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Piras A, Chenghao S, Sebek M, Ispirova G, Menichetti G. CPIExtract: A software package to collect and harmonize small molecule and protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601957. [PMID: 39005430 PMCID: PMC11245042 DOI: 10.1101/2024.07.03.601957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
The binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interaction is not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a tool to interactively extract experimental binding interaction data from multiple databases, perform filtering, and harmonize the resulting information, thus providing a gain of compound-protein interaction data. When compared to a single source, DrugBank, we show that it can collect more than 10 times the amount of annotations. The end-user can apply custom filtering to the aggregated output data and save it in any generic tabular file suitable for further downstream tasks such as network medicine analyses for drug repurposing and cross-validation of deep learning models.
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Affiliation(s)
- Andrea Piras
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy
| | - Shi Chenghao
- Network Science Institute, Northeastern University, 360 Huntington Ave, 02115, MA, USA
| | - Michael Sebek
- Network Science Institute, Northeastern University, 360 Huntington Ave, 02115, MA, USA
| | - Gordana Ispirova
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, 02115, MA, USA
| | - Giulia Menichetti
- Network Science Institute, Northeastern University, 360 Huntington Ave, 02115, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, 02115, MA, USA
- Harvard Data Science Initiative, Harvard University, 114 Western Avenue, 02134, MA, USA
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Lv B, Li Y, Shi A, Pan J. Model driven method for exploring individual and confounding effects in spontaneous adverse event reporting databases. Expert Opin Drug Saf 2024:1-9. [PMID: 38073472 DOI: 10.1080/14740338.2023.2293200] [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: 08/22/2023] [Accepted: 11/28/2023] [Indexed: 06/27/2024]
Abstract
BACKGROUND Spontaneous Adverse Event Reporting (SAER) databases play a crucial role in post-marketing drug surveillance. However, the traditional model-free disproportionality analysis has been challenged by the insufficiency in investigating subgroup and confounders. These issues result in significant low-precision and biases in data mining for SAER. METHODS The Model-Driven Reporting Odds Ratio (MD-ROR) was proposed to bridge the gap between SAER database and explainable models for exploring individual and confounding effects. MD-ROR is grounded in a well-designed model, rather than a 2 × 2 cross table, for estimating AE-drug signals. Consequently, individual and confounding effects can be parameterized based on these models. We employed simulation data and the FDA Adverse Event Reporting System (FAERS) database. RESULT The simulated data indicated the subgroup effects estimated by MD-ROR were unbiased and efficient. Moreover, the adjusted-MD-ROR demonstrated greater robustness against confounding biases than the crude ROR. Applying our method to the FAERS database suggested higher occurrences of drug interactions and cardiac adverse events induced by Midazolam in females compared to males. CONCLUSION The study underscored that MD-ROR holds promise as a method for investigating individual and confounding effects in SAER databases.
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Affiliation(s)
- Bo Lv
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuedong Li
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Aiming Shi
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jie Pan
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Lichtenberg FR. Do newer drugs treat fewer diseases, controlling for time since launch? Evidence from France and the U.S. J Pharm Policy Pract 2024; 17:2357604. [PMID: 38903694 PMCID: PMC11188943 DOI: 10.1080/20523211.2024.2357604] [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] [Indexed: 06/22/2024] Open
Abstract
Background More recently approved drugs have significantly fewer indications than drugs approved many years ago. One possible reason for this may be that, controlling for the number of years since approval or launch, more recently approved drugs have fewer indications (e.g. at the time of launch). The role of precision and personalised medicine has increased, and the goal of precision medicine is to provide a more precise approach for the prevention, diagnosis and treatment of disease. Drugs that have fewer indications may be 'more precise' than drugs that have many indications. Methods We use different kinds of data from two countries - France and the U.S. - to analyze the relationship across many drugs between the number of indications of a drug, the drug's vintage - i.e. the year in which the drug was first marketed or approved - and its age - the number of years it has been marketed. Results All the evidence from both countries indicates that, controlling for drug age, more recently approved drugs tend to have fewer indications than drugs approved many years ago. In the U.S., a 10-year increase in vintage is associated with a 10.7% decline in the effective number of indications of all drugs, and a 19.4% decline in the effective number of indications of drugs approved after 1989. In France, the positive effect on the number of indications of the increase in drug age was more than offset by the negative effect of the increase in drug vintage. Conclusions More recently approved drugs are less likely to be 'general-purpose technologies' (or even multi-purpose technologies) than older drugs. The relative importance of 'precision medicine' has increased in recent decades. Drugs that have fewer indications may be 'more precise' than drugs that have many indications.
