Abstract
Personalized medicine aims at identifying specific targets for treatment considering the gene expression profile of each patient individually. We discuss the challenges for personalized oncology to take off and present an approach based on hub inhibition that we are developing. That is, the subtraction of RNA-seq data of tumoral and non-tumoral surrounding tissues in biopsies allows the identification of up-regulated genes in tumors of patients. Targeting connection hubs in the subnetworks formed by the interactions between the proteins of up-regulated genes is a suitable strategy for the inhibition of tumor growth and metastasis in vitro. The most relevant protein targets may be further analyzed for drug repurposing by computational biology. The subnetworks formed by the interactions between the proteins of up-regulated genes allow the inference by Shannon entropy of the number of targets to be inhibited according to the tumor aggressiveness. There are common targets between tumoral tissues but many others are personalized at a molecular level. We also consider additional measures and more sophisticated modeling. This approach is necessary to improve the rational choice of therapeutic targets and the description of network dynamics. The modeling of attractors through Hopfield Network and ordinary differential equations are given here as examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Calderón-Aparicio A, Orue A (2019) Precision oncology in Latin America: current situation, challenges and perspectives. eCancer Med Sci 13:920
Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674
Krzyszczyk P, Acevedo A, Davidoff EJ, Timmins LM, Marrero-Berrios I, Patel M et al (2018) The growing role of precision and personalized medicine for cancer treatment. Technology (Singap World Sci) 6(3–4):79–100
Wilsdon T, Barron A, Edwards G, Lawlor R (2018) The benefits of personalised medicine to patients, society and healthcare systems. Charles River Assoc 2018:1–72
Carels N, Tilli T, Tuszynski JA (2015) A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases. PLoS ONE 10:e0115054
Garralda E, Dienstmann R, Piris-Giménez A, Braña I, Rodon J, Tabernero J (2019) New clinical trial designs in the era of precision medicine. Mol Oncol 13(3):549–557
Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C (2013) Emerging landscape of oncogenic signatures across human cancers. Nat Genet 45(10):1127–1133
Vuckovic N, Vuckovic BM, Liu Y, Paranjape K (2016) Accelerating clinical genomics to transform cancer care. Intel 1:8. https://www.intel.com/content/dam/www/public/us/en/documents/white-papers/accelerating-clinical-genomics-to-transform-cancer-care-paper.pdf. Accessed by Feb 2020
Falconi A, Lopes G, Parker JL (2014) Biomarkers and receptor targeted therapies reduce clinical trial risk in non-small-cell lung cancer. J Thorac Oncol 9:163–169
Morel CM, McGuire A, Mossialos E (2011) The level of income appears to have no consistent bearing on pharmaceutical prices across countries. Health Aff 30(8):1545–1552
Ramalho OD, Brummel AR, Miller DB (2010) Medication therapy management: 10 years experience in a large integrated health care system. J Manag Care Pharm 16(3):185–195
Morgan G, Ward R, Barton M (2004) The contribution of cytotoxic chemotherapy to 5-year survival in adult malignancies. Clin Oncol (R Coll Radiol) 16:549–560
PMC (Personalized Medicine Coalition) (2014) The case for personalized medicine, 4th edn. http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/pmc_case_for_personalized_medicine.pdf. Accessed by Feb 2020
Coyle K, Boudreau J, Marcato P (2017) Genetic mutations and epigenetic modifications: driving cancer and informing precision medicine. BioMed Res Inter 2017:9620870
