Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES
Source:vignettes/drugbank_nside.Rmd
drugbank_nside.RmdIntroduction
In modern pharmacovigilance, analyzing drugs in isolation is often insufficient. Patients frequently take multiple medications, leading to complex Drug-Drug Interactions (DDIs).
The dbparser package now provides a unified framework to
analyze the complete safety profile of a drug:
- Mechanism: Biological targets and pathways from DrugBank.
- Phenotype (Single Drug): Real-world adverse events from OnSIDES.
- Polypharmacy (Combinations): Interaction risks from TWOSIDES.
This vignette demonstrates the complete workflow: loading all three
databases, merging them into a single dvobject, and
performing a scientific case study on the interaction between
Leuprolide and Calcitriol.
1. Loading and Inspecting Data
We will use the curated sample data files included in the
dbparser package to demonstrate the workflow.
1.1 Load DrugBank (The Hub)
First, we load the mechanistic data.
# Load sample DrugBank data included in the package
drugbank_path <- system.file("two_drugs.RDS", package = "dbparser")
drugbank_db <- readRDS(drugbank_path)
# Review the drugbank object structure
show_dvobject_metadata(drugbank_db)
#> === BASIC INFO ===
#> Class Total_DataFrames Top_Level_Lists Object_Size
#> 1 dvobject 55 3 1.5 Mb
#>
#> === DATABASE METADATA ===
#> Database Type Version Export_Date
#> Original DrugBank 5.1 2025-01-02
#>
#> === DATA.FRAMES ===
#> Path Rows Cols Size
#> drugs$general_information 2 15 8 Kb
#> drugs$drug_classification 2 9 3.8 Kb
#> drugs$synonyms 15 4 3.6 Kb
#> drugs$pharmacology 2 12 18.1 Kb
#> drugs$international_brands 9 3 2.7 Kb
#> drugs$mixtures 5 3 2 Kb
#> drugs$packagers 35 3 6.2 Kb
#> drugs$manufacturers 13 4 2.9 Kb
#> drugs$prices 16 5 4.6 Kb
#> drugs$categories 58 3 9.2 Kb
#> drugs$dosages 189 4 17.4 Kb
#> drugs$atc_codes 4 10 4.3 Kb
#> drugs$patents 35 6 8.6 Kb
#> drugs$drug_interactions 1645 4 514.7 Kb
#> drugs$sequences 1 3 1.3 Kb
#> drugs$calculated_properties 26 4 5.6 Kb
#> drugs$experimental_properties 5 4 2.4 Kb
#> drugs$external_identifiers 30 3 4.7 Kb
#> drugs$pdb_entries 19 2 2.4 Kb
#> drugs$affected_organisms 2 2 1.2 Kb
#> drugs$groups 4 2 1.3 Kb
#> drugs$external_links 5 3 2.1 Kb
#> salts 2 8 2.8 Kb
#> products 190 19 68.6 Kb
#> references$drugs$articles 13 4 6.3 Kb
#> references$drugs$links 11 4 6.1 Kb
#> references$carriers$books 1 4 1.6 Kb
#> references$carriers$articles 15 4 5.4 Kb
#> references$enzymes$books 33 4 8.2 Kb
#> references$enzymes$articles 1573 4 485.5 Kb
#> references$enzymes$links 615 4 164.4 Kb
#> references$enzymes$attachments 55 4 20 Kb
#> references$targets$articles 101 4 27.4 Kb
#> references$targets$links 14 4 7.1 Kb
#> cett$carriers$general_information 1 6 1.9 Kb
#> cett$carriers$actions 2 2 1.2 Kb
#> cett$carriers$polypeptides$general_information 1 20 6.8 Kb
#> cett$carriers$polypeptides$external_identy 7 3 2.2 Kb
#> cett$carriers$polypeptides$synonyms 10 2 2 Kb
#> cett$carriers$polypeptides$pfams 2 3 1.5 Kb
#> cett$carriers$polypeptides$go 2 3 1.5 Kb
#> cett$enzymes$general_information 2 8 2.6 Kb
#> cett$enzymes$actions 7 2 1.5 Kb
#> cett$enzymes$polypeptides$general_information 2 20 14 Kb
#> cett$enzymes$polypeptides$external_identy 15 3 3 Kb
#> cett$enzymes$polypeptides$synonyms 21 2 3 Kb
#> cett$enzymes$polypeptides$pfams 2 3 1.4 Kb
#> cett$enzymes$polypeptides$go 21 3 3.6 Kb
#> cett$targets$general_information 4 6 2.6 Kb
#> cett$targets$actions 10 2 1.9 Kb
#> cett$targets$polypeptides$general_information 4 20 14.2 Kb
#> cett$targets$polypeptides$external_identy 26 3 3.9 Kb
#> cett$targets$polypeptides$synonyms 15 2 2.4 Kb
#> cett$targets$polypeptides$pfams 5 3 2.1 Kb
#> cett$targets$polypeptides$go 37 3 5.6 Kb1.2 Load OnSIDES (Single-Drug Labels)
Next, we load the adverse event data extracted from FDA labels.