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Affiliation(s)
- Frank R. Lichtenberg
- Columbia University, National Bureau of Economic Research, and CESifo, New York, NY, USA
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Saravanan KS, Satish KS, Saraswathy GR, Kuri U, Vastrad SJ, Giri R, Dsouza PL, Kumar AP, Nair G. Innovative target mining stratagems to navigate drug repurposing endeavours. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:303-355. [PMID: 38789185 DOI: 10.1016/bs.pmbts.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.
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Affiliation(s)
- Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Ushnaa Kuri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Soujanya J Vastrad
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ritesh Giri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Adusumilli Pramod Kumar
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Gouri Nair
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
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Oprea TI, Bologa C, Holmes J, Mathias S, Metzger VT, Waller A, Yang JJ, Leach AR, Jensen LJ, Kelleher KJ, Sheils TK, Mathé E, Avram S, Edwards JS. Overview of the Knowledge Management Center for Illuminating the Druggable Genome. Drug Discov Today 2024; 29:103882. [PMID: 38218214 PMCID: PMC10939799 DOI: 10.1016/j.drudis.2024.103882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.
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Affiliation(s)
- Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Stephen Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Anna Waller
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Keith J Kelleher
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Ewy Mathé
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Sorin Avram
- Coriolan Dragulescu Institute of Chemistry, Timisoara, Romania
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, USA.
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Taujale R, Gravel N, Zhou Z, Yeung W, Kochut K, Kannan N. Informatic challenges and advances in illuminating the druggable proteome. Drug Discov Today 2024; 29:103894. [PMID: 38266979 DOI: 10.1016/j.drudis.2024.103894] [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: 10/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/26/2024]
Abstract
The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.
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Affiliation(s)
- Rahil Taujale
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA
| | - Nathan Gravel
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | - Wayland Yeung
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Krystof Kochut
- School of Computing, University of Georgia, Athens, GA, USA
| | - Natarajan Kannan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
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Guan Y, Zhang Y, Zhao X, Wang Y. Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing. Hereditas 2024; 161:6. [PMID: 38273392 PMCID: PMC10809458 DOI: 10.1186/s41065-024-00310-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVE Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools. METHODS The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes. RESULTS There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping. CONCLUSIONS The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.
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Affiliation(s)
- Yin Guan
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Yue Zhang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Xiaoqian Zhao
- Department of Ethics Committee, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Yue Wang
- Department of Rheumatism Immunity Branch, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu, China.
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Hasselgren C, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol 2024; 64:527-550. [PMID: 37738505 DOI: 10.1146/annurev-pharmtox-040323-040828] [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] [Indexed: 09/24/2023]
Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Wang Z, Zhao G, Zhu Z, Wang Y, Xiang X, Zhang S, Luo T, Zhou Q, Qiu J, Tang B, Xia K, Li B, Li J. VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome. Nucleic Acids Res 2024; 52:D1478-D1489. [PMID: 37956311 PMCID: PMC10767961 DOI: 10.1093/nar/gkad1061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/21/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhaopo Zhu
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xudong Xiang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Shiyu Zhang
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Tengfei Luo
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, & Multi-Omics Research Center for Brain Disorders, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China
| | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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Shen L, Sun X, Chen Z, Guo Y, Shen Z, Song Y, Xin W, Ding H, Ma X, Xu W, Zhou W, Che J, Tan L, Chen L, Chen S, Dong X, Fang L, Zhu F. ADCdb: the database of antibody-drug conjugates. Nucleic Acids Res 2024; 52:D1097-D1109. [PMID: 37831118 PMCID: PMC10768060 DOI: 10.1093/nar/gkad831] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.
