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.androgen receptor ͉ drug design ͉ prostate cancer C urrent approaches for discovery of novel chemical leads against a molecular target rely heavily on high-throughput screening (HTS) and to a lesser extent on virtual ligand screening (VLS) techniques. HTS has provided rapid lead identification for numerous drug targets (1-8); however, HTS also has major drawbacks, including a significant level of false positives and false negatives and low hit rates for many targets (9). Successful leads from HTS can also suffer from poor bioavailability and unwanted toxicity profiles of compounds. These problems result partially from the nature of the chemical libraries used for HTS. Furthermore, because the pharmacological properties of most compound libraries are largely unknown, there is an additional high risk that optimization of hits identified with HTS will not be sufficient for their evolution into real drugs.In contrast, retrospective analysis of marketed drugs reveals that their physicochemical and structural properties are clustered around preferred values and scaffolds (10). In addition, some chemical motifs are associated with high biological activity and often confer activity against more than one target/receptor (11-16). These motifs have been referred to as ''privileged structures'' (11). These observations lead to an assumption that the chemical space of potential drugs is limited. Consequently, currently marketed...

Computational Discovery of Novel Low Micromolar Human Pregnane X Receptor Antagonists

“…In addition, we have used docking, an approach that has been widely applied elsewhere for virtual discovery of new leads for nuclear hormone receptors (Schapira et al, 2000(Schapira et al, , 2001(Schapira et al, , 2003a as well as many other therapeutic targets (Leach et al, 2006;Bisson et al, 2007;Zhang et al, 2007). We found that some of the antagonists, such as ketoconazole and itraconazole, generally score well; this may be because the docking program picks up nonspecific van der Waals interactions of large molecules on the outer surface of the AF-2 site.…”

“…In contrast to other nuclear receptors, such as the androgen receptor (Bohl et al, 2004;Bisson et al, 2007) and thyroid hormone receptor (Schapira et al, 2003b), the majority of publications on PXR have focused primarily on agonists (e.g., those capable of inducing drug metabolism and transporter expression) with clinical implications for drug-drug interactions (Ung et al, 2007). Conversely, there have been very few attempts to address antagonism at PXR, which could be used to diminish agonist interactions that are unavoidable with some treatments, such as with anticancer therapies and macrolide antibiotics such as rifampicin (Mani et al, 2005).…”

Structure‐based protein‐protein interaction networks and drug design

“…To understand the side effects and toxicity of rejected drugs, it is important to predict "OffTarget" binding sites. A number of methods have used clustering of proteins into families [98,99], global structure similarity measurement [100,101] and interface similarity measurement [102][103][104][105] to predict "Off-Target" binding or to redesign drugs to enhance efficacy. Lounkine et al have used a similarity ensemble approach to predict off-targets, based on whether a molecule will bind to a target with similar chemical features to those of known targets [106].…”

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

“…A virtual screening protocol identified ER specific ligands from a plant product-based database [32]; from 12 candidates evaluated by a fluorescence polarization binding assay, 3 had >100-fold selectivity to ER over ER . The same approach has also been used to find compounds with good selectivity for ER over ER [32,33]. Bisson et al have used computational methods that led to a nonsteroidal antiandrogen with improved AR antagonistic activity based on an initial screening of FDA approved new drugs [33].…”

“…The same approach has also been used to find compounds with good selectivity for ER over ER [32,33]. Bisson et al have used computational methods that led to a nonsteroidal antiandrogen with improved AR antagonistic activity based on an initial screening of FDA approved new drugs [33]. Several groups have published datasets or performed modeling on ER and AR and these data are readily available for further evaluation with new modeling methods [34][35][36][37][38].…”