Skip to content
2000
Volume 1, Issue 1
  • ISSN: 2772-6215
  • E-ISSN: 2772-6223

Abstract

Abstracts

Anticancer drug development is becoming complex and demanding because human cancer leads to 12% of global human mortality. Chemical and pharmacological breakthroughs play leading roles in updating drug evaluation and development for different types of tumors.

Chemical and pharmacological breakthroughs manifest in different facets. A large proportion of financial and workload increases in drug discovery must be paid off. In front of complexity, difficulties, and financial increase of drug development, evaluative promotion must go miniature-wise and single-cell-wise. Multi-omics knowledge and technology are greatly expanded and understood in depth. This type of technical trend is suitable for current experimental exploration and clinical occasions. Technical and pharmacologic advances are especially emphasized to address this trend.

Presently, the anticancer pharmaceutical study is multi-faceted and risk-taking. To keep up this momentum, multi-disciplinary drug evaluation, clinical selection, and combination principles should be discovered. Past and futuristic chemical and pharmacological interactions and breakthroughs are discussed.

In summary, the landscape of pharmaceutical investigation should be integrated with chemical and pharmacological knowledge in every facet of drug development and clinical personalization.

Loading

Article metrics loading...

/content/journals/csci/10.2174/0127726215312620241029102421
2024-11-04
2025-09-02
Loading full text...

Full text loading...

References

  1. SiegelR.L. MillerK.D. WagleN.S. JemalA. Cancer statistics, 2023.CA Cancer J. Clin.2023731174810.3322/caac.2176336633525
    [Google Scholar]
  2. AhmadA.S. Ormiston-SmithN. SasieniP.D. Trends in the lifetime risk of developing cancer in Great Britain: Comparison of risk for those born from 1930 to 1960.Br. J. Cancer2015112594394710.1038/bjc.2014.60625647015
    [Google Scholar]
  3. FojoT. The high cost of ignorance in oncology.Semin. Oncol.201643662362410.1053/j.seminoncol.2016.11.01028061979
    [Google Scholar]
  4. MinaL.A. SledgeG.W. Rethinking the metastatic cascade as a therapeutic target.Nat. Rev. Clin. Oncol.20118632533210.1038/nrclinonc.2011.5921502993
    [Google Scholar]
  5. LuD.Y. LuT.R. CaoS. Cancer metastases and clinical therapies.Cell Dev. Biol.201214e11010.4172/2168‑9296.1000e110
    [Google Scholar]
  6. LuD.Y. LuT.R. WuH.Y. CaoS. Cancer metastasis treatments.Curr. Drug Ther.201381242910.2174/1574885511308010003
    [Google Scholar]
  7. RuggeriB.A. CampF. MiknyoczkiS. Animal models of disease: Pre-clinical animal models of cancer and their applications and utility in drug discovery.Biochem. Pharmacol.201487115016110.1016/j.bcp.2013.06.02023817077
    [Google Scholar]
  8. Herter-SprieG.S. KungA.L. WongK.K. New cast for a new era: Preclinical cancer drug development revisited.J. Clin. Invest.201312393639364510.1172/JCI6834023999436
    [Google Scholar]
  9. LuDY. LuTR. Antimetastatic drugs, pharmacologic challenge and opportunity.Curr. Drug ther.202510.2174/0115748855284405231212051251
    [Google Scholar]
  10. FaresJ. FaresM.Y. KhachfeH.H. SalhabH.A. FaresY. Molecular principles of metastasis: A hallmark of cancer revisited.Signal Transduct. Target. Ther.2020512810.1038/s41392‑020‑0134‑x32296047
    [Google Scholar]
  11. MervisJ. Productivity counts--But the definition is key.Science2005309573572672710.1126/science.309.5735.72616051784
    [Google Scholar]
  12. HayM. ThomasD.W. CraigheadJ.L. EconomidesC. RosenthalJ. Clinical development success rates for investigational drugs.Nat. Biotechnol.2014321405110.1038/nbt.278624406927
