How can physicians be convinced to overcome concerns of safety, reimbursement, and liability to prescribe novel regimens (personalized drug combinations, dosing, and sequences), without randomized clinical trial evidence?
In this meeting we had a lively dialogue on the “personalization conundrum” for advanced cancer patients – on the one hand, we can identify highly personalized treatments, such as drug combinations, but … on the other hand, high levels of personalization mean that it is unlikely there will be evidence from randomized clinical trials to support the uniquely targeted treatment. So clinicians may lack confidence to prescribe them and payers to cover them. Treating physicians have concerns about safety, reimbursement, and liability, which are heightened when there isn’t randomized clinical trial evidence.
Over the last four months we have held 15 sessions and learned a lot, thanks to some amazing expert insights. Personalized drug combinations (Ally Perlina), dosing, and a strategic sequence of therapies based on evolutionary and game theory (Bob Gatenby) can provide better outcomes for advanced cancer patients.
Clinicians make treatment decisions outside the guidelines, e.g., off-label uses of drugs, for individual patients all the time. When they do, they are guided by their own experience, and the experience of their colleagues. What are those dynamics, and can we encourage more of it?
Testing, mathematical simulation models that predict response, and real world experience from longitudinal studies are three approaches that could give physicians, payers, and patients more confidence to prescribe more personalized therapies
- Testing: We have heard from Tony Letai and Payel Chatterjee of SEngine about functional testing . Blood-based liquid biopsy using ctDNA and surrogate markers of efficacy can be used in cases where fresh tumor tissue is unavailable. (Peter Kuhn is scheduled to discuss.) Blood and other novel tests (Karin Rodland) can enable more frequent monitoring of disease progression, enabling fine-tuning of treatment and personalization.
- Predictive Models: There is a lot of investment and effort in developing models that will predict drug response by large pharmaceutical companies and academics that can be repurposed.
- Real World Evidence: Every patient should be tracked in an observational trial to share results of their unique, personalized, N-of-1 experiments. GCTA (XCELSIOR) is one such unique registry (Jeff Schrager): It allows you to create “n-of-1” arms, does not drop the patients on the floor ever, has no exclusion criteria, and understands “arm” in a dynamic way not available to any other trial model.
Several other ideas were raised to address the personalization conundrum:
- Glenn Sabin proposed that the patient could consent to hold the clinician harmless, lowering liability concerns.
- Ally Perlina recommended having patients raise specific treatment options with their doctor and listen to the specific feedback.
- Saed Sayad pointed to creating a logical process that leverages existing public data.