Abstract
A poor prognosis, relapse and resistance are burning issues during adverse-risk acute myeloid leukaemia (AML) treatment. As a natural medicine, Scutellaria barbata D. Don (SBD) has shown impressive antitumour activity in various cancers. Thus, SBD may become a potential drug in adverse-risk AML treatment. This study aimed to screen the key targets of SBD in adverse-risk AML using the drug–biomarker interaction model through bioinformatics and network pharmacology methods. First, the adverse-risk AML-related critical biomarkers and targets of SBD active ingredient were obtained from The Cancer Genome Atlas database and several pharmacophore matching databases. Next, the protein–protein interaction network was constructed, and topological analysis and pathway enrichment were used to screen key targets and main pathways of intervention of SBD in adverse-risk AML. Finally, molecular docking was implemented for key target verification. The results suggest that luteolin and quercetin are the main active components of SBD against adverse-risk AML, and affected drug resistance, apoptosis, immune regulation and angiogenesis through the core targets AKT1, MAPK1, IL6, EGFR, SRC, VEGFA and TP53. We hope the proposed drug–biomarker interaction model provides an effective strategy for the research and development of antitumour drugs.
Graphic abstract
Similar content being viewed by others
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Short NJ, Rytting ME, Cortes JE (2018) Acute myeloid leukaemia. The Lancet 392:593–606. https://doi.org/10.1016/S0140-6736(18)31041-9
Shallis RM, Wang R, Davidoff A et al (2019) Epidemiology of acute myeloid leukemia: recent progress and enduring challenges. Blood Rev 36:70–87. https://doi.org/10.1016/j.blre.2019.04.005
Stone RM (2009) Prognostic factors in AML in relation to (ab)normal karyotype. Best Pract Res Clin Haematol 22:523–528. https://doi.org/10.1016/j.beha.2009.07.003
Byrd JC (2002) Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 100:4325–4336. https://doi.org/10.1182/blood-2002-03-0772
Marcucci G, Mrozek K, Ruppert AS et al (2005) Prognostic factors and outcome of core binding factor acute myeloid leukemia patients with t(8;21) differ from those of patients with inv(16): a Cancer and Leukemia Group B study. J Clin Oncol 23:5705–5717. https://doi.org/10.1200/JCO.2005.15.610
Dai Z, Wang X, Li Z et al (2008) Scutellaria barbate extract induces apoptosis of hepatoma H22 cells via the mitochondrial pathway involving caspase-3. World J Gastroenterol 14:7321. https://doi.org/10.3748/wjg.14.7321
Marconett CN, Morgenstern TJ, San Roman AK et al (2014) BZL101, a phytochemical extract from the Scutellaria barbata plant, disrupts proliferation of human breast and prostate cancer cells through distinct mechanisms dependent on the cancer cell phenotype. Cancer Biol Ther 10:397–405. https://doi.org/10.4161/cbt.10.4.12424
Yin X, Zhou J, Jie C et al (2004) Anticancer activity and mechanism of Scutellaria barbata extract on human lung cancer cell line A549. Life Sci 75:2233–2244. https://doi.org/10.1016/j.lfs.2004.05.015
Cha Y, Lee E, Lee H et al (2004) Methylene chloride fraction of Scutellaria barbata induces apoptosis in human U937 leukemia cells via the mitochondrial signaling pathway. Clin Chim Acta 348:41–48. https://doi.org/10.1016/j.cccn.2004.04.013
Rugo H, Shtivelman E, Perez A et al (2007) Phase I trial and antitumor effects of BZL101 for patients with advanced breast cancer. Breast Cancer Res Treat 105:17–28. https://doi.org/10.1007/s10549-006-9430-6
Perez AT, Arun B, Tripathy D et al (2010) A phase 1B dose escalation trial of Scutellaria barbata (BZL101) for patients with metastatic breast cancer. Breast Cancer Res Treat 120:111–118. https://doi.org/10.1007/s10549-009-0678-5
Chen Q, Rahman K, Wang S et al (2020) Scutellaria barbata: a review on chemical constituents, pharmacological activities and clinical applications. Curr Pharm Des 26:160–175. https://doi.org/10.2174/1381612825666191216124310
Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715. https://doi.org/10.1038/nrd1470