    [Google Scholar]
  13. LuD.Y. ChenE.H. LuT.R. Anticancer drug development, a matter of money or a matter of idea?Metabolomics201552e134
    [Google Scholar]
  14. SteegP.S. Targeting metastasis.Nat. Rev. Cancer201616420121810.1038/nrc.2016.2527009393
    [Google Scholar]
  15. LuD.Y. LuT.R. ZhuH. DingJ. XuB. WuS.Y. YarlaN.S. Anticancer drug development, getting out from bottleneck.Int J Mol Biol20172100010
    [Google Scholar]
  16. LuD.Y. LuT.R. Anticancer drug development, challenge and dilemma.Nurs. Care Open Access J.202073727510.15406/ncoaj.2020.07.00222
    [Google Scholar]
  17. LuD.Y. XuB. LuT.R. Anticancer drug development, pharmacology update.EC Pharmacol. Toxicol.2020Special Issue16
    [Google Scholar]
  18. LuD.Y. LuT.R. XuB. YarlaN.S. Anticancer drug developments, challenge from historic perspective.EC Pharmacol. Toxicol.2018611922936
    [Google Scholar]
  19. LuD.Y. XuB. LuT.R. Anticancer drug development, evaluative architecture.Lett. Drug Des. Discov.202421583684610.2174/1570180819666220610102444
    [Google Scholar]
  20. HanahanD. Hallmarks of cancer: New dimensions.Cancer Discov.2022121314610.1158/2159‑8290.CD‑21‑105935022204
    [Google Scholar]
  21. BehrenA. ThompsonE.W. AndersonR.L. FerraoP.T. Cancer plasticity and the microenvironment: Implications for immunity and therapy response.Front. Oncol.2019927610.3389/fonc.2019.0027631134142
    [Google Scholar]
  22. SinglaS. SahaiD. MangalN. Clinical trials in oncology: A comprehensive review.EC Pharmacol. Toxicol.202082111
    [Google Scholar]
  23. BedardP.L. HansenA.R. RatainM.J. SiuL.L. Tumour heterogeneity in the clinic.Nature2013501746735536410.1038/nature1262724048068
    [Google Scholar]
  24. LuD.Y. LuT.R. XuB. QiR.X. SastryN.Y. ZhouX.D. DingJ. Cancer metastasis, a clinical dilemma for therapeutics.Curr. Drug Ther.201611216316910.2174/1574885511666160810143216
    [Google Scholar]
  25. CencioniC. ComunanzaV. MiddontiE. VallarielloE. BussolinoF. The role of redox system in metastasis formation.Angiogenesis202124343545010.1007/s10456‑021‑09779‑533909153
    [Google Scholar]
  26. LambertA.W. PattabiramanD.R. WeinbergR.A. Emerging biological principles of metastasis.Cell2017168467069110.1016/j.cell.2016.11.03728187288
    [Google Scholar]
  27. LuD.Y. LuT.R. Drug sensitivity testing, a unique drug selection strategy.Adv. Biomarker Sci. Technol.20202596610.1016/j.abst.2020.11.001
    [Google Scholar]
  28. PopovaA.A. LevkinP.A. Precision medicine in oncology: In vitro drug sensitivity and resistance test (DSRT) for selection of personalized anticancer therapy.Adv. Ther. (Weinh.)202032190010010.1002/adtp.201900100
    [Google Scholar]
  29. ProiettoM. CrippaM. DamianiC. PasqualeV. SaccoE. VanoniM. GilardiM. Tumor heterogeneity: Preclinical models, emerging technologies, and future applications.Front. Oncol.202313116453510.3389/fonc.2023.116453537188201
    [Google Scholar]
  30. KitaevaK.V. RutlandC.S. RizvanovA.A. SolovyevaV.V. Cell culture based in vitro test systems for anticancer drug screening.Front. Bioeng. Biotechnol.2020832210.3389/fbioe.2020.0032232328489
    [Google Scholar]
  31. QianJ. OlbrechtS. BoeckxB. VosH. LaouiD. EtliogluE. WautersE. PomellaV. VerbandtS. BusschaertP. BassezA. FrankenA. BemptM.V. XiongJ. WeynandB. van HerckY. AntoranzA. BosisioF.M. ThienpontB. FlorisG. VergoteI. SmeetsA. TejparS. LambrechtsD. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling.Cell Res.202030974576210.1038/s41422‑020‑0355‑032561858
    [Google Scholar]
  32. PengM. ChengX. XiongW. YiL. WangY. Integrated analysis of a competing endogenomic RNA network reveals a prognostic Inc RNA signature in bladder cancer.Front. Oncol.20211168424210.3389/fonc.2021.68424234408977
