex of differences between molecules in interactions with receptor proteins, an “anti-obesity” score was calculated for each one as a serial number of this molecule in descending order by corresponding IC50, EC50 chemoreactomic constants values.Results. The lipolytic effect is predicted specifically for fonturacetam as a result of activation by this molecule of β3-adrenoceptors, adenosine receptors, glucagon-like peptide, sphingosine phosphate and peroxisome proliferators, as well as specific inhibition of cannabinoid, opioid, histamine, glutamate, nociceptin, orexin and neuropeptide Y receptors. Due to these mechanisms fonturacetam will contribute to normalizing appetite and improving adipose tissue metabolism. The total lipolytic effect score was calculated for all established interactions with receptors and amounted to 4.3±0.9 for fonturacetam, 3.0±1.4 for pramiracetam, and 2.5±1.5 for all other molecules.Conclusion. The results of the analysis suggest that the lipolytic effects of fonturacetam (Actitropil – Pharmstandard, Russia) will be much stronger than for other racetams (piracetam, aniracetam, pramiracetam, levetiracetam). Chemoreactomic analysis of fonturacetam indicated new mechanisms of pharmacological action of the molecule, providing a decrease in excess appetite and body weight normalization. Fonturacetam is the only nootropic drug indicated for the treatment of obesity.

7-hydroxymatairesinol (7(OH)MR) is the main lignan of spruce extracts, characterized by oncoprotective and anti-inflammatory properties. The results of chemoreactome modeling made it possible to formulate the molecular mechanisms of the pharmacological effects of 7(OH)MR for anti-inflammatory (inhibition of 5-lipoxygenase, matrix metalloproteinase MMP2, mitogen-activated kinase p38-alpha, leukotriene b4 receptor, prostacyclin receptor), oncoprotective (antioxidant effect due to inhibition heme oxygenase-2, inhibition of cyclin-dependent kinases 3 and 4, epidermal growth factor, mTOR protein). An assessment of the transcriptomic effects of 7(OH)MR in breast cancer cells showed significant dose-dependent effects on the transcription of 3468 of 12700 genes. The 7(OH)MR molecule, reducing the expression of proliferative genes (401 genes) and chronic inflammation (148 genes), while simultaneously increasing the transcription of oncoprotective immunity genes (100 genes), inhibits the proliferation of tumor cells. Experiments on solid Ehrlich carcinoma showed a significant oncoprotective effect of 7(OH)MR at doses of 60, 120 mg/day, even while taking estrogen (p = 0.007). A clinical study showed that 7(OH)MR (60 mg/day, 1 month) helps normalize estrogen metabolism and improve the clinical symptoms of mastopathy.

Background. Nonsteroidal anti-inflammatory drugs (NSAIDs) are characterized by ulcerogenic effects and used for effective and safe pharmacotherapy of inflammation and pain. The zinc-containing drug acyzole is a promising anti-inflammatory drug that potentially does not have this drawback. Objective: to conduct chemoreactomic modeling of the pharmacological effects of acyzole and zinc derivatives of well-known NSAIDs (diclofenac, nimesulide, ketorolac).Material and methods. The analysis of the pharmacological capabilities of NSAIDs was based on a chemoinformatic approach, i.e. comparing the chemical structure of the studied molecules with the structures of millions of other molecules with established molecular pharmacological properties. The analysis procedure was based on the latest machine learning technologies developed in the theory of topological and metric analysis of feature descriptions.Results. It was shown that acyzole might have an anti-inflammatory effect due to its impact on the activity of cytokines and, partly, on the metabolism of prostaglandins and leuktrienes. The central effects of acyzole are comparable to those of zinc-NSAIDs. The analgesic effect of acyzole may be associated with kinin receptors inhibition, and weak antihistamine and antinociceptin effects. Acizol may also exhibit a gastroprotective effect. It was established that acyzole, to a lesser extent than the reference molecules, negatively affected the metabolism of vitamins and microelements.Conclusion. Chemoreactomic profiling of acyzole indicates prospects for its use as an anti-inflammatory drug.

