Advancing cancer drug discovery towards more agile development of targeted combination therapies
Abstract
Current drug-discovery strategies are typically ‘target-centric’ and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized ‘on-target’ potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.
References
- 1 Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov.3(8),711–715 (2004).Crossref, Medline, CAS, Google Scholar
- 2 Butcher EC. Can cell systems biology rescue drug discovery? Nat. Rev. Drug Discov.4(6),461–467 (2005).Crossref, Medline, CAS, Google Scholar
- 3 Sams-Dodd F. Target-based drug discovery: is something wrong? Drug Discov. Today10(2),139–147 (2005).Crossref, Medline, CAS, Google Scholar
- 4 Dancey JE, Chen HX. Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat. Rev. Drug Discov.5(8),649–659 (2006).Crossref, Medline, CAS, Google Scholar
- 5 Stommel JM, Kimmelman AC, Ying H et al. Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies. Science318(5848),287–290 (2007).Crossref, Medline, CAS, Google Scholar
- 6 Yonesaka K, Zejnullahu K, Okamoto I et al. Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab. Sci. Transl. Med.3(99),99ra86 (2011).Crossref, Medline, Google Scholar
- 7 Hughes B. Novel agents combined get own guidance. Nat. Biotech.29,174 (2011).Crossref, CAS, Google Scholar
- 8 Ainscow E, Carragher N. Addressing kinetic applications in high content screening. Eur. Pharmaceut. Rev.5,44–50 (2008).Google Scholar
- 9 Carragher NO. Advancing high content analysis towards improving clinical efficacy. Eur. Pharmaceut. Rev.1,12–16 (2011).Google Scholar
- 10 Caie PD, Walls RE, Ingleston-Orme A et al. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol. Cancer Ther.9(6),1913–1926 (2010).Crossref, Medline, CAS, Google Scholar
- 11 Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ. Multidimensional drug profiling by automated microscopy. Science306(5699),1194–1198 (2004).Crossref, Medline, CAS, Google Scholar
- 12 Tanaka M, Bateman R, Rauh D et al. An unbiased cell morphology-based screen for new, biologically active small molecules. PLoS Biol.3(5),e128 (2005).Crossref, Medline, Google Scholar
- 13 Yarrow JC, Perlman ZE, Westwood NJ, Mitchison TJ. A high-throughput cell migration assay using scratch wound healing, a comparison of image-based readout methods. BMC Biotechnol.4,21 (2004).Crossref, Medline, Google Scholar
- 14 Alcock P, Bath C, Blackett C, Simpson PB. High content cell based primary screening for oncology targets – a perspective. Eur. Pharmaceut. Rev.3, (2010).Google Scholar
- 15 Bickle M. High-content screening: a new primary screening tool? IDrugs11(11),822–826 (2008).Medline, CAS, Google Scholar
- 16 Young DW, Bender A, Hoyt J et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol.4(1),59–68 (2008).Crossref, Medline, CAS, Google Scholar
- 17 Carragher NO. Profiling distinct mechanisms of tumour invasion for drug discovery: imaging adhesion, signaling and matrix turnover. Clin. Exp. Metastasis26(4),381–397 (2009).Crossref, Medline, CAS, Google Scholar
- 18 Isherwood B, Timpson P, McGhee EJ. Live cell in vitro and in vivo imaging applications. Accelerating drug discovery. Pharmaceutics3(2),141–170 (2011).Crossref, Medline, CAS, Google Scholar
- 19 Zimmermann GR, Lehar J, Keith CT. Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov. Today12(1–2),34–42 (2007).Crossref, Medline, CAS, Google Scholar
- 20 Chou TC, Talalay P. Generalized equations for the analysis of inhibitions of Michaelis–Menten and higher-order kinetic systems with two or more mutually exclusive and nonexclusive inhibitors. Eur. J. Biochem.115(1),207–216 (1981).Crossref, Medline, CAS, Google Scholar
