The HistoReceptomics Profiler (HR Profiler) is a drug discovery informatics tool that indicates where in the human body (in what tissue or cells) a drug is likely to exhibit bioactivity. The bioactivity may be either beneficial (the expected or unexpected benefit of using the drug) or adverse (known or unexpected tissue-specific, side effects from the drug). Specifically, many investigators overlook the fact that, for a drug to be effective against a disease, it must target proteins expressed in tissues related to the disease (Kumar et al., 2016). The HR Profiler algorithm provides this information, by determining a drug’s historeceptomic profile, the list of target-tissue pairs statistically most likely to be affected by the drug.
The HR Profiler offers three functions:
“In what tissues is my drug most active?”
Simply type in the common drug name (e.g., “Gleevec”) or the drug SMILES string (see Glossary for definition of SMILES) and click “Enter” and the results will be returned.
“What genes/drug targets are most specific to tissue X?”
Note that this is different from whether the receptor is overexpressed in the tissue: this search returns whether the receptor is markedly more abundant in a tissue as compared to all the other tissues. Simply pull down to select your tissue of interest and see the results for that tissue. You can change the specificity threshold (p-value): e.g., reducing the number to 0.0001 will return fewer, more specific results.
“In which tissues is drug target/gene X most specifically abundant?”
Note that this is different from whether the receptor is overexpressed in the tissue: this search returns whether the receptor is markedly more abundant in a tissue as compared to all the other tissues. Simply enter the gene name (e.g., DRD4 for the dopamine receptor 4) and click submit.
SMILES: An alphanumeric string that encodes a chemical structure. For example: CCC is propane (three single bonded carbons).
p-value: The chance that the overabundance of the tissue/target in question could occur by random chance based on the variability of the expression of that target across 70 other tissues.
Kim, E.J., Felsovalyi, K., Young, L.M., Shmelkov, S., Grunebaum, M., Cardozo, T. (2018) Molecular basis of atypicality of bupropion inferred from its receptor engagement in nervous system tissues. (2018) Psychopharmacology 235, 2643–2650
Cardozo, T., Shmelkov, E., Felsovalyi, K., Swetnam, J., Butler, T., Malaspina, D., Shmelkov, S. (2017) Chemistry-based molecular signature underlying the atypia of clozapine. Transl Psychiatry, 7, e1036. doi:10.1038/tp.2017.6
Shmelkov, E., Grigoryan, A., Swetnam, J., Xin, J., Tivon, D., Shmelkov, S., Cardozo, T. (2015) Historeceptomic fingerprints for drug-like compounds. Front Physiol, 6, 371. doi: 10.3389/fphys.2015.00371