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PhD degree in Foundations of the Life Sciences and their Ethical Consequences European School of Molecular Medicine (SEMM) and University of Milan Faculty of Medicine Settore disciplinare: FIL/02 Humans, animals, and Petri dishes: Biomedical modeling between experimentation and representation Pierre-Luc Germain IFOM-IEO Campus, Milan Matricola n. R08889 Internal Supervisor Giuseppe Testa, & Lab Supervisor: European Institute of Oncology IFOM-IEO Campus, Milan, Italy External Supervisor: Marcel Weber, University of Geneva Geneva, Switzerland Anno accademico 2012-2013 Contents Introduction1 0.1 Biomedical models as a working category . .2 0.2 Descriptivity and normativity . .4 0.3 The context of enquiry . .6 0.3.1 Ethics of animal experimentation . .6 0.3.2 Funding policy and research prioritization . .8 0.4 Plan . .9 1 Do biomedical models model? 13 1.1 Philosophical accounts of scientific models . 14 1.1.1 Models as mediators . 15 1.1.2 Models as representations . 16 1.1.3 Similarity and adequacy of representation . 18 1.1.4 Modeling as surrogacy or indirect access . 20 1.1.5 Model of, model for . 24 1.2 Surrogacy in biomedical models . 27 1.2.1 Cutting lineages at the joints . 28 1.2.2 Samples in mosaic individuals . 30 1.2.3 Material idealizations . 33 1.2.4 Abandoning the model/sample distinction in biomedical research . 35 1.3 Animal models, model animals . 36 1.3.1 Model individuation . 38 1.4 Models or experimental systems? . 41 2 Evaluating biomedical models 45 2.1 Introduction . 45 2.2 Screens and predictivity . 47 2.2.1 The critique of animal testing . 47 2.2.2 Multi-step screening . 50 2.2.3 The missing base rate . 53 2.2.4 Sensitivity and specificity . 55 2.3 The Cancer Chemotherapy National Service Center . 57 2.3.1 Screening in the context of drug discovery . 57 2.3.2 The CCNSC { from attrition to annotation . 60 2.4 Assumptions of the received views . 66 2.4.1 Assumption 1: Biomedical models function as surrogates . 66 2.4.2 Assumption 2: Modeling is a unidirectional process . 69 2.4.3 Assumption 3: Modeling is a dyadic relationship . 70 2.4.4 A new account of biomedical models . 71 i 3 The instrumental role of biomedical models 73 3.1 Introduction . 73 3.1.1 Kinds of kinds of biomedical models . 74 3.2 Living instruments . 76 3.2.1 Example 1: The Ascheim-Zondek test for pregnancy . 78 3.2.2 Example 2: Induced pluripotent stem cell models of cancer . 79 3.2.3 Example 3: Xenograft models of cancer . 81 3.2.4 Example 4: Genetically engineered models of cancer . 85 3.3 Characterization of the instrumental role . 89 3.3.1 Production and observation instruments . 91 3.3.2 Replica and instruments . 95 3.3.3 The growing importance of the instrumental role . 98 3.4 Proximate functions of biomedical models . 100 3.4.1 Models as tools for thinking . 100 3.4.2 Models as replica or surrogates . 101 3.4.3 Models as instruments . 101 3.4.4 Models as reagents or factories . 101 3.5 Conclusion . 102 4 Models and theory 105 4.1 Introduction . 105 4.2 Extrapolation and the theoretical . 107 4.2.1 Claude Bernard and the foundations of experimental medicine . 107 4.2.2 Theoretical constructs . 111 4.2.3 Theoretical constructs as bridges . 115 4.3 Cancer stem cells between theory and operations . 116 4.3.1 The theoretical grounding of early cancer xenografts . 116 4.3.2 Epistemic iteration . 120 4.3.3 The Cancer Stem Cell framework . 121 4.3.4 Melanoma-initiating cells . 124 4.4 Spaces of representation . 128 4.4.1 The closure of representation . 130 4.5 Conclusion . 132 5 In vitro models and distributed modeling 135 5.1 Introduction . 135 5.2 Stem cell models . 136 5.2.1 A brief history of iPSCs . 136 5.2.2 Repeated development . 138 5.2.3 Cell types between in vivo and in vitro . 143 5.2.4 Turning the inside out and recomposing it . 146 5.3 The iPSC research paradigm . 149 5.3.1 iPSC models and human variation . 149 5.3.2 In vitro symptoms as intermediary anchorpoints . 152 5.3.3 Translating phenotypes . 154 5.3.4 Comparative cellular models . 158 5.4 Distributed modeling . 160 5.4.1 Against the dyadic view . 160 5.4.2 The role of the patient . 163 5.4.3 Structures of models as a locus of evaluation . 165 ii 5.4.4 Connectivity . 166 5.5 The new model organism? . 169 Conclusion 173 6.6 Modeling in applied research . 176 Appendix 179 A Test statistics and the coin-tossing argument 179 A.1 Fundamentals of test evaluation . 179 A.2 Base rates and the coin-tossing argument . 