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2000
Volume 21, Issue 3
  • ISSN: 1573-3947
  • E-ISSN: 1875-6301

Abstract

Breast cancer remains a significant global health challenge, necessitating innovative approaches to improve treatment efficacy while minimizing side effects. This review explores the promising advancements in breast cancer drug delivery driven by the transformative potential of bioinformatics and Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate genomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers. The integration of multi-omics data facilitates a comprehensive understanding of the disease, guiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in biomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment. AI, particularly machine learning and deep learning, has revolutionized breast cancer research. Machine learning models empower accurate diagnosis through image analysis, improve survival prediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural networks, enable precise tumor detection and classification from medical imaging data, notably mammograms and MRI scans. Additionally, natural language processing techniques facilitate the mining of vast scientific literature, uncovering hidden insights and identifying potential drug targets. Network-based approaches integrated with AI algorithms facilitate the identification of central proteins as promising drug targets within complex biological networks. This review also examines AI-optimized nanoformulations designed to enhance targeted drug delivery. AI-guided design of drug-loaded nanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery, offering promising solutions to overcome the challenges of conventional drug delivery.

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2025-11-03

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References

  1. AmorosoN. PomaricoD. FanizziA. DidonnaV. GiottaF. La ForgiaD. LatorreA. MonacoA. PantaleoE. PetruzzellisN. TamborraP. ZitoA. LorussoV. BellottiR. MassafraR. A roadmap towards breast cancer therapies supported by explainable artificial intelligence.Appl. Sci.20211111488110.3390/app11114881
     [Google Scholar]
  2. LeeJH NanA Combination drug delivery approaches in metastatic breast cancer.J Drug Deliv.2012201291537510.1155/2012/915375
     [Google Scholar]
  3. WainbergM. MericoD. DelongA. FreyB.J. Deep learning in biomedicine.Nat. Biotechnol.201836982983810.1038/nbt.423330188539
     [Google Scholar]
  4. ChanH.C.S. ShanH. DahounT. VogelH. YuanS. Advancing drug discovery via artificial intelligence.Trends Pharmacol. Sci.201940859260410.1016/j.tips.2019.06.00431320117
     [Google Scholar]
  5. ZhangZ. LiJ. HeT. DingJ. Bioinformatics identified 17 immune genes as prognostic biomarkers for breast cancer: Application study based on artificial intelligence algorithms.Front. Oncol.20201033010.3389/fonc.2020.0033032296631
     [Google Scholar]
  6. CortiC. CobanajM. DeeE.C. CriscitielloC. TolaneyS.M. CeliL.A. CuriglianoG. Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care.Cancer Treat. Rev.202311210249810.1016/j.ctrv.2022.10249836527795
     [Google Scholar]
  7. LiuB. HeH. LuoH. ZhangT. JiangJ. Artificial intelligence and big data facilitated targeted drug discovery.Stroke Vasc. Neurol.20194420621310.1136/svn‑2019‑00029032030204
     [Google Scholar]
  8. ShaoD. DaiY. LiN. CaoX. ZhaoW. ChengL. RongZ. HuangL. WangY. ZhaoJ. Artificial intelligence in clinical research of cancers.Brief. Bioinform.2022231bbab52310.1093/bib/bbab52334929741
     [Google Scholar]