    [Google Scholar]
  33. Ortiz-OteroN. MarshallJ.R. LashB. KingM.R. Chemotherapy-induced release of circulating-tumor cells into the bloodstream in collective migration units with cancer-associated fibroblasts in metastatic cancer patients.BMC Cancer202020187310.1186/s12885‑020‑07376‑132917154
    [Google Scholar]
  34. YoungH.S. McGowanL.M. JepsonK.A. AdamsJ.C. Impairment of cell adhesion and migration by inhibition of protein disulphide isomerases in three breast cancer cell lines.Biosci. Rep.20204010BSR2019327110.1042/BSR2019327133095243
    [Google Scholar]
  35. HeissigB. SalamaY. OsadaT. OkumuraK. HattoriK. The multifaceted role of plasminogen in cancer.Int. J. Mol. Sci.2021225230410.3390/ijms2205230433669052
    [Google Scholar]
  36. LanderE.S. Initial impact of the sequencing of the human genome.Nature2011470733318719710.1038/nature0979221307931
    [Google Scholar]
  37. RahimzadehV. BartlettG. Policies and practices of data-intensive primary care in the precision-medicine era.Intern. Med. Rev. (Wash. D. C.)20173911410.18103/imr.v3i9.558
    [Google Scholar]
  38. WeiJ. NiN. MengW. HuanY. GaoY. Early urinary protein changes during tumor formation in a NuTu-19 tail vein injection rat model.Sci. Rep.20201011170910.1038/s41598‑020‑68674‑z32678190
    [Google Scholar]
  39. LuD.Y. LuT.R. ChenX.L. XuB. DingJ. Plasma fibrinogen concentrations in patients with solid tumor and therapeutic improvements by combining anticoagulants and fibrinolytical agents.Adv. Pharmacoepidemiol. Drug Saf.201544e133
    [Google Scholar]
  40. DvorakH.F. WeaverV.M. TlstyT.D. BergersG. Tumor microenvironment and progression.J. Surg. Oncol.2011103646847410.1002/jso.2170921480238
    [Google Scholar]
  41. LuD.Y. LuT.R. XuB. DingJ. ChenE-H. WuH.Y. WuS-Y. Sastry YarlaN. ZhuH. Antimetastatic therapy at aberrant sialylation in cancer cells, a potential hotspot.Clin. Proteom. Bioinform.20172111810.15761/CPB.1000118
    [Google Scholar]
  42. LuD.Y. ChenX.L. DingJ. Treatment of solid tumors and metastases by fibrinogen-targeted anticancer drug therapy.Med. Hypotheses200768118819310.1016/j.mehy.2006.06.04516956730
    [Google Scholar]
  43. PritykoDA. BurkovIV. SafonovVV. KlimovDE. GusevL. Palliative care for children. Problems and ways to solve them.EC Clin. Exp. Anat.2019292329
    [Google Scholar]
  44. LuD.Y. ChenY.Z. ShenY. XuB. LuD.F. Medical treatment for chronic or aggressive diseases, palliative therapy and nursery.Nov. Res. Sci.20203255610.31031/NRS.2020.3.000556
    [Google Scholar]
  45. WatsonJ. SalisburyC. BanksJ. WhitingP. HamiltonW. Predictive value of inflammatory markers for cancer diagnosis in primary care: A prospective cohort study using electronic health records.Br. J. Cancer2019120111045105110.1038/s41416‑019‑0458‑x31015558
    [Google Scholar]
  46. LuD.Y. ChenY.Z. LuT.R. XuB. LuD.F. Cancer metastasis, palliative treatment and nursery.Ann. Pharmacol. Pharm.2020511175
    [Google Scholar]
  47. LinL.H. ChouH.C. ChangS.J. LiaoE.C. TsaiY.T. WeiY.S. ChenH.Y. LinM.W. WangY.S. ChienY.A. YuX.R. ChanH.L. Targeting UDP‐glucose dehydrogenase inhibits ovarian cancer growth and metastasis.J. Cell. Mol. Med.20202420118831190210.1111/jcmm.15808
    [Google Scholar]
  48. FuY. LiA. WuJ. KunzR.F. SunR. DingZ. WuJ. DongC. Fibrinogen and fibrin differentially regulate the local hydrodynamic environment in neutrophil-tumor cell-endothelial cell adhesion system.Appl. Sci. (Basel)20201117910.3390/app11010079
    [Google Scholar]
  49. LuD.Y. WuF.G. ZhenZ.M. LuT.R. WuH.Y. CheJ.Y. XuB. Different spontaneous pulmonary metastasis inhibitions against lewis lung carcinoma in mice by bisdioxopiperazine compounds of different treatment schedules.Sci. Pharm.2010781132010.3797/scipharm.0910‑1621179367
