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Will King
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Will King
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Will King
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Will King
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\documentclass[../Main.tex]{subfiles}
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\graphicspath{{\subfix{Assets/img/}}}
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\begin{document}
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In 1938, President Franklin D.
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Roosevelt signed the Food, Drug, and Cosmetic Act, establishing the Food and
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Drug Administration's (FDA) authority to require pre-market approval of
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pharmaceuticals [Com14].
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This created a regulatory framework where pharmaceutical companies must
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demonstrate safety and efficacy through clinical trials before bringing drugs
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to market.
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The costs of these trials - both in time and money - form a significant barrier
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to entry in pharmaceutical markets.
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Understanding what causes clinical trials to fail is therefore crucial to
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predict the impact of policies, intended or unintended.
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Existing research has examined how drugs progress through development
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pipelines, but we know relatively little about the relative contribution of different
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challenges to the early termination of clinical trials.
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%HWANG et al do discuss a few different reasons
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When a trial terminates early due to operational challenges rather than safety
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or efficacy concerns, potentially effective treatments may be delayed or
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abandoned entirely.
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%Example of GLP-1s
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This paper provides the first empirical framework to separate
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market-driven and safety/efficacy based terminations from
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one form of operational failure
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-- enrollment challenges --
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in Phase III clinical trials.
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Using a novel dataset constructed from administrative data registered on
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ClinicalTrials.gov, I exploit variation in enrollment timing and market
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conditions to identify how extending the enrollment period affects trial completion.
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Specifically, I answer the question:
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\textit{
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``How does the probability of trial termination change
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when the enrollment period is extended?''
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}
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This approach differs from previous work that focuses for the most part
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on the drug development
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pipeline and progression between clinical trial phases.
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% In 1938 President Franklin D Rosevelt signed the Food, Drug, and Cosmetic Act,
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% granting the Food and Drug Administration (FDA) authority to require
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% pre-market approval of pharmaceuticals.
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% \cite{commissioner_milestonesusfood_2023}
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% As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed
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% compounds with Structured Product Labels (SPLs)
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% and 10,983 previously-marketed SPLs
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% \cite{commissioner_nsde_2024},
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% %from nsde table. Get number of unique application_nubmers_or_citations with most recent end date as null.
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% In 1999, they began requiring that drug developers register and
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% publish clinical trials on \url{https://clinicaltrials.gov}.
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% This provides a public mechanism where clinical trial sponsors are
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% responsible to explain what they are trying to acheive and how it will be
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% measured, as well as provide the public the ability to search and find trials
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% that they might enroll in.
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% Multiple derived datasets such as the Cortellis Investigational Drugs dataset
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% or the AACT dataset from the Clinical Trials Transformation Intiative
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% integrate these data.
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% This brings up a question:
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% Can we use this public data on clinical trials to identify what effects the
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% success or failure of trials?
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% In this work, I use updates to records on
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% \url{https://ClinicalTrials.gov}
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% to do exactly that, disentangle the effect of participant enrollment
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% and competing drugs on the market affect the success or failure of
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% clinical trials.
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\subsection{Background}
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%Describe how clinical trials fit into the drug development landscape and how they proceed
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Clinical trials are a required part of drug development.
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Not only does the FDA require that a series of clinical trials demonstrate sufficient safety and efficacy of
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a novel pharmaceutical compound or device, producers of derivative medicines may be required to ensure that
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their generic small molecule compound -- such as ibuprofen or levothyroxine -- matches the
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performance of the originiator drug if delivery or dosage is changed.
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For large molecule generics (termed biosimilars) such as Adalimumab
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(Brand name Humira, with biosimilars Abrilada, Amjevita, Cyltezo, Hadlima, Hulio,
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Hyrimoz, Idacio, Simlandi, Yuflyma, and Yusimry),
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the biosimilars are required to prove they have similar efficacy and safety to the
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reference drug.
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%TODO? Decide whether to include this or not
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%When registering these clinical trials
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% discuss how these are registered and what data is published.
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% Include image and discuss stages
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% Discuss challenges faced
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% Introduce my work
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In the world of drug development, these trials are classified into different
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phases of development\footnote{
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\cite{anderson_fdadrugapproval_2022}
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provide an overview of this process
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while
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\cite{commissioner_drugdevelopmentprocess_2020}
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describes the process in detail.}.
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Pre-clinical studies primarily establish toxicity and potential dosing levels.