Background. The search for an effective and safe pharmacotherapy for tumor diseases includes the evaluation of the action of candidate molecules on various types of tumor cells. Vitamin B12 and its derivatives are promising molecules whose properties can be controlled through chemical modifications.Objective: conducting in silico chemoreactom screening and in vitro experimental study of aquacobalamin and heptamethyl ester of cyanoaquacobyrinic acid (HECСA).Material and methods. Chemoreactome screening was carried out on the basis of a problem-oriented theory of chemograph isomorphism analysis, which is an extension of the algebraic approach to machine learning and recognition problems. Trainable algorithms for calculating chemical distances between molecules were used, on the basis of which the values of half-maximal inhibitory concentration (IC50) were calculated. Screening was carried out for 470 cultures of human tumor cells, including SNB19 (astrocytoma), HCT116 (colon cancer), HeLa (cervical carcinoma), BT-474 (breast duct carcinoma), and A549 (lung carcinoma) cell lines. Dicyanocobyric acid heptamethyl ester ((CN)2Cby(OCH3)7) was obtained by boiling a solution of vitamin B12 in methanol with sulfuric acid (1.0 M) for 4 days. HECСA ((CN)(H2O)Cby(OCH3)7) was obtained by vacuum drying an aqueous solution of (CN)2Cby(OCH3)7 (pH 4.0 and 25 °С). The ester structure and purity were confirmed by 1H nuclear magnetic resonance data, elemental analysis, and MALDI-ToF (matrix-assisted laser desorption/ionization time of flight) mass spectroscopy. Experimental studies of tumor cell cultures were carried out using the MTT testwith aquacobalamin and HECСA on cell lines of immortalized (telomerized) fibroblasts (Fb-hTERT), lung carcinoma (A549), and breast duct cancer (BT-474).Results. Chemoreactome screening of the effects of molecules on tumor cells made it possible to obtain estimates of cell growth IC50 for 470 tumor cell lines. Depending on cell line and vitamin B12 derivative molecule, IC50 values varied in a fairly wide range: from 15 to 2000 nM. In vitro studies on cultures of two human tumor cell lines (BT-474 and A549) and telomerized Fb-hTERT fibroblasts confirmed the cytotoxic effect of aquacobalamin and its hydrophobic derivative HECСA. It was shown that aquacobalamin had weak cytotoxic properties in the concentration range of 3.125–200 µg/l (IC50 > 200 nM), and HECСA significantly reduces the survival of BT-474 and A549 tumor cell lines at high concentrations (100–200 µg/l, IC50 about 100 nM).Conclusion. Correspondence was shown between the results of in silico chemoreactome screening and in vitro cell culture studies: IC50 values for HECСA were significantly lower than for aquacobalamin, and the conversion factor from chemoreactome estimates to experimental ones was almost the same (2.64 for BT-474, and 2.63 for A549). The results of chemoreactome screening for other tumor cell lines can be used to plan further cell experiments with vitamin B12 derivatives.

Objective: comprehensive analysis of the spectrum of antibacterial action of bactеriocins.Material and methods. Chemomicrobiome analysis of bacteriocins A/B, C, S, 28b, RS-2020 was performed to assess the minimum inhibitory concentration (MIC) values for 152 strains of pathogenic bacteria and the area under the growth curve (AUC) values for a representative sample of normobiota (38 human commensal bacteria).Results. Compared to other molecules, bacteriocin C was characterized by lower MIC constants for a wide range of pathogenic bacterial strains. Thus, it more effectively inhibited strains of pathogens of bacterial pneumonia (H. influenzae, S. mutans, S. pneumoniae, S. pyogenes), nosocomial infections (K. pneumoniae, P. aeruginosa, S. aureus, S. epidermidis, S. pneumoniae), skin diseases (M. audouinii, T. mentagrophytes, etc.), urinary tract infections (E. cloacae, P. mirabilis and P. vulgaris), Fusobacterium necrophorum and Candida fungi. At the same time, bacteriocin C to a lesser extent than the reference molecules inhibited the growth of the normophysiological microbiota of the Bacteroides, Enterococcus genera, non-pathogenic Escherichia, yeast S. cerevisiae and others. By stimulating butyrate (butyric anion) producing microorganisms, bacteriocin C can exhibit prebiotic properties.Conclusion. The main structural features of the bacteriocin C molecule associated with the antibacterial effect on pathogenic microbiota were identified and described.

Summary: Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Background. The enzymatic cofactor coenzyme Q10 (CoQ10) is involved in the synthesis of adenosine triphosphate in mitochondria. The CoQ10 biosynthesis declines when using statin drugs.Objective: systematization of the impact of coenzyme Q10 on chronic, sluggish systemic inflammation leading to the development of atherosclerosis, liver and pancreas pathologies, and neurodegeneration.Material and methods. Systematic computer analysis of 16,788 publications on CoQ10 found by the query “coenzyme Q10 OR ubiquinone” in PubMed/MEDLINE database of biomedical publications was carried out. Current methods of topological and metric data analysis developed at the scientific school of Academician of the Russian Academy of Sciences Yu.I. Zhuravlev were used.Results. CoQ10 is involved in the regulation of inflammation and exhibits lipid-lowering, hepatoprotective, nephroprotective, anti-asthenic, cardioprotective, and neuroprotective effects. CoQ10 favorably affects carbohydrate metabolism and a wide range of neurological diseases.Conclusion. CoQ10 supplementation improves glycemic control in insulin resistance, kidney function, and is useful in the treatment of migraine and neurodegenerative pathologies (Parkinson’s disease, etc.).

Background. Therapy of hyperlipidemia with synthetic statin drugs is characterized by a number of side effects. In particular, statin-dependent myopathy occurs as a result of statin inhibition of biosynthesis of cofactor coenzyme Q10 (CoQ10), which plays a central role in the electron transport chains during adenosine triphosphate (ATP) synthesis in mitochondria.Objective: systematic analysis of scientific publications on CoQ10 pharmacology with an emphasis on its effect on the lipid profile.Material and methods. The analysis included 16,788 articles found by “coenzyme Q10 OR ubiquinone" query in the PubMed/MEDLINE biomedical publications database. Topological and metric big data analysis methods were used developed in the scientific school of Academician of the Russian Academy of Sciences Yu.I. Zhuravlev.Results. A much wider range of CoQ10 pharmacological effects was established than simply supporting ATP biosynthesis. Coenzyme Q10 exhibits pronounced lipid-lowering, anti-asthenic, and anti-inflammatory effects and can be successfully used in the treatment of asthenic conditions, cardiovascular pathology (including hyperlipidemia and statin-induced myopathy), carbohydrate metabolism disorders (insulin resistance), and diverse neurological diseases. CoQ10 supplements reduce mortality by 50% in patients with cardiovascular diseases, improve glycemic control in insulin resistance, kidney function, and are useful in the treatment of migraine and neurodegenerative pathologies (Parkinson's disease, etc.).Conclusion. Experimental data and evidence base indicate the potential of using CoQ10 in patients with hyperlipidemia and other cardiovascular system diseases.

