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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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2
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Zheng YH, Pan GJ, Quan Y, Zhang HY. Construction of microgravity biological knowledge graph and its applications in anti-osteoporosis drug prediction. LIFE SCIENCES IN SPACE RESEARCH 2024; 41:64-73. [PMID: 38670654 DOI: 10.1016/j.lssr.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/11/2023] [Accepted: 01/24/2024] [Indexed: 04/28/2024]
Abstract
Microgravity in the space environment can potentially have various negative effects on the human body, one of which is bone loss. Given the increasing frequency of human space activities, there is an urgent need to identify effective anti-osteoporosis drugs for the microgravity environment. Traditional microgravity experiments conducted in space suffer from limitations such as time-consuming procedures, high costs, and small sample sizes. In recent years, the in-silico drug discovery method has emerged as a promising strategy due to the advancements in bioinformatics and computer technology. In this study, we first collected a total of 184,915 literature articles related to microgravity and bone loss. We employed a combination of dependency path extraction and clustering techniques to extract data from the text. Afterwards, we conducted data cleaning and standardization to integrate data from several sources, including The Global Network of Biomedical Relationships (GNBR), Curated Drug-Drug Interactions Database (DDInter), Search Tool for Interacting Chemicals (STITCH), DrugBank, and Traditional Chinese Medicines Integrated Database (TCMID). Through this integration process, we constructed the Microgravity Biology Knowledge Graph (MBKG) consisting of 134,796 biological entities and 3,395,273 triplets. Subsequently, the TransE model was utilized to perform knowledge graph embedding. By calculating the distances between entities in the model space, the model successfully predicted potential drugs for treating osteoporosis and microgravity-induced bone loss. The results indicate that out of the top 10 ranked western medicines, 7 have been approved for the treatment of osteoporosis. Additionally, among the top 10 ranked traditional Chinese medicines, 5 have scientific literature supporting their effectiveness in treating bone loss. Among the top 20 predicted medicines for microgravity-induced bone loss, 15 have been studied in microgravity or simulated microgravity environments, while the remaining 5 are also applicable for treating osteoporosis. This research highlights the potential application of MBKG in the field of space drug discovery.
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Affiliation(s)
- Yu-Han Zheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Guan-Jing Pan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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3
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Nicholson DN, Himmelstein DS, Greene CS. Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts. BioData Min 2022; 15:26. [PMID: 36258252 PMCID: PMC9578183 DOI: 10.1186/s13040-022-00311-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 09/17/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Knowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via manual curation, which is challenging to scale with an exponentially rising publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to annotate textual data automatically. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This bottleneck makes populating a knowledge graph with multiple nodes and edge types practically infeasible. Thus, we sought to accelerate the label function creation process by evaluating how label functions can be re-used across multiple edge types. RESULTS We obtained entity-tagged abstracts and subsetted these entities to only contain compounds, genes, and disease mentions. We extracted sentences containing co-mentions of certain biomedical entities contained in a previously described knowledge graph, Hetionet v1. We trained a baseline model that used database-only label functions and then used a sampling approach to measure how well adding edge-specific or edge-mismatch label function combinations improved over our baseline. Next, we trained a discriminator model to detect sentences that indicated a biomedical relationship and then estimated the number of edge types that could be recalled and added to Hetionet v1. We found that adding edge-mismatch label functions rarely improved relationship extraction, while control edge-specific label functions did. There were two exceptions to this trend, Compound-binds-Gene and Gene-interacts-Gene, which both indicated physical relationships and showed signs of transferability. Across the scenarios tested, discriminative model performance strongly depends on generated annotations. Using the best discriminative model for each edge type, we recalled close to 30% of established edges within Hetionet v1. CONCLUSIONS Our results show that this framework can incorporate novel edges into our source knowledge graph. However, results with label function transfer were mixed. Only label functions describing very similar edge types supported improved performance when transferred. We expect that the continued development of this strategy may provide essential building blocks to populating biomedical knowledge graphs with discoveries, ensuring that these resources include cutting-edge results.
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Affiliation(s)
- David N. Nicholson
- grid.25879.310000 0004 1936 8972Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Daniel S. Himmelstein
- grid.25879.310000 0004 1936 8972Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Casey S. Greene
- grid.430503.10000 0001 0703 675XDepartment of Biomedical Informatics, University of Colorado School of Medicine and Center for Health Artificial Intellegence (CHAI), University of Colorado School of Medicine, Aurora, USA
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4
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Firoozbakht F, Rezaeian I, Rueda L, Ngom A. Computationally repurposing drugs for breast cancer subtypes using a network-based approach. BMC Bioinformatics 2022; 23:143. [PMID: 35443626 PMCID: PMC9020161 DOI: 10.1186/s12859-022-04662-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 03/30/2022] [Indexed: 11/22/2022] Open
Abstract
‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.
