How can attrition rates be reduced in cancer drug discovery?

Bibliography

• Trotta F, Leufkens HG, Schellens JH. Evaluation of oncology drugs at the European Medicines Agency and US Food and Drug Administration: when differences have an impact on clinical practice. J Clin Oncol 2011;29(16):2266-72
   PubMed Web of Science ®Google Scholar
• Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3(8):711-15
   PubMed Web of Science ®Google Scholar
• Tonkens R. An overview of the drug development process. Physician Exec 2005;31(3):48-52
   Google Scholar
• Garrido-Laguna I, Hidalgo M, Kurzrock R. The inverted pyramid of biomarker-driven trials. Nat Rev Clin Oncol 2011;8(9):562-6
   PubMed Web of Science ®Google Scholar
• Scannell JW, Blanckley A, Boldon H, Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 2012;11(3):191-200
   PubMed Web of Science ®Google Scholar
• Swinney DC, Anthony J. How were new medicines discovered? Nat Rev Drug Discov 2011;10(7):507-19
   PubMed Web of Science ®Google Scholar
• Reed JC. NCATS could mitigate pharma valley of death. Genet Eng Biotechnol News 2011;31(10):6
   Web of Science ®Google Scholar
• Williams M. Productivity shortfalls in drug discovery: contributions from the preclinical sciences? J Pharmacol Exp Ther 2011;336(1):3-8
   PubMed Web of Science ®Google Scholar
• Pammolli F, Magazzini L, Riccaboni M. The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 2011;10(6):428-38
   PubMed Web of Science ®Google Scholar
• Paul SM, Mytelka DS, Dunwiddie CT. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov 2010;9(3):203-14
   PubMed Web of Science ®Google Scholar
• Hait WN. Anticancer drug development: the grand challenges. Nat Rev Drug Discov 2010;9(4):253-4
   PubMed Web of Science ®Google Scholar
• Adams DJ. The Valley of death in anticancer drug development: a reassessment. Trends Pharmacol Sci 2012;33(4):173-80
   PubMed Web of Science ®Google Scholar
• Goodwin R, Giaccone G, Calvert H. Targeted agents: how to select the winners in preclinical and early clinical studies? Eur J Cancer 2012;48(2):170-8
   PubMed Web of Science ®Google Scholar
• Johnson JI, Decker S, Zaharevitz D. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer 2001;84(10):1424-31
   PubMed Web of Science ®Google Scholar
• Moreno L, Chesler L, Hargrave D. Preclinical drug development for childhood cancer. Expert Opin Drug Discov 2011;6(1):49-64
   PubMed Web of Science ®Google Scholar
• Lee Y, Kawagoe R, Sasai K. Loss of suppressor-of-fused function promotes tumorigenesis. Oncogene 2007;26(44):6442-7
   PubMed Web of Science ®Google Scholar
• Chesler L, Weiss WA. Genetically engineered murine models–contribution to our understanding of the genetics, molecular pathology and therapeutic targeting of neuroblastoma. Semin Cancer Biol 2011;21(4):245-55
   PubMed Web of Science ®Google Scholar
• Hingorani SR, Petricoin EF, Maitra A. Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell 2003;4(6):437-50
   PubMed Web of Science ®Google Scholar
• Hidalgo M, Bruckheimer E, Rajeshkumar NV. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther 2011;10(8):1311-16
   PubMed Web of Science ®Google Scholar
• Moore AS, Faisal A, de Castro DG. Selective FLT3 inhibition of FLT3-ITD(+) acute myeloid leukaemia resulting in secondary D835Y mutation: a model for emerging clinical resistance patterns. Leukemia; 2012;26(7):1462-70
   PubMed Web of Science ®Google Scholar
• Yauch RL, Dijkgraaf GL, Alicke B. Smoothened mutation confers resistance to a Hedgehog pathway inhibitor in medulloblastoma. Science 2009;326(5952):572-4
   PubMed Web of Science ®Google Scholar
• Rudin CM, Hann CL, Laterra J. Treatment of medulloblastoma with hedgehog pathway inhibitor GDC-0449. N Engl J Med 2009;361(12):1173-8
   PubMed Web of Science ®Google Scholar
• Sullivan RJ, Flaherty KT. Resistance to BRAF-targeted therapy in melanoma. Eur J Cancer 2013. DOI: http://dx.doi.org/10.1016/j.ejca.2012.11.019
   Google Scholar
• Das Thakur M, Salangsang F, Landman AS. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 2013. DOI: 10.1038/nature11814
   PubMed Web of Science ®Google Scholar
• Britschgi A, Andraos R, Brinkhaus H. JAK2/STAT5 inhibition circumvents resistance to PI3K/mTOR blockade: a rationale for cotargeting these pathways in metastatic breast cancer. Cancer Cell 2012;22(6):796-811
   PubMed Web of Science ®Google Scholar
• Humphrey RW, Brockway-Lunardi LM, Bonk DT. Opportunities and challenges in the development of experimental drug combinations for cancer. J Natl Cancer Inst 2011;103(16):1222-6
   PubMed Web of Science ®Google Scholar
• LoRusso PM, Canetta R, Wagner JA. Accelerating cancer therapy development: the importance of combination strategies and collaboration. Summary of an institute of medicine workshop. Clin Cancer Res 2012;18(22):6101-9