Summary: Cardiotoxicity remains a major cause of drug withdrawal, partially due to lacking predictability of animal models. Additionally, risk of cardiotoxicity following treatment of cancer patients is treatment limiting. It is unclear which patients will develop heart failure following therapy. Human pluripotent stem cell (hPSC)-derived cardiomyocytes present an unlimited cell source and may offer individualized solutions to this problem. We developed a platform to predict molecular and functional aspects of cardiotoxicity. Our platform can discriminate between the different cardiotoxic mechanisms of existing and novel anthracyclines Doxorubicin, Aclarubicin, and Amrubicin. Doxorubicin and Aclarubicin unlike Amrubicin substantially affected the transcriptome, mitochondrial membrane integrity, contractile force and transcription factor availability. Cardiomyocytes recovered fully within two or three weeks, corresponding to the intermittent clinical treatment regimen. Our system permits the study of hPSC-cardiomyocyte recovery and the effects of accumulated dose after multiple dosing, allowing individualized cardiotoxicity evaluation, which effects millions of cancer patients treated annually.

Biguanide derivatives exhibit a wide variety of therapeutic applications, including anti-cancer effects. Metformin is an effective anti-cancer agent against breast cancer, lung cancer, and prostate cancer. In the crystal structure (PDB ID: 5G5J), it was found that metformin is found in the active site of CYP3A4, and the associated anti-cancer effect was explored. Taking clues from this work, pharmacoinformatics research has been carried out on a series of known and virtual biguanide, guanylthiourea (GTU), and nitreone derivatives. This exercise led to the identification of more than 100 species that exhibit greater binding affinity toward CYP3A4 in comparison to that of metformin. Selected six molecules were subjected to molecular dynamics simulations, and the results are presented in this work. [ABSTRACT FROM AUTHOR]

Summary: The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.

This document is an editorial published in Frontiers in Pharmacology titled "Pharmacoinformatics: new developments and challenges in drug design." It discusses the field of pharmacoinformatics, which combines computational sciences and pharmacology to streamline drug discovery and development. The editorial highlights 10 original research articles that demonstrate the transformative potential of computational approaches in pharmacology. These articles cover a range of therapeutic areas and offer insights into drug design, efficacy, and mechanisms of action. The research topics include deep learning-based multi-drug synergy prediction models, network pharmacology-based approaches, bioinformatics in phytomedicine research, computational analysis of viral proteases, design of chemical libraries, and synthesis and evaluation of novel compounds. The editorial concludes by emphasizing the interdisciplinary nature of pharmacoinformatics and its potential to accelerate drug discovery and enhance our understanding of complex biological systems. The authors hope that this research inspires further collaboration and the development of more effective and personalized therapeutic strategies. [Extracted from the article]

Summary: Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Nearly all living cells undergo DNA replication in order to duplicate their genome; this multi-step biological process is tightly regulated by different cellular machineries and numerous multi-protein complexes. MCM2 (Minichromosome maintenance protein 2) is a key player in this game and its expression profile has been strongly correlated with cancer formation and progression. Thus, inhibiting MCM2 overexpression can significantly reduce oncogenesis; however, until now there has been no record of any natural drug-like molecule that can overcome MCM2-associated cancer. Therefore, this meticulous study has made an effort to identify novel efficacious drug-like natural compounds with the potential to cease cancer progression, targeting MCM2. This investigation observed 402 different previously identified flavonoids from Catharanthus roseus and studied their potency, efficacy, and safety against MCM2 using a chemoinformatics approach. The initial molecular docking screening identified the compounds that had higher binding affinity with the targeted protein. Further, an in silico pharmacokinetics study and toxicity evaluation were carried out in order to confirm that four natural flavonoids (CHEMSPIDER: 172169, CID: 440832, CID: 8546, CID: 182232) were effective and safe in the human body, as strong inhibitors of MCM2. Lastly, molecular dynamics simulations of the potent inhibitors were executed in order to disclose the stability of CID: 8546 at the active site of the protein. Therefore, the results of this computational approach may act as an initial instruction for future in vitro and in vivo testing carried out in order to identify natural drug-like compounds that can beat cancer, targeting MCM2.

Natural compounds are increasingly becoming an important source of drug leads for computer-aided drug design approaches. Cucurbita maxima has been observed to have medicinal properties and can, therefore, be a potential source of novel drug leads. However, before compounds can be synthesized in the lab for tests, modern approaches require that the candidate compounds be screened for drug-likeness characteristics and toxicity, among others. In this work, the computational tools, SwissADME and DataWarrior were used to screen C. maxima compounds for their potential consideration as drug leads. A total of 130 compounds, downloaded from the LOTUS natural products database, were computationally analysed. The data set presented in this work will be useful to researchers searching for novel drug leads based on natural compounds.

