First chemical feature-based pharmacophore modeling of potent retinoidal retinoic acid metabolism blocking agents (RAMBAs): identification of novel RAMBA scaffolds.

The first three-dimensional (3D) pharmacophore model was developed for potent retinoidal retinoic acid metabolism blocking agents (RAMBAs) with IC(50) values ranging from 0.0009 to 5.84nM. The seven common chemical features in these RAMBAs as deduced by the Catalyst/HipHop program include five hydrophobic groups (hydrophobes), and two hydrogen bond acceptors. Using the pharmacophore model as a 3D search query against NCI and Maybridge conformational Catalyst formatted databases; we retrieved several compounds with different structures (scaffolds) as hits. Twenty-one retrieved hits were tested for RAMBA activity at 100nM concentration. The most potent of these compounds, NCI10308597 and HTS01914 showed inhibitory potencies less (54.7% and 53.2%, respectively, at 100nM) than those of our best previously reported RAMBAs VN/12-1 and VN/14-1 (90% and 86%, respectively, at 100nM). Docking studies using a CYP26A1 homology model revealed that our most potent RAMBAs showed similar binding to the one observed for a series of RAMBAs reported previously by others. Our data shows the potential of our pharmacophore model in identifying structurally diverse and potent RAMBAs. Further refinement of the model and searches of other robust databases is currently in progress with a view to identifying and optimizing new leads.

First pharmacophore-based identification of androgen receptor down-regulating agents: discovery of potent anti-prostate cancer agents.

A qualitative 3D pharmacophore model (a common feature based model or Catalyst HipHop algorithm) was developed for well-known natural product androgen receptor down-regulating agents (ARDAs). The four common chemical features identified included: one hydrophobic group, one ring aromatic group, and two hydrogen bond acceptors. This model served as a template in virtual screening of the Maybridge and NCI databases that resulted in identification of six new ARDAs (EC(50) values 17.5-212 microM). Five of these molecules strongly inhibited the growth of human prostate LNCaP cells. These novel compounds may be used as leads to develop other novel anti-prostate cancer agents.

Possibilities for transfer of relevant data without revealing structural information.

In this paper, we discuss how we safely exchanged proprietary data between third parties in the early years of predictive ADME/Tox model development. At that time, industry scientists wanted to evaluate predictive models, but were not willing to share their structures with software vendors. At the same time, model developers were willing to run the scientists' structures through their models, but they were not willing to reveal which descriptors were important for a particular predictive model. We developed a process where scientists could perform calculations on a broad number of commercially available public descriptors and forward results as a property file, instead of their structures. Meanwhile, the model developer could extract descriptors used in the predictive model, run the model, and pass results back to the scientist. On the following pages, we discuss the pros and cons of this approach, and we address questions such as: Can structural information that is proprietary be compromised from descriptors in ADME/Tox models? Can ADME/Tox predictions be made purely from descriptors, without the explicit knowledge of chemical structures, proprietary or otherwise?

Novel retinoic acid metabolism blocking agents endowed with multiple biological activities are efficient growth inhibitors of human breast and prostate cancer cells in vitro and a human breast tumor xenograft in nude mice.

Novel retinoic acid metabolism blocking agents (RAMBAs) have been synthesized and characterized. The synthetic features include introduction of nucleophilic ligands at C-4 of all-trans-retinoic acid (ATRA) and 13-cis-retinoic acid, and modification of terminal carboxylic acid group. Most of our compounds are powerful inhibitors of hamster liver microsomal ATRA metabolism enzyme(s). The most potent compound is methyl (2E,4E,6E,8E)-9-(3-imidazolyl-2,6,6-trimethylcyclohex-1-enyl)-3,7-dimethylnona-2,4,6,8-tetraenoate (5) with an IC(50) value of 0.009 nM, which is 666,667 times more potent than the well-known RAMBA, liarozole (Liazal, IC(50) = 6000 nM). Quite unexpectedly, there was essentially no difference between the enzyme inhibitory activities of the two enantiomers of compound 5. In MCF-7 cell proliferation assays, the RAMBAs also enhance the ATRA-mediated antiproliferative activity in a concentration dependent manner. The novel atypical RAMBAs, in addition to being highly potent inhibitors of ATRA metabolism in microsomal preparations and in intact human cancer cells (MCF-7, T47D, and LNCaP), also exhibit multiple biological activities, including induction of apoptosis and differentiation, retinoic acid receptor binding, and potent antiproliferative activity on a number of human cancer cells. Following subcutaneous administration to mice bearing human breast MCF-7 tumor xenografts, 6 (VN/14-1, the free carboxylic acid of 5) was well-tolerated and caused significant tumor growth suppression ( approximately 85.2% vs control, p = 0.022). Our RAMBAs represent novel anticancer agents with unique multiple mechanisms of action. The most potent compounds are strong candidates for development as therapeutic agents for the treatment of a variety of cancers.

Three dimensional pharmacophore modeling of human CYP17 inhibitors. Potential agents for prostate cancer therapy.

We report here a molecular modeling investigation of steroidal and nonsteroidal inhibitors of human cytochrome P450 17alpha-hydroxylase-17,20-lyase (CYP17). Using the pharmacophore perception technique, we have generated common-feature pharmacophore model(s) to explain the putative binding requirements for two classes of human CYP17 inhibitors. Common chemical features in the steroid and nonsteroid human CYP17 enzyme inhibitors, as deduced by the Catalyst/HipHop program, are one to two hydrogen bond acceptors (HBAs) and three hydrophobic groups. For azole-steroidal ligands, the 3beta-OH group of ring A and the N-3 of the azole ring attached to ring D at C-17 act as hydrogen bond acceptors. A model that permits hydrogen bond interaction between the azole functionality on ring D and the enzyme is consistent with experimental deductions for type II CYP17 inhibitors where a sixth ligating atom interacts with Fe(II) of heme. In general, pharmacophore models derived for steroid and nonsteroidal compounds bear striking similarities to all azole sites mapping the HBA functionality and to three hydrophobic features describing the hydrophobic interactions between the ligands and the enzyme. Using the pharmacophore model derived for azole-steroidal inhibitors as a 3D search query against several 3D multiconformational Catalyst formatted databases, we identified several steroidal compounds with potential inhibition of this enzyme. Biological testing of some of these compounds show low to high inhibitory potency against the human CYP17 enzyme. This shows the potential of our pharmacophore model in identifying new and potent CYP17 inhibitors. Further refinement of the model is in progress with a view to identifying and optimizing new leads.