    [Google Scholar]
  50. ParkerA.L. BenguiguiM. FornettiJ. GoddardE. LucottiS. Insua-RodríguezJ. WiegmansA.P. Current challenges in metastasis research and future innovation for clinical translation.Clin. Exp. Metastasis202239226327710.1007/s10585‑021‑10144‑535072851
    [Google Scholar]
  51. Ruiz-EspigaresJ. NietoD. MoroniL. JiménezG. MarchalJ.A. Evolution of metastasis study models toward metastasis-on-a-chip: The ultimate model?Small20211714200600910.1002/smll.20200600933705602
    [Google Scholar]
  52. LuDY. LuTR. Anti-metastatic drug developments, utility of more animal models.Mathews J. Pharm. Sci.2022611110.30654/MJPS.10011
    [Google Scholar]
  53. JelgersmaC. VajkoczyP. How to target spinal metastasis in experimental research: An overview of currently used experimental mouse model and future prospects.Int. J. Mol. Sci.20212211542010.3390/ijms2211542034063821
    [Google Scholar]
  54. MunkleyJ. ScottE. Targeting aberrant sialylation to treat cancer.Medicines (Basel)20196410210.3390/medicines604010231614918
    [Google Scholar]
  55. ChenF. QiX. QianM. DaiY. SunY. Tackling the tumor microenvironment: What challenge does it pose to anticancer therapies?Protein Cell201451181682610.1007/s13238‑014‑0097‑125185441
    [Google Scholar]
  56. HofbauerL.C. BozecA. RaunerM. JakobF. PernerS. PantelK. Novel approaches to target the microenvironment of bone metastasis.Nat. Rev. Clin. Oncol.202118848850510.1038/s41571‑021‑00499‑933875860
    [Google Scholar]
  57. MonteroJ. SarosiekK.A. DeAngeloJ.D. MaertensO. RyanJ. ErcanD. PiaoH. HorowitzN.S. BerkowitzR.S. MatulonisU. JänneP.A. AmreinP.C. CichowskiK. DrapkinR. LetaiA. Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.Cell2015160597798910.1016/j.cell.2015.01.04225723171
    [Google Scholar]
  58. EduatiF. UtharalaR. MadhavanD. NeumannU.P. LongerichT. CramerT. Saez-RodriguezJ. MertenC.A. A microfluidics platform for combinatorial drug screening on cancer biopsies.Nat. Commun.201891243410.1038/s41467‑018‑04919‑w29934552
    [Google Scholar]
  59. LiangS. WillisJ. DouJ. MohantyV. HuangY. VilarE. ChenK. Sensei: How many samples to tell a change in cell type abundance?BMC Bioinformatics2022231210.1186/s12859‑021‑04526‑534983369
    [Google Scholar]
  60. LiuZ. LiH. DangQ. WengS. DuoM. LvJ. HanX. Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy.Cell. Mol. Life Sci.2022791157710.1007/s00018‑022‑04608‑436316529
    [Google Scholar]
  61. BlauA. BrownA. MahantaL. AmrS. The translational genomic core at partners personalized medicine: facilitating the transition of research towards personalized medicine.J. Pers. Med.2016611010.3390/jpm601001026927185
    [Google Scholar]
  62. XuZ. GaoY. HaoY. LiE. WangY. ZhangJ. WangW. GaoZ. WangQ. Application of a microfluidic chip-based 3D co-culture to test drug sensitivity for individualized treatment of lung cancer.Biomaterials201334164109411710.1016/j.biomaterials.2013.02.04523473962
    [Google Scholar]
  63. CuiH. WangX. WesslowskiJ. TronserT. RosenbauerJ. SchugA. DavidsonG. PopovaA.A. LevkinP.A. Assembly of multi-sphoroid cellular architectures by programmable droplet merging.Adv. Mater.2021334200643410.1002/adma.20200643433325613
    [Google Scholar]
  64. RosenfeldA. GöcklerT. KuzinaM. ReischlM. SchepersU. LevkinP.A. Designing inherently photodegradable cell-adhesive hydrogels for 3D cell culture.Adv. Healthc. Mater.20211016210063210.1002/adhm.20210063234111332
    [Google Scholar]
  65. Sontheimer-PhelpsA. HassellB.A. IngberD.E. Modelling cancer in microfluidic human organs-on-chips.Nat. Rev. Cancer2019192658110.1038/s41568‑018‑0104‑630647431