The pharmacoinformation approach to the assessment and modeling of drugs involves the use of modern methods of data mining. These methods include: 1) analysis of big data (selection of texts of scientific publications, search for new biomarkers); 2) computer analysis of texts (automatic classification of texts by content, identification of pseudoscientific texts); 3) analysis of metric maps (visualization and analysis of complex patterns, including clustering) and 4) chemoinformation analysis, including the assessment of the effect of drugs on the transcriptome, proteome and microbiome of a person. The article provides examples of the application of these methods of pharmacoinformatics to chondroprotectors containing standardized forms of chondroitin sulfate and glucosamine sulfate.

Natural compounds are increasingly becoming an important source of drug leads for computer-aided drug design approaches. Cucurbita maxima has been observed to have medicinal properties and can, therefore, be a potential source of novel drug leads. However, before compounds can be synthesized in the lab for tests, modern approaches require that the candidate compounds be screened for drug-likeness characteristics and toxicity, among others. In this work, the computational tools, SwissADME and DataWarrior were used to screen C. maxima compounds for their potential consideration as drug leads. A total of 130 compounds, downloaded from the LOTUS natural products database, were computationally analysed. The data set presented in this work will be useful to researchers searching for novel drug leads based on natural compounds.

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes. [ABSTRACT FROM AUTHOR]

The Danshen-Shanzha Decoction (DSD) is a renowned herbal combination consisting of the root of Salvia miltiorrhiza Bunge (known as Danshen in Chinese) and the fruits of Crataegus pinnatifida Bunge (known as Shanzha in Chinese), which has exhibited remarkable clinical efficacy in the treatment of coronary heart disease (CHD) in traditional Chinese medicine, with its earliest recorded application dating to around 202 BCE during the Han Dynasty. Despite significant advancements in the fundamental research and clinical applications of DSD over the past few decades, the precise bioactive components as well as the underlying mechanisms responsible for its protective effect on CHD remain unelucidated. The present study was designed to elucidate the bioactive components and potential mechanism of DSD in the treatment of CHD using in silico technologies integrated with pharmacoinformatic methods and experimental validation. The chemical components of DSD were analyzed and identified using UPLC-Q-TOF-MS. Pharmacoinformatic-based methods were employed to comprehensively investigate the principal active components and targets of DSD for treating CHD. GO and KEGG pathway analyses were utilized to elucidate the underlying mechanism responsible for DSD's efficacy against CHD. Molecular docking and molecular dynamics simulation were performed to assess the binding affinity between active components and putative targets. Furthermore, surface plasmon resonance (SPR) was carried out to verify the affinity and kinetic characteristics of major components to STAT3 protein. Subsequently, a series of in vitro experiments, including cell viability test, flow cytometric analysis, ELISA and western blotting, were conducted to validate the predicted results in an oxygen-glucose deprivation (OGD)-stimulated H9c2 model. A total of 96 compounds were characterized by UPLC-Q-TOF-MS, and 281 overlapping targets were identified through pharmacoinformatic-based methods. Among these, ten critical compounds were determined as the core active components of DSD. The core targets associated with the development of CHD included STAT3, SRC, TP53, JUN, and AKT1. Notably, Dihydrotanshinone I and (+)-Epicatechin exhibited strong binding affinity towards STAT3. The potential mechanisms by which DSD modulates the pathological progression of CHD were predicted to involve inflammation, oxidative stress, and apoptosis. Importantly, the cytoprotective effect of DSD against apoptosis was confirmed in OGD-stimulated H9c2 cells, as evidenced by the upregulation of Bcl-2 expression and downregulation of both Bax and cleaved caspase-3 expressions upon DSD treatment. Furthermore, DSD significantly enhanced the phosphorylated protein expressions of JAK2 and STAT3 compared to the OGD group, suggesting its potential role in modulating related signaling pathways. The current study successfully fills the gap in the understanding of the chemical profiles of DSD, predicting its active components, potential targets, and molecular mechanisms in the treatment of CHD. These findings not only provide a valuable strategy but also robust data support for future investigations into DSD, thereby facilitating the identification of novel therapeutic targets for traditional Chinese medicines in the battle against CHD. [Display omitted] • Danshen-Shanzha Decoction (DSD) showed efficacy in treating Coronary Heart Disease (CHD). • In silico & experimental validation explored mechanisms of DSD against CHD. • 96 compounds from DSD were identified by UPLC-Q-TOF-MS. • Dihydrotanshinone I exhibit strong binding to STAT3. • The anti-apoptotic effect of DSD may link to JAK2/STAT3 activation. [ABSTRACT FROM AUTHOR]

Summary: Pharmacologically active compounds with known biological targets were evaluated for inhibition of SARS-CoV-2 infection in cell and tissue models to help identify potent classes of active small molecules and to better understand host-virus interactions. We evaluated 6,710 clinical and preclinical compounds targeting 2,183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target, and cell interactome produced cellular networks important for infection. This analysis revealed 389 small molecules with micromolar to low nanomolar activities, representing >12 scaffold classes and 813 host targets. Representatives were evaluated for mechanism of action in stable and primary human cell models with SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of host factor dependencies and treatments for viral diseases.

Summary: Targeted inhibition of aberrant signaling is an important treatment strategy in cancer, but responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and given at low dosages. Here, we present a pipeline to identify promising multi-drug combinations. We perturbed an isogenic PI3K mutant and wild-type cell line pair with a limited set of drugs and recorded their signaling state and cell viability. We then reconstructed their signaling networks and mapped the signaling response to changes in cell viability. The resulting models, which allowed us to predict the effect of unseen combinations, indicated that no combination selectively reduces the viability of the PI3K mutant cells. However, we were able to validate 25 of the 30 combinations that we predicted to be anti-selective. Our pipeline enables efficient prioritization of multi-drug combinations from the enormous search space of possible combinations.