# Load sample OnSIDES data included in the package
onsides_path <- system.file("onside.RDS", package = "dbparser")
onsides_db <- readRDS(onsides_path)
# Review the onsides_db object structure
show_dvobject_metadata(onsides_db)
#> === BASIC INFO ===
#> Class Total_DataFrames Top_Level_Lists Object_Size
#> 1 dvobject 8 0 2.3 Mb
#>
#> === DATABASE METADATA ===
#> Database Type Version Export_Date
#> Original OnSIDES 3.1.1 April 2025
#>
#> === DATA.FRAMES ===
#> Path Rows Cols Size
#> product_adverse_effect 52745 7 2.2 Mb
#> high_confidence 2 2 1.4 Kb
#> product_label 95 5 39.7 Kb
#> product_to_rxnorm 160 2 5.5 Kb
#> vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb
#> vocab_meddra_adverse_effect 347 3 31.6 Kb
#> vocab_rxnorm_ingredient 3 3 2.1 Kb
#> vocab_rxnorm_product 17 3 4.9 Kb1.3 Load TWOSIDES (Drug Interactions)
Finally, we load the dataset containing adverse events reported when two drugs are taken simultaneously.
# Load sample TWOSIDES data included in the package
twosides_path <- system.file("twoside_raw.zip", package = "dbparser")
if(file.exists(twosides_path)) {
twosides_db <- parseTWOSIDES(twosides_path)
} else {
# Fallback for demonstration if file is not yet in the build
# Mocking structure similar to actual TWOSIDES output
twosides_db <- list(drug_drug_interactions = data.table(
drug_1_rxnorm_id = "42375", drug_1_concept_name = "Leuprolide",
drug_2_rxnorm_id = "1894", drug_2_concept_name = "Calcitriol",
condition_meddra_id = "10002034", condition_concept_name = "Anaemia",
PRR = "13.33", mean_reporting_frequency = "0.12"
))
class(twosides_db) <- c("TWOSIDESDb", "dvobject", "list")
attr(twosides_db, "original_db_info") <- list(type="TWOSIDES", version="2.0")
}
# Review structure
show_dvobject_metadata(twosides_db)
#> === BASIC INFO ===
#> Class Total_DataFrames Top_Level_Lists Object_Size
#> 1 dvobject 1 0 217.6 Kb
#>
#> === DATABASE METADATA ===
#> Database Type Version Export_Date
#> Original TWOSIDESDB Unknown Unknown
#>
#> === DATA.FRAMES ===
#> Path Rows Cols Size
#> drug_drug_interactions 1706 13 216.6 Kb2. Merging the Databases (Chaining)
We integrate the databases in a chain. First, we merge OnSIDES into DrugBank, and then we merge TWOSIDES into the result. This creates a single “Hub and Spoke” object centered on DrugBank.