  9. VoraL.K. GholapA.D. JethaK. ThakurR.R.S. SolankiH.K. ChavdaV.P. Artificial intelligence in pharmaceutical technology and drug delivery design.Pharmaceutics2023157191610.3390/pharmaceutics1507191637514102
     [Google Scholar]
  10. BegA ParveenR. Review of bioinformatics tools and techniques to accelerate ovarian cancer research.Intern. J. Bioinfo. Intel. Comput.202211111010.61797/ijbic.v1i1.116
     [Google Scholar]
  11. HeT. HuangL. LiJ. WangP. ZhangZ. Potential prognostic immune biomarkers of overall survival in ovarian cancer through comprehensive bioinformatics analysis: A novel artificial intelligence survival prediction system.Front. Med.2021858749610.3389/fmed.2021.58749634109184
     [Google Scholar]
  12. CuiC. DingX. WangD. ChenL. XiaoF. XuT. ZhengM. LuoX. JiangH. ChenK. Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug–drug links based on graph neural network.Bioinformatics202137182930293710.1093/bioinformatics/btab19133739367
     [Google Scholar]
  13. PandiyanS. WangL. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence.Comput. Biol. Med.202215010614010.1016/j.compbiomed.2022.10614036179510
     [Google Scholar]
  14. DuM. OuyangY. MengF. MaQ. LiuH. ZhuangY. PangM. CaiT. CaiY. Nanotargeted agents: An emerging therapeutic strategy for breast cancer.Nanomedicine201914131771178610.2217/nnm‑2018‑048131298065
     [Google Scholar]
  15. AshfaqU.A. RiazM. YasmeenE. YousafM.Z. Recent advances in nanoparticle-based targeted drug-delivery systems against cancer and role of tumor microenvironment.Crit. Rev. Ther. Drug Carrier Syst.201734431735310.1615/CritRevTherDrugCarrierSyst.201701784529199588
     [Google Scholar]
  16. LiyanageP.Y. HettiarachchiS.D. ZhouY. OuhtitA. SevenE.S. OztanC.Y. CelikE. LeblancR.M. Nanoparticle-mediated targeted drug delivery for breast cancer treatment.Biochim. Biophys. Acta Rev. Cancer20191871241943310.1016/j.bbcan.2019.04.00631034927
     [Google Scholar]
  17. SadatS. SaeidniaS. NazaraliA. HaddadiA. Nano-pharmaceutical formulations for targeted drug delivery against HER2 in breast cancer.Curr. Cancer Drug Targets2015151718610.2174/156800961566615010511504725564255
     [Google Scholar]
  18. HoB.N. PfefferC.M. SinghA.T.K. Update on nanotechnology-based drug delivery systems in cancer treatment.Anticancer Res.201737115975598129061776
     [Google Scholar]
  19. ZhouY. YuanY. LiL. WangX. QuanY. LiuC. YuM. HuX. MengX. ZhouZ. ZhangC.Y. ChenX. LiuM. WangC. HER2-intronic miR-4728-5p facilitates HER2 expression and accelerates cell proliferation and migration by targeting EBP1 in breast cancer.PLoS One2021162e024583210.1371/journal.pone.024583233529238
     [Google Scholar]
  20. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery.Mol. Divers.20212531315136010.1007/s11030‑021‑10217‑333844136
     [Google Scholar]
  21. WaksA.G. WinerE.P. Breast cancer treatment: A review.JAMA2019321328830010.1001/jama.2018.1932330667505
     [Google Scholar]
  22. KeyT.J. VerkasaloP.K. BanksE. Epidemiology of breast cancer.Lancet Oncol.20012313314010.1016/S1470‑2045(00)00254‑011902563
     [Google Scholar]
  23. NayarisseriA. KhandelwalR. TanwarP. MadhaviM. SharmaD. ThakurG. Speck-PlancheA. SinghS.K. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery.Curr. Drug Targets202122663165510.2174/18735592MTEzsMDMnz33397265
     [Google Scholar]
  24. TroyanskayaO TrajanoskiZ CarpenterA ThrunS RazavianN OliverN Artificial intelligence and cancer.Nat. can.20201214915210.1038/s43018‑020‑0034‑6
     [Google Scholar]
  25. Macías-GarcíaL. Martínez-BallesterosM. Luna-RomeraJ.M. García-HerediaJ.M. García-GutiérrezJ. Riquelme-SantosJ.C. Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance.Artif. Intell. Med.202011010197610.1016/j.artmed.2020.10197633250148
