The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell- and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates.
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Waldmeier, P., Bozyczko-Coyne, D., Williams, M. & Vaught, J. L. Recent clinical failures in Parkinson's disease with apoptosis inhibitors underline the need for a paradigm shift in drug discovery for neurodegenerative diseases. Biochem. Pharmacol. 72, 1197–1206 (2006).
Bolognesi, M. L., Matera, R., Minarini, A., Rosini, M. & Melchiorre, C. Alzheimer's disease: new approaches to drug discovery. Curr. Opin. Chem. Biol. 13, 303–308 (2009).
Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).
Mangialasche, F., Solomon, A., Winblad, B., Mecocci, P. & Kivipelto, M. Alzheimer's disease: clinical trials and drug development. Lancet Neurol. 9, 702–716 (2010).
Hoelder, S., Clarke, P. A. & Workman, P. Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol. Oncol. 6, 155–176 (2012).
Morens, D. M., Folkers, G. K. & Fauci, A. S. The challenge of emerging and re-emerging infectious diseases. Nature 430, 242–249 (2004).
Marston, H. D., Folkers, G. K., Morens, D. M. & Fauci, A. S. Emerging viral diseases: confronting threats with new technologies. Sci. Transl Med. 6, 253ps210 (2014).
Arrowsmith, J. Trial watch: Phase II failures: 2008–2010. Nat. Rev. Drug Discov. 10, 328–329 (2011).
Laverty, H. et al. How can we improve our understanding of cardiovascular safety liabilities to develop safer medicines? Br. J. Pharmacol. 163, 675–693 (2011).
Bass, N. M. in Current Diagnosis & Treatment in Gastroenterology (eds Friedman, S. E., Grendell, J. H. & McQuaid, K. R.) 664–679 (Lang Medical Books/McGraw-Hill, 2003).
Masters, J. R. & Stacey, G. N. Changing medium and passaging cell lines. Nat. Protoc. 2, 2276–2284 (2007).
Nestor, C. E. et al. Rapid reprogramming of epigenetic and transcriptional profiles in mammalian culture systems. Genome Biol. 16, 11 (2015).
Morris, C. C. Maintenace and loss in tissue culture of specific cell characteristics. Adv. Appl. Microbiol. 4, 117–212 (1962).
Carreau, A., Hafny-Rahbi, B. E., Matejuk, A., Grillon, C. & Kieda, C. Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia. J. Cell. Mol. Med. 15, 1239–1253 (2011).
Newby, D., Marks, L. & Lyall, F. Dissolved oxygen concentration in culture medium: assumptions and pitfalls. Placenta 26, 353–357 (2005).
Sullivan, M., Galea, P. & Latif, S. What is the appropriate oxygen tension for in vitro culture? Mol. Hum. Reprod. 12, 653 (2006).
Halliwell, B. Cell culture, oxidative stress, and antioxidants: avoiding pitfalls. Biomed. J. 37, 99–105 (2014).
Tiede, L. M., Cook, E. A., Morsey, B. & Fox, H. S. Oxygen matters: tissue culture oxygen levels affect mitochondrial function and structure as well as responses to HIV viroproteins. Cell Death Dis. 2, e246 (2011).
Redshaw, Z. & Loughna, P. T. Oxygen concentration modulates the differentiation of muscle stem cells toward myogenic and adipogenic fates. Differentiation 84, 193–202 (2012).
Gebhardt, R. & Matz-Soja, M. Liver zonation: novel aspects of its regulation and its impact on homeostasis. World J. Gastroenterol. 20, 8491–8504 (2014).
Bhatia, S. N. & Ingber, D. E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014).
Wells, R. G. The role of matrix stiffness in regulating cell behavior. Hepatology 47, 1394–1400 (2008).
Discher, D. E., Janmey, P. & Wang, Y.-l. Tissue cells feel and respond to the stiffness of their substrate. Science 310, 1139–1143 (2005).
Levental, I., Georges, P. C. & Janmey, P. A. Soft biological materials and their impact on cell function. Soft Matter 3, 299–306 (2007).
Engler, A. J. et al. Myotubes differentiate optimally on substrates with tissue-like stiffness: pathological implications for soft or stiff microenvironments. J. Cell Biol. 166, 877–887 (2004).
Semler, E. J., Ranucci, C. S. & Moghe, P. V. Mechanochemical manipulation of hepatocyte aggregation can selectively induce or repress liver-specific function. Biotechnol. Bioeng. 69, 359–369 (2000).
Huh, D. et al. Reconstituting organ-level lung functions on a chip. Science 328, 1662–1668 (2010).
Wells, R. G. Cellular sources of extracellular matrix in hepatic fibrosis. Clin. Liver Dis. 12, 759–768 (2008).
Teranishi, Y. et al. Involvement of hepatic stellate cell cytoglobin in acute hepatocyte damage through the regulation of CYP2E1-mediated xenobiotic metabolism. Lab Invest. 95, 515–524 (2015).
Roberts, R. A. et al. Role of the Kupffer cell in mediating hepatic toxicity and carcinogenesis. Toxicol. Sci. 96, 2–15 (2007).
Lobsiger, C. S. & Cleveland, D. W. Glial cells as intrinsic components of non-cell autonomous neurodegenerative disease. Nat. Neurosci. 10, 1355–1360 (2007).