# Step 1: Merge DrugBank + OnSIDES
db_plus_onsides <- merge_drugbank_onsides(drugbank_db, onsides_db)
# Step 2: Merge Result + TWOSIDES
# The function is chainable and automatically detects the existing data
final_db <- merge_drugbank_twosides(db_plus_onsides, twosides_db)
# Review final object structure
# Note the presence of 'drugbank', 'onsides', 'twosides', and 'integrated_data'
show_dvobject_metadata(final_db)
#> === BASIC INFO ===
#> Class Total_DataFrames
#> 1 DrugBankTWOSIDESDb, DrugBankOnSIDESDb, dvobject 68
#> Top_Level_Lists Object_Size
#> 1 4 4.3 Mb
#>
#> === DATABASE METADATA ===
#> Database Type Version Export_Date
#> First Database DrugBank 5.1 2025-01-02
#> Second Database TWOSIDESDB Unknown Unknown
#>
#> === DATA.FRAMES ===
#> Path Rows Cols Size
#> drugbank$drugs$general_information 2 15 8 Kb
#> drugbank$drugs$drug_classification 2 9 3.8 Kb
#> drugbank$drugs$synonyms 15 4 3.6 Kb
#> drugbank$drugs$pharmacology 2 12 18.1 Kb
#> drugbank$drugs$international_brands 9 3 2.7 Kb
#> drugbank$drugs$mixtures 5 3 2 Kb
#> drugbank$drugs$packagers 35 3 6.2 Kb
#> drugbank$drugs$manufacturers 13 4 2.9 Kb
#> drugbank$drugs$prices 16 5 4.6 Kb
#> drugbank$drugs$categories 58 3 9.2 Kb
#> drugbank$drugs$dosages 189 4 17.4 Kb
#> drugbank$drugs$atc_codes 4 10 4.3 Kb
#> drugbank$drugs$patents 35 6 8.6 Kb
#> drugbank$drugs$drug_interactions 1645 4 514.7 Kb
#> drugbank$drugs$sequences 1 3 1.3 Kb
#> drugbank$drugs$calculated_properties 26 4 5.6 Kb
#> drugbank$drugs$experimental_properties 5 4 2.4 Kb
#> drugbank$drugs$external_identifiers 30 3 4.7 Kb
#> drugbank$drugs$pdb_entries 19 2 2.4 Kb
#> drugbank$drugs$affected_organisms 2 2 1.2 Kb
#> drugbank$drugs$groups 4 2 1.3 Kb
#> drugbank$drugs$external_links 5 3 2.1 Kb
#> drugbank$salts 2 8 2.8 Kb
#> drugbank$products 190 19 68.6 Kb
#> drugbank$references$drugs$articles 13 4 6.3 Kb
#> drugbank$references$drugs$links 11 4 6.1 Kb
#> drugbank$references$carriers$books 1 4 1.6 Kb
#> drugbank$references$carriers$articles 15 4 5.4 Kb
#> drugbank$references$enzymes$books 33 4 8.2 Kb
#> drugbank$references$enzymes$articles 1573 4 485.5 Kb
#> drugbank$references$enzymes$links 615 4 164.4 Kb
#> drugbank$references$enzymes$attachments 55 4 20 Kb
#> drugbank$references$targets$articles 101 4 27.4 Kb
#> drugbank$references$targets$links 14 4 7.1 Kb
#> drugbank$cett$carriers$general_information 1 6 1.9 Kb
#> drugbank$cett$carriers$actions 2 2 1.2 Kb
#> drugbank$cett$carriers$polypeptides$general_information 1 20 6.8 Kb
#> drugbank$cett$carriers$polypeptides$external_identy 7 3 2.2 Kb
#> drugbank$cett$carriers$polypeptides$synonyms 10 2 2 Kb
#> drugbank$cett$carriers$polypeptides$pfams 2 3 1.5 Kb
#> drugbank$cett$carriers$polypeptides$go 2 3 1.5 Kb
#> drugbank$cett$enzymes$general_information 2 8 2.6 Kb
#> drugbank$cett$enzymes$actions 7 2 1.5 Kb
#> drugbank$cett$enzymes$polypeptides$general_information 2 20 14 Kb
#> drugbank$cett$enzymes$polypeptides$external_identy 15 3 3 Kb
#> drugbank$cett$enzymes$polypeptides$synonyms 21 2 3 Kb
#> drugbank$cett$enzymes$polypeptides$pfams 2 3 1.4 Kb
#> drugbank$cett$enzymes$polypeptides$go 21 3 3.6 Kb
#> drugbank$cett$targets$general_information 4 6 2.6 Kb
#> drugbank$cett$targets$actions 10 2 1.9 Kb
#> drugbank$cett$targets$polypeptides$general_information 4 20 14.2 Kb
#> drugbank$cett$targets$polypeptides$external_identy 26 3 3.9 Kb
#> drugbank$cett$targets$polypeptides$synonyms 15 2 2.4 Kb
#> drugbank$cett$targets$polypeptides$pfams 5 3 2.1 Kb
#> drugbank$cett$targets$polypeptides$go 37 3 5.6 Kb
#> onsides$product_adverse_effect 52745 7 2.2 Mb
#> onsides$high_confidence 2 2 1.4 Kb
#> onsides$product_label 95 5 39.7 Kb
#> onsides$product_to_rxnorm 160 2 5.5 Kb
#> onsides$vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb
#> onsides$vocab_meddra_adverse_effect 347 3 31.6 Kb
#> onsides$vocab_rxnorm_ingredient 3 3 2.1 Kb
#> onsides$vocab_rxnorm_product 17 3 4.9 Kb
#> integrated_data$vocab_rxnorm_ingredient_enriched 3 4 2.3 Kb
#> integrated_data$high_confidence_enriched 2 3 1.6 Kb
#> integrated_data$DrugBank_RxCUI_Mapping 2 2 1.2 Kb
#> integrated_data$drug_drug_interactions 1706 17 300.9 Kb
#> twosides$drug_drug_interactions 1706 13 216.6 Kb3. Single Drug Analysis: Leuprolide (DB00007)
Scientific Context: Leuprolide is a GnRH analog. We first establish its baseline side effect profile from OnSIDES.
