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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024:1-27. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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2
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Tarasova OA, Rudik AV, Biziukova NY, Filimonov DA, Poroikov VV. Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. J Cheminform 2022; 14:55. [PMID: 35964150 PMCID: PMC9375066 DOI: 10.1186/s13321-022-00633-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/12/2022] [Indexed: 11/24/2022] Open
Abstract
Motivation Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical–chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. Methods and results We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. Conclusion The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00633-4.
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Affiliation(s)
- O A Tarasova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia.
| | - A V Rudik
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - N Yu Biziukova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - D A Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - V V Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
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Selvaraj V, Rathinavel T, Ammashi S, Nasir Iqbal M. Polyphenolic Phytochemicals Exhibit Promising SARS-COV-2 Papain Like Protease (PLpro) Inhibition Validated through a Computational Approach. Polycycl Aromat Compd 2022. [DOI: 10.1080/10406638.2022.2103578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Vasuki Selvaraj
- Department of Biotechnology, Sona College of Arts and Science, Salem, India
| | | | - Subramanian Ammashi
- PG and Research Department of Biochemistry, Rajah Serfoji Government College, Thanjavur, India
| | - Muhammad Nasir Iqbal
- Department of Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
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Poleksic A. Overcoming Sparseness of Biomedical Networks to Identify Drug Repositioning Candidates. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2377-2384. [PMID: 33591920 DOI: 10.1109/tcbb.2021.3059807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on exploration of diverse biochemical concepts and their relationships, including drug's adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs matrix completion to overcome the sparseness of biomedical data and to enrich the set of relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.
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Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection. Sci Rep 2022; 12:7843. [PMID: 35551258 PMCID: PMC9096754 DOI: 10.1038/s41598-022-11879-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
As there are no clear on-target mechanisms that explain the increased risk for thrombosis and viral infection or reactivation associated with JAK inhibitors, the observed elevated risk may be a result of an off-target effect. Computational approaches combined with in vitro studies can be used to predict and validate the potential for an approved drug to interact with additional (often unwanted) targets and identify potential safety-related concerns. Potential off-targets of the JAK inhibitors baricitinib and tofacitinib were identified using two established machine learning approaches based on ligand similarity. The identified targets related to thrombosis or viral infection/reactivation were subsequently validated using in vitro assays. Inhibitory activity was identified for four drug-target pairs (PDE10A [baricitinib], TRPM6 [tofacitinib], PKN2 [baricitinib, tofacitinib]). Previously unknown off-target interactions of the two JAK inhibitors were identified. As the proposed pharmacological effects of these interactions include attenuation of pulmonary vascular remodeling, modulation of HCV response, and hypomagnesemia, the newly identified off-target interactions cannot explain an increased risk of thrombosis or viral infection/reactivation. While further evidence is required to explain both the elevated thrombosis and viral infection/reactivation risk, our results add to the evidence that these JAK inhibitors are promiscuous binders and highlight the potential for repurposing.
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Repurposing approved therapeutics for new indication: Addressing unmet needs in psoriasis treatment. CURRENT RESEARCH IN PHARMACOLOGY AND DRUG DISCOVERY 2021; 2:100041. [PMID: 34909670 PMCID: PMC8663928 DOI: 10.1016/j.crphar.2021.100041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Psoriasis is a chronic inflammatory autoimmune condition manifested by the hyperproliferation of keratinocytes with buildup of inflammatory red patches and scales on skin surfaces. The available treatment options for the management of psoriasis have various drawbacks, and the clinical need for effective therapeutics for this disease remain unmet; therefore, the approaches of drug repurposing or drug repositioning could potentially be used for treating indications of psoriasis. The undiscovered potential of drug repurposing or repositioning compensates for the limitations and hurdles in drug discovery and drug development processes. Drugs initially approved for other indications, including anticancer, antidiabetic, antihypertensive, and anti-arthritic activities, are being investigated for their potential in psoriasis management as a new therapeutic indication by using repurposing strategies. This article envisages the potential of various therapeutics for the management of psoriasis. Psoriasis is an autoimmune inflammatory skin disorder with complex physiology. Conventional treatments for psoriasis cause severe adverse effects; therefore an unmet need remains for safer and more effective therapies for psoriasis. Various drugs that effectively decrease the inflammation and proliferation of skin cells can be repurposed for the management of psoriasis. Repurposed drugs provide various incentives to the pharmaceutical industry.
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Trindade JDS, Freire-de-Lima CG, Côrte-Real S, Decote-Ricardo D, Freire de Lima ME. Drug repurposing for Chagas disease: In vitro assessment of nimesulide against Trypanosoma cruzi and insights on its mechanisms of action. PLoS One 2021; 16:e0258292. [PMID: 34679091 PMCID: PMC8535186 DOI: 10.1371/journal.pone.0258292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022] Open
Abstract
Chagas disease is a neglected illness caused by Trypanosoma cruzi and its treatment is done only with two drugs, nifurtimox and benznidazole. However, both drugs are ineffective in the chronic phase, in addition to causing serious side effects. This context of therapeutic limitation justifies the continuous research for alternative drugs. Here, we study the in vitro trypanocidal effects of the non-steroidal anti-inflammatory drug nimesulide, a molecule that has in its chemical structure a toxicophoric nitroaromatic group (NO2). The set of results obtained in this work highlights the potential for repurposing nimesulide in the treatment of this disease that affects millions of people around the world.