McCormack, E. et al. Bi-specific TCR-anti CD3 redirected T-cell targeting of NY-ESO-1- and LAGE-1-positive tumors. Cancer Immunol. Immunother. 62, 773–785 (2013).
Pieters, R. et al. In vitro drug sensitivity of cells from children with leukemia using the MTT assay with improved culture conditions. Blood 76, 2327–2336 (1990).
Hollingsworth, S. J. & Biankin, A. V. The challenges of precision oncology drug development and implementation. Publ. Health Genom. 18, 338–348 (2015).
Biankin, A. V., Piantadosi, S. & Hollingsworth, S. J. Patient-centric trials for therapeutic development in precision oncology. Nature 526, 361–370 (2015).
Iwadate, Y., Fujimoto, S., Namba, H. & Yamaura, A. Promising survival for patients with glioblastoma multiforme treated with individualised chemotherapy based on in vitro drug sensitivity testing. Br. J. Cancer 89, 1896–1900 (2003).
Bosanquet, A. G. & Bell, P. B. Ex vivo therapeutic index by drug sensitivity assay using fresh human normal and tumor cells. J. Exp. Ther. Oncol. 4, 145–154 (2004).
Villman, K., Blomqvist, C., Larsson, R. & Nygren, P. Predictive value of in vitro assessment of cytotoxic drug activity in advanced breast cancer. Anticancer Drugs 16, 609–615 (2005).
Eriksson, A. et al. Drug screen in patient cells suggests quinacrine to be repositioned for treatment of acute myeloid leukemia. Blood Cancer J. 5, e307 (2015).
Pemovska, T. et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).
Yamada, S. et al. Distinctive multidrug sensitivity and outcome of acute erythroblastic and megakaryoblastic leukemia in children with Down syndrome. Int. J. Hematol. 74, 428–436 (2001).
Aljitawi, O. S. et al. A novel three-dimensional stromal-based model for in vitro chemotherapy sensitivity testing of leukemia cells. Leuk. Lymphoma 55, 378–391 (2014).
Bakker, E., Qattan, M., Mutti, L., Demonacos, C. & Krstic-Demonacos, M. The role of microenvironment and immunity in drug response in leukemia. Biochim. Biophys. Acta 1863, 414–426 (2016).
Saeed, K. et al. Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur. Urol. http://dx.doi.org/10.1016/j.eururo.2016.04.019 (2016).
Suprynowicz, F. A. et al. Conditionally reprogrammed cells represent a stem-like state of adult epithelial cells. Proc. Natl Acad. Sci. USA 109, 20035–20040 (2012).
Yuan, H. et al. Use of reprogrammed cells to identify therapy for respiratory papillomatosis. N. Engl. J. Med. 367, 1220–1227 (2012).
van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).
Pemovska, T. et al. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature 519, 102–105 (2015).
Rees, M. G. et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12, 109–116 (2016).
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Perlman, Z. E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).
Caie, P. D. et al. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol. Cancer Ther. 9, 1913–1926 (2010).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Avior, Y., Sagi, I. & Benvenisty, N. Pluripotent stem cells in disease modelling and drug discovery. Nat. Rev. Mol. Cell Biol. 17, 170–182 (2016).
Singh, V. K., Kumar, N., Kalsan, M., Saini, A. & Chandra, R. Mechanism of induction: induced pluripotent stem cells (iPSCs). J. Stem Cells 10, 43–62 (2015).
Bar-Nur, O., Russ, H. A., Efrat, S. & Benvenisty, N. Epigenetic memory and preferential lineage-specific differentiation in induced pluripotent stem cells derived from human pancreatic islet beta cells. Cell Stem Cell 9, 17–23 (2011).
Kim, K. et al. Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat. Biotechnol. 29, 1117–1119 (2011).
Sakurai, T. et al. A non-inheritable maternal Cas9-based multiple-gene editing system in mice. Sci. Rep. 6, 20011 (2016).
Martella, A., Pollard, S. M., Dai, J. & Cai, Y. Mammalian synthetic biology: time for big MACs. ACS Synth. Biol. http://dx.doi.org/10.1021/acssynbio.6b00074 (2016).
Annaluru, N., Ramalingam, S. & Chandrasegaran, S. Rewriting the blueprint of life by synthetic genomics and genome engineering. Genome Biol. 16, 125 (2015).
Miller, J. D. et al. Human iPSC-based modeling of late-onset disease via progerin-induced aging. Cell Stem Cell 13, 691–705 (2013).
Liang, P. et al. Drug screening using a library of human induced pluripotent stem cell-derived cardiomyocytes reveals disease-specific patterns of cardiotoxicity. Circulation 127, 1677–1691 (2013).
Matsa, E. et al. Drug evaluation in cardiomyocytes derived from human induced pluripotent stem cells carrying a long QT syndrome type 2 mutation. Eur. Heart J. 32, 952–962 (2011).
Navarrete, E. G. et al. Screening drug-induced arrhythmia [corrected] using human induced pluripotent stem cell-derived cardiomyocytes and low-impedance microelectrode arrays. Circulation 128, S3–S13 (2013).
