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Evidence for personalised medicine: mechanisms, correlation, and new kinds of black box

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Abstract

Personalised medicine (PM) has been discussed as a medical paradigm shift that will improve health while reducing inefficiency and waste. At the same time, it raises new practical, regulatory, and ethical challenges. In this paper, we examine PM strategies epistemologically in order to develop capacities to address these challenges, focusing on a recently proposed strategy for developing patient-specific models from induced pluripotent stem cells (iPSCs) so as to make individualised treatment predictions. We compare this strategy to two main PM strategies—stratified medicine and computational models. Drawing on epistemological work in the philosophy of medicine, we explain why these two methods, while powerful, are neither truly personalised nor, epistemologically speaking, novel strategies. Both are forms of correlational black box. We then argue that the iPSC models would count as a new kind of black box. They would not rely entirely on mechanistic knowledge, and they would utilise correlational evidence in a different way from other strategies—a way that would enable personalised predictions. In arguing that the iPSC models would present a novel method of gaining evidence for clinical practice, we provide an epistemic analysis that can help to inform the practical, regulatory, and ethical challenges of developing an iPSC system.

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Notes

  1. Personalised medicine also includes the production of personalised medical devices and implants, as well as, potentially, patient-specific 3D-bioprinted tissues or organs. For overviews of recent developments, see, e.g., [1, 2]. This paper focuses on pharmacological treatments.

  2. Despite variations in terminology—with PM also discussed in relation to aims of prediction and prevention, for example [3, 4]—there is some agreement that the personalisation of treatment drawing on such data is the most fundamental aspect of PM [5,6,7].

  3. In the United States, precision medicine is the usual term [5, p. 24].

  4. The same principle underpins new methods of online advertising, in which companies aggregate apparently inconsequential data to generate sophisticated ‘virtual identities’ with surprising predictive power [16].

  5. While some of the epilepsies are known to have a genetic component, some involve a large number of genes and proteins, seemingly not in isolation, which would also make it difficult to develop a way to stratify patients by omics data.

  6. This is not to say that mechanistic evidence plays no role in producing such knowledge. The two kinds of evidence work together in various ways: mechanistic evidence can guide judgments about what instances of correlation may be causative and help establish the direction of causation; correlation can aid in sorting out causal links where masking or counteracting causal factors problematise developing mechanistic understanding [30, 31]. Reasoning about mechanisms plays a role in the choice of which connections to test for in correlational studies, as well as in their design and interpretation [32] and in the definition of interventions [33]. Both play roles in theory development and in answering practical clinical questions [34].

  7. Most discussions focus on applying the results of correlational studies to groups other than the experimental group, rather than to particular individuals (the problem of external validity). However, the issue of applying these results to individuals is similar enough that many of the points made in the literature obtain.

  8. While stratified medicine treatments rely in this justificatory way on correlational evidence, it is worth noting that both kinds of evidence may be involved in their development. On the one hand, the approach has been spurred on by observations within trials where a drug has been found to have little to no benefit on average but significant benefit for some individuals. Researchers could then search for a shared feature of these individuals or their conditions. An example is panitumumab (Vectibix), a treatment for metastatic colorectal cancer, which was found to increase survival by two years for patients whose cancer has no mutations in the codon 12 and 13 of the KRAS gene, but by only a few weeks for metastatic colorectal cancer patients overall [47, p. 17]. On the other hand, reasoning about mechanisms can suggest treatment strategies that will work for some patients. For instance, ivacaftor (Kalydeco), a treatment for cystic fibrosis patients with a G551D mutation on their CFTR gene (one among many mutations that can cause cystic fibrosis), was developed from a primarily pathophysiological rationale before being trialled [48].

  9. Some have also proposed model development drawing on theoretical knowledge, but this possibility is much further away, if it is indeed a possibility at all [49]. Our characterisation might be disputed if advanced simulation techniques are thought to constitute a new epistemic method [50, 51]; for a reply, see [52]. We leave this issue aside as we nonetheless consider the method to have the characteristics of a black box.

  10. The amount of argument made for this observation in the literature may make this point appear more controversial than, we think, it can plausibly be taken to be. It has occurred, in part, because the idea is at odds with the way that models—particularly, models as theories or parts of theories—have been discussed in relation to new mechanist approaches. On this view, models are regarded as explanations of mechanisms, and mechanistic explanations should aim at completeness and specificity [56]. In addition, the tendency to focus on explanatory rather than predictive models, and on basic rather than applied sciences, tends to imply the view that abstraction and idealisation are limitations for models to overcome, particularly when combined with traditional assumptions about the relation between prediction and explanation [54, 57, 58]. While our main concern in this paper is to develop an understanding of a potential method in PM, we welcome the opportunity to provide a counter to these foci.

  11. Models of this kind are dubbed ‘phenomenal models’ by Craver [55, p. 841]. A similar notion is discussed by Weisberg as models that aim for a ‘MAXOUT’ ideal [53, p. 109].

  12. These procedures would not occur for every patient, but rather would be part of working out how to define the process of making and using the model, from collection of the sample to interpretation of the results. The same clearly defined procedure would then be used for each patient.

  13. We note that this is consistent with the experience of one of the authors who is a practicing scientist. Eric Winsberg makes a similar point in relation to simulations [51].

  14. Thank you to two anonymous reviewers for pushing us to clarify this point.

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Acknowledgements

This research was supported by Australian Research Council (Grant ID CE140100012). The authors would like to thank colleagues in the Ethics, Policy and Public Engagement theme at the Centre of Excellence for Electromaterials Science (ACES) for their comments on drafts.

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Authors and Affiliations

  1. Monash University, Clayton, VIC, Australia

    Mary Jean Walker

  2. University of Melbourne, Parkville, VIC, Australia

    Justin Bourke

  3. Macquarie University, North Ryde, NSW, Australia

    Katrina Hutchison

  4. Australian Research Council Centre of Excellence for Electromaterials Science, Wollongong, NSW, Australia

    Mary Jean Walker, Justin Bourke & Katrina Hutchison

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Correspondence to Mary Jean Walker.

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Walker, M.J., Bourke, J. & Hutchison, K. Evidence for personalised medicine: mechanisms, correlation, and new kinds of black box. Theor Med Bioeth 40, 103–121 (2019). https://doi.org/10.1007/s11017-019-09482-z

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