Merge branch 'rewrite_section'
Will King
1 year ago
commit
bdb7ddb27c
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layout {
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tab name="Main and Compile" cwd="~/research/PhD_Deliverables/jmp/Latex/Paper/" hide_floating_panes=true focus=true {
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// This tab is where I manage main from.
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// it opens up Main.txt for my JMP, opens the pdf in okular (in a floating tab), and then get's ready to build the pdf.
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pane edit="Main.tex" focus=true // This is the editor
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// this is the compilation window
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pane size="60%" command="compiletex" {
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args "Main.tex"
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start_suspended true
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// This is the ls of sections
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pane size="35%" command="ls"{
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floating_panes {
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// here is where I run okular from, it is auto hidden
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tab name="sections" cwd="~/research/PhD_Deliverables/jmp/Latex/Paper/sections/" {
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pane split_direction="horizontal" {
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pane command="watch" {
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args "--color" "git status"
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// requires `git config --global color.status always` to be set
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pane command="git" {
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args "log" "-n 10" "--all" "--oneline" "--graph" "--stat" "--decorate"
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layout {
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tab name="Main and Compile" cwd="~/research/phd_deliverables/jmp/Latex/Paper" hide_floating_panes=true focus=true {
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// This tab is where I manage main from.
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// it opens up Main.txt for my JMP, opens the pdf in okular (in a floating tab), and then get's ready to build the pdf.
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pane size=1 borderless=true {
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plugin location="tab-bar"
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}
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pane split_direction="vertical" {
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pane edit="./Main.tex" focus=true // This is the editor
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pane split_direction="horizontal" {
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// this is the compilation window
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pane size="60%" command="comlatex.sh" {
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args "Main.tex"
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start_suspended true
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}
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// This is the ls of sections
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pane size="35%" command="ls"{
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args "sections/"
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}
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}
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}
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floating_panes {
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// here is where I run okular from, it is auto hidden
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pane command="okular" {
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args "Main.pdf"
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}
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pane size=2 borderless=true {
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tab name="sections" cwd="~/research/phd_deliverables/jmp/Latex/Paper/sections" {
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pane size=1 borderless=true {
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}
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}
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tab name="git" cwd="~/research/phd_deliverables/jmp/Latex/Paper/" {
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pane size=1 borderless=true {
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plugin location="tab-bar"
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}
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pane split_direction="vertical" {
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pane split_direction="horizontal" {
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pane command="watch" {
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args "--color" "git status"
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// requires `git config --global color.status always` to be set
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}
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pane size="30%" {
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focus true
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}
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}
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pane command="git" {
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args "log" "-n 10" "--all" "--oneline" "--graph" "--stat" "--decorate"
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}
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}
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pane size=2 borderless=true {
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plugin location="status-bar"
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}
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}
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@ -0,0 +1,18 @@
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NEXT STEPS IN WRITING
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- insert a description of the general approach I use:
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- predicting, based on snapshots, the likelihood of termination.
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- this needs to go between the description of the snapshots and the
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causal inference introduction.
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- Then I can use what I've written about the graph, and follow up with more information about the data.
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Overall this would look like
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- [x] Introduction of the question and general issues of confoundedness.
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- [x] Clinical Trials Data Sources
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- [x] Explain basic econometric modelling approach
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- [ ] Then explain the graph, nodes, and confoundedness in more detail
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- [ ] Then go over the rest of the data.
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- [ ] Finally
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- Discuss the number of datapoints.
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- review major challenges to causal identification. (no enrollment model small data size)
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@ -0,0 +1,34 @@
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Outlining for jmp
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<intro>
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Introduction and problem statement
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*Explain what I am doing:*
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</intro>
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<literature
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Describe what has been done
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- measuring failure rates & impact
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Introduce different types of failure
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- Scientific
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- Strategic
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- Operational
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Efforts to measure failures
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Medbio story to illuistrate failure modes.
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Operational and strategic failures undermine scientific process of discovery
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*My effort is to separate...*: place my work in context
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Introduce clinical trials' progressions, stages, and statuses.
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</literature>
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<causal model>
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Derive causal model
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</causal model>
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<data>
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Summarize data sources
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</data>
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<econometrics>
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Introduce econometric model
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</econmetrics>
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<results>
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Discuss econometric results
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</results>
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Conclusion
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Appendicies
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- in-depth data source info
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- More econometric results
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@ -0,0 +1,58 @@
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In 19xx the United States Food and Drug Administration (FDA) was created to "QUOTE".
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As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed compounds with Structured Product Labels (SPL)
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and 10,983 previously-marketed SPLs.