Mioulane, M., Foldes, G., Ali, N. N., Schneider, M. D. & Harding, S. E. Development of high content imaging methods for cell death detection in human pluripotent stem cell-derived cardiomyocytes. J. Cardiovasc. Transl. Res. 5, 593–604 (2012).
Xu, X. et al. Prevention of β-amyloid induced toxicity in human iPS cell-derived neurons by inhibition of cyclin-dependent kinases and associated cell cycle events. Stem Cell Res. 10, 213–227 (2013).
Usher, L. C. et al. A chemical screen identifies novel compounds that overcome glial-mediated inhibition of neuronal regeneration. J. Neurosci. 30, 4693–4706 (2010).
Dimos, J. T. et al. Induced pluripotent stem cells generated from patients with ALS can be differentiated into motor neurons. Science 321, 1218–1221 (2008).
Spence, J. R. et al. Directed differentiation of human pluripotent stem cells into intestinal tissue in vitro. Nature 470, 105–109 (2011).
Nalayanda, D. D. et al. An open-access microfluidic model for lung-specific functional studies at an air-liquid interface. Biomed. Microdevices 11, 1081–1089 (2009).
Ebert, A. D. et al. Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature 457, 277–280 (2009).
Marchetto, M. C. et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 143, 527–539 (2010).
Koch, P. et al. Presenilin-1 L166P mutant human pluripotent stem cell-derived neurons exhibit partial loss of gamma-secretase activity in endogenous amyloid-beta generation. Am. J. Pathol. 180, 2404–2416 (2012).
Burkhardt, M. F. et al. A cellular model for sporadic ALS using patient-derived induced pluripotent stem cells. Mol. Cell Neurosci. 56, 355–364 (2013).
Chung, C. Y. et al. Identification and rescue of alpha-synuclein toxicity in Parkinson patient-derived neurons. Science 342, 983–987 (2013).
Peng, J., Liu, Q., Rao, M. S. & Zeng, X. Using human pluripotent stem cell-derived dopaminergic neurons to evaluate candidate Parkinson's disease therapeutic agents in MPP+ and rotenone models. J. Biomol. Screen 18, 522–533 (2013).
Chiu, P. J. et al. Validation of a [3H]astemizole binding assay in HEK293 cells expressing HERG K+ channels. J. Pharmacol. Sci. 95, 311–319 (2004).
Huang, X. P., Mangano, T., Hufeisen, S., Setola, V. & Roth, B. L. Identification of human Ether-a-go-go related gene modulators by three screening platforms in an academic drug-discovery setting. Assay Drug Dev. Technol. 8, 727–742 (2010).
Gintant, G., Sager, P. T. & Stockbridge, N. Evolution of strategies to improve preclinical cardiac safety testing. Nat. Rev. Drug Discov. 15, 457–471 (2016).
Ivashchenko, C. Y. et al. Human-induced pluripotent stem cell-derived cardiomyocytes exhibit temporal changes in phenotype. Am. J. Physiol. Heart Circ. Physiol. 305, H913–H922 (2013).
Ma, Z. et al. Self-organizing human cardiac microchambers mediated by geometric confinement. Nat. Commun. 6, 7413 (2015).
Schaaf, S. et al. Human engineered heart tissue as a versatile tool in basic research and preclinical toxicology. PLoS ONE 6, e26397 (2011).
Meyer, T., Leisgen, C., Gonser, B. & Gunther, E. QT-screen: high-throughput cardiac safety pharmacology by extracellular electrophysiology on primary cardiac myocytes. Assay Drug Dev. Technol. 2, 507–514 (2004).
Hansen, A. et al. Development of a drug screening platform based on engineered heart tissue. Circ. Res. 107, 35–44 (2010).
Bridgland-Taylor, M. H. et al. Optimisation and validation of a medium-throughput electrophysiology-based hERG assay using IonWorks HT. J. Pharmacol. Toxicol. Methods 54, 189–199 (2006).
Farre, C. et al. Port-a-patch and patchliner: high fidelity electrophysiology for secondary screening and safety pharmacology. Comb. Chem. High Throughput Screen 12, 24–37 (2009).
Sirenko, O. et al. Multiparameter in vitro assessment of compound effects on cardiomyocyte physiology using iPSC cells. J. Biomol. Screen 18, 39–53 (2013).
Lu, H. R. et al. High throughput measurement of Ca++ dynamics in human stem cell-derived cardiomyocytes by kinetic image cytometery: a cardiac risk assessment characterization using a large panel of cardioactive and inactive compounds. Toxicol. Sci. 148, 503–516 (2015).
Cerignoli, F. et al. High throughput measurement of Ca2+ dynamics for drug risk assessment in human stem cell-derived cardiomyocytes by kinetic image cytometry. J. Pharmacol. Toxicol. Methods 66, 246–256 (2012).
Pointon, A., Abi-Gerges, N., Cross, M. J. & Sidaway, J. E. Phenotypic profiling of structural cardiotoxins in vitro reveals dependency on multiple mechanisms of toxicity. Toxicol. Sci. 132, 317–326 (2013).
Peters, M. F., Lamore, S. D., Guo, L., Scott, C. W. & Kolaja, K. L. Human stem cell-derived cardiomyocytes in cellular impedance assays: bringing cardiotoxicity screening to the front line. Cardiovasc. Toxicol. 15, 127–139 (2015).
