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Burley SK, Wu-Wu A, Dutta S, Ganesan S, Zheng SXF. Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019-2023. Oncogene 2024:10.1038/s41388-024-03077-2. [PMID: 38886570 DOI: 10.1038/s41388-024-03077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019-2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA.
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| | - Amy Wu-Wu
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
| | - Steven X F Zheng
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
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Burley SK. Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development. J Biol Chem 2021; 296:100559. [PMID: 33744282 PMCID: PMC8059052 DOI: 10.1016/j.jbc.2021.100559] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 02/02/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
The Protein Data Bank (PDB) is an international core data resource central to fundamental biology, biomedicine, bioenergy, and biotechnology/bioengineering. Now celebrating its 50th anniversary, the PDB houses >175,000 experimentally determined atomic structures of proteins, nucleic acids, and their complexes with one another and small molecules and drugs. The importance of three-dimensional (3D) biostructure information for research and education obtains from the intimate link between molecular form and function evident throughout biology. Among the most prolific consumers of PDB data are biomedical researchers, who rely on the open access resource as the authoritative source of well-validated, expertly curated biostructures. This review recounts how the PDB grew from just seven protein structures to contain more than 49,000 structures of human proteins that have proven critical for understanding their roles in human health and disease. It then describes how these structures are used in academe and industry to validate drug targets, assess target druggability, characterize how tool compounds and other small-molecules bind to drug targets, guide medicinal chemistry optimization of binding affinity and selectivity, and overcome challenges during preclinical drug development. Three case studies drawn from oncology exemplify how structural biologists and open access to PDB structures impacted recent regulatory approvals of antineoplastic drugs.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA.
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QSAR investigations and structure-based virtual screening on a series of nitrobenzoxadiazole derivatives targeting human glutathione-S-transferases. J Mol Struct 2020. [DOI: 10.1016/j.molstruc.2020.128015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Xu F, Chen JX, Yang XB, Hong XB, Li ZX, Lin L, Chen YS. Analysis of Lung Adenocarcinoma Subtypes Based on Immune Signatures Identifies Clinical Implications for Cancer Therapy. Mol Ther Oncolytics 2020; 17:241-249. [PMID: 32346613 PMCID: PMC7183104 DOI: 10.1016/j.omto.2020.03.021] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 03/26/2020] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of this cancer type heterogeneity. LUAD subtypes were identified on the basis of the immunogenomic profiling of 29 immune signatures. We named three LUAD subtypes: Immunity High, Immunity Medium, and Immunity Low. The Immunity High subtype was characterized by immune activation, e.g., increased immune scores, elevated stromal scores and the highest infiltration of CD8+ T cells, and decreased tumor purities. Activated expressions of human leukocyte antigen (HLA) genes, immune checkpoint molecules, and T helper 1 (Th1)/interferon-gamma (IFNγ) gene signature were also observed in the Immunity High subtype. N 6-methyladenosine (m6A) RNA methylation, associated with cancer initiation and progression, was reduced in the Immunity High subtype. Functional and signaling pathway enrichment analysis further showed that differentially expressed genes between the Immunity High subtype and the other subtypes mainly participated in immune response and some cancer-associated pathways. In addition, the Immunity High subtype exhibited more sensitivity to immunotherapy and chemotherapy. Finally, candidate compounds that aimed at LUAD subtype differentiation were identified. Comprehensively characterizing the LUAD subtypes based on immune signatures may help to provide potential strategies for LUAD treatment.
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Affiliation(s)
- Feng Xu
- Department of Respiratory Medicine, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
| | - Jie-xin Chen
- Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
| | - Xiong-bin Yang
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
| | - Xin-bin Hong
- Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
| | - Zi-xiong Li
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
| | - Ling Lin
- Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
- Corresponding author Ling Lin, Department of Rheumatology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China.
| | - Yong-song Chen
- Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China
- Corresponding author Yong-song Chen, Department of Endocrinology, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, Guangdong 515041, P.R. China.
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Westbrook JD, Soskind R, Hudson BP, Burley SK. Impact of the Protein Data Bank on antineoplastic approvals. Drug Discov Today 2020; 25:837-850. [PMID: 32068073 DOI: 10.1016/j.drudis.2020.02.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/08/2020] [Accepted: 02/07/2020] [Indexed: 12/14/2022]
Abstract
Open access to 3D structure information from the Protein Data Bank (PDB) facilitated discovery and development of >90% of the 79 new antineoplastic agents (54 small molecules, 25 biologics) with known molecular targets approved by the FDA 2010-2018. Analyses of PDB holdings, the scientific literature and related documents for each drug-target combination revealed that the impact of public-domain 3D structure data was broad and substantial, ranging from understanding target biology (∼95% of all targets) to identifying a given target as probably druggable (∼95% of all targets) to structure-guided lead optimization (>70% of all small-molecule drugs). In addition to aggregate impact assessments, illustrative case studies are presented for three protein kinase inhibitors, an allosteric enzyme inhibitor and seven advanced-stage melanoma therapeutics.
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Affiliation(s)
- John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Rose Soskind
- Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
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Duarte Y, Márquez-Miranda V, Miossec MJ, González-Nilo F. Integration of target discovery, drug discovery and drug delivery: A review on computational strategies. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2019; 11:e1554. [PMID: 30932351 DOI: 10.1002/wnan.1554] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/14/2018] [Accepted: 01/23/2019] [Indexed: 12/22/2022]
Abstract
Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Yorley Duarte
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Valeria Márquez-Miranda
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Matthieu J Miossec
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Fernando González-Nilo
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile.,Centro Interdisciplinario de Neurociencias de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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Yildiz I, Sen O, Erenler R, Demirtas I, Behcet L. Bioactivity–guided isolation of flavonoids from Cynanchum acutum L. subsp. sibiricum (willd.) Rech. f. and investigation of their antiproliferative activity. Nat Prod Res 2017; 31:2629-2633. [DOI: 10.1080/14786419.2017.1289201] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ilyas Yildiz
- Faculty of Art and Science, Department of Chemistry, Gaziosmanpasa University, Tokat, Turkey
| | - Ozkan Sen
- Faculty of Art and Science, Department of Chemistry, Gaziosmanpasa University, Tokat, Turkey
| | - Ramazan Erenler
- Faculty of Art and Science, Department of Chemistry, Gaziosmanpasa University, Tokat, Turkey
| | - Ibrahim Demirtas
- Faculty of Natural Sciences, Department of Chemistry, Cankiri Karatekin University, Cankiri, Turkey
| | - Lutfi Behcet
- Faculty of Art and Science, Department of Biology, Bingol University, Bingol, Turkey
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Gao C, Wang D, Zhang Y, Huang XX, Song SJ. Kaurane and abietane diterpenoids from the roots of Tripterygium wilfordii and their cytotoxic evaluation. Bioorg Med Chem Lett 2016; 26:2942-2946. [DOI: 10.1016/j.bmcl.2016.04.026] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 04/07/2016] [Accepted: 04/12/2016] [Indexed: 01/31/2023]
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