Drug discovery of small molecules targeting the higher-order hTERT promoter G-quadruplex.

DNA G-quadruplexes (G4s) are now widely accepted as viable targets in the pursuit of anticancer therapeutics. To date, few small molecules have been identified that exhibit selectivity for G4s over alternative forms of DNA, such as the ubiquitous duplex. We posit that the lack of current ligand specificity arises for multiple reasons: G4 atomic models are often small, monomeric, single quadruplex structures with few or no druggable pockets; targeting G-tetrad faces frequently results in the enrichment of extended electron-deficient polyaromatic end-pasting scaffolds; and virtual drug discovery efforts often under-sample chemical search space. We show that by addressing these issues we can enrich for non-standard molecular templates that exhibit high selectivity towards G4s over other forms of DNA. We performed an extensive virtual screen against the higher-order hTERT core promoter G4 that we have previously characterized, targeting 12 of its unique loop and groove pockets using libraries containing 40 million drug-like compounds for each screen. Using our drug discovery funnel approach, which utilizes high-throughput fluorescence thermal shift assay (FTSA) screens, microscale thermophoresis (MST), and orthogonal biophysical methods, we have identified multiple unique G4 binding scaffolds. We subsequently used two rounds of catalogue-based SAR to increase the affinity of a disubstituted 2-aminoethyl-quinazoline that stabilizes the higher-order hTERT G-quadruplex by binding across its G4 junctional sites. We show selectivity of its binding affinity towards hTERT is virtually unaffected in the presence of near-physiological levels of duplex DNA, and that this molecule downregulates hTERT transcription in breast cancer cells.

Evidence for the contribution of non-covalent steroid interactions between DNA and topoisomerase in the genotoxicity of steroids.

Fifty two steroids and 9 Vitamin D analogs were docked into ten crystallographically-defined DNA dinucleotide sites and two human topoisomerase II ATP binding sites using two computational programs, Autodock and Surflex. It is shown that both steroids and Vitamin D analogs exhibit a propensity for non-covalent intercalative binding to DNA. A higher predicted binding affinity was found, however, for steroids and the ATP binding site of topoisomerase; in fact these drugs exhibited among the highest topo II binding observed in over 1370 docked drugs. These findings along with genotoxicity data from 26 additional steroids not subjected to docking analysis, support a mechanism wherein the long known, but poorly understood, clastogenicity of steroids may be attributable to inhibition of topoisomerase. A "proof of principle" experiment with dexamethasone demonstrated this to be the likely mechanism of clastogenicity of, at least, this steroid. The generality of this proposed mechanism of genotoxicity across the steroids and vitamin-D analogs is discussed.

Prediction of noncovalent Drug/DNA interaction using computational docking models: studies with over 1350 launched drugs.

Noncovalent chemical/DNA interactions, for example, intercalation and groove-binding, may be more important to genomic integrity than previously appreciated, and there may very well be genotoxic consequences of that binding. It is of importance, then, to develop methods allowing a determination or prediction of such interactions. This would have particular utility in the pharmaceutical industry where genotoxicity is, for the most part, disallowed in new drug entities. We have previously used DNA docking simulations to assess if molecules had structure and charge characteristics which could accommodate noncovalent binding via, for example, electrostatic/hydrogen bonding. We here extend those earlier studies by examining a series of over 1,350 "launched" drugs for ability to noncovalently bind 10 different DNA sequences using two computational programs: Autodock and Surflex. These drugs were also evaluated for binding to the crystallographic ATP-binding site of human topoisomerase II. The results obtained clearly demonstrate multiple series of noncovalent DNA binding structure activity relationships which would not have been predicted based on cursory structural examination. Many drugs within these series are genotoxic although not via any commonly recognized structural covalent alerts. The present studies confirm previously implicated features such as N-dialkyl groups and specific N-aryl ketones as potential genotoxic chemical moieties acting through noncovalent mechanisms. These initial studies provide considerable evidence that DNA intercalation may be an important, largely overlooked, source of drug-induced genotoxicity and further suggest involvement of topoisomerase in that genotoxicity.

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

Structure-based drug design: from nucleic acid to membrane protein targets.

The in silico methods for drug discovery are becoming increasingly powerful and useful. That, in combination with increasing computer processor power, in our case using a novel distributed computing grid, has enabled us to greatly enhance our virtual screening efforts. Herein we review some of these efforts using both receptor and ligand-based virtual screening, with the goal of finding new anti-cancer agents. In particular, nucleic acids are a neglected set of targets, especially the different morphologies of duplex, triplex, and quadruplex DNA, many of which have increasing biological relevance. We also review examples of molecular modeling to understand receptors and using virtual screening against G-protein coupled receptor membrane proteins.