West J, Bianconi G, Severini S, Teschendorff AE (2012) Differential network entropy reveals cancer system hallmarks. Sci Rep 2:802
Maciejko L, Smalley M, Goldman A (2017) Cancer immunotherapy and personalized medicine: Emerging technologies and biomarker-based approaches. J Mol Biomark Diagn 8(5):350
Ersek JL, Nadler E, Freeman-Daily J, Mazharuddin S, Kim ES (2017) Clinical pathways and the patient perspective in the pursuit of value-based oncology care. Am Soc Clin Oncol Educ. Book. 37:597–606. https://doi.org/10.14694/EDBK_174794
Lavi O, Gottesman MM, Levy D (2012) The dynamics of drug resistance: a mathematical perspective. Drug Resist Updates 15(1–2):90–97
Harrington JA, Hernandez-Guerrero TC, Basu B (2017) Early phase clinical trial designs e state of play and adapting for the future. Clin Oncol (R Coll Radiol) 29:770–777
Citron ML, Berry DA, Cirrincione C, Hudis C, Winer EP, Gradishar WJ et al (2003) Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J Clin Oncol 21:1431–1439
Norton L, Simon R (1977) Tumor size, sensitivity to therapy, and design of treatment schedules. Cancer Treat Rep 61:1307–1317
Goldie JH, Coldman AJ, Gudauskas GA (1982) Rationale for the use of alternating non-cross-resistant chemotherapy. Cancer Treat Rep 66:439–449
Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9):793–795
Catharina L, de Menezes MA, Carels N (2018) System biology to access target relevance in the research and development of molecular inhibitors. In: da Silva FAB, Carels N, Paes Silva Junior F (Eds) Theoretical and applied aspects of system biology. Computational biology, 1st edn. Springer International Publishing, Cham, pp 221–242
Verma M (2012) Personalized medicine and cancer. J Pers Med 2:1–14
FDA (Food and Drug Administration) (2019) Policy for device software functions and mobile medical applications—Guidance for industry and food and drug administration staff. https://www.fda.gov/media/80958/download. Accessed by Feb 2020
FDA (Food and Drug Administration) (2020) Table of pharmacogenomic biomarkers in drug labeling. https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling. Accessed by Feb 2020
Gelifescience (2019) Delivering precision health: the role of molecular diagnostics. https://thepathologist.com/fileadmin/pdf/GE-app-note-0919-supplied.pdf. Accessed by Feb 2020
McShane LM, Polley MY (2013) Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical robustness and clinical utility. Clin Trials 10:653–665
van de Vijver MJ, He YD, van ’T Veer LJ, Dai H, Hart AA, Voskuil DW et al (2002) A gene expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009
Vadas A, Bilodeau TJ, Oza C (2019) The evolution of biomarker use in clinical trials for cancer treatments: key findings and implications. L.E.K. Consulting 2019:1–25. https://www.lek.com/insights/sr/evolution-biomarker-use-clinical-trials-cancer-treatments. Accessed by Feb 2020
BIS Research (2017) Global precision medicine market to reach $141.70 billion by 2026, reports BIS Research. PR Newswire website. https://www.prnewswire.com/news-releases/global-precision-medicine-market-to-reach-14170-billion-by-2026-reports-bis-research-664364683.html. Accessed by Feb 2020
Novartis (2019). https://www.hcp.novartis.com/contentassets/4c6d6843d6cf4231804e0e7d7b865ec3/18-nvsonc-0005-poatr5_trend_report.pdf. Accessed by Feb 2020
Matchett KB, Lynam-Lennon N, Watson RW, Brown JAL (2017) Advances in precision medicine: tailoring individualized therapies. Cancers 9(11):146
Hong B, Zu Y (2013) Detecting circulating tumor cells: current challenges and new trends. Theranostics 3:377–94
Sheridan C (2019) Investors keep the faith in cancer liquid biopsies. Nat Biotechnol 37(9):972–974
New J (2019) The promise of data-driven drug development. Center for Data Innovation 1–33. http://www2.datainnovation.org/2019-data-driven-drug-development.pdf. Accessed by Feb 2020.