Rappaz, B. et al. Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy. Opt. Express 23, 13333–13347 (2015).
Grosberg, A. et al. Muscle on a chip: in vitro contractility assays for smooth and striated muscle. J. Pharmacol. Toxicol. Methods 65, 126–135 (2012).
Banerjee, I. et al. Cyclic stretch of embryonic cardiomyocytes increases proliferation, growth, and expression while repressing Tgf-β signaling. J. Mol. Cell Cardiol. 79, 133–144 (2015).
Macias-Vidal, J. et al. The proteasome inhibitor bortezomib reduced cholesterol accumulation in fibroblasts from Niemann-Pick type C patients carrying missense mutations. FEBS J. 281, 4450–4466 (2014).
Millman, J. R. et al. Generation of stem cell-derived β-cells from patients with type 1 diabetes. Nat. Commun. 7, 11463 (2016).
Hua, H. et al. iPSC-derived β cells model diabetes due to glucokinase deficiency. J. Clin. Invest. 123, 3146–3153 (2013).
Smith, A. S., Davis, J., Lee, G., Mack, D. L. & Kim, D. H. Muscular dystrophy in a dish: engineered human skeletal muscle mimetics for disease modeling and drug discovery. Drug Discov. Today http://dx.doi.org/10.1016/j.drudis.2016.04.013 (2016).
Chen, I. Y., Matsa, E. & Wu, J. C. Induced pluripotent stem cells: at the heart of cardiovascular precision medicine. Nat. Rev. Cardiol. 13, 333–349 (2016).
Ebert, A. D. et al. Characterization of the molecular mechanisms underlying increased ischemic damage in the aldehyde dehydrogenase 2 genetic polymorphism using a human induced pluripotent stem cell model system. Sci. Transl Med. 6, 255ra130 (2014).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Sander, J. D. & Joung, J. K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014).
Doudna, J. A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).
Chen, B. & Huang, B. Imaging genomic elements in living cells using CRISPR/Cas9. Methods Enzymol. 546, 337–354 (2014).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).
Agrotis, A. & Ketteler, R. A new age in functional genomics using CRISPR/Cas9 in arrayed library screening. Front. Genet. 6, 300 (2015).
Musunuru, K. Genome editing of human pluripotent stem cells to generate human cellular disease models. Dis. Model. Mech. 6, 896–904 (2013).
Xue, H., Wu, J., Li, S., Rao, M. S. & Liu, Y. Genetic modification in human pluripotent stem cells by homologous recombination and CRISPR/Cas9 system. Methods Mol. Biol. 1307, 173–190 (2016).
Moore, J. D. The impact of CRISPR-Cas9 on target identification and validation. Drug Discov. Today 20, 450–457 (2015).
Xue, W. et al. CRISPR-mediated direct mutation of cancer genes in the mouse liver. Nature 514, 380–384 (2014).
Anastasov, N. et al. A 3D-microtissue-based phenotypic screening of radiation resistant tumor cells with synchronized chemotherapeutic treatment. BMC Cancer 15, 466 (2015).
Chau, D. Y., Johnson, C., MacNeil, S., Haycock, J. W. & Ghaemmaghami, A. M. The development of a 3D immunocompetent model of human skin. Biofabrication 5, 035011 (2013).
Wenzel, C., Otto, S., Prechtl, S., Parczyk, K. & Steigemann, P. A novel 3D high-content assay identifies compounds that prevent fibroblast invasion into tissue surrogates. Exp. Cell Res. 339, 35–43 (2015).
Wenzel, C. et al. 3D high-content screening for the identification of compounds that target cells in dormant tumor spheroid regions. Exp. Cell Res. 323, 131–143 (2014).
Krausz, E. et al. Translation of a tumor microenvironment mimicking 3D tumor growth co-culture assay platform to high-content screening. J. Biomol. Screen 18, 54–66 (2013).
Vukicevic, S. et al. Identification of multiple active growth factors in basement membrane Matrigel suggests caution in interpretation of cellular activity related to extracellular matrix components. Exp. Cell Res. 202, 1–8 (1992).
Sieh, S. et al. Phenotypic characterization of prostate cancer LNCaP cells cultured within a bioengineered microenvironment. PLoS ONE 7, e40217 (2012).
Phelps, E. A., Landazuri, N., Thule, P. M., Taylor, W. R. & Garcia, A. J. Bioartificial matrices for therapeutic vascularization. Proc. Natl Acad. Sci. USA 107, 3323–3328 (2010).
Xu, T. et al. Hybrid printing of mechanically and biologically improved constructs for cartilage tissue engineering applications. Biofabrication 5, 015001 (2013).
Mironi-Harpaz, I., Berdichevski, A. & Seliktar, D. Fabrication of PEGylated fibrinogen: a versatile injectable hydrogel biomaterial. Methods Mol. Biol. 1181, 61–68 (2014).
Harrington, H. et al. Immunocompetent 3D model of human upper airway for disease modeling and in vitro drug evaluation. Mol. Pharm. 11, 2082–2091 (2014).
Altekar, M. et al. Assay optimization: a statistical design of experiments approach. Clin. Lab Med. 27, 139–154 (2007).