Hutchinson L, Kirk R (2011) High drug attrition rates—Where are we going wrong? Nat Rev Clin Oncol 8:189–190. https://doi.org/10.1038/nrclinonc.2011.34
Yuan H, Ma Q, Cui H et al (2017) How can synergism of traditional medicines benefit from network pharmacology? Molecules 22:1135. https://doi.org/10.3390/molecules22071135
Wu J, Hong S, Xie X, Liu W (2020) A network pharmacology-based study on the anti-lung cancer effect of dipsaci radix. Evid Based Complement Altern 2020:1–9. https://doi.org/10.1155/2020/7424061
Huang J, Chen F, Zhong Z et al (2020) Interpreting the pharmacological mechanisms of Huachansu capsules on hepatocellular carcinoma through combining network pharmacology and experimental evaluation. Front Pharmacol. https://doi.org/10.3389/fphar.2020.00414
Zeng Q, Li L, Jin Y et al (2019) A network pharmacology approach to reveal the underlying mechanisms of Paeonia lactiflora pall. On the treatment of Alzheimer’s disease. Evid Based Complement Altern 2019:1–12. https://doi.org/10.1155/2019/8706589
Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. J Appl Genet 52:413–435. https://doi.org/10.1007/s13353-011-0057-x
Liu C, Li H, Wang K et al (2019) Identifying the antiproliferative effect of astragalus polysaccharides on breast cancer: coupling network pharmacology with targetable screening from The Cancer Genome Atlas. Front Oncol 9:368. https://doi.org/10.3389/fonc.2019.00368
Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol 19:A68–A77. https://doi.org/10.5114/wo.2014.47136
Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007
Gregg ME, Datta S, Lorenz D (2018) A log rank test for clustered data with informative within-cluster group size. Stat Med 37:4071–4082. https://doi.org/10.1002/sim.7899
Ru J, Li P, Wang J et al (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 6:13. https://doi.org/10.1186/1758-2946-6-13
Tian S, Li Y, Wang J et al (2011) ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints. Mol Pharm 8:841–851. https://doi.org/10.1021/mp100444g
Xu X, Zhang W, Huang C et al (2012) A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci 13:6964–6982. https://doi.org/10.3390/ijms13066964
Tao W, Xu X, Wang X et al (2013) Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol 145:1–10. https://doi.org/10.1016/j.jep.2012.09.051
Li F, Duan J, Zhao M et al (2019) A network pharmacology approach to reveal the protective mechanism of Salvia miltiorrhiza–Dalbergia odorifera coupled-herbs on coronary heart disease. Sci Rep 9:19343. https://doi.org/10.1038/s41598-019-56050-5
Szklarczyk D, Santos A, von Mering C et al (2016) STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384. https://doi.org/10.1093/nar/gkv1277
Gfeller D, Grosdidier A, Wirth M et al (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 42:W32–W38. https://doi.org/10.1093/nar/gku293
Wu Z, Li W, Liu G, Tang Y (2018) Network-based methods for prediction of drug-target interactions. Front Pharmacol 9:1134. https://doi.org/10.3389/fphar.2018.01134
Yu H, Chen J, Xu X et al (2012) A systematic prediction of multiple drug–target interactions from chemical, genomic, and pharmacological data. PLoS ONE 7:e37608. https://doi.org/10.1371/journal.pone.0037608
Kim S, Chen J, Cheng T et al (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47:D1102–D1109. https://doi.org/10.1093/nar/gky1033
Sterling T, Irwin JJ (2015) ZINC 15—ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
The UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515. https://doi.org/10.1093/nar/gky1049
Tang Y, Li M, Wang J et al (2015) CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127:67–72. https://doi.org/10.1016/j.biosystems.2014.11.005
Mering CV (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31:258–261. https://doi.org/10.1093/nar/gkg034
Scardoni G, Petterlini M, Laudanna C (2009) Analyzing biological network parameters with CentiScaPe. Bioinformatics 25:2857–2859. https://doi.org/10.1093/bioinformatics/btp517
Kanehisa M, Furumichi M, Tanabe M et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092
Yu G, Wang L, Han Y, He Q (2012) clusterProfiler an R package for comparing biological themes among gene clusters. OMICS J Integr Biol 16:284–287. https://doi.org/10.1089/omi.2011.0118