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Affiliation(s)
- Forough Firoozbakht
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada
| | - Iman Rezaeian
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada.,Rocket Innovation Studio, 156 Chatham St W, Windsor, ON, Canada
| | - Luis Rueda
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada.
| | - Alioune Ngom
- School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON, Canada
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5
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Bahmad HF, Demus T, Moubarak MM, Daher D, Alvarez Moreno JC, Polit F, Lopez O, Merhe A, Abou-Kheir W, Nieder AM, Poppiti R, Omarzai Y. Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing. Med Sci (Basel) 2022; 10:medsci10010015. [PMID: 35225948 PMCID: PMC8883996 DOI: 10.3390/medsci10010015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.
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Affiliation(s)
- Hisham F. Bahmad
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Correspondence: or ; Tel.: +1-786-961-0216
| | - Timothy Demus
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
| | - Maya M. Moubarak
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
- CNRS, IBGC, UMR5095, Universite de Bordeaux, F-33000 Bordeaux, France
| | - Darine Daher
- Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon;
| | - Juan Carlos Alvarez Moreno
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Francesca Polit
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
| | - Olga Lopez
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Ali Merhe
- Department of Urology, Jackson Memorial Hospital, University of Miami, Leonard M. Miller School of Medicine, Miami, FL 33136, USA;
| | - Wassim Abou-Kheir
- Department of Anatomy, Cell Biology, and Physiological Sciences, Faculty of Medicine, American University of Beirut, Beirut 1107-2020, Lebanon; (M.M.M.); (W.A.-K.)
| | - Alan M. Nieder
- Division of Urology, Columbia University, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (T.D.); (A.M.N.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Robert Poppiti
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
| | - Yumna Omarzai
- Arkadi M. Rywlin M.D. Department of Pathology and Laboratory Medicine, Mount Sinai Medical Center, Miami Beach, FL 33140, USA; (J.C.A.M.); (F.P.); (R.P.); (Y.O.)
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA;
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6
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Negi V, Yang J, Speyer G, Pulgarin A, Handen A, Zhao J, Tai YY, Tang Y, Culley MK, Yu Q, Forsythe P, Gorelova A, Watson AM, Al Aaraj Y, Satoh T, Sharifi-Sanjani M, Rajaratnam A, Sembrat J, Provencher S, Yin X, Vargas SO, Rojas M, Bonnet S, Torrino S, Wagner BK, Schreiber SL, Dai M, Bertero T, Al Ghouleh I, Kim S, Chan SY. Computational repurposing of therapeutic small molecules from cancer to pulmonary hypertension. SCIENCE ADVANCES 2021; 7:eabh3794. [PMID: 34669463 PMCID: PMC8528428 DOI: 10.1126/sciadv.abh3794] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/27/2021] [Indexed: 05/05/2023]
Abstract
Cancer therapies are being considered for treating rare noncancerous diseases like pulmonary hypertension (PH), but effective computational screening is lacking. Via transcriptomic differential dependency analyses leveraging parallels between cancer and PH, we mapped a landscape of cancer drug functions dependent upon rewiring of PH gene clusters. Bromodomain and extra-terminal motif (BET) protein inhibitors were predicted to rely upon several gene clusters inclusive of galectin-8 (LGALS8). Correspondingly, LGALS8 was found to mediate the BET inhibitor–dependent control of endothelial apoptosis, an essential role for PH in vivo. Separately, a piperlongumine analog’s actions were predicted to depend upon the iron-sulfur biogenesis gene ISCU. Correspondingly, the analog was found to inhibit ISCU glutathionylation, rescuing oxidative metabolism, decreasing endothelial apoptosis, and improving PH. Thus, we identified crucial drug-gene axes central to endothelial dysfunction and therapeutic priorities for PH. These results establish a wide-ranging, network dependency platform to redefine cancer drugs for use in noncancerous conditions.
