ChEMBL periodically curates clinical trial information into its DRUG_INDICATION table. However, there’s some weird inconsistencies in the way it references disease concepts in external vocabularies. This blog post is an exploration of that table.

As of ChEMBL v35, the DRUG_INDICATION table contains the following columns:

  1. DRUGIND_ID - a unique identifier for the chemical-indication pair
  2. MOLREGNO - a foreign key to the molecules table
  3. MAX_PHASE_FOR_IND - the maximum phase achieved by clinical trials of the chemical-indication pair
  4. MESH_ID - the local unique identifier from Medical Subject Headings (MeSH) for the indication
  5. MESH_HEADING - the label in MeSH for the given MeSH ID
  6. EFO_ID - A compact URI (CURIE) for a term in the Experimental Factor Ontology (EFO) ( in theory)
  7. EFO_TERM - a label for the CURIE in the EFO_ID column

This is already strange, considering that for cell lines, tissues, and targets, ChEMBL has created its own table which contains the cross-references to external vocabularies. Here, they’re baked into the pivot table.

The funny business is about the EFO_ID column:

  1. It’s strange that the MESH_ID column uses local unique identifiers but the EFO_ID column uses compact URIs (CURIEs). CURIEs are a syntax for referencing an entity in an ontology or database that takes the form of <prefix>:<local unique identifier>. The prefix is usually the acronym for the resource and the local unique identifier is the ID inside the resource (usually a number). More on this in my previous post.
  2. The CURIEs in the EFO_ID column aren’t all using EFO as the prefix!

Let’s have a look at what’s actually in the EFO_ID column by using the chembl_downloader Python package to automatically download the latest version of ChEMBL and run SQL queries over it.

import chembl_downloader

chembl_downloader.query("""\
   SELECT DISTINCT efo_id, efo_term
   FROM DRUG_INDICATION
   WHERE efo_id NOT LIKE 'EFO:%'
""")
efo_id efo_term
HP:0001945 Fever
Orphanet:309005 Disorder of lipid metabolism
HP:0003124 Hypercholesterolemia
Orphanet:79211 Combined hyperlipidemia
HP:0000023 Inguinal hernia
… …

Using a bit of SQL string processing to identify the prefixes and the Bioregistry to retrieve the name and homepage gives a bit more context about what the prefixes in CURIEs in the EFO_ID column represent.

import bioregistry
import chembl_downloader

sql = """\
SELECT prefix, count(prefix) as count
FROM (
    SELECT substr(efo_id, 0, instr(efo_id, ":")) as prefix
    FROM DRUG_INDICATION
)
GROUP BY prefix
HAVING count(prefix) > 0
ORDER BY count(prefix) DESC
"""

df = chembl_downloader.query(sql)
df["name"] = df["prefix"].map(bioregistry.get_name)
df["homepage"] = df["prefix"].map(bioregistry.get_homepage)
prefix count name homepage
EFO 37,603 Experimental Factor Ontology http://www.ebi.ac.uk/efo
MONDO 13,532 Mondo Disease Ontology https://monarch-initiative.github.io/mondo
HP 3,381 Human Phenotype Ontology http://www.human-phenotype-ontology.org/
Orphanet 359 Orphanet http://www.orpha.net/consor/
MP 281 Mammalian Phenotype Ontology https://www.informatics.jax.org/vocab/mp_ontology/
GO 50 Gene Ontology http://geneontology.org/
DOID 45 Human Disease Ontology http://www.disease-ontology.org
CHEBI 19 Chemical Entities of Biological Interest http://www.ebi.ac.uk/chebi
UBERON 1 Uber Anatomy Ontology http://uberon.org

The ones that stand out to me are CHEBI, UBERON, and GO, since these resources are respectively for chemicals, anatomical entities, and biological processes/cellular components/molecular functions.

