Abstract: Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

Abstract: MOTIVATION: Much effort has been invested in the identification of protein-protein interactions using text mining and machine learning methods. The extraction of functional relationships between chemical compounds and proteins from literature has received much less attention, and no ready-to-use open-source software is so far available for this task. METHOD: We created a new benchmark dataset of 2,613 sentences from abstracts containing annotations of proteins, small molecules, and their relationships. Two kernel methods were applied to classify these relationships as functional or non-functional, named shallow linguistic and all-paths graph kernel. Furthermore, the benefit of interaction verbs in sentences was evaluated. RESULTS: The cross-validation of the all-paths graph kernel (AUC value: 84.6%, F1 score: 79.0%) shows slightly better results than the shallow linguistic kernel (AUC value: 82.5%, F1 score: 77.2%) on our benchmark dataset. Both models achieve state-of-the-art performance in the research area of relation extraction. Furthermore, the combination of shallow linguistic and all-paths graph kernel could further increase the overall performance slightly. We used each of the two kernels to identify functional relationships in all PubMed abstracts (29 million) and provide the results, including recorded processing time. AVAILABILITY: The software for the tested kernels, the benchmark, the processed 29 million PubMed abstracts, all evaluation scripts, as well as the scripts for processing the complete PubMed database are freely available at https://github.com/KerstenDoering/CPI-Pipeline.

Abstract: Pharmacovigilance is the pharmacological science that focuses on the safe and appropriate use of drugs.Variability in response to drug therapy in both terms of safety and efficacy is highly related to patient's personal genomics. Hence, pharmacovigilance considers pharmacogenomics methodologies in the evaluation of medicinal products. The aim of this work is to introduce the pharmacovigilance/ pharmacogenomics insilico pipeline (PHARMIP) that uses the drug (or drug candidate) digital structure and the advances in bioinformatics tools and databases to figure-out the genetic factors underlying the drug reported adverse reactions (ADRs).PHARMIP uses user-friendly freely available bioinformatics resources to help pharmacovigilance and pharmacogenomics scientists with minimal bioinformatics experience to retrieve helpful information for their daily basis activities. Also, PHARMIP could help the advances in precision medicine in a drug-centric approach as it can be used to reveal genetic risk factors for certain drug ADRs. Domperidone was used as an example to the application of PHARMIP as the pipeline was initially developed during the insilico exploration of domperidone cardiotoxic ADRs. Method is composed of 3 main steps: *Preparing the drug off-label targets (OLT) list.*Retrieving the related diseases/ adverse reactions (DA) list.*Analysis of DA list to get answers.

Abstract: Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/.

Abstract: Purpose: Maytansine (DM1) is a potent anticancer drug and limited in clinical application due to its poor water solubility and toxic side effects. Zein is widely used in nano drug delivery systems due to its good biocompatibility. In this study, we prepared DM1-loaded zein nanoparticles (ZNPs) to achieve tumor targeting and reduce toxic side effects of DM1. Methods: ZNPs were prepared by phase separation and Box-Behnken design was used to optimize the formulation. Then, confocal fluorescence microscope and flow cytometry were used to determine cellular uptake of ZNPs. A549 cells were cultured in vitro to study cytotoxicity and used to establish tumor xenografts in nude mice. Biodistribution and antitumor activity of ZNPs were performed in vivo experiments. In addition, we also performed histological and immunohistochemical examinations on tumors and viscera. Results: The optimal prescription was obtained by using 120 muL zein added to 2 mL water under stirring in 300 rpm. The encapsulation efficiency and drug loading were 82.97 +/- 0.80% and 3.32 +/- 0.03%, respectively. We found that DM1-loaded ZNPs have a strong inhibitory effect on A549 cells, which stemmed from the ability of ZNPs to enhance cellular uptake. Furthermore, we demonstrated that DM1-loaded ZNPs exhibits a better antitumor efficacy than DM1, which tumor inhibition rate were 97.3% and 92.7%, respectively. The biodistribution revealed that ZNPs could targeted to tumor. Finally, we confirmed by histological that DM1-loaded ZNPs are nontoxic. Conclusion: DM1-loaded ZNPs have considerable antitumor activity. Thus, DM1-loaded ZNPs are a promising treatment of non-small cell lung cancer.