     [Google Scholar]
  26. ElmoreJ.G. ArmstrongK. LehmanC.D. FletcherS.W. Screening for breast cancer.JAMA2005293101245125610.1001/jama.293.10.124515755947
     [Google Scholar]
  27. PolyakK. Breast cancer: Origins and evolution.J. Clin. Invest.2007117113155316310.1172/JCI3329517975657
     [Google Scholar]
  28. SunY.S. ZhaoZ. YangZ.N. XuF. LuH.J. ZhuZ.Y. ShiW. JiangJ. YaoP.P. ZhuH.P. Risk factors and preventions of breast cancer.Int. J. Biol. Sci.201713111387139710.7150/ijbs.2163529209143
     [Google Scholar]
  29. SinghS. SinghS. LillardJ.W.Jr SinghR. Drug delivery approaches for breast cancer.Int. J. Nanomed2017126205621810.2147/IJN.S14032528883730
     [Google Scholar]
  30. ChenY. GaoD. WangY. LinS. JiangY. A novel 3D breast-cancer-on-chip platform for therapeutic evaluation of drug delivery systems.Anal. Chim. Acta201810369710610.1016/j.aca.2018.06.03830253842
     [Google Scholar]
  31. AllahverdiyevAM ParlarE DinparvarS BagirovaM AbamorEŞ Current aspects in treatment of breast cancer based of nanodrug delivery systems and future prospects.Artificial cells nanomed. biotech.201846375576210.1080/21691401.2018.1511573
     [Google Scholar]
  32. TangX. LocW.S. DongC. MattersG.L. ButlerP.J. KesterM. MeyersC. JiangY. AdairJ.H. The use of nanoparticulates to treat breast cancer.Nanomedicine201712192367238810.2217/nnm‑2017‑020228868970
     [Google Scholar]
  33. UnsoyG. KhodadustR. YalcinS. MutluP. GunduzU. Synthesis of Doxorubicin loaded magnetic chitosan nanoparticles for pH responsive targeted drug delivery.Eur. J. Pharm. Sci.20146224325010.1016/j.ejps.2014.05.02124931189
     [Google Scholar]
  34. UnsoyG. YalcinS. KhodadustR. MutluP. OnguruO. GunduzU. Chitosan magnetic nanoparticles for pH responsive Bortezomib release in cancer therapy.Biomed. Pharmacother.201468564164810.1016/j.biopha.2014.04.00324880680
     [Google Scholar]
  35. VijayanV. ShaliniK. YugesvaranV. YeeT.H. BalakrishnanS. PalanimuthuV.R. Effect of paclitaxel-loaded PLGA nanoparticles on MDA-MB type cell lines: Apoptosis and cytotoxicity studies.Curr. Pharm. Des.201824283366337510.2174/138161282466618090311030130179118
     [Google Scholar]
  36. ViniR. SreejaS. Punica granatum and its therapeutic implications on breast carcinogenesis: A review.Biofactors2015412788910.1002/biof.120625857627
     [Google Scholar]
  37. CoradiniD. PellizzaroC. MiglieriniG. DaidoneM.G. PerbelliniA. Hyaluronic acid as drug delivery for sodium butyrate: Improvement of the anti-proliferative activity on a breast-cancer cell line.Int. J. Cancer199981341141610.1002/(SICI)1097‑0215(19990505)81:3<411::AID‑IJC15>3.0.CO;2‑F10209956
     [Google Scholar]
  38. HanH.J. EkweremaduC. PatelN. Advanced drug delivery system with nanomaterials for personalised medicine to treat breast cancer.J. Drug Deliv. Sci. Technol.2019521051106010.1016/j.jddst.2019.05.024
     [Google Scholar]
  39. JinS. YeK. Targeted drug delivery for breast cancer treatment.Recent Pat. Antican. Drug Discov.20138214315310.2174/157489281130802000323394116
     [Google Scholar]
  40. YapK.M. SekarM. FuloriaS. WuY.S. GanS.H. Mat RaniN.N.I. SubramaniyanV. KokareC. LumP.T. BegumM.Y. ManiS. MeenakshiD.U. SathasivamK.V. FuloriaN.K. Drug delivery of natural products through nanocarriers for effective breast cancer therapy: A comprehensive review of literature.Int. J. Nanomedicine2021167891794110.2147/IJN.S32813534880614
     [Google Scholar]
  41. MuruganC. RayappanK. ThangamR. BhanumathiR. ShanthiK. VivekR. ThirumuruganR. BhattacharyyaA. SivasubramanianS. GunasekaranP. KannanS. Combinatorial nanocarrier based drug delivery approach for amalgamation of anti-tumor agents in breast cancer cells: An improved nanomedicine strategy.Sci. Rep.2016613405310.1038/srep3405327725731
     [Google Scholar]
  42. ChavdaVP ViholD PatelA RedwanEM UverskyVN Introduction to bioinformatics, ai, and ml for pharmaceuticals.Bioinfo. Tool Pharma. Drug Prod. Devel.20232718
     [Google Scholar]
  43. ChavdaV. AnandK. ApostolopoulosV. Bioinformatics Tools for Pharmaceutical Drug Product Development.John Wiley & Sons202310.1002/9781119865728
     [Google Scholar]
  44. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.01033099022
     [Google Scholar]
  45. TanP ChenX ZhangH WeiQ LuoK Artificial intelligence aids in development of nanomedicines for cancer management.In: In-Seminars in Cancer BiologyAcademic Press202389617510.1016/j.semcancer.2023.01.005
     [Google Scholar]
  46. RajVS PriyadarshiniA YadavMK PandeyRP GuptaA VibhutiA Artificial intelligence in bioinformatics.In: Biomedical Data Mining for Information Retrieval: Methodologies, Techniques and ApplicationsWiley2021215110.1002/9781119711278.ch2
     [Google Scholar]
  47. WangN. YangB. ZhangX. WangS. ZhengY. LiX. LiuS. PanH. LiY. HuangZ. ZhangF. WangZ. Network pharmacology-based validation of caveolin-1 as a key mediator of Ai Du Qing inhibition of drug resistance in breast cancer.Front. Pharmacol.20189110610.3389/fphar.2018.0110630333750
     [Google Scholar]
  48. Hernández-LemusE. Martínez-GarcíaM. Pathway-based drug-repurposing schemes in cancer: The role of translational bioinformatics.Front. Oncol.20211060568010.3389/fonc.2020.60568033520715
     [Google Scholar]
  49. WangM.D. ShinD.M. SimonsJ.W. NieS. Nanotechnology for targeted cancer therapy.Expert Rev. Anticancer Ther.20077683383710.1586/14737140.7.6.83317555393
     [Google Scholar]
  50. WardC. LangdonS.P. MullenP. HarrisA.L. HarrisonD.J. SupuranC.T. KunklerI.H. New strategies for targeting the hypoxic tumour microenvironment in breast cancer.Cancer Treat. Rev.201339217117910.1016/j.ctrv.2012.08.00423063837
     [Google Scholar]
  51. UlianoJ. NicolòE. CorvajaC. Taurelli SalimbeniB. TrapaniD. CuriglianoG. Combination immunotherapy strategies for triple-negative breast cancer: current progress and barriers within the pharmacological landscape.Expert Rev. Clin. Pharmacol.202215121399141310.1080/17512433.2022.214255936317756
     [Google Scholar]
  52. ParvizpourS. RazmaraJ. OmidiY. Breast cancer vaccination comes to age: Impacts of bioinformatics.Bioimpacts20188322323510.15171/bi.2018.2530211082
     [Google Scholar]
  53. PoustforooshA. FaramarzS. NegahdaripourM. TüzünB. HashemipourH. Tracing the pathways and mechanisms involved in the anti-breast cancer activity of glycyrrhizin using bioinformatics tools and computational methods.J. Biomol. Struct. Dyn.202342281983337042955
     [Google Scholar]
  54. NounouMI ElAmrawyF AhmedN AbdelraoufK GodaS Syed-Sha-QhattalH Breast cancer: Conventional diagnosis and treatment modalities and recent patents and technologies.Breast Cancer2015279173410.4137/BCBCR.S29420
     [Google Scholar]
  55. CoatesA.S. WinerE.P. GoldhirschA. GelberR.D. GnantM. Piccart-GebhartM. ThürlimannB. SennH.J. AndréF. BaselgaJ. BerghJ. BonnefoiH. BursteinH. CardosoF. Castiglione-GertschM. CoatesA.S. ColleoniM. CuriglianoG. DavidsonN.E. Di LeoA. EjlertsenB. ForbesJ.F. GalimbertiV. GelberR.D. GnantM. GoldhirschA. GoodwinP. HarbeckN. HayesD.F. HuoberJ. HudisC.A. IngleJ.N. JassemJ. JiangZ. KarlssonP. MorrowM. OrecchiaR. Kent OsborneC. PartridgeA.H. de la PeñaL. Piccart-GebhartM.J. PritchardK.I. RutgersE.J.T. SedlmayerF. SemiglazovV. ShaoZ-M. SmithI. ThürlimannB. ToiM. TuttA. VialeG. von MinckwitzG. WatanabeT. WhelanT. WinerE.P. XuB. Panel Members Tailoring therapies—improving the management of early breast cancer: St gallen international expert consensus on the primary therapy of early breast cancer 2015.Ann. Oncol.20152681533154610.1093/annonc/mdv22125939896