I wrote the following function to do a bit of exploring, based on the prefix.

import chembl_downloader

def print_indications_with_prefix(prefix: str) -> None:
    sql = f"""\
        SELECT DISTINCT
            MOLECULE_DICTIONARY.chembl_id,
            MOLECULE_DICTIONARY.pref_name,
            DRUG_INDICATION.efo_id,
            DRUG_INDICATION.efo_term
        FROM MOLECULE_DICTIONARY
            JOIN DRUG_INDICATION ON MOLECULE_DICTIONARY.molregno == DRUG_INDICATION.molregno
        WHERE DRUG_INDICATION.efo_id LIKE '{prefix}:%'
        ORDER BY MOLECULE_DICTIONARY.pref_name
        """
    df = chembl_downloader.query(sql)
    df["chembl_id"] = df["chembl_id"].map(lambda s: f"[{s}](https://bioregistry.io/chembl.compound:{s})")
    df["efo_id"] = df["efo_id"].map(lambda s: f"[{s}](https://bioregistry.io/{s})")
    print(df.to_markdown(tablefmt="github", index=False))

Using UBERON returns a single result, which appears to be a mistake / an abuse of the database schema.

chembl_id pref_name efo_id efo_term
CHEMBL4650497 PEGSITACIANINE UBERON:0000029 lymph node

Using CHEBI returns a large number of diagnostic agents. This is part of the “role” hierarchy within ChEBI, and also what I would consider an abuse of the database schema.

chembl_id pref_name efo_id efo_term
CHEMBL1234270 ARFOLITIXORIN CHEBI:44185 methotrexate
CHEMBL5314823 DIGADOGLUCITOL CHEBI:33295 diagnostic agent
CHEMBL4650354 FLORBENGUANE F18 CHEBI:33295 diagnostic agent
CHEMBL5095045 FLORZOLOTAU (18F) CHEBI:33295 diagnostic agent
CHEMBL5314559 FLOTUFOLASTAT F 18 GALLIUM CHEBI:33295 diagnostic agent
CHEMBL4298157 FLUBROBENGUANE F18 CHEBI:33295 diagnostic agent
CHEMBL5314633 IODINE I124 EVUZAMITIDE CHEBI:33295 diagnostic agent
CHEMBL4298185 LONAPEGSOMATROPIN CHEBI:37845 growth hormone
CHEMBL5314761 PEGFOSIMER MANGANESE CHEBI:33295 diagnostic agent
CHEMBL4650497 PEGSITACIANINE CHEBI:33295 diagnostic agent
CHEMBL4297334 PIFLUFOLASTAT F18 CHEBI:33295 diagnostic agent
CHEMBL5314483 RIZEDISBEN CHEBI:33295 diagnostic agent
CHEMBL5314650 TECHNETIUM TC-99M LABELED CARBON CHEBI:33295 diagnostic agent
CHEMBL4298067 TOMARALIMAB CHEBI:35610 antineoplastic agent
CHEMBL5314445 VIDOFLUFOLASTAT(18F) CHEBI:33295 diagnostic agent
CHEMBL4594280 VIPIVOTIDE TETRAXETAN CHEBI:33295 diagnostic agent
CHEMBL4594411 XENON XE-129, HYPERPOLARIZED CHEBI:33295 diagnostic agent
CHEMBL5314610 ZOPOCIANINE CHEBI:33295 diagnostic agent
CHEMBL5314611 ZOPOCIANINE SODIUM CHEBI:33295 diagnostic agent

Using GO returns aging, regulation of ovulation (both positive and negative), and wound healing as the four unique biological processes. This is a little less controversial than UBERON and CHEBI, but it still has a bit of a mismatch for the idea of an “indication”.