    [Google Scholar]
  66. ZhangY. XuJ. YuY. ShangW. YeA. Anticancer drug sensitivity assay with quantitative heterogeneity testing using single-cell Raman Spectroscope.Molecules20182311290310.3390/molecules2311290330405051
    [Google Scholar]
  67. LuD.Y. LuT.R. YarlaN.S. XuB. Drug sensitivity testing for cancer therapy, key areas.Rev. Recent Clin. Trials202217429129910.2174/157488711766622081909452835986532
    [Google Scholar]
  68. LuD.Y. LuT.R. Drug sensitivity testing for cancer therapy, technique analysis and trend.Curr. Rev. Clin. Exp. Pharmacol.202318131110.2174/277243281666621091010464934515020
    [Google Scholar]
  69. WangJ. LinK. HuH. QieX. HuangW.E. CuiZ. GongY. SongY. In vitro anticancer drug sensitivity sensing through single-cell Raman Spectroscopy.Biosensors (Basel)202111828610.3390/bios1108028634436088
    [Google Scholar]
  70. HammoudM.K. YosefH.K. LechtonenT. AljakouchK. SchulerM. AlsaidiW. DahoI. MaghnoujA. HahnS. El-MashtolyS.F. GerwertK. Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level.Sci. Rep.2018811527810.1038/s41598‑018‑33682‑730323297
    [Google Scholar]
  71. LuD.Y. LuT.R. Anti-metastatic drug development, overview and perspectives.Hos. Pal. Med. Int. J.202362455110.15406/hpmij.2023.06.00217
    [Google Scholar]
  72. Eslami-SZ. Cortés-HernándezL.E. ThomasF. PantelK. Alix-PanabièresC. Functional analysis of circulating tumour cells: The KEY to understand the biology of the metastatic cascade.Br. J. Cancer2022127580081010.1038/s41416‑022‑01819‑135484215
    [Google Scholar]
  73. PantelK. Alix-PanabièresC. Crucial roles of circulating tumor cells in the metastatic cascade and tumor immune escape: Biology and clinical translation.J. Immunother. Cancer20221012e00561510.1136/jitc‑2022‑00561536517082
    [Google Scholar]
  74. LitakJ. CzyżewskiW. SzymoniukM. SakwaL. PasierbB. LitakJ. HoffmanZ. KamieniakP. RolińskiJ. Biological and clinical aspects of metastatic spinal tumors.Cancers (Basel)20221419459910.3390/cancers1419459936230523
    [Google Scholar]
  75. LeeS.H. ChoiY. Communication between the skeletal and immune systems.Osteoporos. Sarcopenia201512819110.1016/j.afos.2015.09.004
    [Google Scholar]
  76. LuJ. HuD. ZhangY. MaC. ShenL. ShuaiB. Current comprehensive understanding of denosumab (the RANKL neutralizing antibody) in the treatment of bone metastasis of malignant tumors, including pharmacological mechanism and clinical trials.Front. Oncol.202313113382810.3389/fonc.2023.113382836860316
    [Google Scholar]
  77. LuD.Y. XuB. Cancer bone metastasis, experimental study.Acta Scientific Orthopeadics202251213
    [Google Scholar]
  78. LuD.Y. XuB. Bone cancer and metastatic trials, drug treatment.Acta Sci. Orthop.202149313310.31080/ASOR.2021.04.0355
    [Google Scholar]
  79. AliI. SaleemK. UddinR. HaqueA. El-AzzounyA. Natural products: Human friendly anti-cancer medications.Egypt Pharm J201092133179[NRC].
    [Google Scholar]
  80. LuD.Y. LuT.R. LuY. YarlaN. WuH.Y. Discover natural chemical drugs in modern medicines.Metabolomics201663181
    [Google Scholar]
  81. PuttaS. YarlaN.S. PelusoI. TiwariD.K. ReddyG.V. GirlP.V. KumarN. MaliaR. ChariV.B. ReddyR.D. BadeR. BarrettoG. LuD.Y. TarasovV.V. ChubarevV.M. RibeiroF.F. ScottiL. ScottiL. ScottiM.T. KamalM.A. AlievG. RaoC.V. PerryG. BishayeeA. Anthocyanins: Possible role as multitarget therapeutic agents for prevention and therapy of chronic diseases.Curr. Pharm. Des.201723304475448328831925
    [Google Scholar]
  82. LuD.Y. LuT.R. YarlaN.S. LuY. CheJ.Y. DingJ. XuB. ZhuH. ShenY. WuH.Y. Natural drug cancer treatments, strategies from herbal medicine to chemical or biological drugs.Stud. Nat. Prod. Chem.2020669111510.1016/B978‑0‑12‑817907‑9.00004‑0
