Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013
Article CAS PubMed Google Scholar
Bode AM, Dong Z (2018) Recent advances in precision oncology research. npj Precis Oncol. https://doi.org/10.1038/s41698-018-0055-0
Article PubMed PubMed Central Google Scholar
Tang J, Pearce L, O’Donnell-Tormey J, Hubbard-Lucey VM (2018) Trends in the global immuno-oncology landscape. Nat Rev Drug Discov 17(11):783–784. https://doi.org/10.1038/nrd.2018.167
Article CAS PubMed Google Scholar
Yu JX, Hubbard-Lucey VM, Tang J (2019) Immuno-oncology drug development goes global. Nat Rev Drug Discov. https://doi.org/10.1038/d41573-019-00167-9
Cavalli F (2013) An appeal to world leaders: stop cancer now. The Lancet 381(9865):425–426. https://doi.org/10.1016/s0140-6736(13)60059-8
Maeda H, Khatami M (2018) Analyses of repeated failures in cancer therapy for solid tumors: poor tumor-selective drug delivery, low therapeutic efficacy and unsustainable costs. Clin Transl Med. https://doi.org/10.1186/s40169-018-0185-6
Article PubMed PubMed Central Google Scholar
Hamis S, Powathil GG, Chaplain MAJ (2019) Blackboard to bedside: a mathematical modeling bottom-up approach toward personalized cancer treatments. JCO Clin Cancer Inform 3:1–11. https://doi.org/10.1200/cci.18.00068
Edelman LB, Eddy JA, Price ND (2010) In silicomodels of cancer. Wiley Interdiscip Rev Syst Biol Med 2(4):438–459. https://doi.org/10.1002/wsbm.75
Article CAS PubMed PubMed Central Google Scholar
Bekisz S, Geris L (2020) Cancer modeling: from mechanistic to data-driven approaches, and from fundamental insights to clinical applications. J Comput Sci 46:101198. https://doi.org/10.1016/j.jocs.2020.101198
Lavezzi SM, Borella E, Carrara L, De Nicolao G, Magni P, Poggesi I (2017) Mathematical modeling of efficacy and safety for anticancer drugs clinical development. Expert Opin Drug Discov 13(1):5–21. https://doi.org/10.1080/17460441.2018.1388369
Article CAS PubMed Google Scholar
Craig M (2017) Towards quantitative systems pharmacology models of chemotherapy-induced neutropenia. CPT Pharmacomet Syst Pharmacol 6(5):293–304. https://doi.org/10.1002/psp4.12191
Hosseini I, Gadkar K, Stefanich E, Li C-C, Sun LL, Chu Y-W, Ramanujan S (2020) Mitigating the risk of cytokine release syndrome in a phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling. npj Syst Biol Appl. https://doi.org/10.1038/s41540-020-00145-7
Article PubMed PubMed Central Google Scholar
Piero J, Furlong LI, Sanz F (2018) In silico models in drug development: where we are. Curr Opin Pharmacol 42:111–121. https://doi.org/10.1016/j.coph.2018.08.007
Manolis E, Rohou S, Hemmings R, Salmonson T, Karlsson M, Milligan PA (2013) The role of modeling and simulation in development and registration of medicinal products: output from the EFPIA/EMA modeling and simulation workshop. CPT Pharmacomet Syst Pharmacol 2(2):31. https://doi.org/10.1038/psp.2013.7
Gobburu JVS, Lesko LJ (2009) Quantitative disease, drug, and trial models. Annu Rev Pharmacol Toxicol 49(1):291–301. https://doi.org/10.1146/annurev.pharmtox.011008.145613
Article CAS PubMed Google Scholar
U.S. Food and Drug Administration: PDUFA reauthorization performance goals and procedures fiscal years 2018 through 2022. https://www.fda.gov/downloads/ForIndustry/UserFees/PrescriptionDrugUserFee/UCM5114 38.pdf. Accessed 11 July 2019
Wang Y, Zhu H, Madabushi R, Liu Q, Huang S, Zineh I (2019) Modelinformed drug development: current us regulatory practice and future considerations. Clin Pharmacol Ther 105(4):899–911. https://doi.org/10.1002/cpt.1363