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% \cite{commissioner_drugdevelopmentprocess_2020}.
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Phase I trials are the first attempt to evaluate safety and efficacy in humans.
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Participants typically are healthy individuals, and they measure how the drug
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affects healthy bodies, potential side effects, and adjust dosing levels.
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Sample sizes are often less than 100 participants.
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% \cite{commissioner_drugdevelopmentprocess_2020}.
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Phase II trials typically involve a few hundred participants and is where
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investigators will dial in dosing, research methods, and safety.
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% \cite{commissioner_drugdevelopmentprocess_2020}.
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A Phase III trial is the final trial before approval by the FDA, and is where
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the investigator must demonstrate safety and efficacy with a large number of
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participants, usually on the order of hundreds or thousands.
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% \cite{commissioner_drugdevelopmentprocess_2020}.
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Occassionally, a trial will be a multiphase trial, covering aspects of either
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Phases I and II or Phases II and III.
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After a successful Phase III trial, the sponsor will decide whether or not
|
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to submit an application for approval from the FDA.
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Before filing this application, the developer must have completed
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``two large, controlled clinical trials.''
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% \cite{commissioner_drugdevelopmentprocess_2020}.
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Phase IV trials are used after the drug has received marketing approval to
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validate safety and efficacy in the general populace.
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Throughout this whole process, the FDA is available to assist in decision-making
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regarding topics such as study design, document review, and whether
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they should terminate the trial.
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The FDA also reserves the right to place a hold on the clinical trial for
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safety or other operational concerns, although this is rare.
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\cite{commissioner_drugdevelopmentprocess_2020}.
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In the economics literature, most of the focus has been on describing how
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drug candidates transition between different phases and their probability
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of final approval.
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% Lead into lit review
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% Abrantes-Metz, Adams, Metz (2004)
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\authorcite{abrantes-metz_pharmaceuticaldevelopmentphases_2004}
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described the relationship between
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various drug characteristics and how the drug progressed through clinical trials.
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% This descriptive estimate was notable for using a
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% mixed state proportional hazard model and estimating the impact of
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% observed characteristics in each of the three phases.
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They found that as Phase I and II trials last longer,
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the rate of failure increases.
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In contrast, Phase 3 trials generally have a higher rate of
|
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success than failure after 91 months.
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This may be due to the fact that the purpose of Phases I and II are different
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from the purpose of Phase III.
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Continuing on this theme,
|
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%DiMasi FeldmanSeckler Wilson 2009
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\authorcite{dimasi_trendsrisksassociated_2010}
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examine the completion rate of clinical drug
|
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develompent and find that for the 50 largest drug producers,
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approximately 19\% of their drugs under development between 1993 and 2004
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successfully moved from Phase I to recieving an New Drug Application (NDA)
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or Biologics License Application (BLA).
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They note a couple of changes in how drugs are developed over the years they
|
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study, most notably that
|
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drugs began to fail earlier in their development cycle in the
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latter half of the time they studied.
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They note that this may reduce the cost of new drugs by eliminating late
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and costly failures in the development pipeline.
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Earlier work by
|
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\authorcite{dimasi_valueimprovingproductivity_2002}
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used data on 68 investigational drugs from 10 firms to simulate how reducing
|
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time in development reduces the costs of developing drugs.
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He estimates that reducing Phase III of clinical trials by one year would
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reduce total costs by about 8.9\% and that moving 5\% of clinical trial failures
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from phase III to Phase II would reduce out of pocket costs by 5.6\%.
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|
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A key contribution to this drug development literature is the work by
|
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\authorcite{khmelnitskaya_competitionattritiondrug_2021}
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who created a causal identification strategy
|
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to disentangle strategic exits from exits due to clinical failures
|
||||
in the drug development pipeline.
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She found that overall 8.4\% of all pipeline exits are due to strategic
|
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terminations and that the rate of new drug production would be about 23\%
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higher if those strategic terminatations were elimintated.
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|
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The work that is closest to mine is the work by
|
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\authorcite{hwang_failureinvestigationaldrugs_2016}
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||||
who investigated causes for which late stage (Phase III)
|
||||
clinical trials fail -- with a focus on trials in the USA,
|
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Europe, Japan, Canada, and Australia.
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They identified 640 novel therapies and then studied each therapy's
|
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development history, as outlined in commercial datasets.
|
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They found that for late stage trials that did not go on to recieve approval,
|
||||
57\% failed on efficacy grounds, 17\% failed on safety grounds, and 22\% failed
|
||||
on commercial or other grounds.