    [Google Scholar]
  83. JiaS. ShenM. ZhangF. XieJ. Recent advances in Momordica charantia: functional components and biological activities.Int. J. Mol. Sci.20171812255510.3390/ijms1812255529182587
    [Google Scholar]
  84. LebuanU.Y. KembarenR.F. MartgritaM.M. KholibrinaC.R. Thrombolytic protease characterization from leaves and fruit flesh of the jernang rattan plant (Daemonorops draco).Indones. J. Biotechnol.202328424825310.22146/ijbiotech.82390
    [Google Scholar]
  85. LuD.Y. LuT.R. Herbal medicine in new era.Hos. Pal. Med. Int. J.20193412513010.15406/hpmij.2019.03.00165
    [Google Scholar]
  86. LuD.Y. LuT.R. Drug discoveries from natural resources.J. Prim. Health Care Gen. Pract.20193128
    [Google Scholar]
  87. AgarwalN. MajeeC. ChakraborthyG.S. Natural herbs as anticancer drugs.Int. J. Pharm. Tech. Res.20124311421153
    [Google Scholar]
  88. PattanayakS. Plants in healthcare: Past, present and future.Explor. Anim. Med. Res.202111214014410.52635/EAMR/11.2.140‑144
    [Google Scholar]
  89. PattanayakS. Anti-cancer plants and their therapeutic use as succulent biomedicine capsules.Explor. Anim. Med. Res.202313Ethnomedicine Special015010.52635/eamr/13(S)01‑50
    [Google Scholar]
  90. GuptaS.C. SungB. PrasadS. WebbL.J. AggarwalB.B. Cancer drug discovery by repurposing: Teaching new tricks to old dogs.Trends Pharmacol. Sci.201334950851710.1016/j.tips.2013.06.00523928289
    [Google Scholar]
  91. SuaresA. MedinaM.V. CosoO. Autophagy in viral development and progression of cancer.Front. Oncol.20211160322410.3389/fonc.2021.60322433763351
    [Google Scholar]
  92. Di SottoA. MancinelliR. GullìM. EufemiM. MammolaC.L. MazzantiG. Di GiacomoS. Chemopreventive potential of caryophyllane sesquiterpenes—An overview preliminary evidence.Cancers (Basel)20201210303410.3390/cancers1210303433081075
    [Google Scholar]
  93. PantanoF. CrosetM. DriouchK. Bednarz-KnollN. IulianiM. RibelliG. BonnelyeE. WikmanH. GeraciS. BoninF. SimonettiS. VincenziB. HongS.S. SousaS. PantelK. ToniniG. SantiniD. ClézardinP. Integrin alpha5 in human breast cancer is a mediator of bone metastasis and a therapeutic target for the treatment of osteolytic lesions.Oncogene20214071284129910.1038/s41388‑020‑01603‑633420367
    [Google Scholar]
  94. Hernández-BalmasedaI. GuerraI.R. DeclerckK. Herrera IsidrónJ.A. Pérez-NovoC. Van CampG. De WeverO. GonzálezK. LabradaM. CarrA. Dantas-CassaliG. dos ReisD.C. Delgado-RocheL. NuñezR.R. Delgado-HernándezR. FernándezM.D. Paz-LopesM.T. Vanden BergheW. Marine seagrass extract of Thalassia testudinum suppresses colorectal tumor growth, motility and angiogenesis by autophagic stress and immunogenic cell death pathways.Mar. Drugs20211925210.3390/md1902005233499163
    [Google Scholar]
  95. ZouY. HenryW.S. RicqE.L. GrahamE.T. PhadnisV.V. MaretichP. ParadkarS. BoehnkeN. DeikA.A. ReinhardtF. EatonJ.K. FergusonB. WangW. FairmanJ. KeysH.R. DančíkV. ClishC.B. ClemonsP.A. HammondP.T. BoyerL.A. WeinbergR.A. SchreiberS.L. Plasticity of ether lipids promotes ferroptosis susceptibility and evasion.Nature2020585782660360810.1038/s41586‑020‑2732‑832939090
    [Google Scholar]
  96. AliI. SaleemK. WesselinovaD. HaqueA. Synthesis, DNA binding, hemolytic, and anticancer assays of curcumin I-based ligands and their ruthenium complex (potential treatment of (III) cervical cancer.Med. Chem. Res.20132231386139810.1007/s00044‑012‑0133‑8
    [Google Scholar]
  97. EmranT.B. ShahriarA. MahmudA.R. RahmanT. AbirM.H. SiddiqueeM.F.R. AhmedH. RahmanN. NainuF. WahyudinE. MitraS. DhamaK. HabiballahM.M. HaqueS. IslamA. HassanM.M. Multidrug resistance in cancer: Understanding molecular mechanisms, immunoprevention and therapeutic approaches.Front. Oncol.20221289165210.3389/fonc.2022.89165235814435
