Getting Started with the HistoReceptomics Profiler

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:

  1. Compound Search
  2. Tissue Search
  3. Target Search

Compound Search

“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.

  • For less sensitive but more specific results, turn off the “Docking” switch, which eliminates predicted drug-target information.
  • For drug candidates that are not well-known drugs, try “Similarity” or “Substructure.”

Tissue Search

“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.

Target Search

“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.

Glossary

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.

References & Resources

Ni, E., Kwon, E., Young, L.M., Felsovalyi, K., Fuller, J., Cardozo, T. How polypharmacologic is each chemogenomics library? (2020) Future Drug Discov. ;2(1):FDD26. Published 2020

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

Lough, L., Sherman, D., Ni, E., Young, L.M., Hao, B., Cardozo, T. (2018) Chemical probes of Skp2-mediated p27 ubiquitylation and degradation. Med. Chem. Commun., 9, 1093-1104

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

Cardozo, T., Gupta, P., Ni, E., Young, L. M., Tivon, D., Felsovalyi, K. (2016) Data sources for in vivomolecular profiling of human phenotypes. WIREs Syst Biol Med, 8: 472–484. doi:10.1002/wsbm.1354

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