Susanna Penco, Elena Venco and Alfredo Lio
Although for most pharmaceutical compounds
the final aim is improving human health,
almost all the methods used to identify and
pursue therapeutic targets and to obtain
new potential drugs have traditionally
focused on animal models
Recent studies on attrition rate in pharmacological research show that the pharmaceutical industry finds it difficult to turn new experimental compounds into safe and effective drugs. Although, for most pharmaceutical compounds, the final aim is improving human health, almost all the methods used to identify and pursue therapeutic targets and to obtain new potential drugs have traditionally been centered on animal models. The ability of such methods to predict efficacy and safety in humans needs to be carefully reviewed, in the light of more-predictive and more reliable human-based experimental tools.
The overall cost for the development and the marketing of a new pharmaceutical product ranges between one billion and 1.8 billion US dollars.1 It has been estimated that only one in 10,000 new chemical entities (NCEs), also referred to as new molecular entities (NMEs), discovered in the laboratory succeeds in obtaining marketing approval.2, 3 Recent studies have shown that 95% of experimental drugs fail in the clinical phase.4, 5 The main reasons for these poor results can be ascribed to the lack of therapeutic efficacy and safety.6, 7 Such findings point to the significant inadequacy of the current preclinical tests — mainly in vitro cellular assays and animal based disease models — in screening pharmacological compounds. Many in vitro tests are still performed in a two-dimensional format,8 despite the limitations of this approach,8, 9 and are based on animal cells, which complicates the potential extrapolation of the information they provide to humans.6, 10, 11 In spite of this, such preclinical methods are still considered the ‘gold standard’ in pharmaceutical R&D.
The relevance of animal models
Many animals, including mice, rats, dogs, cats, and non-human primates (NHPs), are used in biomedical and toxicological research as human disease surrogates, so they are defined as ‘animal models’. However, there are a number of important limitations that underlie the lack of successful use of these animal models in furthering the understanding of human disease.
Firstly, there are significant differences among species with regard to their anatomy, metabolism and physiology, which correspond to genetic differences, including differences in regulatory genes. This means that even slight molecular differences can be amplified in the extrapolation process from one species to another. For instance, mice (together with rats, which are the most commonly used species in biomedical research) share with humans slightly more than 90% of their gene sequences. Nevertheless, at least 67 major discrepancies have been found in the immunological functions of mice and humans. This fact is hardly surprising, since these two species separated approximately 65- to 75-million years ago, and have since followed different evolutionary path ways.
About 1% of human genes do not have a homologue in the mouse.14 Biochemistry provides many examples concerning similarities and differences between species. Some of the most significant differences are in the cytochrome P450 enzymes (CYPs), which seem to have evolved from a single ancestral gene over a period of 1.36 billion years. To date, at least 14 families of CYPs genes have been identified in mammals.15 Each member of this gene family has many highly conserved regions in its secondary amino acid structure. However, remarkable differences between species also exist in the primary amino acid sequences. Even small differences in amino acid sequence can imply wide differences in substrate specificity.16 Such variations can explain the divergences in drug response between animal models and humans. The scientific literature provides many examples of therapies that proved successful in animal models, but subsequently failed in clinical trials.17-20
A second important issue surrounding the failure of many animal models is the way in which the disease is induced. Diseases induced ‘artificially’ in animals cannot begin to accurately reproduce the very complex aspects and conditions clinically observed in human patients. This is thought to be one of the most crucial reasons for drug attrition.21, 22
In addition, there are relevant species-specific differences in absorption, distribution, metabolism, excretion and toxicity (ADMET) between animals and humans.23 These processes together make up the important concept of ‘pharmacokinetics’.