Abstract: It is essential for the advancement of science that researchers share, reuse and reproduce each other's workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

Abstract: Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel drug combination discovery. In this study, we proposed a new simplified deep learning model, DeepSignalingSynergy, for drug combination prediction. Compared with existing models that use a large number of chemical-structure and genomics features in densely connected layers, we built the model on a small set of cancer signaling pathways, which can mimic the integration of multi-omics data and drug target/mechanism in a more biological meaningful and explainable manner. The evaluation results of the model using the NCI ALMANAC drug combination screening data indicated the feasibility of drug combination prediction using a small set of signaling pathways. Interestingly, the model analysis suggested the importance of heterogeneity of the 46 signaling pathways, which indicates that some new signaling pathways should be targeted to discover novel synergistic drug combinations.

Abstract: Dietary supplementation of flavonoids has been shown to reduce the severity of neurodegenerative disorders such as dementia, Parkinson's disease, and Alzheimer's disease by their antioxidant effects. However, their low bioavailabilityin vivo raises the question of how much their antioxidant capacity actually contributes to the mitigating effects. The physicochemical properties of flavonoids suggest they could function as mitochondrial uncouplers. Moreover, mitochondrial uncoupling alleviated neurodegeneration in Caenorhabditis elegans during aging in previous research. Therefore, we investigated whether various flavonoids (fisetin, quercetin, apigenin, chrysin, catechin, and naringenin) could reduce neuronal defects by mitochondrial uncoupling in C. elegans. Both neuronal defects and mitochondrial membrane potential were reduced in aged worms in nearly all of the flavonoid treatments suggesting that flavonoids may reduce neurodegeneration in C. elegans. However, there was no significant reduction of neuronal defects in mitophagy-deficient pink-1/pdr-1 double mutants under flavonoid treatments. These results suggest that flavonoids could function as mitochondrial uncouplers to mitigate neurodegeneration in aged C. elegans, possibly via a PINK1/Parkin mitophagy process.

Abstract: Genistein is a type of isoflavone, which has been widely described as an antitumor agent in many cancers. The present study aimed to provide information on the mechanisms of genistein's activity and thus enable a wider range of targeted therapies in hepatitis B virus (HBV)-related liver cancer. We searched the DrugBank database for direct targets of genistein, which were then analyzed through the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database to predict their secondary protein targets. Thirteen primary protein targets of genistein and 209 secondary protein targets-associated genes were identified. The data were integrated into the network of protein targets-associated genes and visualized with the Cytoscape software. We further carried out GO (Gene Ontology) analysis and KEGG (Kyoto Encyclopedia of Gene and Genome) pathway analysis using DAVID (database for annotation, visualization, and integrated discovery) tool. The top 14 KEGG pathways were further assessed, and 19 overlapping genes derived from pathways of hepatitis B and cancer were discovered. The overlapping targets were further mapped in the online tool UALCAN to evaluate the survival rate of hepatocellular carcinoma (HCC) patients. We found that the overexpression of Grb2 (growth factor receptor-binding protein 2) (p < 0.0001) was linked to poor overall survival for liver HCC patients, followed by AKT1 (p = 0.0015) and PIK3CA (p = 0.0088). The present study analyzes the drug-target-disease network and may prove to be a useful tool in gene-phenotype connectivity for genistein in HBV-related liver cancer. Our data also pave the way for further research on Grb2 during the development of chronic HBV infection in liver cancer.

Abstract: One prominent class of drugs is chemical small molecules (CSMs), but the majority of CSMs are of very low druggable potential. Therefore, it is quite important to predict drug-related properties (druggable properties) for candidate CSMs. Currently, a number of druggable properties (e.g., logP and pKa) can be calculated by in silico methods; still the identification of druggable CSMs is a high-risk task, and new quantitative metrics for the druggable potential of CSMs are increasingly needed. Here, we present normalized bond energy (NBE), a new metric for the above purpose. By applying NBE to the DrugBank CSMs whose properties are largely known, we revealed that NBE is able to describe a number of critical druggable properties including logP, pKa, membrane permeability, blood-brain barrier penetration, and human intestinal absorption. Moreover, given that the human endogenous metabolites can serve as important resources for drug discovery, we applied NBE to the metabolites in the Human Metabolome Database. As a result, NBE showed a significant difference in metabolites from various body fluids and was correlated with some important properties, including melting point and water solubility.