Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Abstract

Finding good drug leads de novo from large chemical libraries, real or virtual, is not an easy task. High-throughput screening is often plagued by low hit rates and many leads that are toxic or exhibit poor bioavailability. Exploiting the secondary activity of marketed drugs, on the other hand, may help in generating drug leads that can be optimized for the observed side-effect target, while maintaining acceptable bioavailability and toxicity profiles. Here, we describe an efficient computational methodology to discover leads to a protein target from safe marketed drugs. We applied an in silico "drug repurposing" procedure for identification of nonsteroidal antagonists against the human androgen receptor (AR), using multiple predicted models of an antagonist-bound receptor. The library of marketed oral drugs was then docked into the best-performing models, and the 11 selected compounds with the highest docking score were tested in vitro for AR binding and antagonism of dihydrotestosterone-induced AR transactivation. The phenothiazine derivatives acetophenazine, fluphenazine, and periciazine, used clinically as antipsychotic drugs, were identified as weak AR antagonists. This in vitro biological activity correlated well with endocrine side effects observed in individuals taking these medications. Further computational optimization of phenothiazines, combined with in vitro screening, led to the identification of a nonsteroidal antiandrogen with improved AR antagonism and marked reduction in affinity for dopaminergic and serotonergic receptors that are the primary target of phenothiazine antipsychotics.

Fig. 1.

Regulation of AR transactivation by selected marketed drugs, FPZ and APZ (A) and PCZ (B). The CAT reporter gene assays were conducted as described in Materials and Methods. (−), untreated cells, negative control; (+), 1 nM DHT, positive control; filled bars, 1 nM DHT was added together with ligand at the indicated concentration; empty bars, ligand alone was added at the indicated concentration; dashed bars, raw β-gal activity data. The data are mean ± SEM of three independent experiments. CAT data are normalized against β-gal activity. a.u., arbitrary units. ∗, P < 0.05.

Fig. 2.

Antiandrogen activity of the D4 phenothiazine derivative. The CAT reporter gene assays were conducted as described in Materials and Methods. (A) Inhibition of DHT (1 nM)-stimulated CAT activity, in CV-1 cells transfected with the wild-type AR, by D4 (○) or Casodex (●). CAT activity data are normalized against β-gal activity. (B) D4-stimulated CAT activity in CV-1 cells transfected with the T877A mutant AR. (C) Effect of increasing concentrations of D4 on β-gal activity in wild-type AR-transfected CV-1 cells. (D) PSA assay in LNCaP cells. (−), untreated cells (negative control); (+), 50 nM DHT alone (positive control). The ligand was added to the cells at the indicated concentrations together with 50 nM DHT. The films were digitized, and PSA expression data were normalized by α-tubulin content. (Inset) Endogenous AR expression levels in LNCaP cells. The data are mean ± SEM of three independent experiments. ∗, P <0.05. a.u., arbitrary units.

Fig. 3.

Nuclear translocation experiments. HeLa cells were transiently transfected with AR. (Left) Translocation of the receptor after treatment was assessed by anti-AR antibody labeling. (Center) The nucleus was identified by DAPI staining. (Right) The overlay of AR labeling with the DAPI staining. The treatments used were as follows: CT, untreated cells; DHT, 10 nM; CDX, Casodex (1 μM); D4, compound D4 (1 μM); or D4 + DHT, compound D4 (1 μM) plus DHT (10 nM). Refer to Fig. 2 for the D4 structure. The data are representative of three independent experiments.

Fig. 4.

Primary target activity of the selected marketed drug derivative. Competitive ligand binding experiments with the primary pharmacological targets of FPZ. (A) Serotonin 5HT_2A receptor. (B) Serotonin 5HT_2C receptor. (C) Dopamine D_2L receptor. The data are mean ± SEM of three independent experiments.