Cohen J (2018) Taking a wider view of precision oncology. https://www.forbes.com/sites/joshuacohen/2018/08/02/taking-a-wider-view-of-precision-oncology/#2dd6d94d2022 2018. Accessed by Feb 2020
Madhavan S, Subramaniam S, Brown TD, Chen JL (2018) Art and challenges of precision medicine: Interpreting and integrating genomic data into clinical practice. Am Soc Clin Oncol Ed Book, pp. 546–553
Croston GE (2017) The utility of target-based discovery. Expert Opin Drug Discov 12(5):427–429. https://tandfonline.com/doi/full/10.1080/17460441.2017.1308351. Accessed by February 2020
Conforte AJ, Magalhães M, Tilli TM, da Silva FAB, Carels N (2018) The challenge of translating system biology into targeted therapy of cancer. In: da Silva FAB, Carels N, Paes Silva Junior F (eds) Theoretical and applied aspects of system biology. Computational biology, 1st edn. Springer International Publishing, Cham, pp 175–194
Brummel A, Lustig A, Westrich K, Evans MA, Plank GS, Penso J et al (2014) Best practices: improving patient outcomes and costs in an ACO through comprehensive medication therapy management. J Manag Care Spec Pharm 20(12):1152–1158
Carels N, Spinassé LB, Tilli TM, Tuszynski JA (2016) Toward precision medicine of breast cancer. Theor Biol Med Model 13:7
Jarrett AM, Shah A, Bloom MJ, McKenna MT, Hormuth DA II, Yankeelov TE et al (2019) Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2 + breast cancer. Sci Rep 9:12830
Yankeelov TE, Quaranta V, Evans KJ, Rericha EC (2015) Toward a science of tumor forecasting for clinical oncology. Cancer Res 75(6):918–923
Jarrett AM, Lima EABF, Hormuth DA, McKenna MT, Feng X, Ekrut DA et al (2018) Mathematical models of tumor cell proliferation: a review of the literature. Expert Rev Anticancer Ther 18(12):1271–1286
Stéphanou A, McDougall SR, Anderson ARA, Chaplain MAJ (2005) Mathematical modelling of ow in 2d and 3d vascular networks: applications to antiangiogenic and chemotherapeutic drug stategies. Math Comput Model 41:1137–1156
Stéphanou A, Lesart AC, Deverchère J, Juhem A, Popov A, Estève F (2017) How tumour-induced vascular changes alter angiogenesis: insights from a computational model. J Theor Biol 419:211–226
Mirams GR, Arthurs CJ, Bernabeu MO, Bordas R, Cooper J, Corrias A et al (2013) Chaste: An open source c ++ library for computational physiology and biology. PLoS Comput Biol 9(3):e1002970
Pitt-Francis J, Pathmanathan P, Bernabeu MO, Bordas R, Cooper J, Fletcher AG, et al (2009) Chaste: a test-driven approach to software development for biological modelling. Computer Physics Communications. 180(12):2452–2471. 40 YEARS OF CPC: A celebratory issue focused on quality software for high performance, grid and novel computing architectures
Gillet JP, Gottesman MM (2010) Mechanisms of multidrug resistance in cancer. Methods Mol Biol 596:47–76
Yuaney G, Shah P (2018) Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. In: Proceedings of the 3rd machine learning for health care conference, vol 85, pp 161–226. http://proceedings.mlr.press/v85/yauney18a.html. Accessed by Feb 2020
Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A et al (2017) Detecting cancer metastases on gigapixel pathology images. https://arxiv.org/abs/1703.02442
Patel J (2013) Science of the science, drug discovery and artificial neural networks. Curr Drug Discov Technol 10(1):2–7
Atkinson RD (2018) Drug price controls will be more pain than gain. The Hill. https://thehill.com/opinion/healthcare/416068-drug-price-controls-will-be-more-pain-than-gain. Accessed by Feb 2020; Cost to develop and win marketing approval for a new drug is $2.6 billion. Tufts Center for the Study of Drug Development. 2014; https://static1.squarespace.com/static/5a9eb0c8e2ccd1158288d8dc/t/5ac66adc758d46b001a996d6/1522952924498/pr-coststudy.pdf. Accessed by Feb 2020