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Unfortunately the work of both
|
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\authorcite{hwang_failureinvestigationaldrugs_2016}
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||||
and
|
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\authorcite{khmelnitskaya_competitionattritiondrug_2021}
|
||||
ignore a potentially large cause of failures: operational challenges, i.e. when
|
||||
issues running or funding the trial cause it to fail before achieving its
|
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primary objective.
|
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In a personal review of 199 randomly selected clinical trials which terminated
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before achieving their primary objective,
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I found that
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14.5\% cited safety or efficacy concerns,
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||||
9.1\% cited funding problems (an operational concern),
|
||||
and
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31\% cited enrollment issues (a separate operational concern)\footnote{
|
||||
Note that these figures differ from
|
||||
\authorcite{hwang_failureinvestigationaldrugs_2016}
|
||||
because I sampled from all stages of trials, not just Phase III trials
|
||||
focused on drug development.
|
||||
}.
|
||||
|
||||
|
||||
|
||||
|
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The main contribution of this work is the model I develop to separate
|
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the causal effects of
|
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market conditions (a strategic concern) from the effects of
|
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participant enrollment (an operational concern) on Phase III Clinical trials.
|
||||
This allows me to answer the question posed earlier:
|
||||
\textit{
|
||||
``How does the probability of trial termination change
|
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when the enrollment period is extended?''
|
||||
}
|
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using administrative data.
|
||||
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To understand how I do this, we'll cover some background information on
|
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clinical trials and the administrative data I collected in section
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\ref{SEC:ClinicalTrials},
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explain the approach to causal identification, the required data,
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||||
and describe how the data used matches these requirements in section
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\ref{SEC:CausalAndData}.
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Then we'll cover the econometric model
|
||||
(section \ref{SEC:EconometricModel})
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and results (section
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\ref{SEC:Results}).
|
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Finally, we acknowledge deficiencies in the analysis and potential improvements
|
||||
in section
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\ref{SEC:Improvements},
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then end with my thoughts in the conclusion \ref{SEC:Conclusion}
|
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% \subsection{Market incentives and drug development}
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% %%%%%%%%% What do we know about drug development incentives?
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%
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% \cite{dranove_DoesConsumer_2022} use the implementation of Medicare part D
|
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% to examine whether the production of novel or follow up drugs increases during
|
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% the following 15 years.
|
||||
% They find that when Medicare part D was implemented -- increasing senior
|
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% citizens' ability to pay for drugs -- there was a (delayed) increase
|
||||
% in drug development, with effects concentrated among compounds that were least
|
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% innovative according to their classification of innovations.
|
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% They suggest that this is due to financial risk management, as novel
|
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% pharmaceuticals have a higher probability of failure compared to the less novel
|
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% follow up development.
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% This is what leads risk-adverse companies to prefer follow up development.
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%
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%
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% % Acemoglu and Linn
|
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% % - Market size in innovation
|
||||
% % - Exogenous demographic trends has a large impact on the entry of non-generic drugs and new molecular entitites.
|
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% On the side of market analysis,
|
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% \citeauthor{acemoglu_market_2004}
|
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% (\citeyear{acemoglu_market_2004})
|
||||
% used exogenous deomographics changes to show that the
|
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% entry of novel compounds is highly driven by the underlying aged population.
|
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% They estimate that a 1\% increase in applicable demographics increase the
|
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% entry of new drugs by 6\%, mostly concentrated among generics.
|
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% Among non-generics, a 1\% increase in potential market size
|
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% (as measured by demographic groups) leads to a 4\% increase in novel therapies.
|
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%
|
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% % Gupta
|
||||
% % - Inperfect intellectual property rights in the pharmaceutical industry
|
||||
% \cite{gupta_OneProduct_2020} discovered that uncertainty around which patents
|
||||
% might apply to a novel drug causes a delay in the entry of generics after
|
||||
% the primary patent has expired.
|
||||
% She found that this delay in delivery is around 3 years.
|
||||
%
|
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% % Agarwal and Gaule 2022
|
||||
% % - Retrospective on impact from COVID-19 pandemic
|
||||
% % Not in this version
|
||||
%
|
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% \subsection{Understanding Failures in Drug Development}
|
||||
%
|
||||
% % DISCUSS: Different types of failures
|
||||
% There are myriad of reasons that a drug candidate may not make it to market,
|
||||
% regardless of it's novelty or known safety.