target_drug_id <- "DB00007" # Leuprolide
# 1. Get the RxNorm ID from the bridge table
mapping_df <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>%
filter(drugbank_id == target_drug_id)
target_rxcui <- mapping_df$rxcui[1]
print(paste0("DrugBank ID: ", target_drug_id, " maps to RxNorm CUI: ", target_rxcui))
#> [1] "DrugBank ID: DB00007 maps to RxNorm CUI: 42375"3.1 Extract Baseline Adverse Events (OnSIDES)
# 1. Find product labels linked to this ingredient
product_rxcuis <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>%
filter(ingredient_id == target_rxcui) %>%
pull(product_id)
target_label_ids <- final_db$onsides$product_to_rxnorm %>%
filter(rxnorm_product_id %in% product_rxcuis) %>%
pull(label_id)
# 2. Extract and summarize events
ae_summary <- final_db$onsides$product_adverse_effect %>%
filter(product_label_id %in% target_label_ids) %>%
group_by(effect_meddra_id) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
head(10) %>%
left_join(final_db$onsides$vocab_meddra_adverse_effect,
by = c("effect_meddra_id" = "meddra_id")) %>%
select(effect_meddra_id, meddra_name, Count)
# Fill NAs for demo purposes (if vocab is incomplete in sample)
ae_summary <- ae_summary %>%
mutate(meddra_name = case_when(
effect_meddra_id == 10033336 ~ "Pain",
effect_meddra_id == 10039769 ~ "Rash",
effect_meddra_id == 10052995 ~ "Hot flush",
effect_meddra_id == 10009226 ~ "Constipation",
effect_meddra_id == 10006068 ~ "Bone pain",
TRUE ~ meddra_name
)) %>%
rename(Adverse_Event = meddra_name)
print(ae_summary)
#> # A tibble: 10 × 3
#> effect_meddra_id Adverse_Event Count
#> <int> <chr> <int>
#> 1 10033336 Pain 10659
#> 2 10029197 Nervous 711
#> 3 10039769 Rash 526
#> 4 10017735 Gas 504
#> 5 10010904 Convulsion 424
#> 6 10019211 Headache 351
#> 7 10052995 Hot flush 350
#> 8 10010914 Convulsions 349
#> 9 10009226 Constipation 262
#> 10 10006068 Bone pain 2474. Single Drug Analysis: Calcitriol (DB00136)
We repeat the process for Calcitriol to establish its baseline.
target_drug_id_2 <- "DB00136"
# Get RxCUI
mapping_df_2 <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>%
filter(drugbank_id == target_drug_id_2)
target_rxcui_2 <- mapping_df_2$rxcui[1]
# Get Products & Labels
product_rxcuis_2 <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>%
filter(ingredient_id == target_rxcui_2) %>%
pull(product_id)
target_label_ids_2 <- final_db$onsides$product_to_rxnorm %>%
filter(rxnorm_product_id %in% product_rxcuis_2) %>%
pull(label_id)
# Summarize Events
ae_summary_2 <- final_db$onsides$product_adverse_effect %>%
filter(product_label_id %in% target_label_ids_2) %>%
group_by(effect_meddra_id) %>%
summarise(Count = n()) %>%
arrange(desc(Count)) %>%
head(10) %>%
left_join(final_db$onsides$vocab_meddra_adverse_effect,
by = c("effect_meddra_id" = "meddra_id")) %>%
select(effect_meddra_id, meddra_name, Count) %>%
mutate(meddra_name = ifelse(is.na(meddra_name), paste0("MedDRA_", effect_meddra_id), meddra_name)) %>%
rename(Adverse_Event = meddra_name)5. Polypharmacy Analysis: Drug-Drug Interactions
New Insight: What happens when Leuprolide and Calcitriol are taken together?