Pharmacokinetics is one of the main reasons for candidate compound failure in humans.24 A wide range of species-specific metabolic patterns strongly suggest that data can be hardly (at best) extrapolated from one species to another, both quantitatively and qualitatively — i.e. differences in the amino acid sequence of isozymes may influence both the rate of drug metabolism and the metabolite pattern. 25 An outstanding example of species-specific differences between rats and humans is in coumarin metabolism and toxicity, which appears to be mediated through two major phase I metabolic pathways. The first pathway, involving cytochrome CYP2A enzymes and leading to the conversion of coumarin into the non-toxic metabolite 7-hydroxycoumarin, is very efficient in humans and extremely inefficient in rats. The second pathway involves the detoxification of the epoxide intermediate, coumarin 3,4-epoxide, which spontaneously rearranges to o-hydroxyphenylacetaldehyde and is then oxidised to o-hydroxy – phenylacetic acid. In rats, the rate of conversion to o-hydroxyphenylacetic acid is 50 times lower than in humans. These metabolic discrepancies explain the differences in coumarin-mediated hepatotoxicity between the two species.26
There are many significant examples of drug attrition resulting from the limitations of the animal models used in pharmaceutical R&D:
— More than 150 experimental therapeutics for the treatment of sepsis have been successfully tested in animals. None of them proved useful in humans.27
— A total of 800 new drugs showed promising results in animal models for stroke, but only 97 were approved for the clinical phases. Unfortunately, only two showed some efficacy, with aspirin being one of the two.28, 29
— More than 85 different HIV vaccines have been tested in approximately 200 clinical studies,30 but to date no therapeutic or protective effects on humans have been found. The use of resources has been so extensive that, even if an effective HIV vaccine were found as a result of animal experimentation, animal models could not be considered a suitable predictive experimental method, since the PPV (positive predictive value) would be around 0.01.31
The list of failures gets longer with anti-cancer drugs, and there is also an endless list of failures in relation to neurodegenerative diseases. Indeed, anticancer drugs and treatments for neurological diseases have the highest attrition rate in the development process.32 Awareness of the limits of the predictivity of animal models is rapidly growing.33-39 Even the use of transgenic animals seems to have proved inconclusive in translational medicine.6, 34, 40-42 With regard to neurodegenerative diseases, the results obtained by testing new therapies on animals have been very poor.17, 43, 47
The study of bioavailability is a clear example of the differences in drug response occurring among species, as shown by many studies.48-51 Systematic reviews of the predictive accuracy of animal models in the field of teratogenesis52, 53 and carcinogenesis,54 also showed poor predictive power. In a recent analysis of the registration files of all therapeutic monoclonal antibodies (tmAbs) available in the EU, van Meer et al.55 discovered that the incidence of formation of anti-tmAb antibodies in NHPs and patients was comparable in only 59% of cases. In addition, the type of anti-tmAb antibody response was different in NHPs and humans in the same proportion of cases. The authors concluded that monoclonal antibody immunogenicity in NHPs and humans is significantly different.
In a recent review of the use of the dog model and other animal models in drug toxicology, the authors concluded that its predictive value in current toxicology was very poor.56, 57 The issues associated with extrapolating data from animals to humans are probably due to both inadequate testing procedures and to the failure of models to accurately reproduce human diseases, but evidence is growing that the core of the problem could only be resolved by giving up the use of animals as models.33 Therefore, in the light of controversial predictive value, it is not surprising that some scientists consider preclinical animal studies, “generally scarce, unreliable or nonpredictive”. 58-60
Considering the present stalemate of translational medical science, the development of new, reliable experimental approaches to assure efficiency, convenience and safety in clinical therapies is desperately needed. Long-term Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) studies, comprising a set of in vitro tests based on human biological materials, proved more predictive in testing compounds than did traditional animal-based acute toxicity studies.61
Recently, many important improvements have been made in studying acute toxicity, repeated toxicity62 and reproductive toxicology, as assessed by the ESNATS report.63-64 One particularly promising field is that of organs-on-chips, which are micro-engineered physiological systems aimed at reproducing the physiological properties of human tissues and organs and their interactions. Thanks to these biochips, it has been possible to create a model for acute pulmonary oedema that has permitted the evaluation of new clinical and therapeutic interventions.65 In addition to the lung-on-a-chip, other tools have been successfully developed to mimic the human gut66 and kidney.59 The final aim is clearly to develop a ‘human-on-a-chip’, to fully mimic the functions of and interactions between organs, thus getting closer to the human in vivo situation. Indeed, some already trust this approach as a valid alternative to traditional animal tests.67-70 In addition, the use of human pluripotent stem cells seems to be becoming more widely appreciated in pre-clinical toxicology.71-72
Since the available data show that their predictivity can no longer be assumed, there is an urgent need for reviews and meta-analyses of the animal models currently used in medical research. Moreover, science should focus on the development of more-advanced methods, as a result of the limitations of the current pre-clinical tools, the growing bioethical objections surrounding their use, and the ongoing development
of new in vitro and in silico techniques. These alternative methods should be used ideally in the experimental context of an Integrated Testing Strategy.
