MedxR: Access Drug Regulatory Data via FDA and Health Canada APIs • MedxR

library(MedxR)
library(ggplot2)
library(dplyr)

Introduction

The MedxR package provides a unified interface to access open drug regulatory data from the U.S. Food and Drug Administration (FDA) Open API and the Health Canada Drug Product Database API. It allows users to retrieve real-time or historical information about drug approvals, adverse events, product recalls, and pharmaceutical details, enabling transparent and reproducible analysis of regulatory information across North America.

In addition to API-access functions, the package includes a curated collection of open datasets related to drugs, pharmaceuticals, treatments, and clinical studies. These datasets encompass diverse topics such as, treatment dosages, pharmacological research, placebo effects, drug reactions, misuse of pain relievers, and vaccine effectiveness.

MedxR is designed to support researchers, educators, and healthcare professionals by integrating international RESTful APIs with structured datasets from public, academic, and government sources into a single, easy-to-use R package. It provides a comprehensive foundation for pharmacological research, regulatory analysis, and health data science.

Functions for MedxR

The MedxR package provides several core functions to access real-time and structured information about drugs and pharmaceutical products from public APIs such as the FDA Open API and the Health Canada Drug Product Database API.

These functions allow users to query, retrieve, and analyze regulatory data related to drug approvals, adverse events, product recalls, and drug shortages, ensuring reproducible and transparent access to official medical and regulatory information.

Below is a list of the main functions included in the package:

get_fda_adverse_events(): Search Adverse Events by Drug Name in FDA Adverse Event Reporting System, e.g., get_fda_adverse_events("aspirin").
get_fda_drug_labels(): Search Drug Labels by Drug Name in FDA Drug Labeling Database, e.g., get_fda_drug_labels("aspirin").
get_fda_drugs_approved(): Search FDA-Approved Drugs by Drug Name, e.g., get_fda_drugs_approved("lipitor").
get_fda_ndc_directory(): Search National Drug Code (NDC) Directory by Drug Name, e.g., get_fda_ndc_directory("ibuprofen").
get_hc_active_ingredients(): Get Active Ingredients from Health Canada Drug Product Database.
get_hc_companies(): Get Companies from Health Canada Drug Product Database.
get_hc_din(): Get All DINs from Health Canada Drug Product Database.
get_hc_drug_by_din(): Get a Drug Product by DIN from Health Canada Drug Product Database, e.g., get_hc_drug_by_din("00000213").
get_hc_drug_products(): Retrieve Drug Products from Health Canada Drug Product Database.
get_hc_forms(): Get Pharmaceutical Forms from Health Canada Drug Product Database.
get_hc_search_drug(): Search Drug Products by Brand Name in Health Canada Drug Product Database, e.g., get_hc_search_drug("NEMBUTAL").

These functions allow users to access high-quality and structured information on drugs and regulatory data, which can be seamlessly combined with tools like dplyr and ggplot2 to support a wide range of data analysis, visualization, and pharmacological research tasks.

In the following sections, you’ll find examples on how to work with MedxR in practical scenarios, including retrieving data from the FDA and Health Canada APIs, as well as exploring and analyzing the curated datasets included in the package.

Search Adverse Events by Drug Name in FDA Adverse Event Reporting System



aspirin_adverse_event <- get_fda_adverse_events("aspirin")
aspirin_adverse_event %>% 
  select(report_id, date_received, country, serious, patient_sex) %>%
  head(6)
#> # A tibble: 6 × 5
#>   report_id date_received country serious patient_sex
#>   <chr>     <date>        <chr>   <chr>   <chr>      
#> 1 10003304  2014-03-12    US      No      Female     
#> 2 10003310  2014-03-12    US      No      Female     
#> 3 10003321  2014-03-12    JP      Yes     Male       
#> 4 10003349  2014-03-12    US      Yes     Female     
#> 5 10003432  2014-03-12    US      Yes     Female     
#> 6 10003582  2014-03-12    US      No      Male

