Abstract: BACKGROUND: Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. RESULTS: The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89-0.99. CONCLUSIONS: The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining.

Abstract: Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects nearly eight million people worldwide. There are currently only limited treatment options, which cause several side effects and have drug resistance. Thus, there is a great need for a novel, improved Chagas treatment. Bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) has emerged as a promising pharmacological target. Moreover, some human dihydrofolate reductase (HsDHFR) inhibitors such as trimetrexate also inhibit T. cruzi DHFR-TS (TcDHFR-TS). These compounds serve as a starting point and a reference in a screening campaign to search for new TcDHFR-TS inhibitors. In this paper, a novel virtual screening approach was developed that combines classical docking with protein-ligand interaction profiling to identify drug repositioning opportunities against T. cruzi infection. In this approach, some food and drug administration (FDA)-approved drugs that were predicted to bind with high affinity to TcDHFR-TS and whose predicted molecular interactions are conserved among known inhibitors were selected. Overall, ten putative TcDHFR-TS inhibitors were identified. These exhibited a similar interaction profile and a higher computed binding affinity, compared to trimetrexate. Nilotinib, glipizide, glyburide and gliquidone were tested on T. cruzi epimastigotes and showed growth inhibitory activity in the micromolar range. Therefore, these compounds could lead to the development of new treatment options for Chagas disease.

Abstract: A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs-disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug-disease associations through the analysis of the similarity of GES profiles on known pairs of drug-disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug-disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.

Abstract: The outbreak due to SARS-CoV-2 (or Covid-19) is spreading alarmingly and number of deaths due to infection is aggressively increasing every day. Due to the rapid human to human transmission of Covid-19, we are in need to find a potent drug at the earliest by ruling-out the traditional time-consuming approach of drug development. This is only possible if we use reliable computational approaches for screening compounds from chemical space or by drug repurposing or by finding the phytochemicals and nutraceuticals from plants as they can be immediately used without the need for carrying out drug-trials to test safety and efficacy. A number of plant products were routinely suggested as drugs in traditional Indian and Chinese medicine. Here using molecular docking approach, and combined molecular dynamics and MM-GBSA based free energy calculations approach, we study the potency of the four selected phytochemicals namely andrographolide (AGP1), 14-deoxy 11,12-didehydro andrographolide (AGP2), neoandrographolide (AGP3) and 14-deoxy andrographolide (AGP4) from A. paniculata plant against the four key targets including three non-structural proteins (3 L main protease (3CLpro), Papain-like proteinase (PLpro) and RNA-directed RNA polymerase (RdRp)) and a structural protein (spike protein (S)) of the virus which are responsible for replication, transcription and host cell recognition. The therapeutic potential of the selected phytochemicals against Covid-19 were also evaluated in comparison with a few commercially available drugs. The binding free energy data suggest that AGP3 could be used as a cost-effective drug-analog for treating covid-19 infection in developing countries.Communicated by Ramaswamy H. Sarma.

Abstract: The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug-drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim((R)), using 45 clinical studies (dosing range 0.1-250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or Ctrough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

Abstract: BACKGROUND: Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research. OBJECTIVE: The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution. METHODS: In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P1), Promising Index (P2), Prestige Index (P3), and Collaboration Index (CI). RESULTS: We found that the maxima of P1, P3, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P1 and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P2 was more sensitive to immediate changes. P3 reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug. CONCLUSIONS: In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.

Abstract: As of June 2020, the number of people infected with severe acute respiratory coronavirus 2 (SARS-CoV-2) continues to skyrocket, with more than 6.7 million cases worldwide. Both the World Health Organization (WHO) and United Nations (UN) has highlighted the need for better control of SARS-CoV-2 infections. However, developing novel virus-specific vaccines, monoclonal antibodies and antiviral drugs against SARS-CoV-2 can be time-consuming and costly. Convalescent sera and safe-in-man broad-spectrum antivirals (BSAAs) are readily available treatment options. Here, we developed a neutralization assay using SARS-CoV-2 strain and Vero-E6 cells. We identified the most potent sera from recovered patients for the treatment of SARS-CoV-2-infected patients. We also screened 136 safe-in-man broad-spectrum antivirals against the SARS-CoV-2 infection in Vero-E6 cells and identified nelfinavir, salinomycin, amodiaquine, obatoclax, emetine and homoharringtonine. We found that a combination of orally available virus-directed nelfinavir and host-directed amodiaquine exhibited the highest synergy. Finally, we developed a website to disseminate the knowledge on available and emerging treatments of COVID-19.

Abstract: BACKGROUND: Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules. RESULTS: Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints. CONCLUSION: MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.

Abstract: BACKGROUND: Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources. RESULTS: We gathered data on therapeutic strategies for 300 diseases into the Drug Database for Inborn Errors of Metabolism (DDIEM). Therapeutic approaches, including both successful and ineffective treatments, were manually classified by their mechanisms of action using a new ontology. CONCLUSIONS: We present a manually curated, ontologically formalized knowledgebase of drugs, therapeutic procedures, and mitigated phenotypes. DDIEM is freely available through a web interface and for download at http://ddiem.phenomebrowser.net.

Abstract: The 3-dimensional (3D) structure of therapeutics and other bioactive molecules is an important factor in determining the strength and selectivity of their protein-ligand interactions. Previous efforts have considered the strain introduced and tolerated through conformational changes induced upon protein binding. Herein, we present an analysis of 3-dimentionality for energy-minimized structures from the DrugBank and ligands bound to proteins identified in the Protein Data Bank (PDB). This analysis reveals that the majority of molecules found in both the DrugBank and the PDB tend toward linearity and planarity, with few molecules having highly 3D conformations. Decidedly 3D geometries have been historically difficult to achieve, likely due to the synthetic challenge of making 3D organic molecules, and other considerations, such as adherence to the 'rule-of-five'. This has resulted in the dominance of planar and/or linear topologies of the molecules described here. Strategies to address the generally flat nature of these data sets are explored, including the use of 3D organic fragments and inorganic scaffolds as a means of accessing privileged 3D space. This work highlights the potential utility of libraries with greater 3D topological diversity so that the importance of molecular shape to biological behavior can be more fully understood in drug discovery campaigns.