Network analysis of FDA approved drugs and their targets

Abstract

The global relationship between drugs that are approved for therapeutic use and the human genome is not known. We employed graph-theory methods to analyze the Federal Food and Drug Administration (FDA) approved drugs and their known molecular targets. We used the FDA Approved Drug Products with Therapeutic Equivalence Evaluations 26(th) Edition Electronic Orange Book (EOB) to identify all FDA approved drugs and their active ingredients. We then connected the list of active ingredients extracted from the EOB to those known human protein targets included in the DrugBank database and constructed a bipartite network. We computed network statistics and conducted Gene Ontology analysis on the drug targets and drug categories. We find that drug to drug-target relationship in the bipartite network is scale-free. Several classes of proteins in the human genome appear to be better targets for drugs since they appear to be selectively enriched as drug targets for the currently FDA approved drugs. These initial observations allow for development of an integrated research methodology to identify general principles of the drug discovery process.

Fig 1

Number of active ingredients in each drug record listed in the FDA’s Orange Book.

Fig 2

Number of newly approved active ingredients since 1983. Note that 528 were approved before 1983 but the information about their distribution is not provided in the Orange Book. Since discontinued drugs were removed from this analysis, the number of approved drugs per year is the number of those drugs that are still available.

Fig 3

Visualization of the bipartite drug-target network extracted from DrugBank. Orange nodes represent drugs and blue nodes are known biomolecular targets. The network is made of 1537 nodes (1052 drugs and 485 targets) and 1815 interactions extracted from 2240 research articles.

Fig 4

One hundred seventy nine islands were found in the drug-target bipartite network containing a single giant connected island made of 481 nodes (drugs and targets).

Fig 5

Distribution of drug-target interactions in a bipartite network created from DrugBank.

Fig 6

Gene ontology repeated terms counted for all the drug targets versus repeated terms counted for all human proteins reveal enrichment in receptors, signal transduction components, and transcription factors.