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Affiliation(s)
- Liteng Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yu Guo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zheyuan Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yi Song
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wenxiu Xin
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Xinyue Ma
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Weiben Xu
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wanying Zhou
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Jinxin Che
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lili Tan
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Liangsheng Chen
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Siqi Chen
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Luo Fang
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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12
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Halip L, Avram S, Curpan R, Borota A, Bora A, Bologa C, Oprea TI. Exploring DrugCentral: from molecular structures to clinical effects. J Comput Aided Mol Des 2023; 37:681-694. [PMID: 37707619 PMCID: PMC10692006 DOI: 10.1007/s10822-023-00529-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.
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Affiliation(s)
- Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ana Borota
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Cristian Bologa
- 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.
- Expert Systems Inc, San Diego, CA, USA.
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13
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Abstract
Molecular complexity (MC) lacks a universal definition, but various studies address it in contexts ranging from ligand-receptor interactions to DNA sequencing, with the overarching emphasis being its significance in synthetic organic chemistry and pharmaceutical research. Efforts to quantify MC in drug discovery have been numerous, but a unified approach remains challenging. Strategies based on graph theory, information theory, and substructural feature counts employed to gauge MC are often correlated to molecular weight (MW). Herbert Waldmann and his team introduced a new MC metric called the spacial score (SPS), which is based on factors like atom hybridization and stereoisomeric considerations. While SPS and its normalized version, nSPS, correlate with the natural product likeness score, they do not align with traditional chemical properties. We examined nSPS trends for approved drugs and found no significant changes in MC over eight decades, nor did nSPS capture drug innovation during that period. Furthermore, our analysis indicates that while the majority of approved drugs have an nSPS value between 10 and 20, this metric does not correlate with key drug properties like target bioactivity and oral bioavailability. Mirroring a chemist's intuitive sense of chemical complexity, nSPS addresses the need for a precise empirical tool while a universal definition of MC remains elusive.
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Affiliation(s)
- Tudor I. Oprea
- Expert
Systems Inc, 12760 High
Bluff Dr #370, San Diego, California 92130, United States
- Department
of Internal Medicine, University of New
Mexico, MSC09-5025, Albuquerque, New Mexico 87131, United States
| | - Cristian Bologa
- Department
of Internal Medicine, University of New
Mexico, MSC09-5025, Albuquerque, New Mexico 87131, United States
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14
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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15
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Rita L, Neumann NR, Laponogov I, Gonzalez G, Veselkov D, Pratico D, Aalizadeh R, Thomaidis NS, Thompson DC, Vasiliou V, Veselkov K. Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil. Hum Genomics 2023; 17:57. [PMID: 37420280 DOI: 10.1186/s40246-023-00503-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023] Open
Abstract
Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.
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Affiliation(s)
- Luís Rita
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Natalie R Neumann
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ivan Laponogov
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Guadalupe Gonzalez
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Prescient Design, Genentech | Roche, Basel, Switzerland
| | - Dennis Veselkov
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Domenico Pratico
- Alzheimer's Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - David C Thompson
- Department of Environmental Health Sciences, Yale University, New Haven, CT, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale University, New Haven, CT, USA.
| | - Kirill Veselkov
- Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
- Department of Environmental Health Sciences, Yale University, New Haven, CT, USA.
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16
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Zhao L, Zhang H, Li N, Chen J, Xu H, Wang Y, Liang Q. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. JOURNAL OF ETHNOPHARMACOLOGY 2023; 309:116306. [PMID: 36858276 DOI: 10.1016/j.jep.2023.116306] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds. AIM OF THE STUDY The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine. MATERIALS AND METHODS Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022. RESULTS Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend. CONCLUSION Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.
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Affiliation(s)
- Li Zhao
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Ning Li
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jinman Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hao Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
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