|
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% In this work, I focus on the failure of individual clinical trials, but the
|
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% categories of failure apply to the individual trials as well as the entire
|
||||
% drug development pipeline.
|
||||
% They generally fall into one of the following categories:
|
||||
% \begin{itemize}
|
||||
% \item Scientific Failure: When there are issues regarding
|
||||
% safety and efficacy that must be addressed.
|
||||
% The preeminient question is:
|
||||
% ``Will the drug work for patients?''
|
||||
% %E.Khm, Gupta, etc.
|
||||
% \item Strategic Failure: When the sponsors stop development because of
|
||||
% profitability
|
||||
% %Whether or not the drug will be profitiable, or align with
|
||||
% %the drug developer's future Research \& Development directions i.e.
|
||||
% ``Will producing the drug be beneficial to the
|
||||
% company in the long term?''
|
||||
% %E.Khm, Gupta, GLP-1s, etc.
|
||||
% \item Operational concerns are answers to:
|
||||
% %Whether or not the developer can successfully conduct
|
||||
% %operations to meet scientific or strategic goals, i.e.
|
||||
% ``What has prevented the the company from being able to
|
||||
% finance, develop, produce, and market the drug?''
|
||||
% \end{itemize}
|
||||
% It is likely that a drug fails to complete the development cycle due to some
|
||||
% combination of these factors.
|
||||
%
|
||||
%
|
||||
% %USE MetaBio/CalBio GLP-1 story to illuistrate these different factors.
|
||||
% \cite{flier_DrugDevelopment_2024} documents the case of MetaBio, a company
|
||||
% he was involved in founding that was in the first stages of
|
||||
% developing a GLP-1 based drug for diabetes or obesety before being shut down
|
||||
% in .
|
||||
% MetaBio was a wholy owned subsidiary of CalBio, a metabolic drug development
|
||||
% firm, that recieved a \$30 million -- 5 year investment from Pfizer to
|
||||
% persue development of GLP-1 based therapies.
|
||||
% At the time it was shut down, it faced a few challenges:
|
||||
% \begin{itemize}
|
||||
% \item The compound had a short half life and they were seeking methods to
|
||||
% improve it's effectiveness; a scientific failure.
|
||||
% \item Pfizer imposed a requirement that it be delivered though a route
|
||||
% other than injection (the known delivery mechanism); a strategic failure.
|
||||
% \item When Pfizer pulled the plug, CalBio closed MetaBio because they
|
||||
% could not find other funding sources; an operational failure.
|
||||
% \end{itemize}
|
||||
%
|
||||
% The author states in his conclusion:
|
||||
% \begin{displayquote}
|
||||
% Despite every possibility of success,
|
||||
% MetaBio went down because there were mistaken ideas about what was
|
||||
% possible and what was not in the realm of metabolic therapeutics, and
|
||||
% because proper corporate structure and adequate capital are always
|
||||
% issues when attempting to survive predictable setbacks.
|
||||
% \end{displayquote}
|
||||
%
|
||||
% From this we see that there was a cascade of issues leading to the failure to
|
||||
% develop this novel drug.
|
||||
%
|
||||
%
|
||||
% % I don't think I need to include modelling enrollment here.
|
||||
% % If it is applicable, it can show up in those sections later.
|
||||
%
|
||||
%
|
||||
|
||||
\end{document}
|
||||
@ -0,0 +1,114 @@
|
||||
\documentclass[../Main.tex]{subfiles}
|
||||
\graphicspath{{\subfix{Assets/img/}}}
|
||||
|
||||
\begin{document}
|
||||
|
||||
% Clinical Trials Background Outline
|
||||
% - ClinicalTrials.gov
|
||||
% - Clincial trial progression
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
|
||||
To understand how my administrative clinical trial data is obtained
|
||||
and what it can be used for,
|
||||
let's take a look at how trial investigators record data on
|
||||
\url{ClinicalTrials.gov}.
|
||||
Figure \ref{Fig:Stages} illuistrates the process I describe below.
|
||||
During the Pre-Trial period the trial investigators will design the trial,
|
||||
choose primary and secondary objectives,
|
||||
and decide on how many participants they need to enroll.
|
||||
Once they have decided on these details, they post the trial to \url{ClinicalTrials.com}
|
||||
and decide on a date to begin enrolling trial participants.