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Affiliation(s)
- Joana D’Arc S. Trindade
- Instituto de Química, Departamento de Química Orgânica, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, Brazil
| | - Célio Geraldo Freire-de-Lima
- Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Ilha do Fundão, Rio de Janeiro, Brazil
| | - Suzana Côrte-Real
- Instituto Oswaldo Cruz/Fiocruz, Laboratório de Biologia Estrutural, Rio de Janeiro, Brazil
| | - Debora Decote-Ricardo
- Instituto de Veterinária, Departamento de Microbiologia e Imunologia Veterinária, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, Brazil
| | - Marco Edilson Freire de Lima
- Instituto de Química, Departamento de Química Orgânica, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, Brazil
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KC GB, Bocci G, Verma S, Hassan MM, Holmes J, Yang JJ, Sirimulla S, Oprea TI. A machine learning platform to estimate anti-SARS-CoV-2 activities. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00335-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Hernández-Lemus E, Martínez-García M. Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics. Front Oncol 2021; 10:605680. [PMID: 33520715 PMCID: PMC7841291 DOI: 10.3389/fonc.2020.605680] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.
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Affiliation(s)
- Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Mireya Martínez-García
- Sociomedical Research Unit, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
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10
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Dafniet B, Cerisier N, Audouze K, Taboureau O. Drug-target-ADR Network and Possible Implications of Structural Variants in Adverse Events. Mol Inform 2020; 39:e2000116. [PMID: 32725965 PMCID: PMC8047896 DOI: 10.1002/minf.202000116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 07/28/2020] [Indexed: 12/19/2022]
Abstract
Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the biological complexity of human systems, understanding the underlying mechanisms involved in development of ADRs remains a challenging task. Here, we applied network sciences to analyze a tripartite network between 1000 drugs, 1407 targets, and 6164 ADRs. It allowed us to suggest drug targets susceptible to be associated to ADRs and organs, based on the system organ class (SOC). Furthermore, a score was developed to determine the contribution of a set of proteins to ADRs. Finally, we identified proteins that might increase the susceptibility of genes to ADRs, on the basis of knowledge about genomic structural variation in genes encoding proteins targeted by drugs. Such analysis should pave the way to individualize drug therapy and precision medicine.
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Affiliation(s)
- Bryan Dafniet
- Université de ParisINSERM U1133, CNRS UMR 825175006ParisFrance
| | | | - Karine Audouze
- Université de ParisT3S, INSERM UMR S-112475006ParisFrance
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11
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Govinda KC, Bocci G, Verma S, Hassan M, Holmes J, Yang JJ, Sirimulla S, Oprea TI. REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12915779. [PMID: 33200119 PMCID: PMC7668752 DOI: 10.26434/chemrxiv.12915779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 09/16/2020] [Indexed: 11/09/2022]
Abstract
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.
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Affiliation(s)
- KC Govinda
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Srijan Verma
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Pharmacy, Birla Institute of Technology and Science, Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Mahmudulla Hassan
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Jeremy J. Yang
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Suman Sirimulla
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Abstract
Drug repositioning aims to reuse "old" drugs to treat diseases outside their approved indication(s). Composition-of-matter patents and FDA exclusivities can hinder the immediate availability of some drugs to be repositioned (repurposed). Here, we analyze data from the FDA Orange Book and use current on-market patent validity and exclusivities to classify drugs into on-patent (ONP), off-patent (OFP), and off-market (OFM) sets. In the absence of an unanimously accepted definition for small molecules, these sets include organic molecules and peptides with molecular weight between 100 and 1250, which resulted in 237 ONP drugs, 320 OFM, and 996 OFP drugs, respectively. We discuss the differences between the three categories in terms of primary molecular properties, chemical diversity, mechanism-of-action target classes, and therapeutic areas and comment on the enrichment of OFP drugs in the near future. Given the intellectual property landscape, and in the absence of specific property rights, we suggest that drugs should be prioritized as follows, to improve the repositioning strategy: (i) OFP, (ii) OFM, and (iii) ONP, respectively.
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Affiliation(s)
- Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Boulevard, Timişoara, Timiş 300223, România
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico 87131, United States.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 413 90 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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Wu Q, Taboureau O, Audouze K. Development of an adverse drug event network to predict drug toxicity. Curr Res Toxicol 2020; 1:48-55. [PMID: 34345836 PMCID: PMC8320634 DOI: 10.1016/j.crtox.2020.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/28/2022] Open
Abstract
Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.
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Key Words
- ADE, adverse drug event
- ADR, adverse drug reaction
- AOP, adverse outcome pathway
- Adverse event network
- Computational toxicology
- FAERS, FDA Adverse Event Reporting System
- FDA, Food and Drug Administration
- HMS-PCI, high-throughput mass spectrometric protein complex identification
- LRT, Likelihood Ratio Test
- MedDRA, Medical Dictionary for Regulatory Activities
- Network science
- PPAN, protein-protein association network
- PT, Preferred Term
- Predictive toxicity
- QSAR, Quantitative structure-activity relationships
- SE, side effect
- SOC, System Organ Class
- System toxicology
- TAP–MS, tandem-affinity-purification method coupled to mass spectrometry
- pullS, pull-down score
- wS, weighted score
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Affiliation(s)
- Qier Wu
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
| | - Olivier Taboureau
- Université de Paris, BFA, CNRS UMR 8251, ERL Inserm U1133, CNRS UMR 8251, F-75013 Paris, France
| | - Karine Audouze
- Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France
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Prakash AV, Park JW, Seong JW, Kang TJ. Repositioned Drugs for Inflammatory Diseases such as Sepsis, Asthma, and Atopic Dermatitis. Biomol Ther (Seoul) 2020; 28:222-229. [PMID: 32133828 PMCID: PMC7216745 DOI: 10.4062/biomolther.2020.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 02/11/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
The process of drug discovery and drug development consumes billions of dollars to bring a new drug to the market. Drug development is time consuming and sometimes, the failure rates are high. Thus, the pharmaceutical industry is looking for a better option for new drug discovery. Drug repositioning is a good alternative technology that has demonstrated many advantages over de novo drug development, the most important one being shorter drug development timelines. In the last two decades, drug repositioning has made tremendous impact on drug development technologies. In this review, we focus on the recent advances in drug repositioning technologies and discuss the repositioned drugs used for inflammatory diseases such as sepsis, asthma, and atopic dermatitis.