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Affiliation(s)
- Vinny Negi
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jimin Yang
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Gil Speyer
- Research Computing, Arizona State University, Tempe, AZ, USA
| | - Andres Pulgarin
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Adam Handen
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jingsi Zhao
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yi Yin Tai
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ying Tang
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Miranda K. Culley
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Qiujun Yu
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Patricia Forsythe
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anastasia Gorelova
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Annie M. Watson
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yassmin Al Aaraj
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Taijyu Satoh
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Cardiovascular Medicine, Tohoku University of Graduate School of Medicine, 1-1 Seiryomachi, Aoba-ku, 980-8574 Sendai, Japan
| | - Maryam Sharifi-Sanjani
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Arun Rajaratnam
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - John Sembrat
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Steeve Provencher
- Pulmonary Hypertension and Vascular Biology Research Group, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Xianglin Yin
- Department of Chemistry, Center for Cancer Research, Institute for Drug Discovery, Purdue University, West Lafayette, IN, USA
| | - Sara O. Vargas
- Department of Pathology, Boston Children’s Hospital, MA, USA
| | - Mauricio Rojas
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Ohio State University College of Medicine, Columbus, OH, USA
| | - Sébastien Bonnet
- Pulmonary Hypertension and Vascular Biology Research Group, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | | | - Bridget K. Wagner
- Department of Chemistry and Chemical Biology, Harvard University; Chemical Biology and Therapeutics Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stuart L. Schreiber
- Department of Chemistry and Chemical Biology, Harvard University; Chemical Biology and Therapeutics Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mingji Dai
- Department of Chemistry, Center for Cancer Research, Institute for Drug Discovery, Purdue University, West Lafayette, IN, USA
| | - Thomas Bertero
- Université Côte d’Azur, CNRS, IPMC, Sophia-Antipolis, France
| | - Imad Al Ghouleh
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Stephen Y. Chan
- Center for Pulmonary Vascular Biology and Medicine, Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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Badkas A, De Landtsheer S, Sauter T. Topological network measures for drug repositioning. Brief Bioinform 2021; 22:bbaa357. [PMID: 33348366 PMCID: PMC8294518 DOI: 10.1093/bib/bbaa357] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 12/15/2022] Open
Abstract
Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning.
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8
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Pantziarka P, Capistrano I R, De Potter A, Vandeborne L, Bouche G. An Open Access Database of Licensed Cancer Drugs. Front Pharmacol 2021; 12:627574. [PMID: 33776770 PMCID: PMC7991999 DOI: 10.3389/fphar.2021.627574] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
A global, comprehensive and open access listing of approved anticancer drugs does not currently exist. Partial information is available from multiple sources, including regulatory authorities, national formularies and scientific agencies. Many such data sources include drugs used in oncology for supportive care, diagnostic or other non-antineoplastic uses. We describe a methodology to combine and cleanse relevant data from multiple sources to produce an open access database of drugs licensed specifically for therapeutic antineoplastic purposes. The resulting list is provided as an open access database, (http://www.redo-project.org/cancer-drugs-db/), so that it may be used by researchers as input for further research projects, for example literature-based text mining for drug repurposing.
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Affiliation(s)
- Pan Pantziarka
- The Anticancer Fund, Brussels, Belgium.,The George Pantziarka TP53 Trust, London, United Kingdom
| | | | - Arno De Potter
- Faculty of Medicine, University of Leuven, Leuven, Belgium
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9
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Kumar R, Harilal S, Gupta SV, Jose J, Thomas Parambi DG, Uddin MS, Shah MA, Mathew B. Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work. Eur J Med Chem 2019; 182:111602. [PMID: 31421629 PMCID: PMC7127402 DOI: 10.1016/j.ejmech.2019.111602] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 08/07/2019] [Accepted: 08/07/2019] [Indexed: 02/07/2023]
Abstract
Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.
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Affiliation(s)
- Rajesh Kumar
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Seetha Harilal
- Department of Pharmacy, Kerala University of Health Sciences, Thrissur, Kerala, India
| | - Sheeba Varghese Gupta
- Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, 33612, USA
| | - Jobin Jose
- Department of Pharmaceutics, NGSM Institute of Pharmaceutical Science, NITTE Deemed to be University, Manglore, 575018, India
| | - Della Grace Thomas Parambi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka, Al Jouf, 2014, Saudi Arabia
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh; Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
| | - Muhammad Ajmal Shah
- Department of Pharmacogonosy, Faculty of Pharmaceutical Sciences, Government College University, Faisalabad, Pakistan
| | - Bijo Mathew
- Division of Drug Design and Medicinal Chemistry Research Lab, Department of Pharmaceutical Chemistry, Ahalia School of Pharmacy, Palakkad, 678557, Kerala, India.
