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120 lines
5.3 KiB
TeX
120 lines
5.3 KiB
TeX
\documentclass[../Main.tex]{subfiles}
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\graphicspath{{\subfix{Assets/img/}}}
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\begin{document}
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%%%%%%%%%%%%%%%% What do we know about clinical trials?
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\subsection{What do we know about clinical trials and their success rates?}
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Most studies of clinical trials attempt to model only those trials
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which are involved in the drug approval process.
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% Hwang, Carpenter, Lauffenburger, et al (2016)
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% - Why do investigational new drugs fail during late stage trials?
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\cite{hwang_failure_2016}
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investigated causes for which late stage (Phase III)
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clinical trials fail across the USA, Europe, Japan, Canada, and Australia.
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They found that for late stage trials that did not go on to recieve approval,
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57\% failed on efficacy grounds, 17\% failed on safety grounds, and 22\% failed
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on commercial or other grounds.
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% Abrantes-Metz, Adams, Metz (2004)
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% - What correlates with successfully passing clinical trials and FDA review?
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% -
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\cite{abrantes-metz_pharmaceutical_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 used a
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mixed state proportional hazard model and estimated the impact of
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observed characteristics in each of the three phases.
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They found that as trials last longer, the rate of failure increases for
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Phase I and II trials, while Phase 3 trials generally have a higher rate of
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success than failure after 91 months.
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\cite{hay_ClinicalDevelopment_2014} tracks clinical trials based on
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the number of indications studied.
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They find that 10.4\% of all novel drug development paths for an indication,
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studied in a phase I trial, are ultimately approved by the FDA.
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\cite{wong_EstimationClinical_2019}
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constructed a model where they estimated each, which they used to estimate the
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probability of completing a given phase, conditional on starting a previous phase.
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In doing so, they found that 13.8\% of all drug development programs
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completed successfully, which is higher than the approximately 10\% rate
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others have found\cite{hay_ClinicalDevelopment_2014}.
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One cause of this may be that they considered that a single drug might
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be used tested for multiple indications.
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% Large dataset.
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% they found lower estimates than previous work.
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% Ekaterina Khmelnitskaya (2021)
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% - separates scientific from market failure of the clinical drug pipeline
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%In her doctoral dissertation, Ekaterina Khmelnitskaya
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\cite{khmelnitskaya_CompetitionAttrition_2021} approaches a slightly
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different problem.
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She created a multistage model to track the transition of
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drug candidates between clinical trial phases.
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Her key contribution was to find ways to disentangle strategic exits where
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firms remove novel from the development pipeline and
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exits due to scientific failures
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(where safety and efficacy did not prove sufficient).
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She estimates 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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%%%%%%%%% What do we know about drug development incentives?
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\subsection{What do we know about drug development incentives?}
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% Introduce section
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% key points
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% - multiple types of drugs (generic and brand named)
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% - These respond differently
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% - Dranov et al 2022 - demand pull seems to bias follow up drug development.
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% - increasing demand doesn't necessarily result in new compounds (check this). Risks.
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% - acemoglu and linn 2004 - population size matters.
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% - Note then that separating effects is difficult at the drug development level.
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% - Population ties into the number of drugs available, and operational (recruitment) concerns
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% - In general, there are going to be many confounding variables.
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% -
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%
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%
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% Dranov, Garthwaite, and Hermosilla (2022)
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% - does the demand-pull theory of R&D explain novel compound development?
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% - no, it is biased towards follow-on drug R&D.
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% Acemoglu and Linn
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% - Market size in innovation
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% - 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, %TODO:remove when other sections are written up.
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\citeauthor{acemoglu_market_2004}
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(\citeyear{acemoglu_market_2004})
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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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% Gupta
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% - Inperfect intellectual property rights in the pharmaceutical industry
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\cite{gupta_OneProduct_2020}
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\todo{Sumarize how intellectual property rights affect things}
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% - link to difference between novel and generics from acemoglu and linn
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% Agarwal and Gaule 2022
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% - Retrospective on impact from COVID-19 pandemic
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% Not in this version
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\subsection{What do we know about how Clinical Trials proceed?}
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%interview with Adam George
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% - clinical trials are often handled by contractors
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% - they plan sites, start times, etc from beginning.
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% - Running late is normal.
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% Results on enrollment projection
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% - nothing really good exists.
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% - no cross validation, only tested on a few trials.
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\end{document}
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