1 Esserman, L.J. & Woodcock, J. (2011). Accelerating identification and regulatory approval of investigational cancer drugs. JAMA 306, 2608–2609.
2 Li, A.P. (2004). In vitro approaches to evaluate ADMET drug properties. Current Topics in Medicinal Chemistry 4, 701–706.
3 Berlin, J.A., Glasser, S.C. & Ellenberg, S.S. (2008). Adverse event detection in drug development: Recommendations and obligations beyond phase 3.
American Journal of Public Health 98, 1366–1371.
4 Hartung, T. (2013). Look back in anger — What clinical studies tell us about preclinical work. ALTEX 30, 275–291.
5 Arrowsmith, J. (2012). A decade of change. Nature Reviews Drug Discovery 11, 17–18.
6 Engle, S.J. & Puppala, D. (2013). Integrating human pluripotent stem cells into drug development. Cell Stem Cell 12, 669–677.
7 Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R. & Schacht, A.L. (2010). How to improve R&D productivity: The pharmaceutical
industry’s grand challenge. Nature Reviews Drug Discovery 9, 203–214.
8 Breslin, S. & O’Driscoll, L. (2013). Three-dimensional cell culture: The missing link in drug discovery. Drug Discovery Today 18, 240–249.
9 Arya, N., Sardana, V., Saxena, M., Rangarajan, A. & Katti, D.S. (2012). Recapitulating tumour microenvironment in chitosan–gelatin three-dimensional scaffolds: An improved in vitro tumour model. Journal of the Royal Society, Interface 9, 3288–3302.
10 Knight, A. (2007). Animal experiments scrutinised: Systematic reviews demonstrate poor human clinical and toxicological utility. ALTEX 24, 320–325.
11 van Vliet, E. (2011). Current standing and future prospects for the technologies proposed to transform toxicity testing in the 21st century. ALTEX 28, 17–44.
12 Mestas, J. & Hughes, C.C. (2004). Of mice and not men: Differences between mouse and human immunology. Journal of Immunology 172, 2731–2738.
13 Brady, C.A. (2008). Of mice and men: The potential of high-resolution human immune cell assays to aid the pre-clinical to clinical transition of drug development projects. Drug Discovery World, Winter 2008/09. London, UK: RJ Communications & Media
Ltd. Available at: http://ddw.net-genie.co.uk/enabling_technologies/261930/of_mice_and_men.
html (Accessed 31.08.14).
14 Mouse Genome Sequencing Consortium (2002). Initial sequencing and comparative analysis of the mouse genome. Nature, London 420, 520–562.
15 Nelson, D.R., Koymans, L., Kamataki, T., Stegeman, J.J., Feyereisen, R., Waxman, D.J., Waterman, M.R., Gotoh, O., Coon, M.J., Estabrook, R.W., Gunsalus, I.C. & Nebert, D.W. (1996). P450 superfamily: Update on new sequences, gene mapping, accession numbers and nomenclature. Pharmacogenetics 6, 1–42.