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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 2007, they began requiring that drug developers register and publish clinical trials on \url{https://clinicaltrials.gov}.
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This provides a public mechanism where clinical trial sponsors are responsible to explain
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what they are trying to acheive and how it will be measured, as well as provide the public the ability to
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search and find trials that they might enroll in.
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Data such as this has become part of multiple datasets
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(e.g. the Cortellis Investigational Drugs dataset or the AACT dataset from the Clinical Trials Transformation Intiative)
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used to evaluate what drugs might be entering the market soon.
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This brings up a question: can we use this public data on clinical trials to describe what effects their success or failure?
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In this work, I use updates to records on \url{https://ClinicalTrials.gov} to disentangle
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the effect of participant enrollment and drugs on the market affect the success or failure of clinical trials.
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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.
|
||||||
|
For large molecule generics (termed biosimilars) such as Adalimumab
|
||||||
|
(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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When registering a clinical trial,
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the investigators are required to
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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 phases of development.
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Pre-clinical studies may include
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Phase I trials are the first attempt to evaluate safety and efficacy in humans, and usually \todo{Describe trial phases, get citation}
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Phase II trials typically \todo{}
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A Phase III trial is the final trial befor approval by the FDA
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Phase IV trials are used after approval to ensure safety and efficacy in the general populace ....
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In the economics literature, most of the focus has been on evaluating how drug candidates transition between
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different phases and then on to approval.
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% Now begin introducing work by Chris Adams
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% Lead into lit review
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% Causality
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% Data
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% Economic Model
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% Results
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% Conclusion
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@ -0,0 +1,42 @@
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How do I begin work on stuff
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- next step is causal story. key points include
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- we are trying to separate strategic and operational concerns. (why is this a difficult problem?)
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- we can't trust what we are told
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- terminations could be due to safety, strategic, or operational concerns.
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- explaining confounding between
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- population/market and enrollment.
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-population/market and market conditions.
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- market conditions and enrollment.
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- describe other confounders
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- safety and effectiveness
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- duration <--> enrollment/termination
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- Condition
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- Decision to procede with Phase III trial
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- How do I handle this?
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- Introduce Do-Calculus
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- DAG model
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- What do I need to control for, in some form or other?
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CURRENTLY HERE:
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- Introduce Data
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- Clinical Trial Progression
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- AACT gives us information on
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- terminated/completed status
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- compound-indication pairs
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- MeSH/RxNorm links
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- Snapshots
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- Market Conditions
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- can't directly measure alternate treatments/standards of care.
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- Can get measures of USP - formulary alternatives
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- Can get number of generics or brand names with same drug.
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- Population Sizes
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- IHME Global Burden of Disease dataset. Best measure of impact of a given disease category.
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- DALY's
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- How much data do I have?
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- Econometric model
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- for a given state, what is the probability it will terminate?
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- more accurately for my dist-diff analysis: for a given state, what is the distribution of the probabilities it will terminate?
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- basic bernoulli-logistic model, linear in parameters.
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- What are the specific things I am looking at?
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- number of competing treatments.
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- delaying close of enrollment.
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@ -0,0 +1,318 @@
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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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|
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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_MilestonesUS_2023}.
|
||||||
|
As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed
|
||||||
|
compounds with Structured Product Labels (SPLs)
|
||||||
|
and 10,983 previously-marketed SPLs
|
||||||
|
\cite{commissioner_NSDE_2024}.
|
||||||
|
%from nsde table. Get number of unique application_nubmers_or_citations with most recent end date as null.
|
||||||
|
In 1999, they began requiring that drug developers register and
|
||||||
|
publish clinical trials on \url{https://clinicaltrials.gov}.
|
||||||
|
This provides a public mechanism where clinical trial sponsors are
|
||||||
|
responsible to explain what they are trying to acheive and how it will be
|
||||||
|
measured, as well as provide the public the ability to search and find trials
|
||||||
|
that they might enroll in.
|
||||||
|
Multiple derived datasets such as the Cortellis Investigational Drugs dataset
|
||||||
|
or the AACT dataset from the Clinical Trials Transformation Intiative
|
||||||
|
integrate these data.
|
||||||
|
This brings up a question:
|
||||||
|
Can we use this public data on clinical trials to identify what effects the
|
||||||
|
success or failure of trials?
|
||||||
|
In this work, I use updates to records on
|
||||||
|
\url{https://ClinicalTrials.gov}
|
||||||
|
to do exactly that, disentangle the effect of participant enrollment
|
||||||
|
and competing drugs on the market affect the success or failure of
|
||||||
|
clinical trials.