Calzolari D, Paternostro G, Harrington PL, Piermarocchi C, Duxbury PM (2007) Selective control of the apoptosis signaling network in heterogeneous cell populations. PLoS ONE 2:e547
Calzolari D, Bruschi S, Coquin L, Schofield J, Feala JD, Reed JC et al (2008) Search algorithms as a framework for the optimization of drug combinations. PLoS Comput Biol 4(12):e1000249
Hardman JG, Limbird LE, Gilman AG (2001) Goodman & Gilman’s the pharmacological basis of therapeutics. McGraw-Hill, New York
Pons-Salort M, van der Sanden B, Juhem A, Popov A, Stéphanou A (2012) A computational framework to assess the efficacy of cytotoxic molecules and vascular disrupting agents against solid tumours. Math Model Nat Phenom 7(1):49–77
Thompson MA, Godden JJ, Wham D, Ruggeri A, Mullane MP, Wilson A et al (2019) Coordinating an oncology precision medicine clinic within an integrated health system: lessons learned in year one. J Patient Cent Res Rev 6(1):36–45
Campillos M, Kuhn M, Gavin A, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321(5886):263–266
Cheng F, Zhao Z (2014) Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc 21(e2):e278–e286
Li X, Xu Y, Cui H, Huang T, Wang D, Lian B et al (2017) Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles. Artif Intell Med 83:35–43
DeRose YS, Wang G, Lin Y-C, Bernard PS, Buys SS, Ebbert MT et al (2011) Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med 17:1514–1520
Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM et al (2012) Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 9:338–350
Weeber F, van de Wetering M, Hoogstraat M, Dijkstra KK, Krijgsman O, Kuilman T et al (2015) Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc Natl Acad Sci USA 112:13308–13311
Hidalgo M, Bruckheimer E, Rajeshkumar NV, Garrido-Laguna I, De Oliveira E, Rubio-Viqueira B et al (2011) A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther 10:1311–1316
Izumchenko E, Paz K, Ciznadija D, Sloma I, Katz A, Vasquez-Dunddel D et al (2017) Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors. Ann Oncol 28:2595–2605
Ledford H (2017) Cancer-genome study challenges mouse “avatars”. Nat News. https://doi.org/10.1038/nature.2017.22782
Ben-David U, Ha G, Tseng Y-Y, Greenwald NF, Oh C, Shih J et al (2017) Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet 49(11):1567–1575
Shin SH, Bode AM, Dong Z (2017) Precision medicine: the foundation of future cancer therapeutics. NPJ Precis Oncol 1(1):12
Graham R, Mancher M, Wolman DM, Greenfield S, Steinberg E (eds) (2011) Clinical practice guidelines we can trust. National Academies Press, Washington, DC
Zon RT, Frame JN, Neuss MN, Page RD, Wollins DS, Stranne S et al (2016) American Society of Clinical Oncology policy statement on clinical pathways in oncology. J Oncol Pract 12:261–266
Schork NJ (2015) Personalized medicine: time for one-person trials. Nature 520(7549):609–611
Weber JS, Levit LA, Adamson PC, Bruinooge SS, Burris HA 3rd, Carducci MA et al (2017) Reaffirming and clarifying the American Society of Clinical Oncology’s policy statement on the critical role of phase I trials in cancer research and treatment. J Clin Oncol 35:139–140
Dienstmann R, Rodon J, Tabernero J (2015) Optimal design of trials to demonstrate the utility of genomically-guided therapy: putting precision cancer medicine to the test. Mol Oncol 9:940–950
Xiao G, Ma S, Minna J, Xie Y (2014) Adaptive prediction model in prospective molecular signature based clinical studies. Clin Cancer Res 20:531–539
Woodcock J, LaVange LM (2017) Master protocols to study multiple therapies, multiple diseases, or both. N Engl J Med 377:62–70
Carey LA, Winer EP (2016) I-SPY 2: toward more rapid progress in breast cancer treatment. N Engl J Med 375:83–84
Redig AJ, Jänne PA (2015) Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol 33:975–977