16 Lindberg, L.P. & Negishi, M. (1989). Alteration of mouse cytochrome P450coh substrate specificity by mutation of a single amino-acid residue. Nature,London 339, 632–634.
17 Schnabel, J. (2008). Neuroscience: Standard model. Nature, London 454, 682–685.
18 Gordon, P.H., Moore, D.H., Miller, R.G., Florence, J.M., Verheijde, J.L., Doorish, C., Hilton, J.F., Spitalny, G.M., MacArthur, R.B., Mitsumoto, H., Neville, H.E., Boylan, K., Mozaffar, T., Belsh, J.M., Ravits, J., Bedlack, R.S., Graves, M.C., McCluskey, L.F., Barohn, R.J., Tandan, R.; Western ALS Study Group (2007). Efficacy of minocycline in patients with amyotrophic lateral sclerosis: A phase III randomised trial. Lancet Neurology 6, 1045–1053.
19 Doody, R.S., Raman, R., Farlow, M., Iwatsubo, T., Vellas, B., Joffe, S., Kieburtz, K., He, F., Sun, X., Thomas, R.G., Aisen, P.S.; Alzheimer’s Disease Cooperative Study Steering Committee, Siemers, E., Sethuraman, G., Mohs, R.; Semagacestat Study Group (2013). A phase 3 trial of semagacestat for treatment of Alzheimer’s disease. New England Journal of Medicine 369, 341–350.
20 Gawrylewsky, A. (2007). The trouble with animal models — Why did human trials fail? The Scientist, July 2007. Midland, Ontario, Canada: LabX Media Group. Available at: http://www.the-scientist.com/?articles.view/articleNo/25184/title/The-Troublewith-Animal-Models/ (Accessed 31.08.14).
21 Perel, P., Roberts, I., Sena, E., Wheble, P., Briscoe, C., Sandercock, P., Macleod, M., Mignini, L.E., Jayaram, P. & Khan, K.S. (2007). Comparison of treatment effects between animal experiments and clinical trials: Systematic review. BMJ 334, 197.
22 Pound, P., Ebrahim, S., Sandercock, P., Bracken, M.B., Roberts, I.; Reviewing Animal Trials Systematically (RATS) Group (2004). Where is the evidence that animal research benefits humans? BMJ 328, 514–517.
23 Greek, R. & Hansen, L. (2013). Questions regarding the predictive value of one evolved complex adaptive system for a second: Exemplified by the SOD1 mouse. Progress in Biophysics & Molecular Biology 113, 231– 253.
24 Seymour, M. (2009). The best model for humans is human — How to accelerate early drug development safely. ATLA 37, Suppl. 1, 61–65.
25 Lin, J.F. & Lu, A.Y.H. (1997). Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacological Reviews 49, 403–449.
26 Sutherland, M.L., Fabre, K.M. & Tagle, D.A. (2013). The National Institutes of Health Microphysiological Systems Program focuses on a critical challenge in the drug discovery pipeline. Stem Cell Research & Therapy 4, Suppl. 1, I1.
27 Seok, J., Warren, H.S., Cuenca, A.G., Mindrinos, M.N., Baker, H.V., Xu, W., Richards, D.R., McDonald-Smith, G.P., Gao, H., Hennessy, L., Finnerty, C.C., López, C.M., Honari, S., Moore, E.E., Minei, J.P., Cuschieri, J., Bankey, P.E., Johnson, J.L., Sperry, J., Nathens, A.B., Billiar, T.R., West, M.A., Jeschke, M.G., Klein, M.B., Gamelli, R.L., Gibran, N.S., Brownstein, B.H., Miller-Graziano, C., Calvano, S.E., Mason, P.H., Cobb, J.P., Rahme, L.G., Lowry, S.F., Maier, R.V., Moldawer, L.L., Herndon, D.N., Davis, R.W., Xiao, W., Tompkins, R.G.; Inflammation and Host Response to Injury, Large Scale Collaborative Research Program (2013). Genomic responses in mouse models poorly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences of the USA 110, 3507–3512.
