Abstract: Macroautophagy/autophagy is a complex self-degradative mechanism responsible for clearance of non functional organelles and proteins. A range of factors influences the autophagic process, and disruptions in autophagy-related mechanisms lead to disease states, and further exacerbation of disease. Despite in-depth research into autophagy and its role in pathophysiological processes, the resources available to use it for therapeutic purposes are currently lacking. Herein we report the Autophagy Small Molecule Database (AutophagySMDB; http://www.autophagysmdb.org/ ) of small molecules and their cognate protein targets that modulate autophagy. Presently, AutophagySMDB enlists ~10,000 small molecules which regulate 71 target proteins. All entries are comprised of information such as EC50 (half maximal effective concentration), IC50 (half maximal inhibitory concentration), Kd (dissociation constant) and Ki (inhibition constant), IUPAC name, canonical SMILE, structure, molecular weight, QSAR (quantitative structure activity relationship) properties such as hydrogen donor and acceptor count, aromatic rings and XlogP. AutophagySMDB is an exhaustive, cross-platform, manually curated database, where either the cognate targets for small molecule or small molecules for a target can be searched. This database is provided with different search options including text search, advanced search and structure search. Various computational tools such as tree tool, cataloging tools, and clustering tools have also been implemented for advanced analysis. Data and the tools provided in this database helps to identify common or unique scaffolds for designing novel drugs or to improve the existing ones for autophagy small molecule therapeutics. The approach to multitarget drug discovery by identifying common scaffolds has been illustrated with experimental validation. Abbreviations: AMPK: AMP-activated protein kinase; ATG: autophagy related; AutophagySMDB: autophagy small molecule database; BCL2: BCL2, apoptosis regulator; BECN1: beclin 1; CAPN: calpain; MTOR: mechanistic target of rapamycin kinase; PPARG: peroxisome proliferator activated receptor gamma; SMILES: simplified molecular input line entry system; SQSTM1: sequestosome 1; STAT3: signal transducer and activator of transcription.

Abstract: This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate-depletion and metabolite-formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug-drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug-drug interaction pairs.

Abstract: Protein therapeutic higher order structure (HOS) is a quality attribute that can be assessed to help predict shelf life. To model product shelf-life values, possible sample-dependent pathways of degradation that may affect drug efficacy or safety need to be evaluated. As changes in drug thermal stability over time can be correlated with an increased risk of HOS perturbations, the effect of long-term storage on the product should be measured as a function of temperature. Here, complementary high-resolution mass spectrometry methods for HOS analysis were used to identify storage-dependent changes of biotherapeutics (bevacizumab (Avastin), trastuzumab (Herceptin), rituximab (Rituxan), and the NIST reference material 8671 (NISTmAb)) under accelerated or manufacturer-recommended storage conditions. Collision-induced unfolding ion mobility-mass spectrometry data showed changes in monoclonal antibody folded stability profiles that were consistent with the appearance of a characteristic unfolded population. Orthogonal hydrogen-deuterium exchange-mass spectrometry data revealed that the observed changes in unfolding occurred in parallel to changes in HOS localized to the periphery of the hinge region. Using intact reverse-phase liquid chromatography-mass spectrometry, we identified several mass species indicative of peptide backbone hydrolysis, located between the variable and constant domains of the heavy chain of bevacizumab. Taken together, our data highlighted the capability of these approaches to identify age- or temperature-dependent changes in biotherapeutic HOS.

Abstract: PURPOSE: Many targeted therapies are currently available only via clinical trials. Therefore, routine precision oncology using biomarker-based assignment to drug depends on matching patients to clinical trials. A comprehensive and up-to-date trial database is necessary for optimal patient-trial matching. METHODS: We describe processes for establishing and maintaining a clinical trial database, focusing on genomically informed trials. Furthermore, we present OCTANE (Oncology Clinical Trial Annotation Engine), an informatics framework supporting these processes in a scalable fashion. To illustrate how the framework can be applied at an institution, we describe how we implemented an instance of OCTANE at a large cancer center. OCTANE consists of three modules. The data aggregation module automates retrieval, aggregation, and update of trial information. The annotation module establishes the database schema, implements data integration necessary for automation, and provides an annotation interface. The update module monitors trial change logs, identifies critical change events, and alerts the annotators when manual intervention may be needed. RESULTS: Using OCTANE, we annotated 5,439 oncology clinical trials (4,438 genomically informed trials) that collectively were associated with 1,453 drugs, 779 genes, and 252 cancer types. To date, we have used the database to screen 4,220 patients for trial eligibility. We compared the update module with expert review, and the module achieved 98.5% accuracy, 0% false-negative rate, and 2.3% false-positive rate. CONCLUSION: OCTANE is a general informatics framework that can be helpful for establishing and maintaining a comprehensive database necessary for automating patient-trial matching, which facilitates the successful delivery of personalized cancer care on a routine basis. Several OCTANE components are publically available and may be useful to other precision oncology programs.

Abstract: Pneumonia is an infectious disease caused by bacteria, viruses or fungi that results in millions of deaths globally. Despite the existence of prophylactic methods against some of the major pathogens of the disease, there is no efficient prophylaxis against atypical agents such as Mycoplasma pneumoniae, a bacterium associated with cases of community-acquired pneumonia. Because of the morphological peculiarity of M. pneumoniae, which leads to an increased resistance to antibiotics, studies that prospectively investigate the development of vaccines and drug targets appear to be one of the best ways forward. Hence, in this paper, bioinformatics tools were used for vaccine and pharmacological prediction. We conducted comparative genomic analysis on the genomes of 88 M. pneumoniae strains, as opposed to a reverse vaccinology analysis, in relation to the capacity of M. pneumoniae proteins to bind to the major histocompatibility complex, revealing seven targets with immunogenic potential. Predictive cytoplasmic proteins were tested as potential drug targets by studying their structures in relation to other proteins, metabolic pathways and molecular anchorage, which identified five possible drug targets. These findings are a valuable addition to the development of vaccines and the selection of new in vivo drug targets that may contribute to further elucidating the molecular basis of M. pneumoniae-host interactions.

