You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
153 lines
6.9 KiB
TeX
153 lines
6.9 KiB
TeX
\documentclass[../Main.tex]{subfiles}
|
|
\graphicspath{{\subfix{Assets/img/}}}
|
|
|
|
\begin{document}
|
|
|
|
% TODO:
|
|
%Need to distinguish that this isn't about drug development specifically, but
|
|
% more around clinical trials progress.
|
|
% Thoughts:
|
|
% - What types of failures do clinical trials have an why do clinical trials fail?
|
|
% - What is cited in termination reasons?
|
|
% - Discussion on different types of failures:
|
|
% 1. Failure to find safe and effective drug
|
|
% 2. Failure to collect enough information to determine 1
|
|
% - recruiting
|
|
% - New information (other studies, changes in standards of care, etc)
|
|
% 3. Failure due to other operational/strategic concern.
|
|
% - Issues with PI/Sponsors
|
|
% - profitability expectations
|
|
% - Financial support
|
|
% Trials can fail due to issues in one of four categories
|
|
% - Scientific: The compound-indication combo is not sufficiently safe or efficatious
|
|
% - Strategic: The result from the trial won't meet the company's strategic needs. Profitability for the most part or entry into a drug category they don't want to invest in.
|
|
% - Operational: They can't bring resources to bear to achieve goal, e.g. site planning, PI's, delivering enough financial support, etc.
|
|
% - Tactical: The plans to discover if the approach is scientifically valid are insufficient. Study design, enrollment strategies, etc.
|
|
|
|
% The literature examines many different aspects of these.
|
|
% strategic influences: elasticicity of innovation, demand pull, patents, etc
|
|
% Strategic vs scientific khmelniskaya, hwang.
|
|
% operational: Adam George Interview
|
|
% tactical: enrollment prediction.
|
|
|
|
|
|
% - Discuss how most studies are about clinical trials as part of the drug development pipeline.
|
|
% - Review what we know about causes for failure.
|
|
% - Things that correlate with failures. (adams)
|
|
% - Causes for failure (khmelnitskaya & hwang)
|
|
% - Then talk about studies on clinical trials themselves.
|
|
% - Interview with Adam George
|
|
% - Poor studies of enrollment prediction.
|
|
% -
|
|
% - Then talk about what drives approvals/clinical trial activity for drugs
|
|
% - Elasticity of innovation
|
|
% - No demand pull for novel drugs, but yes for derivatives
|
|
% - Population and Market size interact & drive development (jointly determined)
|
|
% - This doesn't apply to single trials though
|
|
% -
|
|
% - Sumarize
|
|
% - Thus when trying to study what affects clinical trials we must separate
|
|
% - Market & competition effects
|
|
% - Population effects
|
|
% - Multiple trial failure modes (safety & efficacy, Operational etc)
|
|
% -
|
|
% -
|
|
|
|
This paper sits within an intersection of health and industrial organization economics
|
|
that is frequently studied.
|
|
Encouraging a strong supply of novel and generic pharmaceuticals contributes
|
|
in important ways to both public health and fiscal policy.
|
|
Not only to the pathway to drug approval long, as many as 90\% of compounds
|
|
that begin human trials fail to gain approval
|
|
(\cite{khmelnitskaya_competition_2021}).
|
|
Complicating this is the complex regulatory and competitive environment in
|
|
which pharmaceutical companies operate.
|
|
|
|
%%%%%%%%% Why are drugs so expensive?
|
|
|
|
% van der Grond, Uyle-de Groot, Pieters 2017
|
|
% - What causes high costs of drugs?
|
|
% - High level synthesis of discussion regarding causes
|
|
% - Academic and non-academic sources
|
|
% Not particularly applicable
|
|
|
|
%%%%%%%%%%%%%%%% What do we know about clinical trials?
|
|
|
|
% Hwang, Carpenter, Lauffenburger, et al (2016)
|
|
% - Why do investigational new drugs fail during late stage trials?
|
|
\citeauthor{hwang_failure_2016} (\citeyear{hwang_failure_2016})
|
|
investigated causes for which late stage (Phase III)
|
|
clinical trials fail across the USA, Europe, Japan, Canada, and Australia.
|
|
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.
|
|
For context, this current work hopes to be able to distinguish some of the
|
|
mechanisms behind those commercial or other failures.
|
|
|
|
% Abrantes-Metz, Adams, Metz (2004)
|
|
% - What correlates with successfully passing clinical trials and FDA review?
|
|
% -
|
|
In \citeyear{abrantes-metz_pharmaceutical_2004},
|
|
\citeauthor{abrantes-metz_pharmaceutical_2004}
|
|
described the relationship between
|
|
various drug characteristics and how the drug progressed through clinical trials.
|
|
This non-causal estimate was notable for using a
|
|
mixed state proportional hazard model and estimating the impact of
|
|
observed characteristics in each of the three phases.
|
|
They found that as trials last longer, the rate of failure increases for
|
|
Phase I \& II trials, while Phase 3 trials generally have a higher rate of
|
|
success than failure after 91 months.
|
|
|
|
% Ekaterina Khmelnitskaya (2021)
|
|
% - separates scientific from market failure of the clinical drug pipeline
|
|
In her doctoral dissertation, Ekaterina Khmelnitskaya studied the transition of
|
|
drug candidates between clinical trial phases.
|
|
Her key contribution was to find ways to disentangle strategic exits from the
|
|
development pipeline and exits due to clinical failures.
|
|
She found that overall 8.4\% of all pipeline exits are due to strategic
|
|
terminations and that the rate of new drug production would be about 23\%
|
|
higher if those strategic terminatations were elimintated
|
|
(\cite{khmelnitskaya_competition_2021}).
|
|
% causal separation of strategic exits etc.
|
|
|
|
% Waring, Arrosmith, Leach, et al (2015)
|
|
% - Atrition of drug candidates from four major pharma companies
|
|
% - Looked at how phisicochemical properties affected clinical failure due to safety issues
|
|
% not Applicable in this version
|
|
|
|
|
|
|
|
|
|
%%%%%%%%% What do we know about drug development incentives?
|
|
|
|
% Dranov, Garthwaite, and Hermosilla (2022)
|
|
% - does the demand-pull theory of R&D explain novel compound development?
|
|
% - no, it is biased towards follow-on drug R&D.
|
|
% TODO
|
|
|
|
% Acemoglu and Linn
|
|
% - Market size in innovation
|
|
% - Exogenous demographic trends has a large impact on the entry of non-generic drugs and new molecular entitites.
|
|
On the side of market analysis, %TODO:remove when other sections are written up.
|
|
\citeauthor{acemoglu_market_2004}
|
|
(\citeyear{acemoglu_market_2004})
|
|
used exogenous deomographics changes to show that the
|
|
entry of novel compounds is highly driven by the underlying aged population.
|
|
They estimate that a 1\% increase in applicable demographics increase the
|
|
entry of new drugs by 6\%, mostly concentrated among generics.
|
|
Among non-generics, a 1\% increase in potential market size
|
|
(as measured by demographic groups) leads to a 4\% increase in novel therapies.
|
|
|
|
% 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 averages around 3 years.
|
|
|
|
% Agarwal and Gaule 2022
|
|
% - Retrospective on impact from COVID-19 pandemic
|
|
% Not in this version
|
|
|
|
\end{document}
|