We use cookies to improve your experience. By continuing to browse this site, you accept our cookie policy.×

Advancing cancer drug discovery towards more agile development of targeted combination therapies

    ,
    Asier Unciti-Broceta

    Edinburgh Cancer Research UK Centre, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, UK, EH4 2XR

    &
    David A Cameron

    Edinburgh Cancer Research UK Centre, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, UK, EH4 2XR

    Published Online:https://doi.org/10.4155/fmc.11.169

    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

    • Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov.3(8),711–715 (2004).Crossref, Medline, CASGoogle Scholar
    • Butcher EC. Can cell systems biology rescue drug discovery? Nat. Rev. Drug Discov.4(6),461–467 (2005).Crossref, Medline, CASGoogle Scholar
    • Sams-Dodd F. Target-based drug discovery: is something wrong? Drug Discov. Today10(2),139–147 (2005).Crossref, Medline, CASGoogle Scholar
    • Dancey JE, Chen HX. Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat. Rev. Drug Discov.5(8),649–659 (2006).Crossref, Medline, CASGoogle Scholar
    • 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, CASGoogle Scholar
    • 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, MedlineGoogle Scholar
    • Hughes B. Novel agents combined get own guidance. Nat. Biotech.29,174 (2011).Crossref, CASGoogle Scholar
    • Ainscow E, Carragher N. Addressing kinetic applications in high content screening. Eur. Pharmaceut. Rev.5,44–50 (2008).Google Scholar
    • 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, CASGoogle 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, CASGoogle 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, MedlineGoogle 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, MedlineGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 24  Weber L. Applications of genetic algorithms in molecular diversity. Curr. Opin. Chem. Biol.2(3),381–385 (1998).Crossref, Medline, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 30  Frank R, Hargreaves R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov.2(7),566–580 (2003).Crossref, Medline, CASGoogle Scholar
    • 31  Stoughton RB, Friend SH. How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov.4(4),345–350 (2005).Crossref, Medline, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 39  Maggi A, Ciana P. Reporter mice and drug discovery and development. Nat. Rev. Drug Discov.4(3),249–255 (2005).Crossref, Medline, CASGoogle 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, MedlineGoogle 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, CASGoogle Scholar
    • 42  Bullen A. Microscopic imaging techniques for drug discovery. Nat. Rev. Drug Discov.7(1),54–67 (2008).Crossref, Medline, CASGoogle 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, MedlineGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 46  Baena JR, Lendl B. Raman spectroscopy in chemical bioanalysis. Curr. Opin. Chem. Biol.8(5),534–539 (2004).Crossref, Medline, CASGoogle Scholar
    • 47  Downes A, Elfick A. Raman spectroscopy and related techniques in biomedicine. Sensors Basel Sensors10(3),1871–1889 (2010).Crossref, MedlineGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, MedlineGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 59  Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer10(2),130–137 (2010).Crossref, Medline, CASGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle 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, MedlineGoogle 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, CASGoogle 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, CASGoogle 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, CASGoogle Scholar
    • 67  Hunt RA, Otto S. Dynamic combinatorial libraries. new opportunities in systems chemistry. Chem. Commun. (Camb.)47(3),847–858 (2011).Crossref, Medline, CASGoogle Scholar
    • 68  Ramstrom O, Lehn JM. Drug discovery by dynamic combinatorial libraries. Nat. Rev. Drug Discov.1(1),26–36 (2002).Crossref, Medline, CASGoogle 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, MedlineGoogle 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).MedlineGoogle 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, CASGoogle 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, CASGoogle 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