- 21 Chou TC, Talalay P. Quantitative analysis of dose–effect relationships. The combined effects of multiple drugs or enzyme inhibitors. Adv. Enzyme Regul.22,27–55 (1984).Crossref, Medline, CAS, Google Scholar
- 22 Borgatti M, Altomare L, Abonnec M et al. Dielectrophoresis-based ‘lab-on-a-chip’ devices for programmable binding of microspheres to target cells. Int. J. Oncol.27(6),1559–1566 (2005).Medline, CAS, Google Scholar
- 23 Dittrich PS, Manz A. Lab-on-a-chip: microfluidics in drug discovery. Nat. Rev. Drug Discov.5(3),210–218 (2006).Crossref, Medline, CAS, Google Scholar
- 24 Weber L. Applications of genetic algorithms in molecular diversity. Curr. Opin. Chem. Biol.2(3),381–385 (1998).Crossref, Medline, CAS, Google Scholar
- 25 Zinner RG, Barrett BL, Popova E et al. Algorithmic guided screening of drug combinations of arbitrary size for activity against cancer cells. Mol. Cancer Ther.8(3),521–532 (2009).Crossref, Medline, CAS, Google Scholar
- 26 Weber L, Walbaum S, Broger C. A genetic algorithm optimizing biological activity of combinatorial compound libraries. Agnew. Chem. Int. Ed.107,2453–2454 (1995).Google Scholar
- 27 Singh J, Ator MA, Jaeger EP. Application of gentic algorithms to combinatorial synthesis: a computational approach to lead identification and lead optimization. J. Am. Chem. Soc.118,1669–1676 (1996).Crossref, CAS, Google Scholar
- 28 Azmi AS, Wang Z, Philip PA, Mohammad RM, Sarkar FH. Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations. Mol. Cancer Ther.9(12),3137–3144 (2010).Crossref, Medline, CAS, Google Scholar
- 29 Iadevaia S, Lu Y, Morales FC, Mills GB, Ram PT. Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res.70(17),6704–6714 (2010).Crossref, Medline, CAS, Google Scholar
- 30 Frank R, Hargreaves R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov.2(7),566–580 (2003).Crossref, Medline, CAS, Google Scholar
- 31 Stoughton RB, Friend SH. How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov.4(4),345–350 (2005).Crossref, Medline, CAS, Google Scholar
- 32 Kolch W, Pitt A. Functional proteomics to dissect tyrosine kinase signaling pathways in cancer. Nat. Rev. Cancer10(9),618–629 (2010).Crossref, Medline, CAS, Google Scholar
- 33 Voshol H, Ehrat M, Traenkle J, Bertrand E, Van Oostrum J. Antibody-based proteomics: analysis of signaling networks using reverse protein arrays. FEBS J.276(23),6871–6879 (2009).Crossref, Medline, CAS, Google Scholar
- 34 Weissenstein U, Schneider MJ, Pawlak M et al. Protein chip based miniaturized assay for the simultaneous quantitative monitoring of cancer biomarkers in tissue extracts. Proteomics6(5),1427–1436 (2006).Crossref, Medline, CAS, Google Scholar
- 35 Carey MS, Agarwal R, Gilks B et al. Functional proteomic analysis of advanced serous ovarian cancer using reverse phase protein array: TGF-beta pathway signaling indicates response to primary chemotherapy. Clin. Cancer Res.16(10),2852–2860 (2010).Crossref, Medline, CAS, Google Scholar
- 36 Tibes R, Qiu Y, Lu Y et al. Reverse phase protein array. validation of a novel proteomic technology and utility for analysis of primary leukemia specimens and hematopoietic stem cells. Mol. Cancer Ther.5(10),2512–2521 (2006).Crossref, Medline, CAS, Google Scholar
- 37 Politi K, Pao W. How genetically engineered mouse tumor models provide insights into human cancers. J. Clin. Oncol.29(16),2273–2281 (2011).Crossref, Medline, CAS, Google Scholar
- 38 Olive KP, Tuveson DA. The use of targeted mouse models for preclinical testing of novel cancer therapeutics. Clin. Cancer Res.12(18),5277–5287 (2006).Crossref, Medline, CAS, Google Scholar
- 39 Maggi A, Ciana P. Reporter mice and drug discovery and development. Nat. Rev. Drug Discov.4(3),249–255 (2005).Crossref, Medline, CAS, Google Scholar