28 Sena, E., Wheble, P., Sandercock, P. & Macleod, M. (2007). Systematic review and meta-analysis of the efficacy of tirilazad in experimental stroke. Stroke 38, 388–394.
29 van der Worp, H.B., Howells, D.W., Sena, E.S., Porritt, M.J., Rewell, S., O’Collins, V. & Macleod, M.R. (2010). Can animal models of disease reliably inform human studies? PLoS Medicine 7, e1000245.
30 Bailey, J. & Taylor, K. (2009). The SCHER report on non-human primate research — Biased and deeply flawed. ATLA 37, 427–435.
31 Greek, R. & Menache, A. (2013). Systematic reviews of animal models: Methodology versus epistemology. International Journal of Medical Sciences 10, 206–221.
32 Kola, I. & Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nature Reviews Drug Discovery 3, 711–715.
33 Mak, I.W., Evaniew, N. & Ghert, M. (2014). Lost in translation: Animal models and clinical trials in cancer treatment. American Journal of Translational Research 6, 114–118.
34 Enna, S.J. & Williams, M. (2009). Defining the role of pharmacology in the emerging world of translational research. Advances in Pharmacology 57, 1–30.
35 Lazic, S.E. & Essioux, L. (2013). Improving basic and translational science by accounting for litter-to-litter variation in animal models. BMC Neuroscience 14, 37.
36 Leist, M. & Hartung, T. (2013). Inflammatory findings on species extrapolations: Humans are definitely no 70-kg mice. Archives of Toxicology 87, 563–567.
37 Aggarwal, B.B., Danda, D., Gupta, S. & Gehlot, P. (2009). Models for prevention and treatment of cancer: Problems vs promises. Biochemical Pharmacology 78, 1083–1094.
38 Coleman, R.A. (2011). Human tissue in the evaluation of safety and efficacy of new medicines: A viable alternative to animal models? ISRN Pharmaceutics 2011, 806789.
39 Li, A.P. (2014). Biomarkers and human hepatocytes. Biomarkers in Medicine 8, 173–183.
40 Hyman, S.E. (2012). Revolution stalled. Science Translational Medicine 4, 155cm11.
41 Stingl, L., Völkel, M. & Lindl, T. (2009). 20 years of hypertension research using genetically modified animals: No clinically promising approaches in sight. ALTEX 26, 41–51.
42 Bhogal, N. & Combes, R. (2006). The relevance of genetically altered mouse models of human disease. ATLA 34, 429–454.
43 Anon. (2013). Drug testing should be with human iPS cells. Drug Discovery & Development, 06.12.13. Rockaway, NJ, USA: Advantage Media. Available at: http://www.dddmag.com/news/2013/12/drugtesting- should-be-human-ips-cells (Accessed 31.08.14).
44 Mertens, J., Stüber, K., Wunderlich, P., Ladewig, J., Kesavan, J.C., Vandenberghe, R., Vandenbulcke, M., van Damme, P., Walter, J., Brüstle, O. & Koch, P. (2013). APP processing in human pluripotent stem cell-derived neurons is resistant to NSAID-based γ-secretase modulation. Stem Cell Reports 1, 491–498.
45 Rice, J. (2012). Animal models: Not close enough. Nature, London 484, S9.
46 Friese, M.A., Montalban, X., Willcox, N., Bell, J.I., Martin, R. & Fugger, L. (2006). The value of animal models for drug development in multiple sclerosis. Brain 129, 1940–1952.
47 Beal, M.F. (2010). Parkinson’s disease: A model dilemma. Nature, London 466, S8–S10.
48 Grass, G.M. & Sinko, P.J. (2002). Physiology-based pharmacokinetic simulation modelling. Advanced Drug Delivery Reviews 43, 433–451.