Sethi KK, Verma SM (2013) A systematic quantitative approach to rational drug design and discovery of novel human carbonic anhydrase IX inhibitors. J Enzym Inhib Med Chem 29:571–581. https://doi.org/10.3109/14756366.2013.827677
Rose PW, Prlic A, Altunkaya A et al (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D271–D281. https://doi.org/10.1093/nar/gkw1000
Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46:499–511. https://doi.org/10.1021/jm020406h
Liu C, Wang K, Zhuang J et al (2019) The modulatory properties of Astragalus membranaceus treatment on triple-negative breast cancer: an integrated pharmacological method. Front Pharmacol 10:1171. https://doi.org/10.3389/fphar.2019.01171
Wang F, Wu W, Hsiu W et al (2020) Genome-scale metabolic modeling with protein expressions of normal and cancerous colorectal tissues for oncogene inference. Metabolites 10:16. https://doi.org/10.3390/metabo10010016
López-Cortés A, Paz-y-Miño C, Guerrero S et al (2020) OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine. Sci Rep 10:5285. https://doi.org/10.1038/s41598-020-62279-2
Azmi AS, Mohammad RM, Sarkar FH (2012) Can network pharmacology rescue neutraceutical cancer research? Drug Discov Today 17:807–809. https://doi.org/10.1016/j.drudis.2012.06.008
Kibble M, Saarinen N, Tang J et al (2015) Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep 32:1249–1266. https://doi.org/10.1039/C5NP00005J
Ito T, Ishii G, Chiba H, Ochiai A (2007) The VEGF angiogenic switch of fibroblasts is regulated by MMP-7 from cancer cells. Oncogene 26:7194–7203. https://doi.org/10.1038/sj.onc.1210535
Xu L, Hou Y, Tu G et al (2017) Nuclear Drosha enhances cell invasion via an EGFR-ERK1/2-MMP7 signaling pathway induced by dysregulated miRNA-622/197 and their targets LAMC2 and CD82 in gastric cancer. Cell Death Dis 8:e2642. https://doi.org/10.1038/cddis.2017.5
Bufu T, Di X, Yilin Z et al (2018) Celastrol inhibits colorectal cancer cell proliferation and migration through suppression of MMP3 and MMP7 by the PI3K/AKT signaling pathway. Anti-Cancer Drug 29:530–538. https://doi.org/10.1097/CAD.0000000000000621
Du F, Sun L, Chu Y et al (2018) DDIT4 promotes gastric cancer proliferation and tumorigenesis through the p53 and MAPK pathways. Cancer Commun 38:45. https://doi.org/10.1186/s40880-018-0315-y
Miller WP, Yang C, Mihailescu ML et al (2018) Deletion of the Akt/mTORC1 repressor REDD1 prevents visual dysfunction in a rodent model of type 1 diabetes. Diabetes 67:110–119. https://doi.org/10.2337/db17-0728
Havemeyer A, Lang J, Clement B (2011) The fourth mammalian molybdenum enzyme mARC: current state of research. Drug Metab Rev 43:524–539. https://doi.org/10.3109/03602532.2011.608682
Nakazawa H, Sada T, Toriyama M et al (2012) Rab33a mediates anterograde vesicular transport for membrane exocytosis and axon outgrowth. J Neurosci 32:12712–12725. https://doi.org/10.1523/JNEUROSCI.0989-12.2012
Brenner AK, Bruserud Ø (2019) Functional Toll-Like Receptors (TLRs) are expressed by a majority of primary human acute myeloid leukemia cells and inducibility of the TLR signaling pathway is associated with a more favorable phenotype. Cancers 11:973. https://doi.org/10.3390/cancers11070973
Numasaki M (2003) Interleukin-17 promotes angiogenesis and tumor growth. Blood 101:2620–2627. https://doi.org/10.1182/blood-2002-05-1461
Prabhala RH, Pelluru D, Fulciniti M et al (2010) Elevated IL-17 produced by Th17 cells promotes myeloma cell growth and inhibits immune function in multiple myeloma. Blood 115:5385–5392. https://doi.org/10.1182/blood-2009-10-246660
Weaver CT, Hatton RD, Mangan PR, Harrington LE (2007) IL-17 family cytokines and the expanding diversity of effector T cell lineages. Annu Rev Immunol 25:821–852. https://doi.org/10.1146/annurev.immunol.25.022106.141557
Han Y, Ye A, Bi L et al (2014) Th17 cells and interleukin-17 increase with poor prognosis in patients with acute myeloid leukemia. Cancer Sci 105:933–942. https://doi.org/10.1111/cas.12459