Abstract: OBJECTIVE: The aims of this study were to (1) determine whether opportunistically collected data can be used to develop physiologically based pharmacokinetic (PBPK) models in pediatric patients; and (2) characterize age-related maturational changes in drug disposition for the renally eliminated and hepatically metabolized antibiotic trimethoprim (TMP)-sulfamethoxazole (SMX). METHODS: We developed separate population PBPK models for TMP and SMX in children after oral administration of the combined TMP-SMX product and used sparse and opportunistically collected plasma concentration samples to validate our pediatric model. We evaluated predictability of the pediatric PBPK model based on the number of observed pediatric data out of the 90% prediction interval. We performed dosing simulations to target organ and tissue (skin) concentrations greater than the methicillin-resistant Staphylococcus aureus (MRSA) minimum inhibitory concentration (TMP 2 mg/L; SMX 9.5 mg/L) for at least 50% of the dosing interval. RESULTS: We found 67-87% and 71-91% of the observed data for TMP and SMX, respectively, were captured within the 90% prediction interval across five age groups, suggesting adequate fit of our model. Our model-rederived optimal dosing of TMP at the target tissue was in the range of recommended dosing for TMP-SMX in children in all age groups by current guidelines for the treatment of MRSA. CONCLUSION: We successfully developed a pediatric PBPK model of the combination antibiotic TMP-SMX using sparse and opportunistic pediatric pharmacokinetic samples. This novel and efficient approach has the potential to expand the use of PBPK modeling in pediatric drug development.

Abstract: Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug-target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug-target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the number of target proteins for a drug). This study aimed to address this issue by proposing NRLMF with beta distribution rescoring (NRLMFbeta), which is an algorithm to improve the score of NRLMF. The score of NRLMFbeta is equivalent to the value of the original NRLMF score when the concentration of the beta distribution becomes infinity. The beta distribution is known as a conjugative prior distribution of the Bernoulli distribution and can reflect the amount of interaction information to its shape based on Bayesian inference. Therefore, in NRLMFbeta, the beta distribution was used for rescoring the NRLMF score. In the evaluation experiment, we measured the average values of area under the receiver operating characteristics and area under precision versus recall and the 95% confidence intervals. The performance of NRLMFbeta was found to be better than that of NRLMF in the four types of benchmark datasets. Thus, we concluded that NRLMFbeta improved the prediction accuracy of NRLMF. The source code is available at https://github.com/akiyamalab/NRLMFb.

Abstract: Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 (miRNA), GSE68316 (lncRNA/mRNA) and GSE32453 (mRNA) expression profiles downloaded from the Gene Expression Omnibus datasets. Bioinformatics tools were employed to perform function and pathway enrichment analysis, proteinprotein interaction, lncRNAmiRNAmRNA and hub gene networks. Subsequently, DEGs were used as targets to predict drugs. The results indicated that a total of 2,234 DElncs (1,120 upregulated and 1,114 downregulated), 5 DEmiRs (2 upregulated and 3 downregulated) and 42 DEGs (35 upregulated and 7 downregulated) were identified in 4 microarray profiles. Gene ontology analysis revealed that DEGs were mainly involved in actin filament and stress fiber formation and in calcium ion binding, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the hypoxia inducible factor1, transforming growth factorbeta and tumor necrosis factor signaling pathways as the main pathways involved in these processes. The hub genes were screened using cytoHubba. A total of 1,086 lncRNAmiRNAmRNA interactions including 67 lncRNAs, 5 miRNAs and 25 mRNAs were mined in the present study based on prediction websites. Drug prediction indicated that the targeted drugs mainly included angiotensin converting enzyme inhibitors or betablockers. A comprehensive bioinformatics analysis of the molecular regulatory lncRNAmiRNAmRNA network was performed and potential therapeutic applications of drugs were predicted in HCM patients. The data may unravel the future molecular mechanism of HCM.

Abstract: Jinzhen oral liquid (JZ) is a classical traditional Chinese medicine formula used for the treatment of children lung disease. However, the effective substance of JZ is still unclear. In this study, we used lung injury rat model to study the protective effect of JZ, through UPLC-Q-TOF/MS detection coupled with metabolic research and network pharmacology analysis. Fortunately, 31 absorbed prototype constituents and 41 metabolites were identified or tentatively characterized based on UPLC-Q-TOF/MS analysis, and the possible metabolic pathways were hydroxylation, sulfation and glucuronidation. We optimized the data screening in the early stage of network pharmacology by collecting targets based on adsorbed constituents, and further analyzed the main biological processes and pathways. 24 selected core targets were frequently involved in reactive oxygen species metabolic process, dopaminergic synapse pathway and so on, which might play important roles in the mechanisms of JZ for the treatment of lung injury. Overall, the absorbed constituents and their possible metabolic pathways, as well as the absorbed constituent-target-disease network provided insights into the mechanisms of JZ for the treatment of lung injury. Further studies are needed to validate the biological processes and effect pathways of JZ.

Abstract: The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 microM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 microM, 366.8 microM and 391.6 microM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.