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Affiliation(s)
- Annamneedi Venkata Prakash
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Jun Woo Park
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Ju-Won Seong
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
| | - Tae Jin Kang
- Convergence Research Center, Department of Pharmacy and Institute of Chronic Disease, Sahmyook University, Seoul 01795, Republic of Korea
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Zahoránszky-Kőhalmi G, Sheils T, Oprea TI. SmartGraph: a network pharmacology investigation platform. J Cheminform 2020; 12:5. [PMID: 33430980 PMCID: PMC6974502 DOI: 10.1186/s13321-020-0409-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 01/07/2020] [Indexed: 11/18/2022] Open
Abstract
Motivation Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. Results The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. Availability https://smartgraph.ncats.io/.
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Affiliation(s)
- Gergely Zahoránszky-Kőhalmi
- National Center for Advancing Translational Sciences, Rockville, MD, USA. .,Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
| | - Timothy Sheils
- National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.,UNM Comprehensive Cancer Center, Albuquerque, NM, USA.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy At University of Gothenburg, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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16
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Johnson EO, Hung DT. A Point of Inflection and Reflection on Systems Chemical Biology. ACS Chem Biol 2019; 14:2497-2511. [PMID: 31613592 DOI: 10.1021/acschembio.9b00714] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
For the past several decades, chemical biologists have been leveraging chemical principles for understanding biology, tackling disease, and biomanufacturing, while systems biologists have holistically applied computation and genome-scale experimental tools to the same problems. About a decade ago, the benefit of combining the philosophies of chemical biology with systems biology into systems chemical biology was advocated, with the potential to systematically understand the way small molecules affect biological systems. Recently, there has been an explosion in new technologies that permit massive expansion in the scale of biological experimentation, increase access to more diverse chemical space, and enable powerful computational interpretation of large datasets. Fueled by these rapidly increasing capabilities, systems chemical biology is now at an inflection point, poised to enter a new era of more holistic and integrated scientific discovery. Systems chemical biology is primed to reveal an integrated understanding of fundamental biology and to discover new chemical probes to comprehensively dissect and systematically understand that biology, thereby providing a path to novel strategies for discovering therapeutics, designing drug combinations, avoiding toxicity, and harnessing beneficial polypharmacology. In this Review, we examine the emergence of new capabilities driving us to this inflection point in systems chemical biology, and highlight holistic approaches and opportunities that are arising from integrating chemical biology with a systems-level understanding of the intersection of biology and chemistry.
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Affiliation(s)
- Eachan O. Johnson
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Deborah T. Hung
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, United States
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17
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Tambuyzer E, Vandendriessche B, Austin CP, Brooks PJ, Larsson K, Miller Needleman KI, Valentine J, Davies K, Groft SC, Preti R, Oprea TI, Prunotto M. Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov 2019; 19:93-111. [PMID: 31836861 DOI: 10.1038/s41573-019-0049-9] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2019] [Indexed: 12/26/2022]
Abstract
Most rare diseases still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to catalyse the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. With this in mind, we discuss here the technological basis and rare disease applicability of the main therapeutic modalities, including small molecules, monoclonal antibodies, protein replacement therapies, oligonucleotides and gene and cell therapies, as well as drug repurposing. For each modality, we consider its strengths and limitations as a platform for rare disease therapy development and describe clinical progress so far in developing drugs based on it. We also discuss selected overarching topics in the development of therapies for rare diseases, such as approval statistics, engagement of patients in the process, regulatory pathways and digital tools.
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Affiliation(s)
- Erik Tambuyzer
- BioPontis Alliance for Rare Diseases Foundation fup/son, Brussels, Belgium. .,BioPontis Alliance Rare Disease Foundation, Inc, Raleigh, NC, USA.
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium.,Department of Electrical, Computer, and Systems Engineering (ECSE), Case Western Reserve University, Cleveland, OH, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Philip J Brooks
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Kristina Larsson
- Orphan Medicines Office, European Medicines Agency, Amsterdam, Netherlands
| | | | | | - Kay Davies
- MDUK Oxford Neuromuscular Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Stephen C Groft
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Robert Preti
- Hitachi Chemical Regenerative Medicine Business Sector, Allendale, NJ, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Albuquerque, Albuquerque, NM, USA.,UNM Comprehensive Cancer Center, University of New Mexico Health Science Center, Albuquerque, NM, USA
| | - Marco Prunotto
- School of Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
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18
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A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today 2019; 25:248-258. [PMID: 31705979 DOI: 10.1016/j.drudis.2019.10.014] [Citation(s) in RCA: 167] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/18/2019] [Accepted: 10/30/2019] [Indexed: 01/12/2023]
Abstract
Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research.
