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Zhu Y, Yang H, Han L, Mervin LH, Hosseini-Gerami L, Li P, Wright P, Trapotsi MA, Liu K, Fan TP, Bender A. In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine. Front Pharmacol 2023; 14:1116081. [PMID: 36817116 PMCID: PMC9937659 DOI: 10.3389/fphar.2023.1116081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.
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Affiliation(s)
- Yingli Zhu
- Department of Clinical Chinese Pharmacy, School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing, China,Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Yang
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Liwen Han
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,School of Pharmacy and Pharmaceutical Science, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Lewis H. Mervin
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Layla Hosseini-Gerami
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Peihai Li
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Peter Wright
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Maria-Anna Trapotsi
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Kechun Liu
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Tai-Ping Fan
- Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
| | - Andreas Bender
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
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Natural products from Brazilian biodiversity identified as potential inhibitors of PknA and PknB of M. tuberculosis using molecular modeling tools. Comput Biol Med 2021; 136:104694. [PMID: 34365277 DOI: 10.1016/j.compbiomed.2021.104694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/07/2021] [Accepted: 07/23/2021] [Indexed: 11/21/2022]
Abstract
Mycobacterium tuberculosis was discovered in 1882 by Robert Koch but, since its discovery, the tuberculosis (TB) epidemic has endured, being one of the top 10 causes of death worldwide. Drug-resistant TB continues to be a public health threat and bioactive compounds with a new mode of action (MoA) are needed to overcome this. Since natural products are described as important sources for the development of new drugs, the objective of this work was to identify potential ligands from Brazilian natural products (NPs) for M. tuberculosis targets using molecular modeling tools. Using chemogenomics we identified the Serine/Threonine Protein Kinase PknB as a putative target for 13 NPs from a database from Brazilian biodiversity (NuBBE). Literature data supported further investigation of NuBBE105, NuBBE598, NuBBE936, NuBBE964, NuBBE1045, and NuBBE1180 by molecular docking and dynamics. Key interactions were observed with PknB and simulations confirmed stability and favorable binding energies. Considering structural similarity with PknB, we further explored binding of the NPs to PknA, critical for M. tuberculosis survival, and all of them resembled important interactions with the enzyme, showing stable and favorable binding energies, whilst van der Waals interactions seem to play a key role for binding to PknA and PknB. NuBBE936 and NuBBE1180 have already had their antimycobacterial activity reported and our results can provide a basis for their MoA. Finally, the other NPs which have not been tested against M. tuberculosis deserve further investigation, aiming at the discovery of antimycobacterial drug candidates with innovative MoA.
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Mervin LH, Johansson S, Semenova E, Giblin KA, Engkvist O. Uncertainty quantification in drug design. Drug Discov Today 2020; 26:474-489. [PMID: 33253918 DOI: 10.1016/j.drudis.2020.11.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/13/2020] [Accepted: 11/23/2020] [Indexed: 01/03/2023]
Abstract
Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.
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Affiliation(s)
- Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Simon Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Elizaveta Semenova
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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Mervin LH, Afzal AM, Engkvist O, Bender A. Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions. J Chem Inf Model 2020; 60:4546-4559. [DOI: 10.1021/acs.jcim.0c00476] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lewis H. Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Avid M. Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Mölndal SE-431 83, Sweden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1TN, U.K
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