     [Google Scholar]
  56. WalshD. RybickiL. Symptom clustering in advanced cancer.Support. Care Cancer200614883183610.1007/s00520‑005‑0899‑z16482450
     [Google Scholar]
  57. Jiménez-LunaJ. GrisoniF. SchneiderG. Drug discovery with explainable artificial intelligence.Nat. Mach. Intell.202021057358410.1038/s42256‑020‑00236‑4
     [Google Scholar]
  58. VernieriC. CortiF. NichettiF. LigorioF. ManglavitiS. ZattarinE. ReaC.G. CapriG. BianchiG.V. de BraudF. Everolimus versus alpelisib in advanced hormone receptor-positive HER2-negative breast cancer: Targeting different nodes of the PI3K/AKT/mTORC1 pathway with different clinical implications.Breast Cancer Res.20202213310.1186/s13058‑020‑01271‑032252811
     [Google Scholar]
  59. AiH. ZhouW. WangZ. QiongG. ChenZ. DengS. microRNAs‐107 inhibited autophagy, proliferation, and migration of breast cancer cells by targeting HMGB1.J. Cell. Biochem.201912058696870510.1002/jcb.2815730506984
     [Google Scholar]
  60. GuG. DustinD. FuquaS.A.W. Targeted therapy for breast cancer and molecular mechanisms of resistance to treatment.Curr. Opin. Pharmacol.2016319710310.1016/j.coph.2016.11.00527883943
     [Google Scholar]
  61. ZanardiE BregniG de BraudF Di CosimoS. Better together: Targeted combination therapies in breast cancer.In: In Seminars in OncologyWB Saunders2015426887895 https://pubmed.ncbi.nlm.nih.gov/26615133/ 10.1053/j.seminoncol.2015.09.029
     [Google Scholar]
  62. WangX. LiuZ. ChuA. SongR. LiuS. ChaiT. SunC. Hsa_circ_0052611 and mir-767-5p guide the warburg effect, migration, and invasion of BRCA cells through modulating SCAI.J. Bioenerg. Biomembr.202355538139610.1007/s10863‑023‑09985‑437743442
     [Google Scholar]
  63. ChaurasiaM. SinghR. SurS. FloraS.J.S. A review of FDA approved drugs and their formulations for the treatment of breast cancer.Front. Pharmacol.202314118447210.3389/fphar.2023.118447237576816
     [Google Scholar]
  64. SharmaA JainN SareenR Nanocarriers for diagnosis and targeting of breast cancer.Biomed Res Int.2013201396082110.1155/2013/960821
     [Google Scholar]
  65. OlovN. Bagheri-KhoulenjaniS. MirzadehH. Combinational drug delivery using nanocarriers for breast cancer treatments: A review.J. Biomed. Mater. Res. A201810682272228310.1002/jbm.a.3641029577607
     [Google Scholar]
  66. StillmanN.R. BalazI. TsompanasM.A. KovacevicM. AzimiS. LafondS. AdamatzkyA. HauertS. Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment.NPJ Comput Mater20217115010.1038/s41524‑021‑00614‑5
     [Google Scholar]
  67. KarathanasisE. ChanL. BalusuS.R. D’OrsiC.J. AnnapragadaA.V. SechopoulosI. BellamkondaR.V. Multifunctional nanocarriers for mammographic quantification of tumor dosing and prognosis of breast cancer therapy.Biomaterials200829364815482210.1016/j.biomaterials.2008.08.03618814908
     [Google Scholar]
  68. MoradiO. MahdavianL. Simulation and computational study of graphene oxide nano-carriers, absorption, and release of the anticancer drug of camptothecin.J. Mol. Model.202127925110.1007/s00894‑021‑04865‑334401965
     [Google Scholar]