We query the integrated_data$drug_drug_interactions
table, which was populated by merge_drugbank_twosides. This
table allows us to look up interactions by DrugBank ID, using the
enriched columns provided by the merge.
# Look for interactions where Drug 1 is Leuprolide AND Drug 2 is Calcitriol (or vice versa)
interaction_data <- final_db$integrated_data$drug_drug_interactions %>%
filter(
(drugbank_id_1 == target_drug_id & drugbank_id_2 == target_drug_id_2) |
(drugbank_id_1 == target_drug_id_2 & drugbank_id_2 == target_drug_id)
) %>%
arrange(desc(as.numeric(PRR))) %>%
select(drug_name_1, drug_name_2, condition_concept_name, PRR, mean_reporting_frequency)
print(interaction_data)
#> drug_name_1 drug_name_2 condition_concept_name PRR
#> <char> <char> <char> <num>
#> 1: Leuprolide Calcitriol Platelet count decreased 15
#> 2: Leuprolide Calcitriol Disturbance in attention 5
#> 3: Leuprolide Calcitriol Haemorrhage 5
#> mean_reporting_frequency
#> <num>
#> 1: 0.0909091
#> 2: 0.0303030
#> 3: 0.03030305.1 Visualization of Interaction Risks
We can visualize the specific adverse events associated with this combination, ranked by their Proportional Reporting Ratio (PRR) (a measure of signal strength).
if(nrow(interaction_data) > 0) {
# Prepare data for canvasXpress
cx_int_data <- data.frame(PRR = as.numeric(interaction_data$PRR))
rownames(cx_int_data) <- interaction_data$condition_concept_name
canvasXpress(
data = t(cx_int_data),
graphType = "Bar",
title = "Polypharmacy Risks: Leuprolide + Calcitriol",
subtitle = "Data from TWOSIDES",
xAxisTitle = "Signal Strength (PRR)",
yAxisTitle = "Adverse Event",
showLegend = FALSE
)
} else {
print("No interaction data found for this specific pair in the sample dataset.")
}6. Comparative Overview
Finally, we can view the single-drug profiles alongside the interaction profile to see if new risks emerge.
# 1. Prepare Single Drug Data
s1 <- ae_summary %>%
mutate(Type = "Leuprolide (Alone)") %>%
select(Adverse_Event, Value=Count, Type)
s2 <- ae_summary_2 %>%
mutate(Type = "Calcitriol (Alone)") %>%
select(Adverse_Event, Value=Count, Type)
# 2. Prepare Interaction Data (Show Top 5)
# Note: Comparing Frequency (Count) vs PRR (Ratio) directly is tricky,
# so we visualize them to show relative importance within their own context.
s3 <- interaction_data %>%
head(5) %>%
mutate(Type = "Interaction (PRR)", Value = as.numeric(PRR)) %>%
select(Adverse_Event = condition_concept_name, Value, Type)
# 3. Combine
combined_data <- bind_rows(s1, s2, s3) %>%
group_by(Type) %>%
slice_max(Value, n = 5) %>%
ungroup()
# 4. Reshape for Matrix
comparison_matrix <- combined_data %>%
pivot_wider(names_from = Type, values_from = Value, values_fill = 0)
cx_compare <- as.data.frame(comparison_matrix[, -1])
rownames(cx_compare) <- comparison_matrix$Adverse_Event
# 5. Plot
canvasXpress(
data = cx_compare,
graphType = "Bar",
title = "Safety Profile Comparison",
subtitle = "Single Drug Frequencies vs. Interaction PRR",
xAxisTitle = "Value",
yAxisTitle = "Adverse Event",
legendPosition = "right"
)7. Conclusion
This analysis highlights the power of the dbparser
integration ecosystem:
- DrugBank provided the identity and mechanism of Leuprolide and Calcitriol.
- OnSIDES quantified their individual side effects (e.g., Pain, Hot Flushes).
- TWOSIDES revealed specific risks when the drugs are combined (e.g., Anaemia).
By chaining merge_drugbank_onsides and
merge_drugbank_twosides, researchers can perform
comprehensive safety assessments across mechanistic, real-world, and
polypharmacy domains in just a few lines of R code.