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). Modern drug development processes often focus too narrowly on the target of interest, while ignoring the tissue locations where that target may be abundant, potentially slowing the approval of effective therapies. 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 four searches:

  1. Compound Search
  2. Tissue Search
  3. Reverse Target Search
  4. 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. Compound search also allows the user to customize the datasets used in generating historeceptomics profiles for compounds of interest:

Cheminformatic database options:

  • Selecting the ChEMBL option allows the user to make use of the most recently updated version of the ChEMBL database’s bioactivity data
  • Selecting Drugbank allows the user to add in data from the Drugbank database, which often contains target information for drugs of interest that is not contained in the ChEMBL database
  • Selecting the Docking option allows the user to add data from GeneCentrix’s proprietary database of in silico drug-target binding data. 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, similarity search allows users to generate historeceptomics profiles for drugs that are similar in structure to a drug of interest.

Genetics database options:

  • Selecting BioGPS causes the historeceptomics profiler to use the GeneAtlas U133A dataset from BioGPS to analyze the tissue-wide expression profiles of targets of interest
  • Selecting GTEx causes the historeceptomics profiler to use the median gene-level TPM by tissue dataset from GTEx to analyze the tissue-wide expression profiles of targets of interest

Tissue Search

“What genes/drug targets are most specific to tissue X?”

Tissue Search returns whether any particular gene is markedly more abundant in a tissue as compared to its expression in all other tissues. Note that this is different from whether the gene is overexpressed in the tissue.

In order to run a tissue search, select your tissue of interest from the pull-down list and see the results for that tissue. The same genetics datasets used in compound search are also available here, but keep in mind that each dataset has slightly different collections of tissues.

Users can also change the specificity threshold (p-value) for tissue searches: e.g., reducing the number to 0.0001 will return fewer, more specific results.

A cross-tissue expression plot of any gene in the Tissue Search result set can be generated by clicking on the chart icon next to the gene’s name.

Reverse Target Search

“What drugs are known to bind to a particular protein?”

Reverse Target Search allows the user to see a list of drugs or drug-like compounds that bind to a target of interest. In order to run a reverse target search, click the capsule icon next to the gene name result of a Tissue Search. Clicking on any of the compounds returned in a Reverse Target Search will automatically populate a new compound search so the user can easily generate the historeceptomics profile of that molecule.

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. The same genetics datasets used in compound search are also available here, but keep in mind that each dataset has slightly different collections of tissues.

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