Search FDA-Approved Drugs by Drug Name


lipitor_drugs_approved <- get_fda_drugs_approved("lipitor")
lipitor_drugs_approved %>% 
  select(application_number, brand, generic, approval_date, strength, form)
#> # A tibble: 4 × 6
#>   application_number brand   generic              approval_date strength     form  
#>   <chr>              <chr>   <chr>                <date>        <chr>        <chr> 
#> 1 NDA020702          LIPITOR ATORVASTATIN CALCIUM 1996-12-17    EQ 80MG BASE TABLET
#> 2 NDA020702          LIPITOR ATORVASTATIN CALCIUM 1996-12-17    EQ 20MG BASE TABLET
#> 3 NDA020702          LIPITOR ATORVASTATIN CALCIUM 1996-12-17    EQ 40MG BASE TABLET
#> 4 NDA020702          LIPITOR ATORVASTATIN CALCIUM 1996-12-17    EQ 10MG BASE TABLET

Get a Drug Product by DIN from Health Canada Drug Product Database


hc_drug_din <- get_hc_drug_by_din("00000213")
hc_drug_din %>% 
  select(drug_code, class_name, din, brand_name, company_name)
#> # A tibble: 1 × 5
#>   drug_code class_name din      brand_name                  company_name                
#>       <int> <chr>      <chr>    <chr>                       <chr>                       
#> 1      2576 Human      00000213 NEMBUTAL SODIUM INJ 50MG/ML ABBOTT LABORATORIES, LIMITED

Top 10 Canadian Companies by Number of Registered Drug Products


# Retrieve data from Health Canada APIs
drug_products <- get_hc_drug_products()
companies <- get_hc_companies()

# Combine both datasets by company name
merged_data <- drug_products %>%
  left_join(companies, by = "company_name") %>%
  filter(!is.na(company_name)) %>%
  group_by(company_name) %>%
  summarise(total_products = n()) %>%
  arrange(desc(total_products)) %>%
  slice_head(n = 10)

# Plot: Top 10 companies by number of registered drug products
ggplot(merged_data, aes(x = reorder(company_name, total_products), y = total_products)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Canadian Companies by Number of Registered Drug Products",
    x = "Company Name",
    y = "Number of Drug Products",
    caption = "Source: Health Canada Drug Product Database (via MedxR)"
  ) +
  theme_minimal(base_size = 13)

Dataset Suffixes

Each dataset in MedxR is labeled with a suffix to indicate its structure and type:

_df: A standard data frame.
_tbl_df: A tibble data frame object.
_list: A list object.
_matrix: A matrix object.

Datasets Included in MedxR

In addition to API access functions, MedxR offers a curated collection of open datasets focused on drugs, pharmaceuticals, treatments, and clinical studies. These datasets cover diverse topics such as treatment dosages, pharmacological studies, placebo effects, drug reactions, misuses of pain relievers, and vaccine effectiveness. Below are some featured examples:

aspirin_infarction_df: A data frame containing results from a meta-analysis on the use of aspirin to prevent death after myocardial infarction.
drug_prices_tbl_df: A tibble containing information on unit prices for various pharmaceutical products. Each record includes a description, currency, cost per unit, unit type, and a parent key identifier.
placebos_df: A data frame containing pain relief data from both analgesics and placebos.

Conclusion

The MedxR package provides a unified interface for accessing real-time drug regulatory data from public APIs and a comprehensive collection of curated pharmaceutical datasets. Offering seamless integration with both the FDA Open API and Health Canada Drug Product Database API, it enables retrieval of critical information on drug approvals, adverse events, recalls, and product details.

The package serves as an essential resource for researchers, healthcare professionals, and educators in pharmacology, medicine, and healthcare, facilitating evidence-based decision making and reproducible research through reliable international data sources and structured datasets from public, academic, and government repositories.