Billingham L, Malottki K, Steven N (2016) Research methods to change clinical practice for patients with rare cancers. Lancet Oncol 17:e70–e80
Rolfo C, Caglevic C, Bretel D, Hong D, Raez LE, Cardona AF et al (2016) Cancer clinical research in Latin America: current situation and opportunities expert opinion from the first ESMO workshop on clinical trials, Lima, 2015. ESMO Open 1(4):e000055
McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM et al (2013) Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration. BMC Med 11:220
Von Hoff DD, Stephenson JJ Jr, Rosen P et al (2010) Pilot study using molecular profiling of patients’ tumors to find potential targets and select treatments for their refractory cancers. J Clin Oncol 28:4877–4883
Radovich M, Kiel PJ, Nance SM, Niland EE, Parsley ME, Ferguson ME et al (2016) Clinical benefit of a precision medicine based approach for guiding treatment of refractory cancers. Oncotarget 7:56491–56500
Haslem DS, Van Norman SB, Fulde G, Knighton AJ, Belnap T, Butler AM et al (2017) A retrospective analysis of precision medicine outcomes in patients with advanced cancer reveals improved progression-free survival without increased health care costs. J Oncol Pract 13:e108–e119
Haslem DS, Chakravarty I, Fulde G, Gilbert H, TudorBP, Lin K et al (2018) Precision oncology in advanced cancer patients improves overall survival with lower weekly healthcare costs. Oncotarget 9:12316–12322
Schwaederle M, Zhao M, Lee JJ, Lazar V, Leyland-Jones B, Schilsky RL et al (2016) Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms: a meta-analysis. JAMA Oncol 2(11):1452–1459
Bellmunt J, De Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L et al (2017) Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med 376(11):1015–1026
Levit LA, Kim ES, McAneny BL, Nadauld LD, Levit K, Schenkel C et al (2019) Implementing precision medicine in community-based oncology programs: three models. J Oncol Pract 15(6):325–329
Moscow JA, Fojo T, Schilsky RL (2018) The evidence framework for precision cancer medicine. Nat Rev Clin Oncol 15:183–192
Jameson JL, Longo DL (2015) Precision medicine—personalized, problematic, and promising. N Engl J Med 372:2229–2234
Schwartzberg L, Kim ES, Liu D, Schrag D (2017) Precision oncology: Who, how, what, when, and when not? Am Soc Clin Oncol Ed Book 37:160–169
Monro HC, Gaffney EA (2009) Modelling chemotherapy resistance in palliation and failed cure. J Theor Biol 257:292–302
Castorina P, Carco D, Guiot C, Deisboeck TS (2009) Tumor growth instability and its implications for chemotherapy. Cancer Res 69:8507–8515
Kapoor S, Rallabandi VP, Sakode C, Padhi R, Roy PK (2013) A patient-specific therapeutic approach for tumour cell population extinction and drug toxicity reduction using control systems-based dose-profile design. Theor Biol Med Model 10:68
Gardner SN (2000) Scheduling chemotherapy: catch 22 between cell kill and resistance evolution. J Theor Med 2:215–232
FDA (Food and Drug Administration) (2016) FDA advances precision medicine initiative by issuing draft guidances on next generation sequencing-based tests. https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm509814.htm. Accessed by Feb 2020
FDA (Food and Drug Administration) (2020b) 21st century cures act. https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/21st-century-cures-act. Accessed by Feb 2020
Howard DH, Bach PB, Berndt ER, Conti RM (2015) Pricing in the market for anticancer drugs. J Econ Perspect 29:139–162
Sultana J, Cutroneo P, Trifiro G (2013) Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother 4:S73–S77
Katz G, Romano O, Foa C, Vataire AL, Chantelard JV, Hervé R et al (2015) Economic impact of gene expression profiling of early stage breast cancer patients in France. PLoS ONE 10(6):e0128880
Jakka S, Rossbach M (2013) An economic perspective on personalized medicine. HUGO J 7:1