|
||||
If the investigators decide to not continue with the trial before enrolling any participants,
|
||||
the trial is marked as ``Withdrawn''.
|
||||
On the other hand, if they begin enrolling participants, there are two methods to do so.
|
||||
The first is to enter a general ``Recruiting'' state, where patients attempt to enroll.
|
||||
The second is to enter an "Enrollment by invitation only" state.
|
||||
After a trial has enrolled their participants, they wil typically move to an
|
||||
"Active, not recruiting" state to inform potential participants that they are
|
||||
not recruiting.
|
||||
Finally, when the investigators have obtained enough data to achieve their primary
|
||||
objective, the clinical trial will be closed, and marked as ``Completed'' in
|
||||
\url{ClinicalTrials.gov}
|
||||
If the trial is closed before achieving the primary objective, the trial is
|
||||
marked as ``Terminated'' on
|
||||
\url{ClinicalTrials.gov}.
|
||||
|
||||
|
||||
\begin{figure}%[H] %use [H] to fix the figure here.
|
||||
\includegraphics[width=\textwidth]{../assets/img/ClinicalTrialStagesAndStatuses}
|
||||
\par \small
|
||||
Diamonds represent decision points while
|
||||
Squares represent states of the clinical trial and Rhombuses represend data obtained by the trial.
|
||||
\caption[Clinical Trial Stages and Progression]{Clinical Trial Stages and Progression}
|
||||
\label{Fig:Stages}
|
||||
\end{figure}
|
||||
|
||||
Note the information we obtain about the trial from the final status:
|
||||
``Withdrawn'', ``Terminated'', or ``Completed''.
|
||||
Although
|
||||
\cite{khmelnitskaya_competitionattritiondrug_2021}
|
||||
describes a clinical failure due to safety or efficacy as a
|
||||
\textit{scientific} failure, it is better described as a compound failure.
|
||||
Discovering that a compound doesn't work as hoped is not a failure but the whole
|
||||
purpose of the clinical trials process.
|
||||
On the other hand, when a trial terminates early due to reasons
|
||||
other than safety or efficacy concerns, the trial operator does not learn
|
||||
if the drug is effective or safe.
|
||||
This is a knowledge-gathering failure where the trial operator
|
||||
did not learn if the drug was effective or not.
|
||||
I prefer describing a clinical trial as being terminated for
|
||||
\begin{itemize}
|
||||
\item Safety or Efficacy concerns
|
||||
\item Strategic concerns
|
||||
\item Operational concerns.
|
||||
\end{itemize}
|
||||
|
||||
Unfortunately it can be difficult to know why a given trial was terminated,
|
||||
in spite of the fact that upon termination, trials typically record a
|
||||
description of \textit{a single} reason for the clinical trial termination.
|
||||
This doesn't necessarily list all the reasons contributing to the trial termination and may not exist for a given trial.
|
||||
For example, if a Principle Investigator leaves for another institution
|
||||
(terminating the trial), is this decison affected by
|
||||
a safety or efficacy concern,
|
||||
a new competitor on the market,
|
||||
difficulting recruiting participants,
|
||||
or a lack of financial support from the study sponsor?
|
||||
Estimating the impact of different problems that trials face from these
|
||||
low-information, post-hoc signals is insufficient.
|
||||
For this reason, I use clinical trial progression to estimate effects.
|
||||
\todo{not sure if this is the best place for this.}
|
||||
|
||||
As a trial goes through the different stages of recruitment, the investigators
|
||||
update the records on ClinicalTrials.gov.
|
||||
Even though there are only a few times that investigators are required
|
||||
to update this information, it tends to be updated somewhat regularly as it is
|
||||
a way to communicate with potential enrollees.
|
||||
When a trial is first posted, it tends to include information
|
||||
such as planned enrollment,
|
||||
planned end dates,
|
||||
the sites at which it is being conducted,
|
||||
the diseases that it is investigating,
|
||||
the drugs or other treatments that will be used,
|
||||
the experimental arms that will be used,
|
||||
and who is sponsoring the trial.
|
||||
As enrollment is opened and closed and sites are added or removed,
|
||||
investigators will update the status and information
|
||||
to help doctors and potential participants understand whether they should apply.
|
||||
|
||||
|
||||
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
|
||||
|
||||
\end{document}
|
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@ -0,0 +1,143 @@
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Binary file not shown.
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After Width: | Height: | Size: 95 KiB |
Loading…
Reference in New Issue