  69. ShoaibT.H. IbraheemW. AbdelrahmanM. OsmanW. SherifA.E. AshourA. IbrahimS.R.M. GhazawiK.F. MiskiS.F. AlmadaniS.A. ALsiyudD.F. MohamedG.A. AlzainA.A. Exploring the potential of approved drugs for triple-negative breast cancer treatment by targeting casein kinase 2: Insights from computational studies.PLoS One2023188e028988710.1371/journal.pone.028988737578958
     [Google Scholar]
  70. TaylorK. TabishT.A. NarayanR.J. Drug release kinetics of DOX-loaded graphene-based nanocarriers for ovarian and breast cancer therapeutics.Appl. Sci.202111231115110.3390/app112311151
     [Google Scholar]
  71. AbazariR. AtaeiF. MorsaliA. SlawinA.M.Z. Carpenter-WarrenC.L. A luminescent amine-functionalized metal–organic framework conjugated with folic acid as a targeted biocompatible pH-responsive nanocarrier for apoptosis induction in breast cancer cells.ACS Appl. Mater. Interfaces20191149454424545410.1021/acsami.9b1647331718155
     [Google Scholar]
  72. HuJ. YoussefianS. ObayemiJ. MalatestaK. RahbarN. SoboyejoW. Investigation of adhesive interactions in the specific targeting of Triptorelin-conjugated PEG-coated magnetite nanoparticles to breast cancer cells.Acta Biomater.20187136337810.1016/j.actbio.2018.02.01129458110
     [Google Scholar]
  73. PaulmuruganR. BhethanabotlaR. MishraK. DevulapallyR. FoygelK. SekarT.V. AnantaJ.S. MassoudT.F. JoyA. Folate receptor–targeted polymeric micellar nanocarriers for delivery of orlistat as a repurposed drug against triple-negative breast cancer.Mol. Cancer Ther.201615222123110.1158/1535‑7163.MCT‑15‑057926553061
     [Google Scholar]
  74. RanaA. MatiyaniM. TewariC. NegiP.B. Chandra AryaM. DasV. PalM. SahooN.G. Functionalized graphene oxide based nanocarrier for enhanced cytotoxicity of Juniperus squamata root essential oil against breast cancer cells.J. Drug Deliv. Sci. Technol.20227210337010.1016/j.jddst.2022.103370
     [Google Scholar]
  75. ShenY. ZhangJ. HaoW. WangT. LiuJ. XieY. XuS. LiuH. Copolymer micelles function as pH-responsive nanocarriers to enhance the cytotoxicity of a HER2 aptamer in HER2-positive breast cancer cells.Int. J. Nanomedicine20181353755310.2147/IJN.S14994229416334
     [Google Scholar]
  76. AlamriA.H. DebnathS. AlqahtaniT. AlqahtaniA. AlshehriS.A. GhoshA. Enhancing plant-derived smart nano inhibitor in targeting mammalian target of rapamycin (mTOR) in breast cancer using Curcuma longa-derived compound curcumin.Environ. Sci. Pollut. Res. Int.20233134464624646910.1007/s11356‑023‑25375‑036719580
     [Google Scholar]
  77. Landeros-MartínezL.L. Chavez-FloresD. Orrantia-BorundaE. Flores-HolguinN. Construction of a nanodiamond–tamoxifen complex as a breast cancer drug delivery vehicle.J. Nanomater.201626821051910.1155/2016/2682105
     [Google Scholar]
  78. KhaledianS. KahriziD. MoradiS. MartinezF. An experimental and computational study to evaluation of chitosan/gum tragacanth coated-natural lipid-based nanocarriers for sunitinib delivery.J. Mol. Liq.202133411607510.1016/j.molliq.2021.116075
     [Google Scholar]
/content/journals/cctr/10.2174/0115733947287709240229104857
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Advancing Breast Cancer Drug Delivery: The Transformative Potential of Bioinformatics and Artificial Intelligence
Curr Cancer Ther Rev 21, 254 (2025); https://doi.org/10.2174/0115733947287709240229104857
/content/journals/cctr/10.2174/0115733947287709240229104857
/content/journals/cctr/10.2174/0115733947287709240229104857
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  • Article Type:     Review Article
Keyword(s): AI algorithms; bioinformatics; breast cancer; complex biological networks; drug delivery; drug targets

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