Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties.
Peter LarssonMaria Cristina De RosaBenedetta RighinoMaxim OlssonBogdan Iulius FloreaEva Forssell-AronssonAnikó KovácsPer KarlssonKhalil HelouToshima Z ParrisPublished in: Scientific reports (2024)
Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.
Keyphrases
- drug induced
- liver injury
- breast cancer cells
- single cell
- poor prognosis
- adverse drug
- genome wide
- multiple myeloma
- gene expression
- electronic health record
- copy number
- dna methylation
- machine learning
- long non coding rna
- newly diagnosed
- small molecule
- body mass index
- molecular dynamics simulations
- clinical decision support
- genome wide identification
- drug administration