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19
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Tarasova OA, Biziukova NY, Filimonov DA, Poroikov VV, Nicklaus MC. Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications. J Chem Inf Model 2019; 59:3635-3644. [PMID: 31453694 DOI: 10.1021/acs.jcim.9b00164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.
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Affiliation(s)
- Olga A Tarasova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Nadezhda Yu Biziukova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Vladimir V Poroikov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research , National Cancer Institute , Frederick , Maryland 21702 , United States
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20
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Pizzorno A, Padey B, Terrier O, Rosa-Calatrava M. Drug Repurposing Approaches for the Treatment of Influenza Viral Infection: Reviving Old Drugs to Fight Against a Long-Lived Enemy. Front Immunol 2019; 10:531. [PMID: 30941148 PMCID: PMC6434107 DOI: 10.3389/fimmu.2019.00531] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 02/27/2019] [Indexed: 12/18/2022] Open
Abstract
Influenza viruses still constitute a real public health problem today. To cope with the emergence of new circulating strains, but also the emergence of resistant strains to classic antivirals, it is necessary to develop new antiviral approaches. This review summarizes the state-of-the-art of current antiviral options against influenza infection, with a particular focus on the recent advances of anti-influenza drug repurposing strategies and their potential therapeutic, regulatory and economic benefits. The review will illustrate the multiple ways to reposition molecules for the treatment of influenza, from adventitious discovery to in silico-based screening. These novel antiviral molecules, many of which targeting the host cell, in combination with conventional antiviral agents targeting the virus, will ideally enter the clinics and reinforce the therapeutic arsenal to combat influenza virus infections.
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21
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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22
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Chen G, Jia Y, Zhu L, Li P, Zhang L, Tao C, Jim Zheng W. Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD. BMC Med Inform Decis Mak 2019; 19:20. [PMID: 30700303 PMCID: PMC6354331 DOI: 10.1186/s12911-019-0738-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Disease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support the discovery of new drugs or new uses of existing drugs. METHODS In this paper, we introduced a mathematical model to represent gene related diseases with a series of associated genes based on the overrepresentation of genes and diseases in PubMed literature. We also illustrated an efficient way to reveal the implicit connections between COPD and other diseases based on this model. RESULTS We applied this approach to analyze the relationships between Chronic Obstructive Pulmonary Disease (COPD) and other diseases under the Lung diseases branch in the Medical subject heading index system and detected 4 novel diseases relevant to COPD. As judged by domain experts, the F score of our approach is up to 77.6%. CONCLUSIONS The results demonstrate the effectiveness of the gene fingerprint model for diseases on the basis of medical literature.
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Affiliation(s)
- Guocai Chen
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
| | - Yuxi Jia
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
- Department of Medical Informatics, School of Public Health, Jilin University, Changchun, Jilin, 130021 China
| | - Lisha Zhu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
| | - Ping Li
- Department of Development Pediatrics, The Second Affiliated Hospital of Jilin University, Changchun, Jilin, 130041 China
| | - Lin Zhang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Jilin University, Changchun, Jilin, 130041 China
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
| | - W. Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
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23
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La MK, Sedykh A, Fourches D, Muratov E, Tropsha A. Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions. Drug Saf 2018; 41:1059-1072. [PMID: 29876834 PMCID: PMC6212308 DOI: 10.1007/s40264-018-0688-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. OBJECTIVE This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. MATERIALS AND METHODS Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. RESULTS Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. CONCLUSIONS The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.
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Affiliation(s)
- Mary K La
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
| | - Alexander Sedykh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
- Sciome LLC, 2 Davis Drive, Research Triangle Park, NC, 27709, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA.
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24
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Bozorgmehr A, Alizadeh F, Ofogh SN, Hamzekalayi MRA, Herati S, Moradkhani A, Shahbazi A, Ghadirivasfi M. What do the genetic association data say about the high risk of suicide in people with depression? A novel network-based approach to find common molecular basis for depression and suicidal behavior and related therapeutic targets. J Affect Disord 2018; 229:463-468. [PMID: 29331709 DOI: 10.1016/j.jad.2017.12.079] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 11/19/2017] [Accepted: 12/31/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Available sources indicate that the risk of suicide in people with major depression is higher than other psychiatric disorders. Although it seems that these two conditions may have a shared cause in some cases, no studies have been conducted to identify a common basis for them. METHODS In this study, following an extensive review of literature, we found almost all the genes that are involved in major depression and suicidal behavior, and we isolated genes shared between the two conditions. Then, we found all physical or functional interactions within three mentioned gene sets and reconstructed three genetic interactive networks. All networks were analyzed topologically and enriched functionally. Finally, using a drug repurposing approach, we found the main available drugs that interacted with the most central genes shared between suicidal behavior and depression. RESULTS The results demonstrated that BDNF, SLC6A4, CREB1, and TNF are the most fundamental shared genes; and generally, disordered dopaminergic, serotonergic, and immunologic pathways in neuronal projections are the main shared deficient pathways. In addition, we found two genes, SLC6A4 and SLC6A2, to be the main therapeutic targets, and Serotonin-Norepinephrine Reuptake Inhibitors (SNRI) and Tricyclic Antidepressants (TCA) to be the most effective drugs for individuals with depression at risk for suicide. CONCLUSIONS Our results, in addition to shedding light on the integrated molecular basis of depression-suicide, offer new therapeutic targets for individuals with depression at high risk for suicide and could pave the way for future preclinical and clinical studies. However, integrative systems biology-based studies highly depend on existing data and related databases, as well as the arrival of new experimental data sources in the future, possibly affecting the current results.