- 40 Ntziachristos V, Bremer C, Weissleder R. Fluorescence imaging with near-infrared light. new technological advances that enable in vivo molecular imaging. Eur. Radiol.13(1),195–208 (2003).Crossref, Medline, Google Scholar
- 41 Weigert R, Sramkova M, Parente L, Amornphimoltham P, Masedunskas A. Intravital microscopy: a novel tool to study cell biology in living animals. Histochem. Cell Biol133(5),481–491 (2010).Crossref, Medline, CAS, Google Scholar
- 42 Bullen A. Microscopic imaging techniques for drug discovery. Nat. Rev. Drug Discov.7(1),54–67 (2008).Crossref, Medline, CAS, Google Scholar
- 43 Canel M, Serrels A, Anderson KI, Frame MC, Brunton VG. Use of photoactivation and photobleaching to monitor the dynamic regulation of E-cadherin at the plasma membrane. Cell Adh. Migr.4(4),491–501 (2010).Crossref, Medline, Google Scholar
- 44 Reyzer ML, Chaurand P, Angel PM, Caprioli RM. Direct molecular analysis of whole-body animal tissue sections by MALDI imaging mass spectrometry. Methods Mol. Biol.656,285–301 (2010).Crossref, Medline, CAS, Google Scholar
- 45 Reyzer ML, Hsieh Y, Ng K, Korfmacher WA, Caprioli RM. Direct analysis of drug candidates in tissue by matrix-assisted laser desorption/ionization mass spectrometry. J. Mass Spectrom.38(10),1081–1092 (2003).Crossref, Medline, CAS, Google Scholar
- 46 Baena JR, Lendl B. Raman spectroscopy in chemical bioanalysis. Curr. Opin. Chem. Biol.8(5),534–539 (2004).Crossref, Medline, CAS, Google Scholar
- 47 Downes A, Elfick A. Raman spectroscopy and related techniques in biomedicine. Sensors Basel Sensors10(3),1871–1889 (2010).Crossref, Medline, Google Scholar
- 48 Saar BG, Freudiger CW, Reichman J, Stanley CM, Holtom GR, Xie XS. Video-rate molecular imaging in vivo with stimulated Raman scattering. Science330(6009),1368–1370 (2010).Crossref, Medline, CAS, Google Scholar
- 49 Capdeville R, Buchdunger E, Zimmermann J, Matter A. Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug. Nat. Rev. Drug Discov.1(7),493–502 (2002).Crossref, Medline, CAS, Google Scholar
- 50 Chen LL, Trent JC, Wu EF et al. A missense mutation in KIT kinase domain 1 correlates with imatinib resistance in gastrointestinal stromal tumors. Cancer Res.64(17),5913–5919 (2004).Crossref, Medline, CAS, Google Scholar
- 51 Cools J, Deangelo DJ, Gotlib J et al. A tyrosine kinase created by fusion of the PDGFRA and FIP1L1 genes as a therapeutic target of imatinib in idiopathic hypereosinophilic syndrome. N. Engl. J. Med.348(13),1201–1214 (2003).Crossref, Medline, CAS, Google Scholar
- 52 Gorre ME, Mohammed M, Ellwood K et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science293(5531),876–880 (2001).Crossref, Medline, CAS, Google Scholar
- 53 Pao W, Miller VA, Politi KA et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med.2(3),e73 (2005).Crossref, Medline, Google Scholar
- 54 Tamborini E, Bonadiman L, Greco A et al. A new mutation in the KIT ATP pocket causes acquired resistance to imatinib in a gastrointestinal stromal tumor patient. Gastroenterology127(1),294–299 (2004).Crossref, Medline, CAS, Google Scholar
- 55 Xin H, Bernal A, Amato FA et al. High-throughput siRNA-based functional target validation. J. Biomol. Screen.9(4),286–293 (2004).Crossref, Medline, CAS, Google Scholar
- 56 Whitehurst AW, Bodemann BO, Cardenas J et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature446(7137),815–819 (2007).Crossref, Medline, CAS, Google Scholar
- 57 Sachse C, Weiss-Haljiti C, Holz C et al. Exploring the full power of combining high throughput RNAi with high content readouts. from target discovery screens to drug modifier studies. In: High Content Screening. Science, Techniques and Applications. Haney SA (Ed.). John Wiley & Sons. 392,145–168 (2008).Google Scholar