ECML (Experts in Chronic Myeloid Leukemia) (2013) The price of drugs for chronic myeloid leukemia (CML) is a reflection of the unsustainable prices of cancer drugs: from the perspective of a large group of CML experts. Blood 121:4439–4442
Morris ZS, Wooding S, Grant J (2011) The answer is 17 years, what is the question? Understanding time lags in translational research. J R Soc Med 104(12):510–520
Carels N, Tilli TM, Tuszynki JA (2015) Optimization of combination chemotherapy based on the calculation of network entropy for protein-protein interactions in breast cancer cell lines. EPJ Nonlinear Biomed Phys 3:6
Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382
Breitkreutz D, Hlatky L, Rietman E, Tuszynski JA (2012) Molecular signaling network complexity is correlated with cancer patient survivability. Proc Natl Acad Sci USA 109(23):9209–9212
Tilli TM, Carels N, Tuszynski JA, Pasdar M (2016) Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development. Oncotarget 7(39):63189–61203
Conforte AJ, Tuszynski JA, da Silva FAB, Carels N (2019) Signaling complexity measured by shannon entropy and its application in personalized medicine. Front Genet 10:1–14
Peng Q, Schork N (2014) Utility of network integrity methods in therapeutic target identification. Front Genet 5:12
Schramm G, Kannabiran N, König R (2010) Regulation patterns in signaling networks of cancer. BMC Syst Biol 4:162
Winterbach W, Mieghem PV, Reinders M, Wang H, de Ridder D (2013) Topology of molecular interaction networks. BMC Syst Biol 7:90
Freeman LCA (1977) Set of measures of centrality based on betweenness. Sociometry 40:35–41
Frainay C, Jourdan F (2017) Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief Bioinform 18:43–56
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442
Teschendorff AE, Banerji CRS, Severini S, Kuehn R, Sollich P (2015) Increased signaling entropy in cancer requires the scale-free property of proteininteraction networks. Sci Rep 5:1–9
West HJ (2016) Can we define and reach precise goals for precision medicine in cancer care? J Clin Oncol 34:3595–3596
Banerji CRS, Severini S, Caldas C, Teschendorff AE (2015) Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer. PLoS Comput Biol 11:e1004115
Huang S, Ernberg I, Kauffman S (2009) Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin Cell Dev Biol 20:869–876
Cornelius SP, Kath WL, Motter AE (2013) Realistic control of network dynamics. Nat Commun 4:1942
Bora RS, Gupta D, Mukkur TKS, Saini KS (2012) RNA interference therapeutics for cancer: challenges and opportunities (review). Mol Med Rep 6:9–15
Ehrke-Schulz E, Schiwon M, Hagedorn C, Ehrhardt A (2017) Establishment of the CRISPR/Cas9 system for targeted gene disruption and gene tagging. Methods Mol Biol 1654:165–176
Crespo I, del Sol A (2013) A general strategy for cellular reprogramming: The importance of transcription factor cross-repression. Stem Cells 31:2127–2135
Sgariglia D, Conforte AJ, de Carvalho VLA, Carels N, da Silva FAB (2018) Cellular reprogramming. In: da Silva FAB, Carels N, Paes Silva Junior F (eds) Theoretical and applied aspects of system biology. Computational biology, 1st edn. Springer International Publishing, pp 41–55
Moes M, Le Béchec A, Crespo I, Laurini C, Halavatyi A, Vetter G et al (2012) A novel network integrating a miRNA-203/SNAI1 feedback loop which regulates epithelial to mesenchymal transition. PLoS ONE 7(4):e35440
Li C, Wang J (2015) Quantifying the landscape for development and cancer from a core cancer stem cell circuit. Cancer Res 75:2607–2618
Ao P, Galas D, Hood L, Zhu X (2008) Cancer as robust intrinsic state of endogenous molecular-cellular network shaped by evolution. Med Hypotheses 70:678–684
Yuan R, Zhang S, Yu J, Huang Y, Lu D, Cheng R et al (2017) Beyond cancer genes: colorectal cancer as robust intrinsic states formed by molecular interactions. Open Biol 7(11)
