Abstract: Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure-property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual "chemical spaces") and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely "consensus chemical space." This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events.

Abstract: In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG(50) data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

Abstract: The World Health Organization classifies Leishmania as one of the 17 "neglected diseases" that burden tropical and sub-tropical climate regions with over half a million diagnosed cases each year. Despite this, currently available anti-leishmania drugs have high toxicity and the potential to be made obsolete by parasite drug resistance. We chose to analyze organoselenides for leishmanicidal potential given the reduced toxicity inherent to selenium and the displayed biological activity of organoselenides against Leishmania. Thus, the biological activities of 77 selenoesters and their N-aryl-propanamide derivatives were predicted using robust in silico models of Leishmania infantum, Leishmania amazonensis, Leishmania major, and Leishmania (Viannia) braziliensis. The models identified 28 compounds with >60% probability of demonstrating leishmanicidal activity against L. infantum, and likewise, 26 for L. amazonesis, 25 for L. braziliensis, and 23 for L. major. The in silico prediction of ADMET properties suggests high rates of oral absorption and good bioavailability for these compounds. In the in silico toxicity evaluation, only seven compounds showed signs of toxicity in up to one or two parameters. The methodology was corroborated with the ensuing experimental validation, which evaluated the inhibition of the Promastigote form of the Leishmania species under study. The activity of the molecules was determined by the IC(50) value (microM); IC(50) values < 20 microM indicated better inhibition profiles. Sixteen compounds were synthesized and tested for their activity. Eight molecules presented IC(50) values < 20 microM for at least one of the Leishmania species under study, with compound NC34 presenting the strongest parasite inhibition profile. Furthermore, the methodology used was effective, as many of the compounds with the highest probability of activity were confirmed by the in vitro tests performed.

Abstract: Cephalosporins are a widely used subclass of beta-lactam antibiotics that demonstrate variable protein binding independent of generation or antibiotic coverage. Prior work analyzed carbon 3 (C3) and carbon 7 (C7) substituents (locations of R(2) and R(1) groups respectively) for protein binding interactions. This study builds upon these results with statistical analysis of additional agents of the class. Chemical structures of 23 cephalosporins were used to identify the presence of 40 functional groups, and correlative relationships were identified using established protein binding data. Four functional groups were significantly correlated with protein binding: tetrazole (positive association), pyridinium, primary amine, and quaternary amine (negative associations). Cephalosporins with a negative charge at physiological pH were associated with increased protein binding. Analysis of tetrazole-containing cephalosporins and ceftriaxone indicates the need for further study of the potential role in protein binding of neutral or negatively charged aromatic nitrogen heterocycles linked at the C3 position by a thiomethylene group.

Abstract: Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

Abstract: Sex differences have been widely observed in human health. However, little is known about the underlying mechanism behind these observed sex differences. We hypothesize that sex-differentiated genetic effects are contributors of these phenotypic differences. Focusing on a collection of drug metabolism enzymes and transporters (DMET) genes, we discover sex-differentiated genetic regulatory mechanisms between these genes and human complex traits. Here, we show that sex-differentiated genetic effects were present at genome-level and at DMET gene regions for many human complex traits. These sex-differentiated regulatory mechanisms are reflected in the levels of gene expression and endogenous serum biomarkers. Through Mendelian Randomization analysis, we identify putative sex-differentiated causal effects in each sex separately. Furthermore, we identify and validate sex differential gene expression of a subset of DMET genes in human liver samples. We observe higher protein abundance and enzyme activity of CYP1A2 in male-derived liver microsomes, which leads to higher level of an active metabolite formation of clozapine, a commonly prescribed antipsychotic drug. Taken together, our results demonstrate the presence of sex-differentiated genetic effects on DMET gene regulation, which manifest in various phenotypic traits including disease risks and drug responses.

Abstract: Medulloblastoma (MB), occurring in the cerebellum, is the most common childhood brain tumor. Because conventional methods decline life quality and endanger children with detrimental side effects, computer models are needed to imitate the characteristics of cancer cells and uncover effective therapeutic targets with minimum toxic effects on healthy cells. In this study, metabolic changes specific to MB were captured by the genome-scale metabolic brain model integrated with transcriptome data. To determine the roles of sphingolipid metabolism in proliferation and metastasis in the cancer cell, 79 reactions were incorporated into the MB model. The pathways employed by MB without a carbon source and the link between metastasis and the Warburg effect were examined in detail. To reveal therapeutic targets for MB, biomass-coupled reactions, the essential genes/gene products, and the antimetabolites, which might deplete the use of metabolites in cells by triggering competitive inhibition, were determined. As a result, interfering with the enzymes associated with fatty acid synthesis (FAs) and the mevalonate pathway in cholesterol synthesis, suppressing cardiolipin production, and tumor-supporting sphingolipid metabolites might be effective therapeutic approaches for MB. Moreover, decreasing the activity of succinate synthesis and GABA-catalyzing enzymes concurrently might be a promising strategy for metastatic MB.

Abstract: The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.

Abstract: Major depressive disorder with suicide behavior (sMDD) is a server mood disorder, bringing tremendous burden to family and society. Although reduced gamma amino butyric acid (GABA) level has been observed in postmortem tissues of sMDD patients, the molecular mechanism by which GABA levels are altered remains elusive. In this study, we generated induced pluripotent stem cells (iPSC) from five sMDD patients and differentiated the iPSCs to GABAergic interneurons (GINs) and ventral forebrain organoids. sMDD GINs exhibited altered neuronal morphology and increased neural firing, as well as weakened calcium signaling propagation, compared with controls. Transcriptomic sequencing revealed that a decreased expression of serotoninergic receptor 2C (5-HT2C) may cause the defected neuronal activity in sMDD. Furthermore, targeting 5-HT2C receptor, using a small molecule agonist or genetic approach, restored neuronal activity deficits in sMDD GINs. Our findings provide a human cellular model for studying the molecular mechanisms and drug discoveries for sMDD.

Abstract: Anticholinergic burden tools have relevant pharmacological gaps that may explain their limited predictive ability for clinical outcomes. The aim of this study was to provide a universal pharmacological-based list of drugs with their documented affinity for muscarinic receptors. A comprehensive literature review was performed to identify the anticholinergic burden tools. Drugs included in these instruments were searched in four pharmacological databases, and the investigation was supplemented with PubMed. The evidence regarding the potential antagonism of the five muscarinic receptors of each drug was assessed. The proportion of drugs included in the tools with an affinity for muscarinic receptors was evaluated. A universal list of drugs with anticholinergic activity was developed based on their documented affinity for the different subtypes of muscarinic receptors and their ability to cross the blood-brain barrier. A total of 23 tools were identified, including 304 different drugs. Only 48.68%, 47.70%, 48.03%, 43.75%, and 42.76% of the drugs had an affinity to the M1, M2, M3, M4, and M5 receptor, respectively, reported in any pharmacological database. The proportion of drugs with confirmed antagonism varied among the tools (36.8% to 100%). A universal pharmacological-based list of 133 drugs is presented. It should be further validated in different clinical settings.