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10
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The use or generation of biomedical data and existing medicines to discover and establish new treatments for patients with rare diseases - recommendations of the IRDiRC Data Mining and Repurposing Task Force. Orphanet J Rare Dis 2019; 14:225. [PMID: 31615551 PMCID: PMC6794821 DOI: 10.1186/s13023-019-1193-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 09/04/2019] [Indexed: 12/12/2022] Open
Abstract
The number of available therapies for rare diseases remains low, as fewer than 6% of rare diseases have an approved treatment option. The International Rare Diseases Research Consortium (IRDiRC) set up the multi-stakeholder Data Mining and Repurposing (DMR) Task Force to examine the potential of applying biomedical data mining strategies to identify new opportunities to use existing pharmaceutical compounds in new ways and to accelerate the pace of drug development for rare disease patients. In reviewing past successes of data mining for drug repurposing, and planning for future biomedical research capacity, the DMR Task Force identified four strategic infrastructure investment areas to focus on in order to accelerate rare disease research productivity and drug development: (1) improving the capture and sharing of self-reported patient data, (2) better integration of existing research data, (3) increasing experimental testing capacity, and (4) sharing of rare disease research and development expertise. Additionally, the DMR Task Force also recommended a number of strategies to increase data mining and repurposing opportunities for rare diseases research as well as the development of individualized and precision medicine strategies.
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11
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Gopalakrishnan V, Jha K, Jin W, Zhang A. A survey on literature based discovery approaches in biomedical domain. J Biomed Inform 2019; 93:103141. [PMID: 30857950 DOI: 10.1016/j.jbi.2019.103141] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/17/2019] [Accepted: 02/19/2019] [Indexed: 02/06/2023]
Abstract
Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.
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Affiliation(s)
| | | | - Wei Jin
- University of North Texas at Denton, TX, United States.
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Xue H, Li J, Xie H, Wang Y. Review of Drug Repositioning Approaches and Resources. Int J Biol Sci 2018; 14:1232-1244. [PMID: 30123072 PMCID: PMC6097480 DOI: 10.7150/ijbs.24612] [Citation(s) in RCA: 327] [Impact Index Per Article: 54.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 06/12/2018] [Indexed: 12/23/2022] Open
Abstract
Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.
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Affiliation(s)
- Hanqing Xue
- School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, China
| | - Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, China
| | - Haozhe Xie
- School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, China
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Wooden B, Goossens N, Hoshida Y, Friedman SL. Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases. Gastroenterology 2017; 152:53-67.e3. [PMID: 27773806 PMCID: PMC5193106 DOI: 10.1053/j.gastro.2016.09.065] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/15/2016] [Accepted: 09/25/2016] [Indexed: 12/13/2022]
Abstract
Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.
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Affiliation(s)
- Benjamin Wooden
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nicolas Goossens
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Division of Gastroenterology and Hepatology, Department of Medical Specialties, Geneva University Hospital, Geneva, Switzerland
| | - Yujin Hoshida
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Scott L Friedman
- Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Naderi M, Alvin C, Ding Y, Mukhopadhyay S, Brylinski M. A graph-based approach to construct target-focused libraries for virtual screening. J Cheminform 2016; 8:14. [PMID: 26981157 PMCID: PMC4791927 DOI: 10.1186/s13321-016-0126-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 03/03/2016] [Indexed: 01/21/2023] Open
Abstract
Background Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family.
Methods Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns. Results We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated. Conclusions eSynth can successfully reconstruct chemically feasible molecules from molecular fragments.
Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. Here, we developed eSynth, an automated method to synthesize new compounds by reconnecting these building blocks following the connectivity patterns via an exhaustive graph-based search algorithm. eSynth opens up a possibility to rapidly construct virtual screening libraries for targeted drug discovery ![]()
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA USA
| | - Chris Alvin
- Department of Computer Science and Information Systems, Bradley University, Peoria, IL USA
| | - Yun Ding
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA USA
| | - Supratik Mukhopadhyay
- Department of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA USA ; Center for Computation and Technology, Louisiana State University, Baton Rouge, LA USA
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Mucke HA, Mucke E. Sources and Targets for Drug Repurposing: Landscaping Transitions in Therapeutic Space. Assay Drug Dev Technol 2015; 13:319-24. [DOI: 10.1089/adt.2015.29009.hmedrrr] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Eva Mucke
- H.M. Pharma Consultancy, Vienna, Austria
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Mucke HA, Mucke E. Sources and Targets for Drug Repurposing: Landscaping Transitions in Therapeutic Space. ACTA ACUST UNITED AC 2015. [DOI: 10.1089/drrr.2015.0001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Regan K, Raje S, Saravanamuthu C, Payne PRO. Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma. Stud Health Technol Inform 2015; 216:663-7. [PMID: 26262134 PMCID: PMC5081134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.
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Affiliation(s)
- Kelly Regan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Satyajeet Raje
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Cartik Saravanamuthu
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Philip R O Payne
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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