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Affiliation(s)
- Ali Bozorgmehr
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Fatemeh Alizadeh
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Sattar Norouzi Ofogh
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | | | - Sara Herati
- Faculty of Nursing, University of Calgary, Alberta, Canada
| | - Atefeh Moradkhani
- Department of Biology, Faculty of Science, Zanjan Branch, Islamic Azad University, Iran
| | - Ali Shahbazi
- Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mohammad Ghadirivasfi
- Research Center for Addiction and Risky Behavior (ReCARB), Iran University of Medical Sciences (IUMS), Tehran, Iran.
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25
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Nelson SJ, Oprea TI, Ursu O, Bologa CG, Zaveri A, Holmes J, Yang JJ, Mathias SL, Mani S, Tuttle MS, Dumontier M. Formalizing drug indications on the road to therapeutic intent. J Am Med Inform Assoc 2018; 24:1169-1172. [PMID: 29016968 PMCID: PMC6259666 DOI: 10.1093/jamia/ocx064] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 06/15/2017] [Indexed: 02/01/2023] Open
Abstract
Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.
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Affiliation(s)
- Stuart J Nelson
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Oleg Ursu
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Amrapali Zaveri
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Subramani Mani
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Mark S Tuttle
- Center for Digital Health Innovation, University of California, San Francisco, CA, USA
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, The Netherlands
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Kumar A, Sharma A. Computational Modeling of Multi-target-Directed Inhibitors Against Alzheimer’s Disease. NEUROMETHODS 2018. [DOI: 10.1007/978-1-4939-7404-7_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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27
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Mdanda S, Baijnath S, Shobo A, Singh SD, Maguire GE, Kruger HG, Arvidsson PI, Naicker T, Govender T. Lansoprazole-sulfide, pharmacokinetics of this promising anti-tuberculous agent. Biomed Chromatogr 2017. [DOI: 10.1002/bmc.4035] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Sipho Mdanda
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Sooraj Baijnath
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Adeola Shobo
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Sanil D. Singh
- Biomedical Resource Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Glenn E.M. Maguire
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Hendrik G. Kruger
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Per I. Arvidsson
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
- Science for Life Laboratory, Drug Discovery and Development Platform and Division of Translational Medicine and Chemical Biology, Development of Medical Biochemistry and Biophysics; Karolinska Institutet; Stockholm Sweden
| | - Tricia Naicker
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
| | - Thavendran Govender
- Catalysis and Peptide Research Unit; University of KwaZulu-Natal, Westville Campus; Durban South Africa
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28
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Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based Approaches in Pharmacology. Mol Inform 2017; 36. [PMID: 28692140 DOI: 10.1002/minf.201700048] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 06/21/2017] [Indexed: 12/23/2022]
Abstract
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.
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Affiliation(s)
- Baptiste Boezio
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
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Krallinger M, Rabal O, Lourenço A, Oyarzabal J, Valencia A. Information Retrieval and Text Mining Technologies for Chemistry. Chem Rev 2017; 117:7673-7761. [PMID: 28475312 DOI: 10.1021/acs.chemrev.6b00851] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.
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Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre , C/Melchor Fernández Almagro 3, Madrid E-28029, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Anália Lourenço
- ESEI - Department of Computer Science, University of Vigo , Edificio Politécnico, Campus Universitario As Lagoas s/n, Ourense E-32004, Spain.,Centro de Investigaciones Biomédicas (Centro Singular de Investigación de Galicia) , Campus Universitario Lagoas-Marcosende, Vigo E-36310, Spain.,CEB-Centre of Biological Engineering, University of Minho , Campus de Gualtar, Braga 4710-057, Portugal
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra , Avenida Pio XII 55, Pamplona E-31008, Spain
| | - Alfonso Valencia
- Life Science Department, Barcelona Supercomputing Centre (BSC-CNS) , C/Jordi Girona, 29-31, Barcelona E-08034, Spain.,Joint BSC-IRB-CRG Program in Computational Biology, Parc Científic de Barcelona , C/ Baldiri Reixac 10, Barcelona E-08028, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA) , Passeig de Lluís Companys 23, Barcelona E-08010, Spain
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30
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Mani S, Cannon D, Ohls R, Oprea T, Mathias S, Ballard K, Ursu O, Bologa C. Protein biomarker druggability profiling. J Biomed Inform 2017; 66:241-247. [PMID: 28131723 DOI: 10.1016/j.jbi.2017.01.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2016] [Revised: 12/31/2016] [Accepted: 01/24/2017] [Indexed: 11/24/2022]
Abstract
Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us.
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Affiliation(s)
- Subramani Mani
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | - Daniel Cannon
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | - Robin Ohls
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | - Tudor Oprea
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | - Stephen Mathias
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | | | - Oleg Ursu
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
| | - Cristian Bologa
- University of New Mexico Health Sciences Center, Albuquerque, NM 87131, United States.
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31
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Ursu O, Holmes J, Knockel J, Bologa CG, Yang JJ, Mathias SL, Nelson SJ, Oprea TI. DrugCentral: online drug compendium. Nucleic Acids Res 2017; 45:D932-D939. [PMID: 27789690 PMCID: PMC5210665 DOI: 10.1093/nar/gkw993] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 09/27/2016] [Accepted: 10/24/2016] [Indexed: 11/13/2022] Open
Abstract
DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.