Buteyn NJ, Santhanam R, Merchand-Reyes G et al (2020) Activation of the intracellular pattern recognition receptor NOD2 promotes acute myeloid leukemia (AML) cell apoptosis and provides a survival advantage in an animal model of AML. J Immunol 204:1988–1997. https://doi.org/10.4049/jimmunol.1900885
Minato N, Kometani K, Hattori M (2007) Regulation of immune responses and hematopoiesis by the Rap1 signal. Adv Immunol 93:229. https://doi.org/10.1016/S0065-2776(06)93006-5
Braun BS, Shannon K (2008) Targeting Ras in myeloid leukemias. Clin Cancer Res 14:2249–2252. https://doi.org/10.1158/1078-0432.CCR-07-1005
Kiu H, Nicholson SE (2012) Biology and significance of the JAK/STAT signalling pathways. Growth Factors 30:88–106. https://doi.org/10.3109/08977194.2012.660936
Naude PJ, den Boer JA, Luiten PG, Eisel UL (2011) Tumor necrosis factor receptor cross-talk. FEBS J 278:888–898. https://doi.org/10.1111/j.1742-4658.2011.08017.x
Chapuis N, Park S, Leotoing L et al (2010) IκB kinase overcomes PI3K/Akt and ERK/MAPK to control FOXO3a activity in acute myeloid leukemia. Blood 116:4240–4250. https://doi.org/10.1182/blood-2009-12-260711
Sykes SM, Lane SW, Bullinger L et al (2011) AKT/FOXO signaling enforces reversible differentiation blockade in myeloid leukemias. Cell 146:697–708. https://doi.org/10.1016/j.cell.2011.07.032
Goichberg P, Kalinkovich A, Borodovsky N et al (2006) cAMP-induced PKCζ activation increases functional CXCR4 expression on human CD34+ hematopoietic progenitors. Blood 107:870–879. https://doi.org/10.1182/blood-2005-03-0941
Huseby S, Gausdal G, Keen TJ et al (2011) Cyclic AMP induces IPC leukemia cell apoptosis via CRE-and CDK-dependent Bim transcription. Cell Death Dis 2:e237. https://doi.org/10.1038/cddis.2011.124
Moshofsky KB, Cho HJ, Wu G et al (2019) Acute myeloid leukemia-induced T-cell suppression can be reversed by inhibition of the MAPK pathway. Blood Adv 3:3038–3051. https://doi.org/10.1182/bloodadvances.2019000574
Venugopal S, Bar-Natan M, Mascarenhas JO (2020) JAKs to STATs: a tantalizing therapeutic target in acute myeloid leukemia. Blood Rev 40:100634. https://doi.org/10.1016/j.blre.2019.100634
Chen P, Jin Q, Fu Q et al (2016) Induction of multidrug resistance of acute myeloid leukemia cells by cocultured stromal cells via upregulation of the PI3K/Akt signaling pathway. Oncol Res 24:215. https://doi.org/10.3727/096504016X14634208143021
Soga T (2013) Cancer metabolism: key players in metabolic reprogramming. Cancer Sci 104:275–281. https://doi.org/10.1111/cas.12085
Yalcin A, Clem B, Makoni S et al (2010) Selective inhibition of choline kinase simultaneously attenuates MAPK and PI3K/AKT signaling. Oncogene 29:139–149. https://doi.org/10.1038/onc.2009.317
Kim DH, Lee NY, Lee M et al (2008) Vascular endothelial growth factor (VEGF) gene (VEGFA) polymorphism can predict the prognosis in acute myeloid leukaemia patients. Brit J Haematol 140:71–79. https://doi.org/10.1111/j.1365-2141.2007.06887.x
Weidenaar AC, ter Elst A, Koopmans-Klein G, Rosati S et al (2011) High acute myeloid leukemia derived VEGFA levels are associated with a specific vascular morphology in the leukemic bone marrow. Cell Oncol 34:289–296. https://doi.org/10.1007/s13402-011-0017-9
Yao H, Li S, Hu J et al (2011) Chromatographic fingerprint and quantitative analysis of seven bioactive compounds of Scutellaria barbata. Planta Med 77:388–393. https://doi.org/10.1055/s-0030-1250353
Li Y, Zhang Q, Sun H et al (2013) Simultaneous determination of flavonoid analogs in Scutellariae Barbatae Herba by β-cyclodextrin and acetonitrile modified capillary zone electrophoresis. Talanta 105:393–402. https://doi.org/10.1016/j.talanta.2012.10.064
Imran M, Rauf A, Abu-Izneid T et al (2019) Luteolin, a flavonoid, as an anticancer agent: a review. Biomed Pharmacother 112:108612. https://doi.org/10.1016/j.biopha.2019.108612
Rauf A, Imran M, Khan IA et al (2018) Anticancer potential of quercetin: a comprehensive review. Phytother Res 32:2109–2130. https://doi.org/10.1002/ptr.6155
Pan H, Jiang Q, Yu Y et al (2015) Quercetin promotes cell apoptosis and inhibits the expression of MMP-9 and fibronectin via the AKT and ERK signalling pathways in human glioma cells. Neurochem Int 80:60–71. https://doi.org/10.1016/j.neuint.2014.12.001