Havel, J., Link, H., Hofinger, M., Franco-Lara, E. & Weuster-Botz, D. Comparison of genetic algorithms for experimental multi-objective optimization on the example of medium design for cyanobacteria. Biotechnol. J. 1, 549–555 (2006).
Shaw, R., Fitzek, M., Mouchet, E., Walker, G. & Jarvis, P. Overcoming obstacles in the implementation of factorial design for assay optimization. Assay Drug Dev. Technol. 13, 88–93 (2015).
Pampaloni, F. et al. Tissue-culture light sheet fluorescence microscopy (TC-LSFM) allows long-term imaging of three-dimensional cell cultures under controlled conditions. Integr. Biol. (Camb.) 6, 988–998 (2014).
Olive, K. P. et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 324, 1457–1461 (2009).
Smyth, M. J., Pietersz, G. A. & McKenzie, I. F. Use of vasoactive agents to increase tumor perfusion and the antitumor efficacy of drug-monoclonal antibody conjugates. J. Natl Cancer Inst. 79, 1367–1373 (1987).
Froeling, F. E., Marshall, J. F. & Kocher, H. M. Pancreatic cancer organotypic cultures. J. Biotechnol. 148, 16–23 (2010).
Chee, C. E. et al. Phase II study of dasatinib (BMS-354825) in patients with metastatic adenocarcinoma of the pancreas. Oncologist 18, 1091–1092 (2013).
Nobis, M. et al. Intravital FLIM-FRET imaging reveals dasatinib-induced spatial control of Src in pancreatic cancer. Cancer Res. 73, 4674–4686 (2013).
Sung, J. H., Kam, C. & Shuler, M. L. A microfluidic device for a pharmacokinetic-pharmacodynamic (PK-PD) model on a chip. Lab. Chip 10, 446–455 (2010).
Maschmeyer, I. et al. A four-organ-chip for interconnected long-term co-culture of human intestine, liver, skin and kidney equivalents. Lab. Chip 15, 2688–2699 (2015).
Mantella, L. E., Quan, A. & Verma, S. Variability in vascular smooth muscle cell stretch-induced responses in 2D culture. Vasc. Cell 7, 7 (2015).
Zhang, X., Huk, D. J., Wang, Q., Lincoln, J. & Zhao, Y. A microfluidic shear device that accommodates parallel high and low stress zones within the same culturing chamber. Biomicrofluidics 8, 054106 (2014).
Raasch, M. et al. Microfluidically supported biochip design for culture of endothelial cell layers with improved perfusion conditions. Biofabrication 7, 015013 (2015).
Kobel, S., Valero, A., Latt, J., Renaud, P. & Lutolf, M. Optimization of microfluidic single cell trapping for long-term on-chip culture. Lab. Chip 10, 857–863 (2010).
Lecault, V. et al. High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays. Nat. Methods 8, 581–586 (2011).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Johansson, H. J. et al. Retinoic acid receptor alpha is associated with tamoxifen resistance in breast cancer. Nat. Commun. 4, 2175 (2013).
Salehi-Reyhani, A. et al. A first step towards practical single cell proteomics: a microfluidic antibody capture chip with TIRF detection. Lab. Chip 11, 1256–1261 (2011).
Toriello, N. M. et al. Integrated microfluidic bioprocessor for single-cell gene expression analysis. Proc. Natl Acad. Sci. USA 105, 20173–20178 (2008).
Brouzes, E. et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl Acad. Sci. USA 106, 14195–14200 (2009).
Bickle, M. The beautiful cell: high-content screening in drug discovery. Anal. Bioanal Chem. 398, 219–226 (2010).
Isherwood, B. et al. Live cell in vitro and in vivo imaging applications: accelerating drug discovery. Pharmaceutics 3, 141–170 (2011).
Kummel, A. et al. Integration of multiple readouts into the z' factor for assay quality assessment. J. Biomol. Screen 15, 95–101 (2010).
Kuhn, J. et al. Label-free cytotoxicity screening assay by digital holographic microscopy. Assay Drug Dev. Technol. 11, 101–107 (2013).
Rappaz, B., Breton, B., Shaffer, E. & Turcatti, G. Digital holographic microscopy: a quantitative label-free microscopy technique for phenotypic screening. Comb. Chem. High Throughput Screen 17, 80–88 (2014).
Rappaz, B., Kuttler, F., Breton, B. & Turcatti, G. in Label-Free Bisensor Methods in Drug Discovery (ed. Fang, Y.) 307–325 (Springer Science+Business Media, 2015).
Koos, K., Molnár, J., Kelemen, L., Tamás, G. & Horvath, P. DIC image reconstruction using an energy minimization framework to visualize optical path length distribution. Sci. Rep. 6, 30420 (2015).
Swoger, J., Pampaloni, F. & Stelzer, E. H. Imaging cellular spheroids with a single (selective) plane illumination microscope. Cold Spring Harb. Protoc. 2014, 106–113 (2014).
Pampaloni, F., Ansari, N. & Stelzer, E. H. High-resolution deep imaging of live cellular spheroids with light-sheet-based fluorescence microscopy. Cell Tissue Res. 352, 161–177 (2013).
Kankaanpaa, P. et al. BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nat. Methods 9, 683–689 (2012).