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Affiliation(s)
- Oleg Ursu
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Stuart J Nelson
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
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32
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Štular T, Lešnik S, Rožman K, Schink J, Zdouc M, Ghysels A, Liu F, Aldrich CC, Haupt VJ, Salentin S, Daminelli S, Schroeder M, Langer T, Gobec S, Janežič D, Konc J. Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction. J Med Chem 2016; 59:11069-11078. [PMID: 27936766 DOI: 10.1021/acs.jmedchem.6b01277] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.
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Affiliation(s)
- Tanja Štular
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Kaja Rožman
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Julia Schink
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Mitja Zdouc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - An Ghysels
- Center for Molecular Modeling, Ghent University , Technologiepark 903, 9052 Zwijnaarde, Belgium
| | - Feng Liu
- AAT Bioquest, Inc. , 520 Mercury Drive, Sunnyvale, California 94085, United States
| | - Courtney C Aldrich
- Department of Medicinal Chemistry, University of Minnesota , 308 Harvard Street Southeast, Minneapolis, Minnesota 55455, United States
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Sebastian Salentin
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Simone Daminelli
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstrasse 14, A-1090 Vienna, Austria
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Janez Konc
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
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33
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Shen F, Lee Y. Knowledge Discovery from Biomedical Ontologies in Cross Domains. PLoS One 2016; 11:e0160005. [PMID: 27548262 PMCID: PMC4993478 DOI: 10.1371/journal.pone.0160005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 07/12/2016] [Indexed: 01/19/2023] Open
Abstract
In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.
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Affiliation(s)
- Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Yugyung Lee
- School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, Missouri, United States of America
- * E-mail:
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34
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Shen F, Liu H, Sohn S, Larson DW, Lee Y. Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery. INTELLIGENT INFORMATION MANAGEMENT 2016; 8:66-85. [PMID: 28983419 PMCID: PMC5626454 DOI: 10.4236/iim.2016.83006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
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Affiliation(s)
- Feichen Shen
- CSEE Department, University of Missouri, Kansas City, MO, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Sunghwan Sohn
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - David W Larson
- Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Yugyung Lee
- CSEE Department, University of Missouri, Kansas City, MO, USA
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35
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Repositioning Clofazimine as a Macrophage-Targeting Photoacoustic Contrast Agent. Sci Rep 2016; 6:23528. [PMID: 27000434 PMCID: PMC4802322 DOI: 10.1038/srep23528] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 03/08/2016] [Indexed: 01/28/2023] Open
Abstract
Photoacoustic Tomography (PAT) is a deep-tissue imaging modality, with potential clinical applications in the diagnosis of arthritis, cancer and other disease conditions. Here, we identified Clofazimine (CFZ), a red-pigmented dye and anti-inflammatory FDA-approved drug, as a macrophage-targeting photoacoustic (PA) imaging agent. Spectroscopic experiments revealed that CFZ and its various protonated forms yielded optimal PAT signals at wavelengths −450 to 540 nm. CFZ’s macrophage-targeting chemical and structural forms were detected with PA microscopy at a high contrast-to-noise ratio (CNR > 22 dB) as well as with macroscopic imaging using synthetic gelatin phantoms. In vivo, natural and synthetic CFZ formulations also demonstrated significant anti-inflammatory activity. Finally, the injection of CFZ was monitored via a real-time ultrasound-photoacoustic (US-PA) dual imaging system in a live animal and clinically relevant human hand model. These results demonstrate an anti-inflammatory drug repurposing strategy, while identifying a new PA contrast agent with potential applications in the diagnosis and treatment of arthritis.
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36
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Lee HM, Kim Y. Drug Repurposing Is a New Opportunity for Developing Drugs against Neuropsychiatric Disorders. SCHIZOPHRENIA RESEARCH AND TREATMENT 2016; 2016:6378137. [PMID: 27073698 PMCID: PMC4814692 DOI: 10.1155/2016/6378137] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 02/24/2016] [Indexed: 01/03/2023]
Abstract
Better the drugs you know than the drugs you do not know. Drug repurposing is a promising, fast, and cost effective method that can overcome traditional de novo drug discovery and development challenges of targeting neuropsychiatric and other disorders. Drug discovery and development targeting neuropsychiatric disorders are complicated because of the limitations in understanding pathophysiological phenomena. In addition, traditional de novo drug discovery and development are risky, expensive, and time-consuming processes. One alternative approach, drug repurposing, has emerged taking advantage of off-target effects of the existing drugs. In order to identify new opportunities for the existing drugs, it is essential for us to understand the mechanisms of action of drugs, both biologically and pharmacologically. By doing this, drug repurposing would be a more effective method to develop drugs against neuropsychiatric and other disorders. Here, we review the difficulties in drug discovery and development in neuropsychiatric disorders and the extent and perspectives of drug repurposing.