49 Akabane, T., Tabata, K., Kadono, K., Sakuda, S., Terashita, S. & Teramura, T. (2010). A comparison of pharmacokinetics between humans and monkeys. Drug Metabolism & Disposition 38, 308–316.
50 Cao, X., Gibbs, S.T., Fang, L., Miller, H.A., Landowski, C.P., Shin, H.C., Lennernas, H., Zhong, Y., Amidon, G.L., Yu, L.X. & Sun, D. (2006). Why is it challenging to predict intestinal drug absorption and oral bioavailability in human using rat model. Pharmaceutical Research 23, 1675–1686.
51 Chiou, W.L., Jeong, H.Y., Chung, S.M. & Wu, T.C. (2000). Evaluation of using dog as an animal model to study the fraction of oral dose absorbed of 43 drugs in humans. Pharmaceutical Research 17, 135–140.
52 Bailey, J., Knight, A. & Balcombe, J. (2005). The future of teratology research is in vitro. Biogenic Amines 19, 97–145.
53 Bailey, J. (2008). Developmental toxicity testing: Protecting future generations? ATLA 36, 718–721.
54 Basketter, D.A., Clewell, H., Kimber, I., Rossi, A., Blaauboer, B., Burrier, R., Daneshian, M., Eskes, C., Goldberg, A., Hasiwa, N., Hoffmann, S., Jaworska, J., Knudsen, T.B., Landsiedel, R., Leist, M., Locke, P., Maxwell, G., McKim, J., McVey, E.A., Ouédraogo, G., Patlewicz, G., Pelkonen, O., Roggen, E., Rovida, C., Ruhdel, I., Schwarz, M., Schepky, A., Schoeters, G., Skinner, N., Trentz, K., Turner, M., Vanparys, P., Yager, J., Zurlo, J. & Hartung, T. (2012). A roadmap for the development of alternative (non-animal) methods for carcinogenicity testing. ALTEX 29, 41–55.
55 van Meer, P.J., Kooijman, M., Brinks, V., Gispen-de Wied, C.C., Silva-Lima, B., Moors, E.H. & Schellekens, H. (2013). Immunogenicity of mAbs in non-human primates during nonclinical safety assessment. MAbs 5, 810–816.
56 Bailey, J., Thew, M. & Balls, M. (2013). An analysis of the use of dogs in predicting human toxicology and drug safety. ATLA 41, 335–350.
57 Bailey, J., Thew, M. & Balls, M. (2014). An analysis of the use of animal models in predicting human toxicology and drug safety. ATLA 42, 181–199.
58 Wang, S.J., Hung, H.M. & O’Neill, R. (2011). Adaptive design clinical trials and trial logistics models in CNS drug development. European Neuropsychopharmacology 21, 159–166.
59 Jang, K.J., Mehr, A.P., Hamilton, G.A., McPartlin, L.A., Chung, S., Suh, K.Y. & Ingber, D.E. (2013). Human kidney proximal tubule-on-a-chip for drug transport and nephrotoxicity assessment. Integrative Biology (Cambridge) 5, 1119–1129.
60 Giri, S., Braumann, U.D., Giri, P., Acikgöz, A., Scheibe, P., Nieber, K. & Bader, A. (2013). Nanostructured self-assembling peptides as a defined extracellular matrix for long-term functional maintenance of primary hepatocytes in a bioartificial liver modular device. International Journal of Nanomedicine 8, 1525–1539.
61 Ekwall, B. (1999). Overview of the final MEIC results: II. The in vitro–in vivo evaluation, including the selection of a practical battery of cell tests for prediction of acute lethal blood concentrations in humans. Toxicology in Vitro 13, 665–673.
62 Mueller, D., Krämer, L., Hoffmann, E., Klein, S. & Noor, F. (2014). 3D organotypic HepaRG cultures as in vitro model for acute and repeated dose toxicity studies. Toxicology in Vitro 28, 104–112.
