Free Access
Issue |
Med Sci (Paris)
Volume 35, Number 12, Décembre 2019
Anticorps monoclonaux en thérapeutique
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Page(s) | 1121 - 1129 | |
Section | Anticorps monoclonaux : de la complexité du passage du laboratoire à l’homme | |
DOI | https://doi.org/10.1051/medsci/2019209 | |
Published online | 06 January 2020 |
- Le Tourneau C, Lee JJ, Siu LL. Dose escalation methods in phase I cancer clinical trials. J Natl Cancer Inst 2009 ; 101: 708–720. [CrossRef] [PubMed] [Google Scholar]
- Le Tourneau C, Stathis A, Vidal L, et al. Choice of starting dose for molecularly targeted agents evaluated in first-in-human phase I cancer clinical trials. J Clin Oncol 2010 ; 28: 1401–1407. [CrossRef] [PubMed] [Google Scholar]
- Tosi D, Laghzali Y, Vinches M, et al. Clinical development strategies and outcomes in first-in-human trials of monoclonal antibodies. J Clin Oncol 2015 ; 33: 2158–2165. [CrossRef] [PubMed] [Google Scholar]
- Viala M, Vinches M, Alexandre M, et al. Strategies for clinical development of monoclonal antibodies beyond first-in-human trials: tested doses and rationale for dose selection. Br J Cancer 2018 ; 118: 679–697. [CrossRef] [PubMed] [Google Scholar]
- Jardim DL, Hess KR, LoRusso P, et al. Predictive value of phase I trials for safety in later trials and final approved dose: analysis of 61 approved cancer drugs. Clin Cancer Res 2014 ; 20: 281–288. [CrossRef] [PubMed] [Google Scholar]
- Mager DE. Target-mediated drug disposition and dynamics. Biochem Pharmacol 2006 ; 72: 1–10. [CrossRef] [PubMed] [Google Scholar]
- Tabrizi MA, Tseng C-ML, Roskos LK. Elimination mechanisms of therapeutic monoclonal antibodies. Drug Discov Today 2006 ; 11: 81–88. [CrossRef] [PubMed] [Google Scholar]
- Golay J, Semenzato G, Rambaldi A, et al. Lessons for the clinic from rituximab pharmacokinetics and pharmacodynamics. mAbs 2013; 5: 826–37. [CrossRef] [PubMed] [Google Scholar]
- Dayde D, Ternant D, Ohresser M, et al. Tumor burden influences exposure and response to rituximab: pharmacokinetic-pharmacodynamic modeling using a syngeneic bioluminescent murine model expressing human CD20. Blood 2009 ; 113: 3765–3772. [Google Scholar]
- Dostalek M, Gardner I, Gurbaxani BM, et al. Pharmacokinetics, pharmacodynamics and physiologically-based pharmacokinetic modelling of monoclonal antibodies. Clin Pharmacokinet 2013 ; 52: 83–124. [CrossRef] [PubMed] [Google Scholar]
- Azzopardi N, Lecomte T, Ternant D, et al. Cetuximab pharmacokinetics influences progression-free survival of metastatic colorectal cancer patients. Clin Cancer Res 2011 ; 17: 6329–6337. [CrossRef] [PubMed] [Google Scholar]
- Cartron G, Hourcade-Potelleret F, Morschhauser F, et al. Rationale for optimal obinutuzumab/GA101 dosing regimen in B-cell non-Hodgkin lymphoma. Haematologica 2016 ; 101: 226–234. [CrossRef] [PubMed] [Google Scholar]
- Thurber G, Schmidt M, Wittrup K. Factors determining antibody distribution in tumors. Trends PharmacolSci 2008 ; 29: 57–61. [Google Scholar]
- Thurber GM, Schmidt MM, Wittrup KD. Antibody tumor penetration: transport opposed by systemic and antigen-mediated clearance. Adv Drug Deliv Rev 2008 ; 60: 1421–1434. [CrossRef] [PubMed] [Google Scholar]
- Topalian SL, Sznol M, McDermott DF, et al. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J Clin Oncol 2014 ; 32: 1020–1030. [CrossRef] [PubMed] [Google Scholar]
- Patnaik A, Kang SP, Rasco D, et al. Phase I study of pembrolizumab (MK-3475; anti-PD-1 monoclonal antibody) in patients with advanced solid tumors. Clin Cancer Res 2015 ; 21: 4286–4293. [CrossRef] [PubMed] [Google Scholar]
- Agrawal S, Feng Y, Roy A, et al. Nivolumab dose selection: challenges, opportunities, and lessons learned for cancer immunotherapy. J Immunother Cancer 2016 ; 4: 72. [Google Scholar]
- Lindauer A, Valiathan C, Mehta K, et al. Translational pharmacokinetic/pharmacodynamic modeling of tumor growth inhibition supports dose-range selection of the anti-PD-1 antibody pembrolizumab: Translational pharmacokinetic/pharmacodynamic modeling. CPT Pharmacometrics Syst Pharmacol 2017; 6 h 11–20. [Google Scholar]
- Liu C, Yu J, Li H, et al. Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis. Clin Pharmacol Ther 2017 ; 101: 657–666. [CrossRef] [PubMed] [Google Scholar]
- Oude Munnink T, Henstra M, Segerink L, et al. Therapeutic drug monitoring of monoclonal antibodies in inflammatory and malignant disease: translating TNF-α experience to oncology. Clin Pharmacol Ther 2016; 99: 419–31. [CrossRef] [PubMed] [Google Scholar]
- Ratain MJ, Goldstein DA. Time is money: optimizing the scheduling of nivolumab. J Clin Oncol 2018 ; 36: 3074–3076. [Google Scholar]
- Tibbitts J, Canter D, Graff R, et al. Key factors influencing ADME properties of therapeutic proteins: a need for ADME characterization in drug discovery and development. mAbs 2016; 8: 229–45. [CrossRef] [PubMed] [Google Scholar]
- Azzopardi N, Dupuis-Girod S, Ternant D, et al. Dose-response relationship of bevacizumab in hereditary hemorrhagic telangiectasia. mAbs 2015; 7: 630–7. [CrossRef] [PubMed] [Google Scholar]
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