    [Google Scholar]
  98. Dianat-MoghadamH. MahariA. SalahlouR. KhaliliM. AziziM. SadeghzadehH. Immune evader cancer stem cells direct the perspective approaches to cancer immunotherapy.Stem Cell Res. Ther.202213115010.1186/s13287‑022‑02829‑935395787
    [Google Scholar]
  99. MortakiD. GkegkesI.D. PsomiadouV. BlontzosN. ProdromidouA. LefkopoulosF. NicolaidouE. Vaginal microbiota and human papillomavirus: A systematic review.J. Turk. Ger. Gynecol. Assoc.202021319320010.4274/jtgga.galenos.2019.2019.005131564082
    [Google Scholar]
  100. MitraA. GultekinM. EllisL.B. BizzariN. BowdenS. TaumbergerN. BracicT. Vieira-BaptistaP. SehouliJ. KyrgiouM. Genital tract microbiota composition profiles and use of prebiotics and probiotics in gynaecological cancer prevention: Review of the current evidence, the European Society of Gynaecological Oncology Prevention Committee Statement.Lancet Microbe20232300257410.1016/52666‑524738141634
    [Google Scholar]
  101. SurayaR. NaganoT. KobayashiK. NishimuraY. Microbiome as a target for cancer therapy.Integr. Cancer Ther.20201910.1177/153473542092072132564632
    [Google Scholar]
  102. MalaviyaA. PaariK.A. MalviyaS. KondapalliV. GhoshA. SamuelR.A. Gut microbiota and cancer correlates.Probiotic Research in TherapeuticsSpringerSingapore202012710.1007/978‑981‑15‑8214‑1_1
    [Google Scholar]
  103. AlsannanB. UpadhyayA. RamanV.V. SinghJ.B. RojA. HaridasK. Obesity and its associated cancer related risk with gynaecology.J. Coast. Life Med.2023111689695
    [Google Scholar]
  104. HollingsheadM.G. GreenbergN. Gottholm-AhaltM. CamalierR. JohnsonB.C. CollinsJ.M. DoroshowJ.H. ROADMAPS: An online database of response data, dosing regimens, and toxicities of approved oncology drugs as single agents to guide preclinical in vivo studies.Cancer Res.202282122219222510.1158/0008‑5472.CAN‑21‑415135472132
    [Google Scholar]
  105. LeeJ. KimY. JinS. YooH. JeongS. JeongE. YoonS. Q-omics: smart software for assisting oncology and cancer research.Mol. Cells2021441184385010.14348/molcells.2021.016934819397
    [Google Scholar]
  106. MasilamaniK. SenthilnathanB. ManoyogambigaM. GowriR. VigneshwarM. SathiyasundarR. RajaganapathyK. Techniques and tools for in silico drug design for the development of anticancer drugs.Int. J. Life Sci. Pharma Res.202313513014810.22376/ijlpr.2023.13.5.P130‑P148
    [Google Scholar]
  107. MuthuramanA. ThiagarajanU.R.K. ParamakrishmanN. Integration of artificial intelligence in pharmacological research with deep and machine learning process.EC Pharmacol. Toxicol.20197115661
    [Google Scholar]
  108. FreedmanD.H. Hunting for new drugs with AI.Nature20195767787S49S5310.1038/d41586‑019‑03846‑031853074
    [Google Scholar]
  109. CuiW. AouidateA. WangS. YuQ. LiY. YuanS. Discovering anticancer drugs via computational methods.Front. Pharmacol.2020111173310.3389/fphar.2020.0073332508653
    [Google Scholar]
  110. KnudsenL. BrandenbergerC. OchsM. Stereology as the 3D tool to quantitate lung architecture.Histochem. Cell Biol.2021155216318110.1007/s00418‑020‑01927‑033051774
    [Google Scholar]
  111. 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]
  112. FranssenL.C. LorenziT. BurgessA.E.F. ChaplainM.A.J. A mathematical framework for modeling the metastatic spread of cancer.Bull. Math. Biol.20198161965201010.1007/s11538‑019‑00597‑x30903592
    [Google Scholar]
  113. AnvariS. NambiarS. PangJ. MaftoonN. Computational models and simulations of cancer metastasis.Arch. Comput. Methods Eng.20212874837485910.1007/s11831‑021‑09554‑1
    [Google Scholar]
