The Law and In Silico Health Technology: Help or Hindrance?

1. The Law and In Silico Health Technology: Help or Hindrance? Dr Marc Stauch, MA (Oxon), Leibniz University Hannover, Germany Conference ofPediatricOncology

2. ‘The law should aim to support new technologies with potential for benefiting mankind, but also needs to protect relevant individual interests – especially where these are embodied in longstanding ethical principles. In the context of health law, the regulatory focus has been on avoiding harm to individual patients and research subjects; the paradox is that, if research and/or care is made harder or delayed by legal or professional ethical restrictions, patients also stand to be harmed, by being denied better treatment.’ I. Introduction: lawand innovative healthtechnologies Conference ofPediatricOncology

3. Clinical researchers, as well as clinicians who want to use innovative methods in care often get frustrated with the law – seen as holding back valuable research that could make life-saving differences to patients - concern seems pretty much universal – in Europe, in America and elsewhere; Before talking more specifically about issues in the regulation of in silico oncology, worth asking why in general law struggles with innovative medical research – especially where new technology in play; Two main reasons: one to do with nature of law, the other to do with medical research… I. Introduction (contd.) Conference ofPediatricOncology

4. (1) Law – tries to govern activities in line with efficient and socially accepted norms – to adopt rules that encourage what is beneficial and discourage what is harmful – implies that, for regulators to come up with well-fitting rules, they need a lot of local knowledge – i.e. to understand, for the specific activity, what are the benefits vs. the risks and the best way (in terms of its effect) of dealing with it – by prohibition, by permitting performance only by qualified persons, by automatic redress after event if things go wrong, etc; I. Introduction (contd.) Conference ofPediatricOncology

5. (2) Medical research – complex activity; uncertain trade-off between risks to present persons and potential benefits to future persons; (interventional) medical research may involve serious risks of physical harm. ICT-driven research adds new factors – in some ways should make things easier: less risk of physical harm; but risks relating to privacy (worries about ICT security of sensitive health data); and concerns about quality and implications of results generated – risk of false positives, negatives; but also accurate information may distress and overload patient – complex, hard to understand field where little precedent to guide rule maker. I. Introduction (contd.) Conference ofPediatricOncology

6. In silico modeling a particular kind of ICT-driven medical research innovation; Useful to divide the activity into two main stages: (1) Building and validating the models; (2) Putting them into clinical practice. At stage 1, key legal concerns relate to health data privacy and security; at stage 2, an added concern relates to patient safety. II. The regulation of in silico modeling Conference ofPediatricOncology

7. Need lots of data – some of it – at mathematical modeling stage - non-personal: molecular, biochemical or biomechanical data re physical properties of tissues and how these interact in tumour development; But ultimately need to ensure models ‘fit’ what happens with real patients – require retrospective data giving info about patients at diagnosis, and how the tumor develops in diverse cases – with one set of genetic factors rather than another, with one treatment rather than another, etc – the larger the data sets the better, to allow fuller analysis… II (1) Building/validatingmodels (contd.) Conference ofPediatricOncology

8. Here privacy rules can be challenging – EU Data Protection Directive, PIPEDA, HIPAA all stress sensitive nature of health data and need to protect patient/research subject autonomy and privacy; three-fold requirement of: (i) Subject consent to research data use (so far as practical); (ii) De-identification of data/information prior to research use (so far as practical), plus other appropriate security measures; (iii) Safeguard of independent approval and oversight, such as from IRBs or DPAs or RECs. II (1) Building/validatingmodels (contd.) Conference ofPediatricOncology

9. Helpful: Rules fairly flexible in practice; reflect ethics and patient views re use of data – promotes trust; cooperation of researchers with oversight/review bodies may also help promote well-planned research; Hindrance: Cost and delay of seeking re-consent and/or ethics approval (including possible inconsistent review body decisions); data may be rendered less useful due to significant amount of de-identification; [In EU GDPR reforms will encourage closer prior involvement of DPAs and ‘one-stop shop’ approval for multi-centre research.] II (1) Building/validatingmodels (contd.) Conference ofPediatricOncology