180 B Biological and technical replicates 183 Acknowledgments 187 Bibliography 189 iii iv List of Abbreviations ADR Abstract Direct Representation BEAR Biological Effects of Atomic Radiation CAM Causal Analogical Models CCNSC Cancer Chemotherapy National Service Center CERN Conseil Europ´eenpour la Recherche Nucl´eaire(European Organization for Nuclear Research) CSC Cancer Stem Cell DNA Deoxyribonucleic Acid DTP Developmental Therapeutics Program (of the NCI) ESC Embryonic Stem Cell FACS Fluorescence-Activated Cell Sorting FN False Negative FP False Positive GWAS Genome-Wide Association Study HAM Hypothetical Analogical Model HGNC Human Genome Organization Gene Nomenclature Consortium hESC Human Embryonic Stem Cell iPSC Induced Pluripotent Stem Cell LPS Lipopolysaccharide MGI Mouse Genome Institute MIC Melanoma-Initiating Cell mRNA Messenger RNA (Ribonucleic Acid) NCI National Cancer Institute (of the United States of America) NEP Neuroepithelial Progenitors NOD/SCID Non-Obese Diabetic/Severe Combined Immuno-Deficiency NPV Negative Predictive Value NRC National Research Council (of the United States of America) NSC Neural Stem Cell PBPK Physiologically-Based Pharmacokinetic PPV Positive Predictive Value PSI-MI Proteomics Standards Initiative for Molecular Interactions RDoc Research Domain Criteria RNA Ribonucleic Acid SVZ Subventricular Zone v TALEN Transcription Activator-Like Effector Nucleases TN True Negative TP True Positive WBS Williams-Beuren Syndrome 7Dup 7q11 micro-Duplication Syndrome Gene names / products ABCB5 ATP (adenosine triphosphate)-Binding Cassette, sub-family B, member 5 cMyc Cellular Myelocytomatosis oncogene (HGNC symbol: MYC) FMR1 Fragile-X Mental Retardation 1 HER2 Human Epidermal Growth Factor Receptor 2 (HGNC symbol: ERBB2) HRAS Harvey Rat Sarcoma Viral oncogene homolog Il2rg Interleukin-2 Gamma Receptor LRRK2 Leucine-Rich Repeat Kinase 2 SOX2 SRY (sex determining region Y)-box 2 vi List of Figures 2.1 The CCNSC pipeline . 63 3.1 The larval phenotype of the zebrafish model . 86 5.1 In vitro cerebral organoid . 141 5.2 Correspondence between in vivo and in vitro ................ 144 5.3 The iPSC research platform . 153 5.4 Translation of disease phenotypes . 157 vii viii Abstract The use and evaluation of biomedical models { in vitro and in vivo models of diseases { is discussed among scientists, philosophers and science policy-makers, in an attempt to maximize the efficiency and relevance of research and minimize unnecessary moral costs. However, such evaluations raise several methodological issues and have generally been hampered by a lack of attention to the precise functions played by biomedical models. For if biomedical models are ultimately expected to inform us about human pathologies, they seldom do so in isolation, and get there through a wide variety of ways. An epistemological understanding of this process is therefore a precondition for their evaluation, and this thesis is an attempt at building such an epistemology. Several of the examples used come from cancer research, especially xenograft models and models used in the context of large-scale drug screenings. Another important set of examples come in vitro models, with a particular focus on disease modeling using induced pluripotent stem cells. I argue that the notion of model, if conceived as to apply to biomedical models, conflates into that of experimental system. I therefore propose an account of biomedical models that does not presuppose a fundamental divide between modeling and experimentation. I show that biomedical models are not simply scaled-down versions of their target, but instead projections of their target in a different space of representation. I argue for an instrumental role of biomedical models, and use this role to explore the diversity of proximal functions fulfilled by biomedical models. I propose the notion of distributed modeling to draw attention to the relations between model systems, and illustrate this by analyzing the interplay between in vitro and in vivo models. Finally, I explore the implications of this account for the evaluation of biomedical models, and more broadly for the topic of scientific representation. ix x Introduction Biomedical research ultimately aims at improving our understanding of human biology and pathology, and our capacity to intervene on it. However, a considerable proportion of this research proceeds in the absence of its main object { in the absence of human patients. It is the main goal of this thesis to understand how this is possible, and to provide an epistemology for biomedical models. I want to draw an account of biomedical models that does not take for granted some important categories in the way we have come to think about biomedical research. What I call the `directional and dyadic view of modeling' understands modeling as a dyadic rela- tionship between a model and a target system, in which facts are first worked out in the model before being extrapolated to the target. This view also presupposes that modeling is fundamentally distinct from something else that remains uncharacterized { what we might call `direct experimentation' { and tends to assume that biomedical models function as surrogates for human patients. I believe that such a view is flawed in many respects, and my goal is to highlight these shortcomings and propose an alternative view of biomedical research.

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