   PubMed Web of Science ®Google Scholar
• Walker I, Newell H. Do molecularly targeted agents in oncology have reduced attrition rates? Nat Rev Drug Discov 2009;8(1):15-16
   PubMed Web of Science ®Google Scholar
• Cummings J, Raynaud F, Jones L. Fit-for-purpose biomarker method validation for application in clinical trials of anticancer drugs. Br J Cancer 2010;103(9):1313-17
   PubMed Web of Science ®Google Scholar
• Basu B, Olmos D, de Bono JS. Targeting IGF-1R: throwing out the baby with the bathwater? Br J Cancer 2011;104(1):1-3
   PubMed Web of Science ®Google Scholar
• Valentijn LJ, Koster J, Haneveld F. Functional MYCN signature predicts outcome of neuroblastoma irrespective of MYCN amplification. Proc Natl Acad Sci USA 2012;109(47):19190-5
   PubMed Web of Science ®Google Scholar
• Dupont Jensen J, Laenkholm AV, Knoop A. PIK3CA mutations may be discordant between primary and corresponding metastatic disease in breast cancer. Clin Cancer Res 2011;17(4):667-77
   PubMed Web of Science ®Google Scholar
• Gasch C, Bauernhofer T, Pichler M. Heterogeneity of epidermal growth factor receptor status and mutations of KRAS/PIK3CA in circulating tumor cells of patients with colorectal cancer. Clin Chem 2013;59(1):252-60
   PubMed Web of Science ®Google Scholar
• Strati A, Markou A, Parisi C. Gene expression profile of circulating tumor cells in breast cancer by RT-qPCR. BMC Cancer 2011;11:422
   PubMed Web of Science ®Google Scholar
• Gerlinger M, Rowan AJ, Horswell S. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366(10):883-92
   PubMed Web of Science ®Google Scholar
• Yap TA, Sandhu SK, Workman P, Envisioning the future of early anticancer drug development. Nat Rev Cancer 2010;10(7):514-23
   PubMed Web of Science ®Google Scholar
• Kummar S, Kinders R, Gutierrez ME . Phase 0 clinical trial of the poly (ADP-ribose) polymerase inhibitor ABT-888 in patients with advanced malignancies. J Clin Oncol 2009;27(16):2705-11
   PubMed Web of Science ®Google Scholar
• Kummar S, Kinders R, Rubinstein L. Compressing drug development timelines in oncology using phase ‘0’ trials. Nat Rev Cancer 2007;7(2):131-9
   PubMed Web of Science ®Google Scholar
• Rodriguez-Pascual J, Sha P, Garcia-Garcia E. A preclinical and clinical study of mycophenolate mofetil in pancreatic cancer. Invest New Drugs 2012;31(1):14-9
   PubMed Web of Science ®Google Scholar
• Freeman GA, Kimmelman J. Publication and reporting conduct for pharmacodynamic analyses of tumor tissue in early-phase oncology trials. Clin Cancer Res 2012;18(23):6478-84
   PubMed Web of Science ®Google Scholar
• Iannone R. Improving publication rates of biomarker results from cancer trials. Clin Cancer Res 2012;18(23):6398-9
   PubMed Web of Science ®Google Scholar
• Yap TA, Olmos D, Brunetto AT. Phase I trial of a selective c-MET inhibitor ARQ 197 incorporating proof of mechanism pharmacodynamic studies. J Clin Oncol 2011;29(10):1271-9
   PubMed Web of Science ®Google Scholar
• Rubinstein L, Crowley J, Ivy P. Randomized phase II designs. Clin Cancer Res 2009;15(6):1883-90
   PubMed Web of Science ®Google Scholar
• Seymour L, Ivy SP, Sargent D. The design of phase II clinical trials testing cancer therapeutics: consensus recommendations from the clinical trial design task force of the national cancer institute investigational drug steering committee. Clin Cancer Res 2010;16(6):1764-9
   PubMed Web of Science ®Google Scholar
• EMA. E.M.A., EMA/EFPIA 2nd Workshop: adaptive Design in Confirmatory Trials (EMA/779520/2009). 2010. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Minutes/2010/04/WC500089206.pdf [Last accessed 12 November 2012]
   Google Scholar
• Hills RK, Burnett AK. Applicability of a “Pick a Winner” trial design to acute myeloid leukemia. Blood 2011;118(9):2389-94
   PubMed Web of Science ®Google Scholar
• Joshua Chen YH, Demets DL, Gordon Lan KK. Some drop-the-loser designs for monitoring multiple doses. Stat Med 2010;29(17):1793-807
   PubMed Web of Science ®Google Scholar
• Mahajan R, Gupta K. Adaptive design clinical trials: methodology, challenges and prospect. Indian J Pharmacol 2010;42(4):201-7
   PubMed Web of Science ®Google Scholar
• Saint-Raymond A, Herold R. Medicines for pediatric oncology: can we overcome the failure to deliver? Expert Rev Clin Pharmacol 2012;5(5):493-5
   PubMed Web of Science ®Google Scholar
• EMA. E.M.A. European Medicines Agency guidance for companies requesting scientific advice and protocol assistance. EMA Scientific Advice 21 May 2010. EMEA-H-4260-01-Rev. 2010. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guideline/2009/10/WC500004089.pdf [Accessed 26 November 2012]
   Google Scholar