Yuan R, Zhu X, Wang G, Li S, Ao P (2017) Cancer as robust intrinsic state shaped by evolution: a key issues review. Rep Prog Phys 80:042701
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558
Maetschke SR, Ragan MA (2014) Characterizing cancer subtypes as attractors of Hopfield networks. Bioinformatics 30:1273–1279
Taherian Fard A, Ragan MA (2017) Modeling the attractor landscape of disease progression: a network-based approach. Front Genet 8:48
Conforte AJ, Alves LD, Coelho FC, Carels N, da Silva FAB (2020) Modeling basins of attraction for breast cancer using Hopfield networks. Front Genet 11:314. https://doi.org/10.3389/fgene.2020.00314
Szedlak A, Paternostro G, Piermarocchi C (2014) Control of asymmetric hopfield networks and application to cancer attractors. PLoS ONE 9:e105842
Cantini L, Caselle M (2019) Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data. Sci Rep 9:1–9
Guo J, Zheng J (2017) HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington’s epigenetic landscape. Bioinformatics 33(14):i102–i109
Kinghorn AD, Pan L, Fletcher JN, Chai H (2011) The relevance of higher plants in lead compound discovery programs. J Nat Prod 74(6):1539–1555
Bernardini S, Tiezzi A, Laghezza Masci V, Ovidi E (2018) Natural products for human health: An historical overview of the drug discovery approaches. Nat Prod Res 32(16):1926–1950
Cragg GM, Newman DJ (2013) Natural products: a continuing source of novel drug leads. Biochem Biophys Acta 1830(6):3670–3695
Breinbauer Rolf, Vetter Ingrid R, Waldmann Herbert (2002) From protein domains to drug candidates: natural products as guiding principles in the design and synthesis of compound libraries. Angew Chem Int Ed Engl 41(16):2878–2890
Bauer Armin, Brönstrup Mark (2014) Industrial natural product chemistry for drug discovery and development. Nat Prod Rep 31(1):35–60
Newman DJ (2008) Natural products as leads to potential drugs: An old process or the new Hope for drug discovery? J Med Chem 51(9):2589–2599
Corson Timothy W, Crews CM (2007) Molecular understanding and modern application of traditional medicines: triumphs and trials. Cell 130(5):769–774
David B, Wolfender J-L, Dias DA (2015) The pharmaceutical industry and natural products: historical status and new trends. Phytochem Rev 14(2):299–315
Kholod Y, Hoag E, Muratore K, Kosenkov D (2018) Computer-aided drug discovery: molecular docking of diminazene ligands to DNA minor groove. J Chem Educ 95(5):882–887
Prato G, Silvent S, Saka S, Lamberto M, Kosenkov D (2015) Thermodynamics of binding of di- and tetrasubstituted naphthalene diimide ligands to DNA G-quadruplex. J Phys Chem B 119(8):3335–3347
Bajorath J (2015) Computer-aided drug discovery. Research 4:630
Chaudhary KK, Mishra N (2016) A review on molecular docking: novel tool for drug discovery. JSM Chem 3(4):1029
de Ruyck J, Guillaume B, Ralf B, Lensink MF (2016) Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem 9:1–11
Pârvu L (2003) QSAR: a piece of drug design. J Cell Mol Med 7(3):333–335
Rao VS, Srinivas K (2011) Modern drug discovery process: An in silico approach. J Bioinform Seq Anal 2(5):89–94
Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12(1):2694–2718
Buckingham J, Glen RC, Hill AP, Hyde RM, Martin GR, Robertson AD et al (1995) Computer-aided design and synthesis of 5-substituted tryptamines and their pharmacology at the 5-HT1D receptor: discovery of compounds with potential anti-migraine properties. J Med Chem 38(18):3566–3580
Koga H, Itoh A, Murayama S, Suzue S, Irikura T (1980) Structure-activity relationships of antibacterial 6,7- and 7,8-disubstituted 1-alkyl-1,4-dihydro-4-oxoquinoline-3-carboxylic acids. J Med Chem 23(12):1358–1363
Klopmand G (1992) Concepts and applications of molecular similarity, by Mark A. Johnson and Gerald M. Maggiora, Eds., John Wiley & Sons, New York, 1990, pp. 393. J Comp Chem 13(4):539–540
