Post-lanosterol inhibition profile based classification of commonly used prescription medications.
Keri A Tallman, Allison C Anderson, Károly Mirnics, Ned A Porter, Zeljka Korade
Abstract
Open AccessCholesterol is an essential structural component of all cells, and the sterol biosynthetic pathway provides critical precursors for essential homeostatic molecules. Sterol biosynthesis can be disrupted by pathogenic variants in genes, as well as commonly prescribed medications. These medications disrupt post-lanosterol biosynthesis at different steps. We attempted to classify their actions based on the biochemical signatures of sterol intermediates. Our previous screening of the NIH Clinical Compound library of >1800 compounds in clinical use suggested that over 30 medications can disrupt post-lanosterol biosynthesis. Of these, we selected 11 compounds for follow up in a human dermal fibroblast model. Using LC-MS/MS we measured 13 post-lanosterol intermediates in control, DHCR7 +/- and DHCR7-/- human dermal fibroblasts exposed to cariprazine, nebivolol, rotigotine, buspirone, lurasidone, fluoxetine, hydroxyzine, amiodarone, spiroxamine, vilazodone and ziprasidone. All tested cells were exposed to 4 concentrations of each medication. We found in all fibroblasts, regardless of DHCR7 genetic makeup, sterol biosynthesis was inhibited by the tested medications. These medications could be classified in 6 groups based on the post-lanosterol profiles they produced - those that were 1) primarily DHCR7 inhibitors (cariprazine, nebivolol and rotigotine); 2) EBP inhibitors (fluoxetine); 3) combined DHCR7 and DHCR14 inhibitors (buspirone and lurasidone); 4) combined EBP and DHCR7 inhibitors (hydroxyzine); 5) combined EBP and DHCR24 inhibitors (amiodarone); and 6) multi-enzyme inhibitors (vilazodone, ziprasidone, spiroxamine). In addition, DHCR7 +/- fibroblasts responded with greater sterol profile disruptions to all medications, while DHCR7 fibroblasts from patients with Smith-Lemli-Opitz syndrome showed generally a more plateaued response. Knowing the inhibition profile-based classification of medications that have a sterol inhibiting side effect might ultimately translate into safety recommendations during pregnancy and could be critical for new drug development.