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Affiliation(s)
- Hyeong-Min Lee
- Department of Cell Biology & Physiology, School of Medicine, University of North Carolina, 115 Mason Farm Road, Chapel Hill, NC 27599, USA
| | - Yuna Kim
- Department of Pediatrics, School of Medicine, Duke University, 905 S. LaSalle Street, Durham, NC 27710, USA
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37
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Shah ET, Upadhyaya A, Philp LK, Tang T, Skalamera D, Gunter J, Nelson CC, Williams ED, Hollier BG. Repositioning "old" drugs for new causes: identifying new inhibitors of prostate cancer cell migration and invasion. Clin Exp Metastasis 2016; 33:385-99. [PMID: 26932199 DOI: 10.1007/s10585-016-9785-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2015] [Accepted: 02/23/2016] [Indexed: 01/29/2023]
Abstract
The majority of prostate cancer (PCa) deaths occur due to the metastatic spread of tumor cells to distant organs. Currently, there is a lack of effective therapies once tumor cells have spread outside the prostate. It is therefore imperative to rapidly develop therapeutics to inhibit the metastatic spread of tumor cells. Gain of cell motility and invasive properties is the first step of metastasis and by inhibiting motility one can potentially inhibit metastasis. Using the drug repositioning strategy, we developed a cell-based multi-parameter primary screening assay to identify drugs that inhibit the migratory and invasive properties of metastatic PC-3 PCa cells. Following the completion of the primary screening assay, 33 drugs were identified from an FDA approved drug library that either inhibited migration or were cytotoxic to the PC-3 cells. Based on the data obtained from the subsequent validation studies, mitoxantrone hydrochloride, simvastatin, fluvastatin and vandetanib were identified as strong candidates that can inhibit both the migration and invasion of PC-3 cells without significantly affecting cell viability. By employing the drug repositioning strategy instead of a de novo drug discovery and development strategy, the identified drug candidates have the potential to be rapidly translated into the clinic for the management of men with aggressive forms of PCa.
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Affiliation(s)
- Esha T Shah
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Akanksha Upadhyaya
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Lisa K Philp
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Tiffany Tang
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - Dubravka Skalamera
- The University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Jennifer Gunter
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Colleen C Nelson
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Elizabeth D Williams
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Brett G Hollier
- Australian Prostate Cancer Research Centre-Queensland, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
- Translational Research Institute, Brisbane, Australia.
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38
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Kell DB, Lurie-Luke E. The virtue of innovation: innovation through the lenses of biological evolution. J R Soc Interface 2015; 12:rsif.2014.1183. [PMID: 25505138 PMCID: PMC4305420 DOI: 10.1098/rsif.2014.1183] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
We rehearse the processes of innovation and discovery in general terms, using as our main metaphor the biological concept of an evolutionary fitness landscape. Incremental and disruptive innovations are seen, respectively, as successful searches carried out locally or more widely. They may also be understood as reflecting evolution by mutation (incremental) versus recombination (disruptive). We also bring a platonic view, focusing on virtue and memory. We use 'virtue' as a measure of efforts, including the knowledge required to come up with disruptive and incremental innovations, and 'memory' as a measure of their lifespan, i.e. how long they are remembered. Fostering innovation, in the evolutionary metaphor, means providing the wherewithal to promote novelty, good objective functions that one is trying to optimize, and means to improve one's knowledge of, and ability to navigate, the landscape one is searching. Recombination necessarily implies multi- or inter-disciplinarity. These principles are generic to all kinds of creativity, novel ideas formation and the development of new products and technologies.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Princess St., Manchester M1 7DN, UK
| | - Elena Lurie-Luke
- Life Sciences Open Innovation, Procter and Gamble, Procter and Gamble Technical Centres Limited, Whitehall Lane, Egham TW20 9NW, UK
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Audouze K, Taboureau O. Chemical biology databases: from aggregation, curation to representation. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 14:25-29. [PMID: 26194584 DOI: 10.1016/j.ddtec.2015.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 03/19/2015] [Accepted: 03/29/2015] [Indexed: 06/04/2023]
Abstract
Systems chemical biology offers a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets and their perturbations by drugs. However, the integration of all these data needs curation and standardization with an appropriate representation in order to get relevant interpretations. In this mini review, we present some databases and services, which integrated together with computational tools and data standardization, could assist scientists in decision making during the different drug development process.
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Affiliation(s)
- Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75013 Paris, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75013 Paris, France.
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40
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Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations. Sci Rep 2015; 5:10182. [PMID: 25960144 PMCID: PMC4426692 DOI: 10.1038/srep10182] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 04/01/2015] [Indexed: 01/05/2023] Open
Abstract
Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.
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41
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Structural and functional characterization of a specific antidote for ticagrelor. Blood 2015; 125:3484-90. [PMID: 25788700 DOI: 10.1182/blood-2015-01-622928] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 03/04/2015] [Indexed: 12/15/2022] Open
Abstract
Ticagrelor is a direct-acting reversibly binding P2Y12 antagonist and is widely used as an antiplatelet therapy for the prevention of cardiovascular events in acute coronary syndrome patients. However, antiplatelet therapy can be associated with an increased risk of bleeding. Here, we present data on the identification and the in vitro and in vivo pharmacology of an antigen-binding fragment (Fab) antidote for ticagrelor. The Fab has a 20 pM affinity for ticagrelor, which is 100 times stronger than ticagrelor's affinity for its target, P2Y12. Despite ticagrelor's structural similarities to adenosine, the Fab is highly specific and does not bind to adenosine, adenosine triphosphate, adenosine 5'-diphosphate, or structurally related drugs. The antidote concentration-dependently neutralized the free fraction of ticagrelor and reversed its antiplatelet activity both in vitro in human platelet-rich plasma and in vivo in mice. Lastly, the antidote proved effective in normalizing ticagrelor-dependent bleeding in a mouse model of acute surgery. This specific antidote for ticagrelor may prove valuable as an agent for patients who require emergency procedures.