Verma RP, Hansch C (2009) Camptothecins: a SAR/QSAR study. Chem Rev 109(1):213–235
Lin S-K (2000) Pharmacophore perception, development and use in drug design. Edited by Osman F. Güner. Molecules 5(7):987–989
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas M-T (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotech J 14:177–184
Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in Review. J Mol Recognit 24(2):149–164
Chemi G, Brogi S (2017) Breakthroughs in computational approaches for drug discovery. J Drug Res Dev 3(1):2470
Sousa SF, Fernandes PA, Ramos MJ (2006) Protein-ligand docking: current status and future challenges. Proteins 65(1):15–26
Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71
Mackerell AD Jr (2004) Empirical force fields for biological macromolecules: overview and issues. J Comput Chem 25(13):1584–1604
Ganesan A, Coote ML, Barakat K (2017) Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug discovery Today 22(2):249–269
De Vivo M (2011) Bridging quantum mechanics and structure-based drug design. Optimization 7:8
Abagyan R, Totrov M (2001) High-throughput docking for lead generation. Curr Opin Chem Biol 5(4):375–382
Borhani DW, Shaw DE (2012) The future of molecular dynamics simulations in drug discovery. J Comput Aided Mol Des 26(1):15–26
Fischer M, Coleman RG, Fraser JS, Shoichet BK (2014) Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 6(7):575–583
Ivetac A, McCammon JA (2011) Molecular recognition in the case of flexible targets. Curr Pharm Des 17(17):1663–1671
Tarcsay Á, Paragi G, Vass M, Jójárt B, Bogár F, Keserű GM (2013) The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 53(11):2990–2999
Tian S, Sun H, Pan P, Li D, Zhen X, Li Y et al (2014) Assessing an Ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. J Chem Inf Model 54(10):2664–2679
Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Nat Acad Sci USA 108(25):10184–10189
Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE (2011) How does a drug molecule find its target binding site? J Am Chem Soc 133(24): 9181–9183
Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23(2):187–199
Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1–2):141–151
Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci USA 99(20):12562–12566
De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59(9):4035–4061
Korzekwa KR, Jones JP, Gillette JR (1990) Theoretical studies on cytochrome P-450 mediated hydroxylation: A predictive model for hydrogen atom abstractions. J Am Chem Soc 112(19):7042–7046
Warshel A, Levitt M (1976) Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. J Mol Biol 103:227–249
Shaik S, Cohen S, Wang Y, Chen H, Kumar D, Thiel W (2010) P450 enzymes: Their structure, reactivity, and selectivity modeled by QM/MM calculations. Chem Rev 110(2):949–1017
Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID et al (2015) Predicting drug metabolism: Experiment and/or computation? Nature Rev Drug discov. 14(6):387–404
Acknowledgements
This study was supported by a fellowship from Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) to AC and a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/Fiocruz (CAPES/Fiocruz) to CL.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Carels, N., Conforte, A.J., Lima, C.R., da Silva, F.A.B. (2020). Challenges for the Optimization of Drug Therapy in the Treatment of Cancer. In: da Silva, F.A.B., Carels, N., Trindade dos Santos, M., Lopes, F.J.P. (eds) Networks in Systems Biology. Computational Biology, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-51862-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-51862-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51861-5
Online ISBN: 978-3-030-51862-2
eBook Packages: Computer ScienceComputer Science (R0)