Cheng S, Huang W, Pang SJ et al (2019) Quercetin inhibits the production of IL-1β-induced inflammatory cytokines and chemokines in ARPE-19 cells via the MAPK and NF-κB signaling pathways. Int J Mol Sci 20:2957. https://doi.org/10.3390/ijms20122957
Fan J, Hsu W, Lee K et al (2019) Dietary flavonoids luteolin and quercetin inhibit migration and invasion of squamous carcinoma through reduction of Src/Stat3/S100A7 signaling. Antioxidants 8:557. https://doi.org/10.3390/antiox8110557
Huang C, Chan C, Chou I et al (2013) Quercetin induces growth arrest through activation of FOXO1 transcription factor in EGFR-overexpressing oral cancer cells. J Nutr Biochem 24:1596–1603. https://doi.org/10.1016/j.jnutbio.2013.01.010
Chen R, Hollborn M, Grosche A et al (2014) Effects of the vegetable polyphenols epigallocatechin-3-gallate, luteolin, apigenin, myricetin, quercetin, and cyanidin in primary cultures of human retinal pigment epithelial cells. Mol Vis 20:242
Kim MC, Lee HJ, Lim B et al (2014) Quercetin induces apoptosis by inhibiting MAPKs and TRPM7 channels in AGS cells. Int J Mol Med 33:1657–1663. https://doi.org/10.3892/ijmm.2014.1704
Vidya Priyadarsini R, Senthil Murugan R, Maitreyi S et al (2010) The flavonoid quercetin induces cell cycle arrest and mitochondria-mediated apoptosis in human cervical cancer (HeLa) cells through p53 induction and NF-κB inhibition. Eur J Pharmacol 649:84–91. https://doi.org/10.1016/j.ejphar.2010.09.020
Polier G, Giaisi M, Kohler R et al (2015) Targeting CDK9 by wogonin and related natural flavones potentiates the anti-cancer efficacy of the Bcl-2 family inhibitor ABT-263. Int J Cancer 136:688–698. https://doi.org/10.1002/ijc.29009
Larocca LM, Teofili L, Sica S et al (1995) Quercetin inhibits the growth of leukemic progenitors and induces the expression of transforming growth factor-beta 1 in these cells. Blood 85:3654–3661
Lee W, Hsiao M, Chang J et al (2015) Quercetin induces mitochondrial-derived apoptosis via reactive oxygen species-mediated ERK activation in HL-60 leukemia cells and xenograft. Arch Toxicol 89:1103–1117. https://doi.org/10.1007/s00204-014-1300-0
Chang J, Chow J, Chang J et al (2017) Quercetin simultaneously induces G0/G1-phase arrest and caspase-mediated crosstalk between apoptosis and autophagy in human leukemia HL-60 cells. Environ Toxicol 32:1857–1868. https://doi.org/10.1002/tox.22408
Alvarez MC, Maso V, Torello CO et al (2018) The polyphenol quercetin induces cell death in leukemia by targeting epigenetic regulators of pro-apoptotic genes. Clin Epigenet. https://doi.org/10.1186/s13148-018-0563-3
Naimi A, Entezari A, Hagh MF et al (2018) Quercetin sensitizes human myeloid leukemia KG-1 cells against TRAIL-induced apoptosis. J Cell Physiol 234:13233–13241. https://doi.org/10.1002/jcp.27995
Chin Y, Kong JY, Han S (2013) Flavonoids as receptor tyrosine kinase FLT3 inhibitors. Bioorg Med Chem Lett 23:1768–1770. https://doi.org/10.1016/j.bmcl.2013.01.049
Funding
This research is funded by the following projects: National Natural Science Foundation of China (No. 81473511 and 81974547).
Author information
Authors and Affiliations
Contributions
R.X., C.L. and T.W. were involved in conceptualization and methodology; T.W. and C.L. contributed to software; Y.J., X.D. and Y.W. helped in validation; X.D., J.W. and Z.L. helped in data curation; T.W. was involved in original draft writing; Y.J., T.W. and R.X. helped in review and editing; T.W. and C.L. were involved in visualization; X.D., Y.J. and Z.L. were involved in supervision; R.X. and Y.W. helped in funding acquisition. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Consent for publication
The manuscript is approved by all authors for publication.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Wang, T., Lyu, Cy., Jiang, Yh. et al. A drug–biomarker interaction model to predict the key targets of Scutellaria barbata D. Don in adverse-risk acute myeloid leukaemia. Mol Divers 25, 2351–2365 (2021). https://doi.org/10.1007/s11030-020-10124-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11030-020-10124-z