63 Krug, A.K., Kolde, R., Gaspar, J.A., Rempel, E., Balmer, N.V., Meganathan, K., Vojnits, K., Baquié, M., Waldmann, T., Ensenat-Waser, R., Jagtap, S., Evans, R.M., Julien, S., Peterson, H., Zagoura, D., Kadereit, S., Gerhard, D., Sotiriadou, I., Heke, M., Natarajan, K., Henry, M., Winkler, J., Marchan, R., Stoppini, L., Bosgra, S., Westerhout, J., Verwei, M., Vilo, J., Kortenkamp, A., Hescheler, J., Hothorn, L., Bremer, S., van Thriel, C., Krause, K.H., Hengstler, J.G., Rahnenführer, J., Leist, M. & Sachinidis, A. (2013). Human embryonic stem cell-derived test systems for developmental neurotoxicity: A transcriptomics approach. Archives of Toxicology 87, 123–143.
64 Hengstler, J.A., Leist, M. & Hescheler, J. (2013). Briefing paper: The Novel FP7 ESNATS Test Systems of Developmental Toxicity: State-of-the art and Future Perspectives, 8pp. Paris, France: ARTTIC Project Office. Available at: http://www.esnats.eu/uploads/images/ESNATS_Briefing_paper_13-09-2013.pdf (Accessed 31.08.14).
65 Huh, D., Leslie, D.C., Matthews, B.D., Fraser, J.P., Jurek, S., Hamilton, G.A., Thorneloe, K.S., McAlexander, M.A. & Ingber, D.E. (2012). A human disease model of drug toxicity-induced pulmonary edema in a lung-on-a-chip microdevice. Science Translational Medicine 4, 159ra147.
66 Kim, H.J. & Ingber, D.E. (2013). Gut-on-a-chip microenvironment induces human intestinal cells to undergo villus differentiation. Integrative Biology (Cambridge) 5, 1130–1140.
67 Moraes, C., Mehta, G., Lesher-Perez, S.C. & Takayama, S. (2011). Organs-on-a-chip: A focus on compartmentalized microdevices. Annals of Biomedical Engineering 40, 1211–1227.
68 Yum, K., Hong, S.G., Healy, K.E. & Lee, L.P. (2014). Physiologically relevant organs on chips. Biotechnology Journal 9, 16–27.
69 Huh, D., Hamilton, G.A. & Ingber, D.E. (2011). From 3D cell culture to organs-on-chips. Trends in Cell Biology 21, 745–754.
70 Marx, U., Walles, H., Hoffmann, S., Lindner, G., Horland, R., Sonntag, F., Klotzbach, U., Sakharov, D., Tonevitsky, A. & Lauster, R. (2012). ‘Human-on-a-chip’ developments: A translational cutting-edge alternative to systemic safety assessment and efficiency evaluation of substances in laboratory animals and man? ATLA 40, 235–257.
71 Mandenius, C.F., Andersson, T.B., Alves, P.M., Batzl-Hartmann, C., Björquist, P., Carrondo, M.J., Chesne, C., Coecke, S., Edsbagge, J., Fredriksson, J.M., Gerlach, J.C., Heinzle, E., Ingelman-Sundberg, M., Johansson, I., Küppers-Munther, B., Müller-Vieira, U., Noor, F. & Zeilinger, K. (2011). Toward preclinical predictive drug testing for metabolism and hepatotoxicity by using in vitro models derived from human embryonic stem cells and human cell lines — A report on the Vitrocellomics EU-project. ATLA 39, 147–171.
72 Cao, L., Tan, L., Jiang, T., Zhu, X.C. & Yu, J.T. (2014). Induced pluripotent stem cells for disease modeling and drug discovery in neurodegenerative diseases. Molecular Neurobiology, E-pub ahead of print [doi: 10.1007/s12035-014-8867-6].