|
||||||
|
|
||||||
|
%Describe how clinical trials fit into the drug development landscape and how they proceed
|
||||||
|
Clinical trials are a required part of drug development.
|
||||||
|
Not only does the FDA require that a series of clinical trials demonstrate sufficient safety and efficacy of
|
||||||
|
a novel pharmaceutical compound or device, producers of derivative medicines may be required to ensure that
|
||||||
|
their generic small molecule compound -- such as ibuprofen or levothyroxine -- matches the
|
||||||
|
performance of the originiator drug if delivery or dosage is changed.
|
||||||
|
For large molecule generics (termed biosimilars) such as Adalimumab
|
||||||
|
(Brand name Humira, with biosimilars Abrilada, Amjevita, Cyltezo, Hadlima, Hulio,
|
||||||
|
Hyrimoz, Idacio, Simlandi, Yuflyma, and Yusimry),
|
||||||
|
the biosimilars are required to prove they have similar efficacy and safety to the
|
||||||
|
reference drug.
|
||||||
|
|
||||||
|
When registering these clinical trials
|
||||||
|
% discuss how these are registered and what data is published.
|
||||||
|
% Include image and discuss stages
|
||||||
|
% Discuss challenges faced
|
||||||
|
|
||||||
|
% Introduce my work
|
||||||
|
|
||||||
|
In the world of drug development, these trials are classified into different
|
||||||
|
phases of development.
|
||||||
|
\cite{FDADrugApprovalProcess_2022}
|
||||||
|
provide an overview of this process
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}
|
||||||
|
while describes the actual details.
|
||||||
|
Pre-clinical studies primarily establish toxicity and potential dosing levels
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
Phase I trials are the first attempt to evaluate safety and efficacy in humans.
|
||||||
|
Participants typically are heathy individuals, and they measure how the drug
|
||||||
|
affects healthy bodies, potential side effects, and adjust dosing levels.
|
||||||
|
Sample sizes are often less than 100 participants.
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
Phase II trials typically involve a few hundred participants and is where
|
||||||
|
investigators will dial in dosing, research methods, and safety.
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
A Phase III trial is the final trial befor approval by the FDA, and is where
|
||||||
|
the investigator must demonstrate safety and efficacy with a large number of
|
||||||
|
participants, usually on the order of hundreds or thousands.
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
Occassionally, a trial will be a multiphase trial, covering aspects of either
|
||||||
|
Phases I and II or Phases II and III.
|
||||||
|
|
||||||
|
|
||||||
|
After a successful Phase III trial, the sponsor will decide whether or not
|
||||||
|
to submit an application for approval from the FDA.
|
||||||
|
Before filing this application, the developer must have completed
|
||||||
|
"two large, controlled clinical trials."
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
Phase IV trials are used after the drug has recieved marketing approval to
|
||||||
|
validate safety and efficacy in the general populace.
|
||||||
|
Throughout this whole process, the FDA is available to assist in decisionmaking
|
||||||
|
regarding topics such as study design, document review, and whether or not
|
||||||
|
they should terminate the trial.
|
||||||
|
The FDA also reserves the right to place a hold on the clinical trial for
|
||||||
|
safety or other operational concerns, although this is rare.
|
||||||
|
\cite{commissioner_DrugDevelopment_2020}.
|
||||||
|
|
||||||
|
In the economics literature, most of the focus has been on evaluating how
|
||||||
|
drug candidates transition between different phases and their probability
|
||||||
|
of final approval.
|
||||||
|
% Lead into lit review
|
||||||
|
% Abrantes-Metz, Adams, Metz (2004)
|
||||||
|
\cite{abrantes-metz_pharmaceutical_2004},
|
||||||
|
described the relationship between
|
||||||
|
various drug characteristics and how the drug progressed through clinical trials.
|
||||||
|
% This descriptive 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 Phase I and II trials last longer,
|
||||||
|
the rate of failure increases.
|
||||||
|
In contrast, Phase 3 trials generally have a higher rate of
|
||||||
|
success than failure after 91 months.
|
||||||
|
This may be due to the fact that the purpose of Phases I and II are different
|
||||||
|
from the purpose of Phase III.
|
||||||
|
|
||||||
|
Continuing on this theme,
|
||||||
|
%DiMasi FeldmanSeckler Wilson 2009
|
||||||
|
\cite{dimasi_TrendsRisks_2010} examine the completion rate of clinical drug
|
||||||
|
develompent and find that for the 50 largest drug producers,
|
||||||
|
approximately 19\% of their drugs under development between 1993 and 2004
|
||||||
|
successfully moved from Phase I to recieving an New Drug Application (NDA)
|
||||||
|
or Biologics License Application (BLA).