Di, Z. et al. Ultra high content image analysis and phenotype profiling of 3D cultured micro-tissues. PLoS ONE 9, e109688 (2014).
Sandercock, A. M. et al. Identification of anti-tumour biologics using primary tumour models, 3D phenotypic screening and image-based multi-parametric profiling. Mol. Cancer 14, 147 (2015).
Zhao, S. & Iyengar, R. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol. 52, 505–521 (2012).
Majumder, B. et al. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat. Commun. 6, 6169 (2015).
Mitsopoulos, C., Schierz, A. C., Workman, P. & Al-Lazikani, B. Distinctive behaviors of druggable proteins in cellular networks. PLoS Comput. Biol. 11, e1004597 (2015).
Bulusu, K. C., Tym, J. E., Coker, E. A., Schierz, A. C. & Al-Lazikani, B. canSAR: updated cancer research and drug discovery knowledgebase. Nucleic Acids Res. 42, D1040–D1047 (2014).
Hansen, J. & Iyengar, R. Computation as the mechanistic bridge between precision medicine and systems therapeutics. Clin. Pharmacol. Ther. 93, 117–128 (2013).
Pavlopoulos, G. A., Hooper, S. D., Sifrim, A., Schneider, R. & Aerts, J. Medusa: a tool for exploring and clustering biological networks. BMC Res. Notes 4, 384 (2011).
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
The Cancer Cell Line Encyclopedia Consortium & The Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015).
Hatzis, C. et al. Enhancing reproducibility in cancer drug screening: how do we move forward? Cancer Res. 74, 4016–4023 (2014).
Vincent, F. et al. Developing predictive assays: the phenotypic screening “rule of 3”. Sci. Transl Med. 7, 293ps215 (2015).
O'Brien, P. J. et al. High concordance of drug-induced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch. Toxicol. 80, 580–604 (2006).
Pilling, J., Garside, H. & Ainscow, E. Development of a quantitative 96-well method to image glycogen storage in primary rat hepatocytes. Mol. Cell Biochem. 341, 73–78 (2010).
Szkolnicka, D. et al. Accurate prediction of drug-induced liver injury using stem cell-derived populations. Stem Cells Transl. Med. 3, 141–148 (2014).
Timpson, P. et al. Organotypic collagen I assay: a malleable platform to assess cell behaviour in a 3-dimensional context. J. Vis. Exp. 56, e3089 (2011).
Kim, E. J. et al. Pilot clinical trial of hedgehog pathway inhibitor GDC-0449 (vismodegib) in combination with gemcitabine in patients with metastatic pancreatic adenocarcinoma. Clin. Cancer Res. 20, 5937–5945 (2014).
Morgan, M. R. et al. Psoriasin (S100A7) associates with integrin β6 subunit and is required for αvβ6-dependent carcinoma cell invasion. Oncogene 30, 1422–1435 (2011).
Moore, K. M. et al. Therapeutic targeting of integrin αvβ6 in breast cancer. J. Natl Cancer Inst. 106, dju169 (2014).
Scannell, J. W. & Bosley, J. When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS ONE 11, e0147215 (2016).
Edwards, A. M. et al. Preclinical target validation using patient-derived cells. Nat. Rev. Drug Discov. 14, 149–150 (2015).
Baragana, B. et al. A novel multiple-stage antimalarial agent that inhibits protein synthesis. Nature 522, 315–320 (2015).
Aulner, N. et al. High content analysis of primary macrophages hosting proliferating Leishmania amastigotes: application to anti-leishmanial drug discovery. PLoS Negl Trop. Dis. 7, e2154 (2013).
De Muylder, G. et al. A screen against Leishmania intracellular amastigotes: comparison to a promastigote screen and identification of a host cell-specific hit. PLoS Negl Trop. Dis. 5, e1253 (2011).
Siqueira-Neto, J. L. et al. An image-based high-content screening assay for compounds targeting intracellular Leishmania donovani amastigotes in human macrophages. PLoS Negl Trop. Dis. 6, e1671 (2012).
Fux, C. A., Shirtliff, M., Stoodley, P. & Costerton, J. W. Can laboratory reference strains mirror “real-world” pathogenesis? Trends Microbiol. 13, 58–63 (2005).
Calmette, A., Boquet, A. & Negre, L. Contribution à l'étude du bacille tuberculeux bilié. Ann. l'Institut Pasteur 9, 561–570 (in French) (1921).
de Kievit, T. R. & Iglewski, B. H. Bacterial quorum sensing in pathogenic relationships. Infect. Immun. 68, 4839–4849 (2000).
Wagner, V. E., Bushnell, D., Passador, L., Brooks, A. I. & Iglewski, B. H. Microarray analysis of Pseudomonas aeruginosa quorum-sensing regulons: effects of growth phase and environment. J. Bacteriol. 185, 2080–2095 (2003).
Ehrlich, G. D. et al. The distributed genome hypothesis as a rubric for understanding evolution in situ during chronic bacterial biofilm infectious processes. FEMS Immunol. Med. Microbiol. 59, 269–279 (2010).
Kim, J. J. et al. Host cell autophagy activated by antibiotics is required for their effective antimycobacterial drug action. Cell Host Microbe 11, 457–468 (2012).
