From 4800f4652826e6f9b18674505d61cbc98d885893 Mon Sep 17 00:00:00 2001 From: Will King Date: Tue, 1 Feb 2022 13:39:31 -0800 Subject: [PATCH] Add 'Concept Paper 2021-09-03' --- Concept-Paper-2021-09-03.md | 71 +++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 Concept-Paper-2021-09-03.md diff --git a/Concept-Paper-2021-09-03.md b/Concept-Paper-2021-09-03.md new file mode 100644 index 0000000..8ec4ba5 --- /dev/null +++ b/Concept-Paper-2021-09-03.md @@ -0,0 +1,71 @@ +Project Concept Proposal +Motivation +Pharmaceutical R&D lies at the crux of various shaping influences. First, the R&D is difficult to complete with many failures for each success. Second, the regulatory agency imposes minimum requirements in both safety and efficacy that must be met, while passing through a path of tests (of which there are multiple paths available). Third, each approved drug must compete in an imperfect market to meet certain therapeutic needs. Although a given drug may have a period of exclusivity related to its chemical formulation, it may be that it always faces competition from chemically dissimilar compounds. Fifth, entry of competing products is not guaranteed. Finally, the way that pharmaceutical compounds are paid for – involving a mixture of private, insurance, and government funds – makes tracking profit incentives quite difficult. +Because the Federal Government pays for a significant share of prescriptions for those over the age of 65 through the medicare program, identifying the effects of proposed laws on expenditures is a concern of the Congressional Budget Office. Examples of general concerns include: + • How will changes in reimbursment rates affect + ◦ R&D in competing drugs? + ◦ Development of innovator drugs? + • How will changes in testing/trial requirements affect entry of new and generic drugs? +Answers to these questions feed back into the fundamental question CBO faces: how will this affect the federal budget down the road. +Background on work this summer +I spent the summer working with CBO’s Health Analysis Division on developing a dataset that, under ideal circumstances, describes how registered aspects of clinical trials change over time. These include: + • How sponsoring organizations change. + • Expected vs Actual + ◦ time to completion + ◦ enrollment + • Roughly when trials transition between recruiting statuses, e.g. percentage of time spent recruiting. + ◦ Possibly measurable at different sites. +The aformentioned ideal circumstances occur when: + • The trial was started after <>, so version information is captured from the beginning of the trial. + • The trial has concluded. +Objectives +The objective of this research project is to estimate +Potential Topics +Some areas of interest. + 1. Estimate Probability of Success of moving between Phases. + 1. Depends on either FDA data or use of an ML matching process (Hard to do right). + 2. Follow E.K.’s approach of splitting Technical vs Scientific failures to progress. + 3. Denormalize the “process” of moving between phase 1 → phase 2 → phase 3 that is talked about and actually represent the real paths taken. + 1. As selection from a set of paths. + 2. Seeking approval for a new indication may not require initial phase trials. This “extraneous” information may be hard to capture/model/control for. + 3. Denormalizing this path may work as a proxy for changes in regulatory strictness? + 1. Probably not, because choice of path is probably dependent on already known information on safety. + 2. Estimate probability of success for approval based on trial data? How do biologics differ from standard small-molecule drugs? + 1. Haven’t put much thought in to this, not sure what the value proposition would be. + 3. Estimate entry rates/Probability Of Success of Biologics/biosimilars + 1. There appear to be lots of competing biosimilars in insulin. + 4. Develop “diagnostic tests” that will allow effects of policy to be forecast partway through implementation. + 1. Something such as measures of phase 1 trials and phase 2 trials to predict phase 3 & approval rates. +Possible Hypotheses +Do Probabilities of Success differ significantly when estimated on normalized or denormalized paths? + • With the FDA data, this will be uniquely answerable. Not sure though what those implications of interest are though. + • The value proposition here is that it might “measure” the risk reducing effect of that extra knowledge from previous studies, evidence, etc. I wonder if this could act as an instrument on the effect of unknown risk when starting a new compound? + ◦ The underlying assumption is that the path depends heavily on whether the compound has been studied previously or not. That may not be true. + ◦ Provides the “risk value” of prior phases. Maybe allows modeling as net future value? +Do Probabilities of Success depend on reimbursment rate? + • The thought here is that if reimbursement rates increase, more R&D is spent on “long-shot” drugs that are likely to develop new fields of research. + • Requires some sort of reimbursement/pricing data. Not necessarily available, and would require a natural experiment. + +Intermediate goals + 1. Find literature that describes each difficult section of the R&D to successful launch process, focusing on the second section. + +Policy questions/ topics +Precision medicines & trials + + + +Insurers paying for participants in a clinical trial. Medicare pays for many, but what effect does this have on recruitment? Possible variation across drugs (legislatively), as identification. + • Endpoint doesn’t matter for recruiting + • Our data may show the “rate” at which people join trials. + ◦ Does medicare coverage for the trial speed that up? + +Use of surragate endpoints. Risk sharing between insurer and developer. + • Cancer: survival rates vs what happened to the cancer + • Reduces length of trial, but requires phase 4 trials + • Effects on recruiting/timing + +Possible Tasks +Lit review +Synthetic data, possible modeling approaches. +What does it take to run API’s again: teach someone else how to run it. +