The search for original publications on fundamental and clinical medicine that would produce results of the highest scientific quality represents an urgent need for every medical researcher. Such publications are essential, in particular, for the development of reliable treatment standards. The Englishlanguage resources PUBMED and EMBASE are essential to help in solving this problem. However, there is an obvious problem in assessing the quality of the studies found. The paper formulates a method for analyzing the texts of biomedical publications, which is based on an algorithmic assessment of the emotional modality of medical texts (so-called sentiment analysis). The use of the topological theory of data analysis made it possible to develop a set of high-precision algorithms for identifying 16 types of sentiments (manipulative turns of speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressiveness of the text, negative emotional background, etc.). On the basis of the developed algorithms, a point scale for assessing the sentiment quality of research was obtained, which we called the "β-score": the higher the β-score, the less the evaluated text contains manipulative language constructions. As a result, the ANTIFAKE system (http://antifake-news.ru) was developed to analyze the sentiment-quality of Englishlanguage scientific texts. An analysis of ~ 20 million abstracts from PUBMED showed that publications with low sentiment quality (β-score

Candida albicans, a prominent fungal infection, induces a wide range of illnesses, ranging from moderate mucosal irritation to fatal systemic problems. The yeast-to-hypha transformation is greatly studied in the C. albicans pathogenicity. It is, nevertheless, an unscrupulous disease that can induce lethal bloodstream infections. Fungi have major applications in industrial, medical and agricultural fields. They are utilized in the synthesis of peptides, micronutrients, metabolites, phenolics and other organic compounds. The current study has incorporated the implementation of bioinformatics techniques to analyse the molecular level binding of Holotricin-3 against the anti-fungal proteins. The findings of the study revealed that Holotricin-3 had highest binding with BgI2p and the findings were further validated by molecular dynamic studies. These observations can be used to implement In vitro experiments. [ABSTRACT FROM AUTHOR]

Healthcare is the most important component in the life of all human beings as each individual wish to have happy, healthy, and wealthy life‐span. Most of the branches of science are dedicated to improve the healthcare. In the era of knowledge mining, informatics is playing a crucial role in different branches of research. Thus, a wide range of informatics‐based fields have emerged in the last three decades that include medical informatics, bioinformatics, cheminformatics, pharmacoinformatics, immunoinformatics, and clinical informatics. In the past, a number of reviews have been focused on the application of an informatics‐based field in the healthcare. In this review, an attempt is made to summarize the major computational resources developed in any informatics‐based field that have an application in healthcare. This review enlists computational resources in following groups ‐ drug discovery, toxicity prediction, vaccine designing, disease biomarkers, and Internet of Things. We mainly focused on freely available, functional resources like data repositories, prediction models, standalone software, mobile apps, and web services. In order to provide service to the community, we developed a health portal that maintain links related to healthcare http://webs.iiitd.edu.in/. This article is categorized under:Application Areas > Health Care [ABSTRACT FROM AUTHOR]

Summary: Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Plant Based Natural Products (PBNPs) are the primary source of natural antioxidants capable of neutralizing or eliminating harmful Reactive Oxygen Species (ROS). Oxidative stress contributes not only to the pathogenesis of type 2 diabetes (T2DM) but also to diabetic related vascular complications by lipid peroxidation. Oxidation induced DNA and protein damage leads to development of vascular complications like coronary heart disease, CVD, stroke, neuropathy, retinopathy, nephropathy, CKD, and other long term complications associated with diabetics. Likewise Multidrug resistance (MDR) is one of the major clinical challenges in cancer treatment and compromises the effectiveness of conventional anticancer chemotherapeutics. P-glycoprotein (P-gp) has been characterized as a major mechanism of MDR. Ellagic acid (EA) is a bioactive secondary metabolite widely distributed in vegetables and fruits (Strawberry, Grapes, Blackberry, Raspberry, Plums etc.) Chemically, EA is 2,3,7,8-tetrahydroxychromeno [5,4, -cde] chromene-5, 10-dione, a heterotetracyclic dimer of Gallic Acid (GA) molecules formed by oxidative aromatic coupling involving intramolecular lactonization. EA is associated with pharmacological activities such as anti-inflammatory, neuroprotective, cardio-protective, antioxidant, anti-mutagenic, multidrug resistance etc. EA has been marketed as a dietary supplement with claimed benefits against cancer, CVD, CKD and other metabolic disorders. However, pharmacological limitation of EA is attributed to its low solubility in water and reduced bioavailability. In the present study, bimolecular potential of EA has been bioprospected in the revised framework of ADMET pharmacoinformatics to further widen its biomedical applications. [ABSTRACT FROM AUTHOR]