Kim, H. J., Li, H., Collins, J. J. & Ingber, D. E. Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip. Proc. Natl Acad. Sci. USA 113, E7–E15 (2016).
Benam, K. H. et al. Small airway-on-a-chip enables analysis of human lung inflammation and drug responses in vitro. Nat. Methods 13, 151–157 (2016).
Guiguemde, W. A. et al. Chemical genetics of Plasmodium falciparum. Nature 465, 311–315 (2010).
Sundaramurthy, V. et al. Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe 13, 129–142 (2013).
Sun, T., Jackson, S., Haycock, J. W. & MacNeil, S. Culture of skin cells in 3D rather than 2D improves their ability to survive exposure to cytotoxic agents. J. Biotechnol. 122, 372–381 (2006).
Pickl, M. & Ries, C. H. Comparison of 3D and 2D tumor models reveals enhanced HER2 activation in 3D associated with an increased response to trastuzumab. Oncogene 28, 461–468 (2009).
McBeath, R., Pirone, D. M., Nelson, C. M., Bhadriraju, K. & Chen, C. S. Cell shape, cytoskeletal tension, and RhoA regulate stem cell lineage commitment. Dev. Cell 6, 483–495 (2004).
Choi, J., Lee, E. K., Choo, J., Yuh, J. & Hong, J. W. Micro 3D cell culture systems for cellular behavior studies: culture matrices, devices, substrates, and in-situ sensing methods. Biotechnol. J. 10, 1682–1688 (2015).
van Duinen, V., Trietsch, S. J., Joore, J., Vulto, P. & Hankemeier, T. Microfluidic 3D cell culture: from tools to tissue models. Curr. Opin. Biotechnol. 35, 118–126 (2015).
Huh, D., Hamilton, G. A. & Ingber, D. E. From 3D cell culture to organs-on-chips. Trends Cell Biol. 21, 745–754 (2011).
Pampaloni, F., Reynaud, E. G. & Stelzer, E. H. The third dimension bridges the gap between cell culture and live tissue. Nat. Rev. Mol. Cell Biol. 8, 839–845 (2007).
Eglen, R. M. & Randle, D. H. Drug discovery goes three-dimensional: goodbye to flat high-throughput screening? Assay Drug Dev. Technol. 13, 262–265 (2015).
Sittampalam, S. et al. Three-dimensional cell culture assays: are they more predictive of in vivo efficacy than 2D monolayer cell-based assays? Assay Drug Dev. Technol. 13, 254–261 (2015).
Knowlton, S., Onal, S., Yu, C. H., Zhao, J. J. & Tasoglu, S. Bioprinting for cancer research. Trends Biotechnol. 33, 504–513 (2015).
Murphy, S. V. & Atala, A. 3D bioprinting of tissues and organs. Nat. Biotechnol. 32, 773–785 (2014).
Shamir, E. R. & Ewald, A. J. Three-dimensional organotypic culture: experimental models of mammalian biology and disease. Nat. Rev. Mol. Cell Biol. 15, 647–664 (2014).
Fitzgerald, K. A. et al. Life in 3D is never flat: 3D models to optimise drug delivery. J. Control Release 215, 39–54 (2015).
Han, S. et al. Constructive remodeling of a synthetic endothelial extracellular matrix. Sci. Rep. 5, 18290 (2015).
Verhulsel, M. et al. A review of microfabrication and hydrogel engineering for micro-organs on chips. Biomaterials 35, 1816–1832 (2014).
Rimann, M. & Graf-Hausner, U. Synthetic 3D multicellular systems for drug development. Curr. Opin. Biotechnol. 23, 803–809 (2012).
Cushing, M. C. & Anseth, K. S. Materials science. Hydrogel cell cultures. Science 316, 1133–1134 (2007).
Worthington, P., Pochan, D. J. & Langhans, S. A. Peptide hydrogels — versatile matrices for 3D cell culture in cancer medicine. Front. Oncol. 5, 92 (2015).
Chen, N., Zhang, Z., Soontornworajit, B., Zhou, J. & Wang, Y. Cell adhesion on an artificial extracellular matrix using aptamer-functionalized PEG hydrogels. Biomaterials 33, 1353–1362 (2012).
Souza, G. R. et al. Three-dimensional tissue culture based on magnetic cell levitation. Nat. Nanotechnol. 5, 291–296 (2010).
Bumpers, H. L., Janagama, D. G., Manne, U., Basson, M. D. & Katkoori, V. Nanomagnetic levitation three-dimensional cultures of breast and colorectal cancers. J. Surg. Res. 194, 319–326 (2015).
Park, J., Koito, H., Li, J. & Han, A. Microfluidic compartmentalized co-culture platform for CNS axon myelination research. Biomed. Microdevices 11, 1145–1153 (2009).
Kane, B. J., Zinner, M. J., Yarmush, M. L. & Toner, M. Liver-specific functional studies in a microfluidic array of primary mammalian hepatocytes. Anal. Chem. 78, 4291–4298 (2006).
Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).
Moffat, J. G., Rudolph, J. & Bailey, D. Phenotypic screening in cancer drug discovery — past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).
Eder, J., Sedrani, R. & Wiesmann, C. The discovery of first-in-class drugs: origins and evolution. Nat. Rev. Drug Discov. 13, 577–587 (2014).