10. But is data privacy actually something of a ‘red herring’, as areason for hold-ups in health data research use? Arguably real problem as much about properly exploiting data when available, namely by making the data (recorded in different health care systems) inter-operable… here far more investment needed in data curation; Here legal hindrance by omission– IP law reform required to properly reward curation: presently ‘confidentiality’ often to do with safeguarding investment of researchers (and sponsors, e.g. Pharma), in default of external IP protection. II (1) Building/validatingmodels (contd.) Conference ofPediatricOncology

11. Before in silico models are authorized for use as clinical support tools they will need to satisfy requirements under medical device regulation; Rules again similar on both sides of the Atlantic (EU medical device regime; Canada medical device regulation; US FDA rules) Basic idea is to ensure appropriate patient safety, and beyond that demonstrate clinical effectiveness. II (2) Puttingmodelsintoclinicalpractice Conference ofPediatricOncology

12. Again patient data needed to show use of model is safe and confers clinical benefit (greater than where clinicians simply base decisions on own judgment) – utmost importance that decisions well-grounded (especially if they concern treatment decisions where you only get ‘one shot’); Rules clear: clinician deploying model needs informed consent of patient (including to use of patient data to help test and refine the model) and – so long as use qualifies as medical research/innovative therapy - also ethics committee approval. II (2) Puttingmodelsintopractice (contd.) Conference ofPediatricOncology

13. Further question for in silico models about how market certification testing should be best carried out; one approach (used re testing of pharmaceutical products) is the multi-phase, double-blind RCT prior to licensing; onerous and lengthy process; potential hindrance; Assuming goal is to get in silico models adopted as quickly as possible (compatible with reasonable patient safety), need for modelers and interested clinicians to have open dialogue with medical device regulatory authorities... II (2) Puttingmodelsintopractice (contd.) Conference ofPediatricOncology

14. Need to present clear arguments – based on differential risks/benefits – that full testing requirements should not apply to predictive in silico models for use in clinical support; Key benefit is allowing clinicians to make more discriminating decisions appropriate to the particular patient; re risks, models not themselves a form of treatment (with direct potential for physical harm), but still risk of false information leading clinician to adopt inappropriate treatment (also some risks that arise from provision of highly accurate information). II (2) Puttingmodelsintopractice (contd.) Conference ofPediatricOncology

15. In fact argument that onerous advance testing (for approval) may not be needed: models can be sufficiently tested ‘on the job’, at least insofar as predictive knowledge they provide to clinicians (in support of their treatment recommendation) is: (i) presented in an objectively quantified way; (ii) specific to a particular patient; and (iii) temporally contiguous (presenting a flowing picture of what is predicted to occur in the case of that patient). II (2) Puttingmodelsintopractice (contd.) Conference ofPediatricOncology

16. If all of the above is true, clinicians should be able to recognize rapidly if the prediction deviates from reality, indicating that the model is not reliable; In such case they would suspend reliance on the model, and revert to standard treatment (they would give absent the model) – patient not put appreciably at risk; equally the prediction discrepancy would be fed back to modelers to assist with ongoing model refinement process. II (2) Puttingmodelsintopractice (contd.) Conference ofPediatricOncology

17. In medium term in silico modeling offers a promising approach to supporting clinical decisions, which if sensibly managed, should not encounter regulatory resistance due to privacy or safety concerns; however: To get there will require large-scale use of interoperable retrospective data – the law should here encourage the data curation needed with proper IP incentives; And once there, clinicians and modelers with well-developed and prima facie plausible models need to interact with medical device authorities to promote their swift adoption. III. Conclusions Conference ofPediatricOncology

18. THANK YOU FOR YOUR ATTENTION! (The research leading to these results has received funding from the EU Seventh Framework Program FP7/2007-2013 under Grant Agreement No 600841.) Conference ofPediatricOncology