Venkatakrishnan K, Graaf PH (2022) Toward project optimus for oncology precision medicine: multi-dimensional dose optimization enabled by quantitative clinical pharmacology. Clin Pharmacol Ther 112(5):927–932. https://doi.org/10.1002/cpt.2742
Ribba B, Holford NH, Magni P, Trocniz I, Gueorguieva I, Girard P, Sarr C, Elishmereni M, Kloft C, Friberg LE (2014) A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT Pharmacomet Syst Pharmacol 3(5):113. https://doi.org/10.1038/psp.2014.12
Castaeda ARS, Torres ER, Goris NAV, Gonzlez MM, Reyes JB, Gonzlez VGS, Schonbek M, Montijano JI, Cabrales LEB (2019) New formulation of the Gompertz equation to describe the kinetics of untreated tumors. PLoS ONE 14(11):0224978. https://doi.org/10.1371/journal.pone.0224978
Bernard A, Kimko H, Mital D, Poggesi I (2012) Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development. Expert Opin Drug Metab Toxicol 8(9):1057–1069. https://doi.org/10.1517/17425255.2012.693480
Article CAS PubMed Google Scholar
Rocchetti M, Simeoni M, Pesenti E, Nicolao GD, Poggesi I (2007) Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur J Cancer 43(12):1862–1868. https://doi.org/10.1016/j.ejca.2007.05.011
Article CAS PubMed Google Scholar
Bonate PL (2011) Modeling tumor growth in oncology. In: Pharmacokinetics in drug development. Springer, pp 1–19. https://doi.org/10.1007/978-1-4419-7937-7_1
Claret L, Girard P, Hoff PM, Cutsem EV, Zuideveld KP, Jorga K, Fagerberg J, Bruno R (2009) Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. J Clin Oncol 27(25):4103–4108. https://doi.org/10.1200/jco.2008.21.0807
Article CAS PubMed Google Scholar
Zheng Y, Narwal R, Jin C, Baverel PG, Jin X, Gupta A, Ben Y, Wang B, Mukhopadhyay P, Higgs BW et al (2018) Population modeling of tumor kinetics and overall survival to identify prognostic and predictive biomarkers of efficacy for durvalumab in patients with urothelial carcinoma. Clin Pharmacol Ther 103(4):643–652. https://doi.org/10.1002/cpt.986
Article CAS PubMed PubMed Central Google Scholar
Azam F, Latif MF, Farooq A, Tirmazy SH, AlShahrani S, Bashir S, Bukhari N (2019) Performance status assessment by using ECOG (eastern cooperative oncology group) score for cancer patients by oncology healthcare professionals. Case Repo Oncol 12(3):728–736. https://doi.org/10.1159/000503095
Wang Y, Sung C, Dartois C, Ramchandani R, Booth BP, Rock E, Gobburu J (2009) Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther 86(2):167–174. https://doi.org/10.1038/clpt.2009.64
Article CAS PubMed Google Scholar
Powathil GG, Swat M, Chaplain MAJ (2015) Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling. Semin Cancer Biol 30:13–20. https://doi.org/10.1016/j.semcancer.2014.02.003
Maffuid K, Cao Y (2023) Utilizing a proximity dependent labeling strategy to study cancer-immune intercellular interactions. J Pharmacol Exp Ther. https://doi.org/10.1124/jpet.123.001761
Altrock PM, Liu LL, Michor F (2015) The mathematics of cancer: integrating quantitative models. Nat Rev Cancer 15(12):730–745. https://doi.org/10.1038/nrc4029
Article CAS PubMed Google Scholar
Kyroudis CA, Dionysiou DD, Kolokotroni EA, Stamatakos GS (2019) Studying the regression profiles of cervical tumours during radiotherapy treatment using a patient-specific multiscale model. Sci Rep. https://doi.org/10.1038/s41598-018-37155-9
Article PubMed PubMed Central Google Scholar
Rieger TR, Allen RJ, Bystricky L, Chen Y, Colopy GW, Cui Y, Gonzalez A, Liu Y, White RD, Everett RA, Banks HT, Musante CJ (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog Biophys Mol Biol 139:15–22.
Comments (0)