Changing Trends in the Active Ingredients of Pain Relievers

Chia-An (Anne) Chen
Posted on Jul 21, 2016

Contributed by Chia-An Chen (Anne Chen). She is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between July 5th to September 23rd, 2016. This post is based on her first project - R Visualization (due on 2nd week of the program). The R code can be found on GitHub.


Introduction

Before getting into the market, a new drug application, known as an NDA, must be filed with the U.S. Food and Drug Administration to ensure its safety and effectiveness. FDA has released several files of these NDA’s at the [email protected] database. See the graph below for the Entity Relationship Diagram. The data used for this exploratory and visualization project is from the table “Application”, “Product”, and “RegActionDate”.

 

FDADrugTablesMap

To explore and understand the dataset, a plot with the number of approved new drug applications vs. year was created (see below). We can see that the number of approved new drug applications has increased over the decades, and the increasing trend may be consistent with the development of technology in the biomedical field.

 

cases_year

The rate of approved new drug application was not calculated due to the nature of the data, which consists of only approved cases. The confidentiality for total number of submissions may serve as protection from revealing the names of applicants who submitted an NDA but failed.

The analysis is divided in to three categories to analyze the drugs: active ingredient, form, and potential. This project looks at how factors in each subcategory change over the time period from 1939 to 2016 and checks if there are hidden trends.

 

I. Active Ingredient

Rather than randomly choosing which active ingredients to study, this project focuses on those compounds that are most frequently used. The plot below shows the top ten active ingredients in approved cases, and further analysis focused on these ten.

 

10_ai_bars

The popularity of these ten ingredients has changed over time, as seen in the graph below. To be noted, the lines do not reflect the actual data but are smooth lines to fit the data in order to avoid noise and more clearly demonstrate the patterns.

 

10_ai_years

On the left is the raw count of the number of approved cases vs. year, and on the right is the normalized plot. To be more specific, the data in each year is normalized with the total count in that year (data in the first plot).

Three patterns are found:

  1. The number of approved drugs that contain Ethinyl Estradiol as an active ingredient, which is usually used in oral contraceptive, has skyrocketed since 1990s.
  1. Dextrose, Sodium Chloride, and Potassium Chloride somehow show the same pattern in the plot. With further investigation, these three are usually combined in use for intravenous injection to provide sugar, restore electrolyte imbalance, and rehydrate the patients. As liquid, this combination of drugs can also serve as diluent for drugs that need to be injected into the vein.
  1. An interesting finding is that the four active ingredients, Acetaminophen, Hydrocodone Bitartrate, Ibuprofen, and Oxycodone Hydrochloride, used in pain relievers, seem to have different patterns, thus the next step is to take a closer look of these four compounds.

 

four_pain

Acetaminophen, Hydrocodone Bitartrate, and Ibuprofen all share similar patterns as having a peak around 1990s then gradually declining afterwards. For Oxycodone Hydrochloride, the trend seems to be going the other way. Since the 1990s there seems to be a steady rise in the use of Oxycodone Hydrochloride.

One hypothesis for this has to do with the pressures on the pharma industry to increase profit margins. Drugs like Acetaminophen, Hydrocodone Bitartrate, and Ibuprofen, are inexpensive and cannot be expected to bring high margins. Under this pressure, it is not surprising that pharma is looking for new compounds, for example, Oxycodone, to market to the public.

Interestingly, the increasing trend of Oxycodone matches with the preference of drug abusers. A nationwide survey conducted by the researchers at Washington University School of Medicine in St. Louis indicates opioid drug abusers favor Oxycodone over other pain relievers. The high Oxycodone produces resulting from its high purity over common pain reliever, like Acetaminophen/Hydrocodone combined drug. If two incidents are correlated in some way, a stricter legislation may be needed to regulate the misuse and abuse of prescribed pain relievers, especially the ones with Oxycodone.

 

II. Form

Besides active ingredients, drugs can be formulated into different forms, like an oral pill or injectable solution. This section provides insight into the changing trend of the forms among the approved drugs. The rationale for data acquisition is similar to the analysis for active ingredients. First, the top ten most popular forms are gathered, and a time series based plot is generated.

 

10_forms

There is a diverging trend for Oral and Injection in forms of drug, and the possible explanation is that people do not like injections if a similar drug in oral form is available. So to make the drug more marketable, those pharmaceutical companies formulate the drug into oral form if possible. Another explanation could lie in the complicated regulation of injectables that shifted industry preference to introduce orals.

 

III. Potential

In general, there are two review types for an NDA, standard and priority. The FDA defines these as follows: standard review stands for a drug with therapeutic qualities that are similar to drugs already in the market, and priority review represents a drug with an advance over therapy that is available to the public.

 

potential_year

As we can see from the plot above, the ratio of normalized review types in approved cases reached a plateau in the 1980s for standard review and remained at quite a steady rate of 75%. For priority review, the rate of approvals seems to have held relatively steady at 25% since 1980. The lower percentage of priority review makes sense because of the difficulty involved in advancing the development of drugs for new or uncured diseases.

 

Summary

  • There is an increasing trend in the use of Oxycodone as an active ingredient in filings for new drug applications. However, there is no suitable explanation for this trend.
  • A decreasing trend in applications for injectable drugs versus an increasing trend in oral drugs may reflect the preference of patients for oral drugs. It also might relate to industry preference for introducing orals due to the complexity of regulating injectables.
  • In general, the number of cases with standard review is three times more than the ones with priority review in approved new drug applications.

About Author

Chia-An (Anne) Chen

Chia-An (Anne) Chen

Anne Chen has a Masters degree in Bioengineering from the University of Pennsylvania. Prior to working at a biotech startup developing a liver cancer diagnosis device, Anne researched and evaluated open-source Electronic Health Records software for small-scale hospitals...
View all posts by Chia-An (Anne) Chen >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Classes Demo Day Demo Lesson Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet Lectures linear regression Live Chat Live Online Bootcamp Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Lectures Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking Realtime Interaction recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp