Drug Mechanism Profiling with CETT Actions

Introduction

DrugBank describes how a drug acts on the proteins it binds. These proteins are grouped into four CETT types: Carriers, Enzymes, Targets and Transporters. For each drug–protein pair DrugBank records one or more actions, such as inhibitor, agonist, antagonist or substrate.

Since this release, the actions and polypeptides$general_information tables of every CETT type include a drugbank_id column. Earlier versions keyed these tables only by the CETT identifier (for example target_id). Many drugs bind the same protein, so that key alone could not say which drug an action belonged to. Identical rows from different drugs were also collapsed into one.

This vignette shows why the drug link matters with a short pharmacology question.

Scientific case: finding a mechanistic substitute for lepirudin

Lepirudin is a recombinant hirudin and a direct thrombin inhibitor. It was used to anticoagulate patients with heparin-induced thrombocytopenia (HIT), a setting where heparins cannot be given. It was later withdrawn from the market. That leaves a common question for formulary and pharmacovigilance work:

Which other drugs act on the same target with the same action? And what else do they act on, so we can spot differences in their safety profile?

We answer this using the two DrugBank records that ship with dbparser:

DB00001, Lepirudin
DB00006, Bivalirudin, a synthetic peptide derived from hirudin

Prerequisites

library(dbparser)
library(dplyr)

1. Parse the DrugBank records

We parse each record and keep only the drug general information and the CETT sections. In a real analysis you would parse the full DrugBank XML once.

parse_record <- function(file_name) {
  parseDrugBank(
    db_path            = system.file("extdata", file_name, package = "dbparser"),
    drug_options       = NULL,
    parse_salts        = FALSE,
    parse_products     = FALSE,
    references_options = NULL,
    cett_options       = c("targets", "enzymes")
  )
}

lepirudin   <- parse_record("drugbank_record_biotech.xml")
bivalirudin <- parse_record("drugbank_record_small_molecule.xml")
dbs         <- list(lepirudin, bivalirudin)

# Stack the same table from every parsed record
stack_table <- function(getter) {
  bind_rows(lapply(dbs, function(db) getter(db)))
}

drugs <- stack_table(function(db) db$drugs$general_information) %>%
  select(drugbank_id, drug_name = name, type)
drugs
#> # A tibble: 2 × 3
#>   drugbank_id drug_name   type          
#>   <chr>       <chr>       <chr>         
#> 1 DB00001     Lepirudin   biotech       
#> 2 DB00006     Bivalirudin small molecule

2. Target actions now carry the drug

target_actions <- stack_table(function(db) db$cett$targets$actions)
target_actions
#> # A tibble: 2 × 3
#>   action    target_id drugbank_id
#>   <chr>     <chr>     <chr>      
#> 1 inhibitor BE0000048 DB00001    
#> 2 inhibitor BE0000048 DB00006

Both drugs inhibit the same target, BE0000048. Each row now says which drug it comes from.

Why the drug link matters

Suppose we only had target_id, which is what earlier versions of dbparser gave us. To attach actions to drugs we would have to join the actions to the target table on target_id alone:

targets <- stack_table(function(db) db$cett$targets$general_information)

targets %>%
  select(drugbank_id, target_id, target_name = name) %>%
  inner_join(select(target_actions, -drugbank_id), by = "target_id")
#> # A tibble: 4 × 4
#>   drugbank_id target_id target_name action   
#>   <chr>       <chr>     <chr>       <chr>    
#> 1 DB00001     BE0000048 Prothrombin inhibitor
#> 2 DB00001     BE0000048 Prothrombin inhibitor
#> 3 DB00006     BE0000048 Prothrombin inhibitor
#> 4 DB00006     BE0000048 Prothrombin inhibitor

That join gives four rows instead of two. Each drug gets the other drug’s action as well as its own. Here both actions happen to be inhibitor, so the mistake is easy to miss. With a promiscuous target that many drugs act on in different ways, this kind of join quietly mixes up agonists and antagonists. Joining on both keys gives the correct result:

drug_target_actions <- targets %>%
  select(drugbank_id, target_id, target_name = name, known_action) %>%
  inner_join(target_actions, by = c("drugbank_id", "target_id")) %>%
  inner_join(drugs, by = "drugbank_id")

drug_target_actions
#> # A tibble: 2 × 7
#>   drugbank_id target_id target_name known_action action    drug_name   type     
#>   <chr>       <chr>     <chr>       <chr>        <chr>     <chr>       <chr>    
#> 1 DB00001     BE0000048 Prothrombin yes          inhibitor Lepirudin   biotech  
#> 2 DB00006     BE0000048 Prothrombin yes          inhibitor Bivalirudin small mo…

3. Map the targets to genes

The polypeptide table gives each target’s UniProt accession and gene symbol. It now also carries drugbank_id, so we can join it on the same two keys. Gene symbols are the usual way into pathway, expression or genetic-association resources.

target_genes <- stack_table(
  function(db) db$cett$targets$polypeptides$general_information) %>%
  select(drugbank_id, target_id, uniprot_id = polypeptide_id, gene_name,
         cellular_location)

drug_target_genes <- drug_target_actions %>%
  inner_join(target_genes, by = c("drugbank_id", "target_id")) %>%
  select(drug_name, target_name, gene_name, uniprot_id, action, known_action,
         cellular_location)

drug_target_genes
#> # A tibble: 2 × 7
#>   drug_name   target_name gene_name uniprot_id action    known_action
#>   <chr>       <chr>       <chr>     <chr>      <chr>     <chr>       
#> 1 Lepirudin   Prothrombin F2        P00734     inhibitor yes         
#> 2 Bivalirudin Prothrombin F2        P00734     inhibitor yes         
#> # ℹ 1 more variable: cellular_location <chr>

A drug shares lepirudin’s mechanism if it has the same gene and the same action.

lepirudin_mechanism <- drug_target_genes %>%
  filter(drug_name == "Lepirudin") %>%
  distinct(gene_name, action)

drug_target_genes %>%
  semi_join(lepirudin_mechanism, by = c("gene_name", "action")) %>%
  filter(drug_name != "Lepirudin") %>%
  select(drug_name, gene_name, action, known_action)
#> # A tibble: 1 × 4
#>   drug_name   gene_name action    known_action
#>   <chr>       <chr>     <chr>     <chr>       
#> 1 Bivalirudin F2        inhibitor yes

Bivalirudin inhibits prothrombin (thrombin, gene F2), so it shares lepirudin’s primary mechanism. DrugBank marks this as a known therapeutic action (known_action = "yes"). This agrees with clinical practice, where bivalirudin is a parenteral direct thrombin inhibitor used in the same situations, for example percutaneous coronary intervention in patients with or at risk of HIT.

5. Compare the wider interaction profiles

Two drugs with the same primary target can still differ in what else they act on. We stack the target and enzyme actions into one profile per drug. Each action is kept with its own drug, so the comparison is straightforward.

cett_profile <- function(cett_type) {
  id_col <- paste0(sub("s$", "", cett_type), "_id")

  info <- stack_table(function(db) db$cett[[cett_type]]$general_information)
  acts <- stack_table(function(db) db$cett[[cett_type]]$actions)
  if (NROW(info) == 0 || NROW(acts) == 0) {
    return(NULL)
  }

  info %>%
    select(drugbank_id, cett_id = all_of(id_col), protein = name,
           known_action) %>%
    inner_join(rename(acts, cett_id = all_of(id_col)),
               by = c("drugbank_id", "cett_id")) %>%
    mutate(cett_type = cett_type)
}

profile <- bind_rows(cett_profile("targets"), cett_profile("enzymes")) %>%
  inner_join(drugs, by = "drugbank_id") %>%
  select(drug_name, cett_type, protein, action, known_action)

profile
#> # A tibble: 3 × 5
#>   drug_name   cett_type protein         action    known_action
#>   <chr>       <chr>     <chr>           <chr>     <chr>       
#> 1 Lepirudin   targets   Prothrombin     inhibitor yes         
#> 2 Bivalirudin targets   Prothrombin     inhibitor yes         
#> 3 Bivalirudin enzymes   Myeloperoxidase inhibitor unknown

Bivalirudin also has an inhibitor action recorded on the enzyme myeloperoxidase (MPO), but with known_action = "unknown". This means DrugBank lists the interaction without a confirmed role in how the drug works. Lepirudin has no such entry. A reviewer could follow up on this as a hypothesis, for example by checking the literature or adverse-event data (see vignette("drugbank_nside")). It is not an established clinical difference.

Summary

The actions and polypeptides$general_information tables for carriers, enzymes, targets and transporters now include drugbank_id.
Join CETT tables on both drugbank_id and the CETT id (target_id, enzyme_id, …). Joining on the CETT id alone can attach one drug’s actions to another drug.
With the drug link in place, short dplyr pipelines answer mechanism questions such as “which drugs share this target and action?” across a whole DrugBank release.

Introduction

Scientific case: finding a mechanistic substitute for lepirudin

Prerequisites

1. Parse the DrugBank records

2. Target actions now carry the drug

Why the drug link matters

3. Map the targets to genes

5. Compare the wider interaction profiles

Summary

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Drug Mechanism Profiling with CETT Actions

Introduction

Scientific case: finding a mechanistic substitute for lepirudin

Prerequisites

1. Parse the DrugBank records

2. Target actions now carry the drug

Why the drug link matters

3. Map the targets to genes

4. Find drugs that share lepirudin’s mechanism

5. Compare the wider interaction profiles

Summary