  114. GerleeP. JohanssonM. Inferring rates of metastatic dissemination using stochastic network models.PLOS Comput. Biol.2019154e100686810.1371/journal.pcbi.100686830933969
    [Google Scholar]
  115. LuD.Y. LuT.R. LuY. WuH.Y. YarlaN.S. The acquisition of mathematical language in biomedical articles.J. Cell Developmental Biol.2017118
    [Google Scholar]
  116. ChenS. JiangW. DuY. YangM. PanY. LiH. CuiM. Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy.Front. Genet.202314127695910.3389/fgene.2023.127695937900181
    [Google Scholar]
  117. LuD.Y. ChenE.H. WuH.Y. LuT.R. XuB. DingJ. Anticancer drug combination, how far we can go through?Anticancer. Agents Med. Chem.2017171212810.2174/187152061666616040411202827039923
    [Google Scholar]
  118. LuD.Y. LuT.R. YarlaN.S. WuH.Y. XuB. DingJ. ZhuH. Drug combination in clinical cancer treatment.Rev. Recent Clin. Trials201712320221128782482
    [Google Scholar]
  119. LuD.Y. LuT.R. XuB. DingJ. YiL. YarlaN.S. Perspectives of personalized cancer therapy.Adv. Biotechnol. Microbiol.20174355563710.19080/AIBM.2017.04.555638
    [Google Scholar]
  120. LuD.Y. Personalized Cancer Chemotherapy: An Effective Way of Enhancing Outcomes in ClinicsUKWoodhead Publishing, Elsevier1st ed2014
    [Google Scholar]
  121. LuD.Y. LuT.R. XuB. CheJ-Y. ShenY. YarlaN.S. Individualized cancer therapy, future approaches.Curr. Pharmacogenomics Person. Med.201816215616310.2174/1875692116666180821095434
    [Google Scholar]
  122. LuD.Y. LuT.R. CheJ.Y. YarlaN.S. Individualized cancer therapy, what is the next generation?EC Cancer201826286297
    [Google Scholar]
  123. AliI. Nano drugs: novel agents for cancer chemotherapy.Curr. Cancer Drug Targets201111213113410.2174/15680091179432845721062238
    [Google Scholar]
  124. GauroR. NandaveM. JainV.K. JainK. Advances in dendrimer-mediated targeted drug delivery to the brain.J. Nanopart. Res.20212337610.1007/s11051‑021‑05175‑8
    [Google Scholar]
  125. MukhtarM. BilalM. RahdarA. BaraniM. ArshadR. BehlT. BriscC. BanicaF. BungauS. Nanomaterials for diagnosis and treatment of brain cancer: Recent update.Chemosensors (Basel)20208411710.3390/chemosensors8040117
    [Google Scholar]
  126. Reig-VanoB. TylkowskiB. MontanéX. GiamberiniM. Alginate-based hydrogels for cancer therapy and research.Int. J. Biol. Macromol.202117042443610.1016/j.ijbiomac.2020.12.16133383080
    [Google Scholar]
  127. Sharifi-RadJ. QuispeC. ButnariuM. RotariuL.S. SytarO. SestitoS. RapposelliS. AkramM. IqbalM. KrishnaA. KumarN.V.A. BragaS.S. CardosoS.M. JafernikK. EkiertH. Cruz-MartinsN. SzopaA. VillagranM. MardonesL. MartorellM. DoceaA.O. CalinaD. Chitosan nanoparticles as a promising tool in nanomedicine with particular emphasis on oncological treatment.Cancer Cell Int.202121131810.1186/s12935‑021‑02025‑434167552
    [Google Scholar]
  128. JainV. KumarH. AnodH.V. ChandP. GuptaN.V. DeyS. KesharwaniS.S. A review of nanotechnology-based approaches for breast cancer and triple-negative breast cancer.J. Control. Release202032662864710.1016/j.jconrel.2020.07.00332653502
    [Google Scholar]
  129. MayM. Why drug delivery is the key to new medicines.Nat. Med.20222861100110210.1038/s41591‑022‑01826‑y35668179
    [Google Scholar]
  130. LuD.Y. LuY. Several approaches for anticancer drug development progress.Nurs. Care Open Access J.202283858610.15406/ncoaj.2022.08.00244
    [Google Scholar]
  131. LuD.Y. LuT.R. ChenE.H. Sastry YarlaN. XuB. DingJ. HuangM. ZhuH. Keep up the pace of drug development evolution and expenditure.Cancer Rep. Rev.20182516510.15761/CRR.1000165
    [Google Scholar]
/content/journals/csci/10.2174/0127726215312620241029102421
Loading
/content/journals/csci/10.2174/0127726215312620241029102421
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test