|
||||||
|
They note a couple of changes in how drugs are developed over the years they
|
||||||
|
study, most notably that
|
||||||
|
drugs began to fail earlier in their development cycle in the
|
||||||
|
latter half of the time they studied.
|
||||||
|
They note that this may reduce the cost of new drugs by eliminating late
|
||||||
|
and costly failures in the development pipeline.
|
||||||
|
|
||||||
|
Earlier work by
|
||||||
|
\authorcite{dimasi_ValueImproving_2002}
|
||||||
|
used data on 68 investigational drugs from 10 firms to simulate how reducing
|
||||||
|
time in development reduces the costs of developing drugs.
|
||||||
|
He estimates that reducing Phase III of clinical trials by one year would
|
||||||
|
reduce total costs by about 8.9\% and that moving 5\% of clinical trial failures
|
||||||
|
from phase III to Phase II would reduce out of pocket costs by 5.6\%.
|
||||||
|
|
||||||
|
Like much of the work in this field, the focus of the the work by
|
||||||
|
\citeauthor{dimasi_ValueImproving_2002}
|
||||||
|
and
|
||||||
|
\citeauthor{dimasi_TrendsRisks_2010}
|
||||||
|
tends to be on the drug development pipeline, i.e. the progression between
|
||||||
|
phases and towards marketing approval.
|
||||||
|
A key contribution to this drug development literature is the work by
|
||||||
|
\authorcite{khmelnitskaya_CompetitionAttrition_2021}
|
||||||
|
on a causal identification strategy
|
||||||
|
to disentangle strategic exits from exits due to clinical failures
|
||||||
|
in the drug development pipeline.
|
||||||
|
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.
|
||||||
|
|
||||||
|
The work that is closest to mine is the work by
|
||||||
|
\authorcite{hwang_FailureInvestigational_2016}
|
||||||
|
who investigated causes for which late stage (Phase III)
|
||||||
|
clinical trials fail -- with a focus on trials in the USA,
|
||||||
|
Europe, Japan, Canada, and Australia.
|
||||||
|
They identified 640 novel therapies and then studied each therapy's
|
||||||
|
development history, as outlined in commercial datasets.
|
||||||
|
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.
|
||||||
|
|
||||||
|
% Begin Discussing what I do. Then introduce
|
||||||
|
Unlike the majority of the literature, I focus on the progress of
|
||||||
|
individual clinical trials, not on the drug development pipeline.
|
||||||
|
In both
|
||||||
|
\authorcite{khmelnitskaya_CompetitionAttrition_2021}
|
||||||
|
and
|
||||||
|
\authorcite{hwang_FailureInvestigational_2016}
|
||||||
|
the authors describe failures due to safety, efficacy, or strategic concerns.
|
||||||
|
There is another category of concerns that arise for individual clinical trials,
|
||||||
|
that of operational failures.
|
||||||
|
Operational failures can arise when a trial struggles to recruit participants,
|
||||||
|
the principle investigator or other key member leaves for another opportunity,
|
||||||
|
or other studies prove that the trial requires a protocol change.
|
||||||
|
|
||||||
|
% In a personal review of 199 randomly selected clinical trials from the AACT
|
||||||
|
% database, the
|
||||||
|
% \begin{table}
|
||||||
|
% \caption{}\label{tab:}
|
||||||
|
% \begin{center}
|
||||||
|
% \begin{tabular}[c]{|l|l|}
|
||||||
|
% \hline
|
||||||
|
% Reason & Percentage Mentioned \\
|
||||||
|
% \hline
|
||||||
|
% Safety or Efficacy & 14.5\% \\
|
||||||
|
% Funding Problems & 9.1\% \\
|
||||||
|
% Enrollment Issues & 31\% \\
|
||||||
|
% \hline
|
||||||
|
% \end{tabular}
|
||||||
|
% \end{center}
|
||||||
|
% \end{table}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
This paper proposes the first model to separate the causal effects of
|
||||||
|
market conditions (a strategic concern) from the effects of
|
||||||
|
participant enrollment (an operational concern) on Phase III Clinical trials.
|
||||||
|
This will allow me to answer the questions:
|
||||||
|
\begin{itemize}
|
||||||
|
\item What is the marginal effect on trial completion of an additional
|
||||||
|
generic drug on the market?
|
||||||
|
\item What is the marginal effect on trial completion of a delay in
|
||||||
|
closing enrollment?