chembl_id pref_name efo_id efo_term
CHEMBL1566 ACARBOSE GO:0007568 aging
CHEMBL600 ACETYLCYSTEINE GO:0007568 aging
CHEMBL1399 ANASTROZOLE GO:0007568 aging
CHEMBL25 ASPIRIN GO:0007568 aging
CHEMBL1201556 BECAPLERMIN GO:0042060 wound healing
CHEMBL5315086 BETULA PUBESCENS BARK GO:0042060 wound healing
CHEMBL1200800 CALCIUM ACETATE GO:0007568 aging
CHEMBL1200539 CALCIUM CARBONATE GO:0007568 aging
CHEMBL113313 CAPROMORELIN GO:0007568 aging
CHEMBL1042 CHOLECALCIFEROL GO:0007568 aging
CHEMBL2108185 CORIFOLLITROPIN ALFA GO:0060279 positive regulation of ovulation
CHEMBL429910 DAPAGLIFLOZIN GO:0007568 aging
CHEMBL1421 DASATINIB GO:0007568 aging
CHEMBL139 DICLOFENAC GO:0007568 aging
CHEMBL367149 DOCONEXENT GO:0007568 aging
CHEMBL1200969 DUTASTERIDE GO:0007568 aging
CHEMBL135 ESTRADIOL GO:0007568 aging
CHEMBL2108390 FIBRIN GO:0007568 aging
CHEMBL500468 GHRELIN GO:0007568 aging
CHEMBL389621 HYDROCORTISONE GO:0007568 aging
CHEMBL13817 IBUTAMOREN GO:0007568 aging
CHEMBL460026 ICOSAPENT GO:0007568 aging
CHEMBL2109042 INFLUENZA VIRUS VACCINE GO:0007568 aging
CHEMBL471737 IVABRADINE GO:0007568 aging
CHEMBL444814 L-CITRULLINE GO:0007568 aging
CHEMBL191 LOSARTAN GO:0007568 aging
CHEMBL1201419 LUTROPIN ALFA GO:0060279 positive regulation of ovulation
CHEMBL2107951 MALTODEXTRIN GO:0007568 aging
CHEMBL267936 MECAMYLAMINE GO:0007568 aging
CHEMBL1201716 MECASERMIN GO:0007568 aging
CHEMBL1431 METFORMIN GO:0007568 aging
CHEMBL650 METHYLPREDNISOLONE GO:0007568 aging
CHEMBL4074884 MITOQUINONE MESYLATE GO:0007568 aging
CHEMBL438497 NICOTINAMIDE RIBOSIDE GO:0007568 aging
CHEMBL3 NICOTINE GO:0007568 aging
CHEMBL1201574 ONABOTULINUMTOXINA GO:0007568 aging
CHEMBL1234886 OXYGEN GO:0007568 aging
CHEMBL395429 OXYTOCIN GO:0007568 aging
CHEMBL90593 PRASTERONE GO:0007568 aging
CHEMBL50 QUERCETIN GO:0007568 aging
CHEMBL165 RESVERATROL GO:0007568 aging
CHEMBL413 SIROLIMUS GO:0007568 aging
CHEMBL1200574 SODIUM CHLORIDE GO:0007568 aging
CHEMBL93268 SODIUM NITRITE GO:0007568 aging
CHEMBL136478 SODIUM NITROPRUSSIDE GO:0007568 aging
CHEMBL1201621 SOMATROPIN GO:0007568 aging
CHEMBL48802 SULFORAPHANE GO:0007568 aging
CHEMBL386630 TESTOSTERONE GO:0007568 aging
CHEMBL2107067 TESTOSTERONE UNDECANOATE GO:0007568 aging
CHEMBL3545347 TXA127 GO:0007568 aging

There wasn’t really a point to this post other than to show off a quirk I found in ChEMBL. This is useful to be aware of when automatically processing the database in bulk, e.g., for building a knowledge graph.

There are two other follow-up questions I would have about this table:

  1. Are there any EFO terms that are outside the disease hierarchy (i.e., not a child of EFO:0000408)?
  2. Why are there DOID terms? The combination of EFO and MONDO should cover everything. Answering this question actually isn’t so difficult given my recent work on assembling mappings with SeMRA, specifically for the disease landscape. I’ll try to come back to this in a future post.

If you made it this far: what did you think about my clickbait title?