Hosseinkhani, H., Hosseinkhani, M., Hattori, S., Matsuoka, R. & Kawaguchi, N. Micro and nano-scale in vitro 3D culture system for cardiac stem cells. J. Biomed. Mater. Res. A 94, 1–8 (2010).
Puschmann, T. B. et al. HB-EGF affects astrocyte morphology, proliferation, differentiation, and the expression of intermediate filament proteins. J. Neurochem. 128, 878–889 (2014).
Seliktar, D. Designing cell-compatible hydrogels for biomedical applications. Science 336, 1124–1128 (2012).
Miyagawa, Y. et al. A microfabricated scaffold induces the spheroid formation of human bone marrow-derived mesenchymal progenitor cells and promotes efficient adipogenic differentiation. Tissue Eng. Part A 17, 513–521 (2011).
Kelm, J. M., Timmins, N. E., Brown, C. J., Fussenegger, M. & Nielsen, L. K. Method for generation of homogeneous multicellular tumor spheroids applicable to a wide variety of cell types. Biotechnol. Bioeng. 83, 173–180 (2003).
Carragher, N. O. Profiling distinct mechanisms of tumour invasion for drug discovery: imaging adhesion, signalling and matrix turnover. Clin. Exp. Metastasis 26, 381–397 (2009).
Kenny, H. A., Krausz, T., Yamada, S. D. & Lengyel, E. Use of a novel 3D culture model to elucidate the role of mesothelial cells, fibroblasts and extra-cellular matrices on adhesion and invasion of ovarian cancer cells to the omentum. Int. J. Cancer 121, 1463–1472 (2007).
Du, G., Fang, Q. & den Toonder, J. M. Microfluidics for cell-based high throughput screening platforms-A review. Anal. Chim. Acta 903, 36–50 (2016).
Esch, E. W., Bahinski, A. & Huh, D. Organs-on-chips at the frontiers of drug discovery. Nat. Rev. Drug Discov. 14, 248–260 (2015).
Baker, M. Tissue models: a living system on a chip. Nature 471, 661–665 (2011).
Wood, L., Kamm, R. & Asada, H. Stochastic modeling and identification of emergent behaviours of an endothelial cell population in angiogenic pattern formation. Int. J. Robot. Res. 30, 659–677 (2011).
The authors thank H. Ebner for assistance in the writing of this manuscript. E.D.N is supported by the program Paris Alliance of Cancer Research Institutes (PACRI), Investissements d'Avenir, launched by the French government with the reference ANR-11-PHUC-002. N.A. and S.L.S. are grateful for support from the 7th Framework Programme of the European Commission (LEISHDRUG project, 223414) and the French Government (L'Agence nationale de la recherche (ANR)) programmes: Investissements d'Avenir programme ('Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases'; grant ANR-10-LABX-62-IBEID); France BioImaging (FBI; grant ANR-10-INSB-04-01) and the Fondation Française pour la Recherche Médicale (FRM; Grands Équipements Program). N.O.C. acknowledges a fellowship award from Research Councils UK (RCUK). P.H. acknowledges support from the Hungarian National Brain Research Program (grant MTA-SE-NAP B-BIOMAG). V.P. and P.H. acknowledge support from the TEKES Finland Distinguished Professor Programme (FiDiPro) Fellow Grant (40294/13). M.C.M is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the The Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) foundation, and received funding from the Severo Ochoa Center of Excellence (MINECO award SEV-2015-0505), MINECO (grant BIO2014-62200-EXP) and the Innovative Training Networks (ITN) EU Horizon 2020 (EU-H2020) programme (grant 641639 BIOPOL). V.P. and P.Ö. received funding from the European Union's 7th Framework Programme (FP7/2007–2013; grant 258068); EU-FP7 Systems Microscopy Network of Excellence (NoE) project, the Sigrid Juselius Foundation, the Cancer Society of Finland, the Academy of Finland (Centre of Excellence in Translational Cancer Biology), the Magnus Ehrnrooth foundation and the TEKES FiDiPro Fellow Grant (40294/13), and TEKES New Generation Biobanking Grant (40294/11). Research in the Kallioniemi group at the Science for Life Laboratory received funding from K. Wallenberg and A. Wallenberg (grant 2015.0291), and the Karolinska Institutet. G.T. is supported by École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss National Science Foundation/ National Centres of Competence in Research (SNF/NCCR) in Chemical Biology. D.E. acknowledges research support from Cancer Research UK (CRUK) and the Higher Education Funding Council for England (HEFCE).
L.P. is a founder and shareholder of OcellO B.V., a contract research organization that offers drug screening services. The content of the article is not influenced in any way by his involvement.
P.H. is the founder and a shareholder of Single-cell technologies Inc., a biodata analysis company. The content of the article is not influenced in any way by his involvement.
A.M.D. is the inventor of the suspension technology referred to in the article as Happy Cell. He is also a board member, director and shareholder of the company that distributes this technology. The content of the article is not influenced in any way by his involvement.
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Horvath, P., Aulner, N., Bickle, M. et al. Screening out irrelevant cell-based models of disease. Nat Rev Drug Discov 15, 751–769 (2016). https://doi.org/10.1038/nrd.2016.175
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