|
||||||
|
\end{itemize}
|
||||||
|
To undderstand how I do this, we'll cover some background information on
|
||||||
|
clinical trials in section \ref{SEC:ClinicalTrials},
|
||||||
|
explain the data in section \ref{SEC:DataSources},
|
||||||
|
and then examine causal identification and econometric model in sections
|
||||||
|
\ref{SEC:CausalIdentificationAndModel}.
|
||||||
|
Finally I'll review the results and conclusion in sections
|
||||||
|
\ref{SEC:Results}
|
||||||
|
and
|
||||||
|
\ref{SEC:Conclusion}
|
||||||
|
respectively.
|
||||||
|
|
||||||
|
% \subsection{Market incentives and drug development}
|
||||||
|
% %%%%%%%%% What do we know about drug development incentives?
|
||||||
|
%
|
||||||
|
% \cite{dranove_DoesConsumer_2022} use the implementation of Medicare part D
|
||||||
|
% to examine whether the production of novel or follow up drugs increases during
|
||||||
|
% the following 15 years.
|
||||||
|
% They find that when Medicare part D was implemented -- increasing senior
|
||||||
|
% citizens' ability to pay for drugs -- there was a (delayed) increase
|
||||||
|
% in drug development, with effects concentrated among compounds that were least
|
||||||
|
% innovative according to their classification of innovations.
|
||||||
|
% They suggest that this is due to financial risk management, as novel
|
||||||
|
% pharmaceuticals have a higher probability of failure compared to the less novel
|
||||||
|
% follow up development.
|
||||||
|
% This is what leads risk-adverse companies to prefer follow up development.
|
||||||
|
%
|
||||||
|
%
|
||||||
|
% % 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,
|
||||||
|
% \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 in delivery is around 3 years.
|
||||||
|
%
|
||||||
|
% % Agarwal and Gaule 2022
|
||||||
|
% % - Retrospective on impact from COVID-19 pandemic
|
||||||
|
% % Not in this version
|
||||||
|
%
|
||||||
|
% \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.
|
||||||
|
% In this work, I focus on the failure of individual clinical trials, but the
|
||||||
|
% 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 +1,93 @@
|
|||||||
|
\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} operate.
|
||||||
|
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{khm} 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 true failure in that we did not learn if the drug was effective or not.
|
||||||
|
Unfortunately, although termination documentation typically includes a
|
||||||
|
description of a 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.
|
||||||
|
|
||||||
|
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}
|
||||||
@ -0,0 +1,54 @@
|
|||||||
|
--get a list of the most recent activations that exist for a given application.
|
||||||
|
create temp table nsde_activations as
|
||||||
|
select
|
||||||
|
application_number_or_citation,
|
||||||
|
count(distinct package_ndc) as package_count,
|
||||||
|
max(marketing_start_date) as most_recent_start,
|
||||||
|
max(marketing_end_date) as most_recent_end,
|
||||||
|
max(inactivation_date) as most_recent_inactivation,
|
||||||
|
max(reactivation_date) as most_recent_reactivation
|
||||||
|
from spl.nsde
|
||||||
|
group by application_number_or_citation
|
||||||
|
;
|
||||||
|
|
||||||
|
select count(*) from nsde_activations
|
||||||
|
where most_recent_end is null
|
||||||
|
;
|
||||||
|
/*
|
||||||
|
count
|
||||||
|
-----
|
||||||
|
6602
|
||||||
|
*/
|
||||||
|
|
||||||
|
|
||||||
|
select count(*) from nsde_activations
|
||||||
|
where most_recent_end is NOT null
|
||||||
|
;
|
||||||
|
/*
|
||||||
|
count
|
||||||
|
-----
|
||||||
|
10983
|
||||||
|
*/
|
||||||
|
|
||||||
|
/*
|
||||||
|
So, the current number of marketed compounds is how many NDA or ANDA (ANADA?) compounds there are.
|
||||||
|
|
||||||
|
*/
|
||||||
|
|
||||||
|
-- get count of drugs that you can select by first 3 letters
|
||||||
|
select
|
||||||
|
left(application_number_or_citation, 3) as first_3,
|
||||||
|
count(*) as row_count
|
||||||
|
from nsde_activations
|
||||||
|
group by first_3
|
||||||
|
;
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
select
|
||||||
|
left(application_number_or_citation, 3) as first_3,
|
||||||
|
count(*) as row_count
|
||||||
|
from nsde_activations
|
||||||
|
where first_3 in ()
|
||||||
|
group by first_3
|
||||||
|
;
|
||||||
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Reference in New Issue