Introduction. The English-language databases PubMed/MEDLINE and Embase are valuable information resources for finding original publications in basic and clinical medicine. Currently, there are no artificial intelligence systems to evaluate the quality of these publications.Aim. Development and testing of a system for sentiment analysis (i.e. analysis of emotional modality) of biomedical publications.Materials and methods. The technique of analysis of the “Big data” of biomedical publications was formulated on the basis of the topological theory of sentiment analysis. Algorithms have been developed that allow for the classification of texts from 16 sentiment classes with 90% accuracy (manipulative speech, research without positive results, propaganda, falsification of results, negative personal attitude, aggressive text, negative emotional background, etc.). Based on the algorithms, a scale for assessing the sentiment quality of research (β-score) is proposed.Results. Abstracts of 19.9 million publications registered in PubMed/MEDLINE over the past 50 years (1970–2019) were analyzed. It was shown that publications with low sentiment quality (the value of the β-score of the text is less than zero, which corresponds to the prevalence of manipulative and negative sentiments in the text) comprise only 18.5% (3.68 out of 19.9 million). The greatest values of the β-score were characterized by publications on sports medicine, systems biology, nutrition, on the use of applied mathematics and data mining in medicine. The rubrication of the entire array of publications by 27,840 headings (MESH-system of PubMed/MEDLINE) indicated an increase in the β-score by years (i.e., the positive dynamics of sentiment quality of the texts of publications) for 27,090 of the studied headings. The most intense positive dynamics was found for research in genetics, physiology, pharmacology, and gerontology. 249 headings with sharply negative dynamics of sentiment quality and with a pronounced increase in the manipulative sentiments characteristic of the tabloid press were highlighted. Separate assessments of international experts are presented that confirm the patterns identified.Conclusion. The proposed artificial intelligence system allows a researcher to make an effective assessment of the sentiment quality of biomedical research papers, filtering out potentially inappropriate publications disguised as “evidence-based”.

Objective: to elucidate whether cerebrolysin contains peptide fragments that promote geroprotection.Material and methods. The peptide composition of cerebrolysin underwent a comprehensive mass spectrometric analysis, followed by a systemic biological assessment.Results and discussion. Thirty-six peptides with geroprotective properties were isolated in the multipeptide composition of cerebrolysin. These peptides included those of mimetics of adrenomedullin and enkephalins; those of inhibitors of seven targeted human proteins (the protein kinases MAPK1, VPRBP, and PKC, the gamma-secretase PS1, the kinases CDK1, SGK1 and mTOR). The established cerebrolysin peptides were shown to be competitive inhibitors of the seven targeted proteins. In particular, inhibition of PKC and mTOR stimulated the increased autophagy (the utilization of waste and abnormal proteins), which contributes to an increase in the survival of cells and model organisms.Conclusion. Cerebrolysin can have geroprotective effects, by inhibiting the seven targeted proteins, by activating endorphinergic neurotransmission, and can be indirectly involved in vascular tone normalization.

Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than −9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.

Plants and phytocompounds gained more attention because of their unrivalled variety of chemical diversity. In this view, the present study was executed to predict the anticancer potential of Solanum torvum Swartz. fruits derived phytocompounds against one of the breast cancer target proteins (MAPK14, PDB ID: 5ETA, resolution: 2.80 Å) through pharmacoinformatics-based screening and molecular dynamics simulation tools. Initially, a graph theoretical network approach was used to visualize the genes, enzymes, and proteins involved in the signalling pathway of breast cancer and identify the significant target protein (MAPK14). A total of thirty-three active compounds were selected from S. torvum sw. through the IMPPAT database, and their structures were drawn by Chemsketch software. The drug-like behaviours of the compounds were assessed through pharmacokinetics and physicochemical characterization studies. Five compounds, namely chlorogenin (-10.90 kcal × mol -1 ), corosolic acid (-10.80 kcal × mol -1 ), solaspigenin (-10.80 kcal × mol -1 ), paniculogenin (-10.70 kcal × mol -1 ), spirostane-3,6-dione (-10.70 kcal × mol -1 ) exhibited top binding score against MAPK14, these are higher than that of the standard drug (Doxorubicin) (-8.60 kcal × mol -1 ). Additionally, the five top-binding compounds revealed better drug-likeness traits and the lowest toxicity profiles. MD simulation studies confirmed the stability of the top five scored compounds with the MAPK14 binding pockets. According to these findings, the selected five compounds might be used as significant MAPK14 inhibitors and can be used as new medicines for the treatment of breast cancer.Communicated by Ramaswamy H. Sarma.

Summary: Forecasting pharmacokinetics (PK) for individual patients is a fundamental problem in clinical pharmacology. One key challenge is that PK models constructed using data from one dosing regimen must predict PK data for different dosing regimen(s). We propose a deep learning approach based on neural ordinary differential equations (neural-ODE) and tested its generalizability against a variety of alternative models. Specifically, we used the PK data from two different treatment regimens of trastuzumab emtansine. The models performed similarly when the training and the test sets come from the same dosing regimen. However, for predicting a new treatment regimen, the neural-ODE model showed substantially better performance. To date, neural-ODE is the most accurate PK model in predicting untested treatment regimens. This study represents the first time neural-ODE has been applied to PK modeling and the results suggest it is a widely applicable algorithm with the potential to impact future studies.