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42
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Talwar P, Silla Y, Grover S, Gupta M, Grewal GK, Kukreti R. Systems Pharmacology and Pharmacogenomics for Drug Discovery and Development. SYSTEMS AND SYNTHETIC BIOLOGY 2015. [DOI: 10.1007/978-94-017-9514-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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43
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Extending in silico mechanism-of-action analysis by annotating targets with pathways: application to cellular cytotoxicity readouts. Future Med Chem 2014; 6:2029-56. [DOI: 10.4155/fmc.14.137] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. Results: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. Conclusion: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.
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Reddy AS, Tan Z, Zhang S. Curation and analysis of multitargeting agents for polypharmacological modeling. J Chem Inf Model 2014; 54:2536-43. [PMID: 25133604 PMCID: PMC4170814 DOI: 10.1021/ci500092j] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
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In
drug discovery and development, the conventional “single drug,
single target” concept has been shifted to “single drug,
multiple targets” – a concept coined as polypharmacology.
For studies in this emerging field, dedicated and high-quality databases
of multitargeting ligands would be exceedingly beneficial. To this
end, we conducted a comprehensive analysis of the structural and chemical/biological
profiles of polypharmacological agents and present a Web-based database
(Polypharma). All of these compounds curated herein
have been cocrystallized with more than one unique protein with intensive
reports of their multitargeting activities. The present study provides
more insight of drug multitargeting and is particularly useful for
polypharmacology modeling. This specialized curation has been made
publically available at http:/imdlab.org/polypharma/
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Affiliation(s)
- A Srinivas Reddy
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, University of Texas MD Anderson Cancer Center , Houston, Texas 77030, United States
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Kangas JD, Naik AW, Murphy RF. Efficient discovery of responses of proteins to compounds using active learning. BMC Bioinformatics 2014; 15:143. [PMID: 24884564 PMCID: PMC4030446 DOI: 10.1186/1471-2105-15-143] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 05/07/2014] [Indexed: 11/13/2022] Open
Abstract
Background Drug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive. Results This paper describes methods for making this global approach practical by constructing predictive models for many target responses to many compounds and using them to guide experimentation. We demonstrate for the first time that by jointly modeling targets and compounds using descriptive features and using active machine learning methods, accurate models can be built by doing only a small fraction of possible experiments. The methods were evaluated by computational experiments using a dataset of 177 assays and 20,000 compounds constructed from the PubChem database. Conclusions An average of nearly 60% of all hits in the dataset were found after exploring only 3% of the experimental space which suggests that active learning can be used to enable more complete characterization of compound effects than otherwise affordable. The methods described are also likely to find widespread application outside drug discovery, such as for characterizing the effects of a large number of compounds or inhibitory RNAs on a large number of cell or tissue phenotypes.
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Affiliation(s)
| | | | - Robert F Murphy
- Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Ave,, Pittsburgh, PA 15213, USA.
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Xie L, Ge X, Tan H, Xie L, Zhang Y, Hart T, Yang X, Bourne PE. Towards structural systems pharmacology to study complex diseases and personalized medicine. PLoS Comput Biol 2014; 10:e1003554. [PMID: 24830652 PMCID: PMC4022462 DOI: 10.1371/journal.pcbi.1003554] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
- Ph.D. Program in Computer Science, Biology, and Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America
- * E-mail:
| | - Xiaoxia Ge
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Hepan Tan
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Li Xie
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Yinliang Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Thomas Hart
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Xiaowei Yang
- School of Public Health, Hunter College, The City University of New York, New York, New York, United States of America
| | - Philip E. Bourne
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
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Cummings JL, Zhong K. Repackaging FDA-approved drugs for degenerative diseases: promises and challenges. Expert Rev Clin Pharmacol 2014; 7:161-5. [PMID: 24502586 DOI: 10.1586/17512433.2014.884923] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Repurposing refers to the therapeutic use of a drug or drug candidate for a disease other than that for which it was originally intended. Repurposing is attractive as a drug development strategy since much is known about approved agents including their drug-likeness and pharmacokinetic features, dosing, safety, tolerability, formulation and manufacturing. Time savings are also robust accounting for several years of the drug development cycle. Tissue and cell-based assays, epidemiologic information and human studies identify approved drugs that might be repurposed from use in Alzheimer's disease and other neurodegenerative disorders. The total number of compounds available for repurposing that are brain-penetrant is relatively small. Intellectual property and patent protection issues for repurposed drugs are hurdles for this approach to drug development. Repurposing may contribute importantly to development of new therapies for neurodegenerative disorders.
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Affiliation(s)
- Jeffrey L Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV 89106, USA
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Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr Top Med Chem 2014; 14:1356-64. [PMID: 24805064 PMCID: PMC5344042 DOI: 10.2174/1568026614666140506121116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2014] [Revised: 02/05/2014] [Accepted: 02/05/2014] [Indexed: 12/22/2022]
Abstract
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.
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Affiliation(s)
| | | | | | - Alexander Tropsha
- 100K Beard Hall, Campus Box 7568, University of North Carolina, Chapel Hill, NC 27599-7568, USA.
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Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges. INTERACTIVE KNOWLEDGE DISCOVERY AND DATA MINING IN BIOMEDICAL INFORMATICS 2014. [DOI: 10.1007/978-3-662-43968-5_16] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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