- 58 Erfle H, Neumann B, Liebel U et al. Reverse transfection on cell arrays for high content screening microscopy. Nat. Protoc.2(2),392–399 (2007).Crossref, Medline, CAS, Google Scholar
- 59 Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer10(2),130–137 (2010).Crossref, Medline, CAS, Google Scholar
- 60 Hopkins AL, Mason JS, Overington JP. Can we rationally design promiscuous drugs? Curr. Opin. Struct. Biol.16(1),127–136 (2006).Crossref, Medline, CAS, Google Scholar
- 61 Karaman MW, Herrgard S, Treiber DK et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol.26(1),127–132 (2008).Crossref, Medline, CAS, Google Scholar
- 62 Vieth M, Sutherland JJ, Robertson DH, Campbell RM. Kinomics: characterizing the therapeutically validated kinase space. Drug Discov. Today10(12),839–846 (2005).Crossref, Medline, CAS, Google Scholar
- 63 Durrant JD, Amaro RE, Xie L et al. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput. Biol.6(1),e1000648 (2010).Crossref, Medline, Google Scholar
- 64 Apsel B, Blair JA, Gonzalez B et al. Targeted polypharmacology. discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nat. Chem. Biol.4(11),691–699 (2008).Crossref, Medline, CAS, Google Scholar
- 65 Learn CA, Hartzell TL, Wikstrand CJ et al. Resistance to tyrosine kinase inhibition by mutant epidermal growth factor receptor variant III contributes to the neoplastic phenotype of glioblastoma multiforme. Clin Cancer Res10(9),3216–3224 (2004).Crossref, Medline, CAS, Google Scholar
- 66 Bhat VT, Caniard AM, Luksch T, Brenk R, Campopiano DJ, Greaney MF. Nucleophilic catalysis of acylhydrazone equilibration for protein-directed dynamic covalent chemistry. Nat. Chem.2(6),490–497 (2010).Crossref, Medline, CAS, Google Scholar
- 67 Hunt RA, Otto S. Dynamic combinatorial libraries. new opportunities in systems chemistry. Chem. Commun. (Camb.)47(3),847–858 (2011).Crossref, Medline, CAS, Google Scholar
- 68 Ramstrom O, Lehn JM. Drug discovery by dynamic combinatorial libraries. Nat. Rev. Drug Discov.1(1),26–36 (2002).Crossref, Medline, CAS, Google Scholar
- 69 Wu Y, Amonkar MM, Sherrill BH et al. Impact of lapatinib plus trastuzumab versus single-agent lapatinib on quality of life of patients with trastuzumab-refractory HER2+ metastatic breast cancer. Ann. Oncol. (2011).Google Scholar
- 70 Croom KF, Dhillon S. Bevacizumab. A review of its use in combination with paclitaxel or capecitabine as first-line therapy for HER2-negative metastatic breast cancer. Drugs71(16),2213–2229 (2011).Crossref, Medline, Google Scholar
- 71 Lai TL, Lavori PW, Shih MC. Adaptive trial designs. Annu. Rev. Pharmacol. Toxicol. doi:10.1146/annurev-pharmtox- 010611-134504 (2011) (Epub ahead of print).Medline, Google Scholar
- 72 Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther.86(1),97–100 (2009).Crossref, Medline, CAS, Google Scholar
- 73 Stead M, Cameron D, Lester N et al. Strengthening clinical cancer research in the United Kingdom. Br. J. Cancer104(10),1529–1534 (2011).Crossref, Medline, CAS, Google Scholar
- 101 US Food and Drug Administration. Draft guidance for industry. Codevelopment of two or more unmarketed investigational drugs for use in combination (2010). www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM236669.pdfGoogle Scholar
- 102 Stampede Trial. www.stampedetrial.org/PDF/MRCCTU STAMPEDE PROTOCOLV7.1.pdfGoogle Scholar
- 103 US Food and Drug Administration. Draft guidance for industry. Adaptive design clinical trials for drugs and biologics (2010). www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdfGoogle Scholar