Quercetin (3,3′,4′,5,7-Pentahydroxyflavone) is the one among the bioactive secondary metabolite (BASM) in neem seed of Azadirachta indica A. Juss. Quercetin (Que) and its derivatives hold promising pharmacological effects. Antidiabetic, anti-inflammatory, antioxidant, antimicrobial, anti-Alzheimer's, antiarthritic, cardiovascular, and wound-healing effects of Que have been extensively investigated, recently lot of work has been carried out on its anticancer activity against different cancer cell lines. Recently, in silico/in vitro studies have demonstrated that Que interferes with different stages of coronavirus entry and replication cycle (PLpro, 3CLpro, and NTPase/helicase). Due to its pleiotropic effects in human health and disease and lack of systemic toxicity, Que and its derivatives could be tested for their efficacy on human target system in future clinical trials. In the present study, an attempt has been made to evaluate the physicochemical, druggable properties of Que from A. indica to prospect its ADMET properties. [ABSTRACT FROM AUTHOR]

Azadirachtin (AZA) is the most abundant bioactive secondary metabolite (BASM) in neem seed oil extract (NSOE) of Azadirachta indica A. Juss. AZA is localised in different parts of the plant (seeds, fruits, flowers, leaves, stem, bark and root) however, with varying degree of concentration. It has been documented that maximum concentration of AZA is present to the tune of 48000 μg g-1 in the seeds. It has been established that the environmental conditions determines the overall content and composition of BASM in different parts of the plant. Neem plant parts are most commonly used as therapeutic agents in remote villages in India for its ethnomedicinal therapeutic potentials; however, its physicochemical, druggable and pharmacological properties inadequately described. In the present study an attempt has been made to evaluate the physicochemical, druggable and pharmacological properties of Azadirachtin in NSOE of A. indica from ADMET perspectives. [ABSTRACT FROM AUTHOR]

Cymbopogon martinii is a grass from genus Cymbopogon (lemongrasses) native to India, but widely cultivated in other places for its aromatic essential oil. C. martinii known as Palmarosa smells sweet with rose-like odor. Geraniol, a terpene alcohol present in Cymbopogon martinii essential oil (CMEO) is much valued for its typical aroma and medicinal uses. In addition to the pleasant odor, Geraniol is known for fungicidal, nematicidal, acaricidal, insecticidal, repellent properties hence, used as Natural Pest Control Agent (NPCA) exhibiting low toxicity. Furthermore, geraniol has been suggested to exemplify a new class of chemoprevention agents in the treatment of cancer. Biological activities such as antimicrobial, anti-oxidant, anti-inflammatory and vascular effects have been investigated. In the present study, GCMS based in-silico ADMET pharmacoinformatics aspects (Physicochemical, Lipophilicity, Medicinal Chemistry, Druglikeness, Absorption, Water Solubility, Distribution, Metabolism, Pharmacokinetics, Excretion, Environmental Toxicity, Tox21 Pathway and Toxicophore Rules) with PASS prediction of geraniol from CMEO has been bioprospected from human health perspective point of view. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Objective. To optimize selection of neuroprotective treatment protocols in patients with chronic cerebral ischemia taking account of synergism in drug interactions for an individual personalized approach to increase treatment efficacy. Materials and Methods. Differential chemoreactome analysis of synergism between ethylmethylhydroxypyridine succinate (EMHPS) and a number of single-component neuroprotective substances (piracetam, vinpocetine, citicoline, choline alfoscerate); proteome analysis of polypeptide neuroprotectors (Cerebrolysin and others); expert analysis of the composition of a multicomponent neuroprotector (Cytoflavin). Results. Piracetam, citicoline (Neupilept), and choline alfoscerate (Cereton) effectively enhanced the pharmacological properties of EMHPS. Expert assessments of synergism between the properties of EMHPS, polypeptide neuroprotectors (Cerebrolysin), and other multicomponent formulations (Cytoflavin), which are also used in the adjuvant therapy in combination with EMHPS, are presented. Conclusions. In real clinical practice there is particular interest in objective evaluation of combined treatment protocols. EMHPS can provide a favorable background for maximizing treatment efficacy using additional drugs. [ABSTRACT FROM AUTHOR]

Summary: RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.

The emergence of drug resistance against the known hookworm drugs namely albendazole and mebendazole and their reduced efficacies necessitate the need for new drugs. Chemically diverse natural products present plausible templates to augment hookworm drug discovery. The present work utilized pharmacoinformatics techniques to predict African natural compounds ZINC95486082, ZINC95486052 and euphohelionon as potential inhibitory molecules of the hookworm Necator americanus β tubulin gene. A library of 3390 compounds was screened against a homology-modelled structure of β tubulin. The docking results obtained from AutoDock Vina was validated with an acceptable area under the curve (AUC) of 0.714 computed from the receiver operating characteristic (ROC) curve. The three selected compounds had favourable binding affinities and were predicted to form no interactions with the resistance-associated mutations Phe167, Glu198 and Phe200. The compounds were predicted as anthelmintics using a Bayesian-based technique and were pharmacologically profiled to be druglike. Further molecular dynamics simulations and MM-PBSA calculations showed the compounds as promising anthelmintic drug leads. Novel critical residues comprising Leu246, Asn247 and Asn256 were also predicted for binding. Euphohelionon was selected as a template for the de novo fragment-based design of five compounds labelled A1, A2, A3, A4 and A5; with four of them having SAscore values below 6, denoting easy synthesis. All the five de novo molecules docked firmly in the binding pocket of the β tubulin with no binding interactions with the three known resistance mutation residues. Binding energies of −8.2, −7.6, −7.3, −7.2 and −6.8 kcal/mol were obtained for A1, A2, A3, A4 and A5, respectively. The identified compounds can serve as treasure troves from which future potent anthelmintics can be designed. The current study strives to assuage the hookworm disease burden, especially making available molecules with the potential to circumvent the chemoresistance.

