Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

doi:10.1073/pnas.0609752104

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

*Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA 92037;
^†Department of Oncodevelopmental Biology, The Burnham Institute, La Jolla, CA 92037;
^‡College of Pharmacy, Ohio State University, Columbus, OH 43210;
^§Department of Pharmacology, University of Melbourne, Victoria 3010, Australia; and
^¶Drug Discovery Biology Laboratory, Department of Pharmacology, Monash University, Victoria 3800, Australia

Edited by Etienne-Emile Baulieu, Collège de France, Le Kremlin-Bicetre Cedex, France, and approved June 6, 2007 (received for review November 2, 2006)

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.

Footnotes

^‖To whom correspondence should be addressed. E-mail: abagyan{at}scripps.edu
Author contributions: W.H.B. and A.V.C. contributed equally to this work; W.H.B., A.V.C., A.C., J.T.D., P.M.S., X.-K.Z., and R.A. designed research; W.H.B., A.V.C., N.B.-S., B.L., J.C., N.G., and L.T.M. performed research; W.H.B., A.V.C., L.T.M., A.C., J.T.D., P.M.S., X.-K.Z., and R.A. analyzed data; and A.V.C., P.M.S., X.-K.Z., and R.A. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0609752104/DC1.
Abbreviations:

AR,

androgen receptor;

HTS,

high-throughput screening;

VLS,

virtual ligand screening;

LBD,

ligand binding domain;

DHT,

dihydrotestosterone;

CAT,

chloramphenicol acetyl transferase;

PSA,

prostate-specific antigen;

FPZ,

fluphenazine;

APZ,

acetophenazine;

PCZ,

periciazine;

GPCR,

G protein-coupled receptor.