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

DXR (1-deoxy-d-xylulose-5-phosphate reductoisomerase) is an essential enzyme in the Methylerythritol 4-phosphate (MEP) pathway, which is used by M. tuberculosis and a few other pathogens. This essential enzyme in the isoprenoid synthesis pathway has been previously reported as an important target for antibiotic drug design. However, till now, there is no record of any drug-like safe molecule to inhibit MtbDXR. Numerous plant species have been traditionally used for tuberculosis therapies. In this study, we selected six plant species with anti-tubercular properties. The chemoinformatic screening was performed on 352 phytochemicals from those plants against the MtbDXR protein. After molecular docking analysis, we filtered the top five compounds, CID: 5280443 (Apigenin), CID: 3220 (Emodin), CID: 5280863 (Kaempferol), CID: 5280445 (Luteolin), and CID: 6101979 (beta-Hydroxychalcone), based on binding affinity. Molecular dynamics simulations disclosed the stability of the compounds at the active site of the proteins. Finally, in silico ADME and toxicity evaluations confirmed the compounds to be effective and safe for oral administration. Thus, our findings identified three drug-like safe molecules- Apigenin, Kaempferol, and beta-Hydroxychalcone, that showed good stability in the protein's active site. The results of this computational approach may act as an initial instruction for future in vitro and in vivo testing to identify natural drug-like compounds to treat tuberculosis.Communicated by Ramaswamy H. Sarma.

This article describes the results of Laboratory Information Management System implementation in an expert institution dealing with medicines evaluation. The purpose of the study was to evaluate the feasibility of providing digital support to the laboratories of the Federal State Budgetary Institution «Scientific Centre for Expert Evaluation of Medicinal Products» of the Ministry of Health of the Russian Federation and to assess the functionality of the information system in terms of coverage of functional capabilities of various classes of laboratory software. The implementation of laboratory automation systems is required by the current regulations and is in line with international approaches to organisation of the quality management. The information system implementation made it possible to computerize management of drug samples, distribution of assignments among responsible employees, storage and management of documents, and reporting of test results. It created opportunities for direct integration with laboratory equipment allowing for creation of analytical protocols without manual data transfer. The implemented solution meets all requirements and standards for the functional capabilities of laboratory information systems. A similar system could be used in chemical, pharmacological, medical and biochemical laboratories, its implementation will make it possible to meet both national and international standards. These innovations are part of the concept of CALS/PLM technologies integration into institutions management programmes.

Resume. Different cell types respond differently to the effects of vitamin D3. The paper presents the results of a dose-dependent differential chemotranscriptome analysis of vitamin D3 in relation to breast tumor cells (MCF7 line) and neuron progenitor cells (NPC line). Expression of the genes involved in immunomodulation (192 genes) was significantly increased in tumor cells and in the intracellular signaling from receptors (275 genes) and the expression of genes involved in maintaining energy metabolism (482 genes), cell division / proliferation (387 genes), DNA repair (391 gene), synthesis and transport of proteins (188 genes) and in maintaining chronic inflammation (factors of TNF / NF-kB. 105 genes). In neuronal cells, similar changes in the expression of these categories of genes occurred to a much lesser extent and did not reach statistical significance. The reduction in DNA repair in tumor cells stimulates their apoptosis, the decrease in energy metabolism reduces the ability of tumor cells to divide and to resist therapeutic effects. It is interesting to note that vitamin D3 contributed to a decrease in the expression of genes supporting the cellular response to gamma radiation (9 genes) and contributed to the enhancement of the antitumor effects of vitamin A (5 genes). Also, vitamin D3 reduced the expression of genes that express potential target proteins of antitumor drugs (casein kinase, c-src tyrosine kinase, c-myc, etc.). Thus, vitamin D3 suppressed the division of tumor cells in a dose-dependent manner, without adversely affecting the survival of neurons.

Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research. [ABSTRACT FROM AUTHOR]

Objective: by using the pharmacoinformation profiling, to comprehensively assess all possible effects of the molecules of mexidol, choline alfoscerate, piracetam, glycine, and semax in accordance with the anatomical therapeutic and chemical (ATC) classification system.Material and methods. Chemoreactomic, pharmacoinformation, and geriatric information analyses of the properties of the molecules are based on chemoreactomic methodology. The chemoreactomic analysis uses the information from the PubChem, HMDB, and String databases; the pharmacoinformation analysis applies the information from the international ATC classification and a combined sample of data from the Therapeutic Target Database (TTD), SuperTarget, Manually Annotated Targets and Drugs Online Resource (MATADOR), and Potential Drug Target Database (PDTD); geriatric information analysis employs the data on the geroprotective effect of individual substances from the PubChem database and the data available in the literature data on geroprotection from the PubMed database, which have been collected through the artificial intelligence system.Results and discussion. Mexidol is characterized by the maximum set of positive effects (the drug is used to treat CNS and cardiovascular diseases and metabolic disorders and has anti-inflammatory and anti-infective properties, etc.). Mexidol and glycine are predicted to cause the lowest frequency of adverse reactions, such as itching, constipation, paresthesia, vomiting, etc. Geriatric information assessments of changes in the life span of model organisms have shown that mexidol contributes to the higher life expectancy of C. elegans (by 22.7±10%), Drosophila (by 14.4±15%), and mice (by 14.6±3%); the control drugs do by no more than 6.1%.Conclusion. The results of the study indicate that mexidol has a high potential to be used as a geroprotector.