ENCODE: A new tool for drug discovery?

Only a small proportion (<2%) of the total genome codes for proteins and the remainder had up to now been termed non-coding or ‘junk DNA’. The aim of the ENCODE (Encyclopaedia of DNA elements) project was to attempt to characterize these undefined regions.  The consortium has recently published 30 papers detailing, amongst much data, regions of transcription and regulatory areas that were previously unreported.

One of these papers by Maurano et al., used a technique to map sites of regulatory elements within the DNA and compare these with noncoding variant polymorphisms associated with common diseases that have been identified through genome-wide association studies (GWAS).

The group examined many different cell types including primary cells, immortalized, malignancy derived or pluripotent cell lines, hematopoietic cells, progenitor cells as well as some fetal tissue samples. They used Deoxyribonuclease 1 (DNase1) hypersensitive sites (DHSs) of increased chromatin accessibility as a marker for binding sites of regulatory elements such as transcription factors and thus mapped the regulatory regions in this material. In total, they identified DHS positions spanning 42.2% of the genome, a higher density of regulatory regions than previously appreciated. They then examined the position of single nucleotide polymorphisms (SNPs) identified by GWAS and found a 40% enrichment of these SNPs in DHSs. This analysis shows that the common genetic variants associated with disease are often located at recognition sequences of transcription factors. The authors also demonstrated that these regulatory regions may control the expression of genes that are distant (>250kb) rather than solely the expression of the nearest gene.

Further interesting data from the consortium was obtained through the study of cancer lines. Over 40 cancer lines of different origin were examined and data obtained showing that cancer lines possess regulatory DNA regions that are not present in normal cells (Stamatoyannopoulous, J. A., 2012).

The new information provided by ENCODE is not yet readily applicable to drug discovery, however, this data could provide a map of transcriptional and regulatory regions that could help to identify novel therapeutic targets. In a recent article in Nature Drug Discovery, Michael Snyder one of the principal investigators of the ENCODE consortium explains that changes in gene expression through a change in regulatory sequence could enable identification of proteins that could make useful drug targets.

Applications that could be useful in drug discovery settings include the use of knockdown technologies to screen for biological effects, or zinc finger nuclease technology that can introduce mutations to regulatory elements to determine if changes in these regulatory regions are causal of disease.

Systematic localization of common disease-associated variation in regulatory DNA.

Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, Shafer A, Neri F, Lee K, Kutyavin T, Stehling-Sun S, Johnson AK, Canfield TK, Giste E, Diegel M, Bates D, Hansen RS, Neph S, Sabo PJ, Heimfeld S, Raubitschek A, Ziegler S, Cotsapas C, Sotoodehnia N, Glass I, Sunyaev SR, Kaul R, Stamatoyannopoulos JA.

Science. 2012 Sep 7;337(6099):1190-5. doi: 10.1126/science.1222794. Epub 2012 Sep 5.

What does our genome encode?, Stamatoyannopoulous, J. A. 2012, Genome Research 22: 1602-1611

An audience with Michael Snyder, Nature reviews Drug Discovery Oct 2012. 11: 744

The ENCODE papers are available online at go.nature.com/iN6Ezx.


Faster metabolism

However well new compounds in development perform in vitro, the real confirmation is if they have desired effect in the body, and without major side effects. A key parameter in this understanding is the effect of the body’s metabolism on the compounds. It is highlighted and discussed in this article , where the authors have developed an early method to determine the functional effect of the metabolites formed on the drug target.

The Authors took human H4 receptor ligands which had been well characterised as active inverse agonists in a 384 well functional cell based assay using H4 receptor linked  to a reporter gene (β-Galactosidase ) and incubated them with liver microsomes (containing the cytochrome enzymes). The cytochrome enzymes converted the compounds into their respective metabolites (as would occur in the liver). The metabolites were then separated and identified using a LC/MS (electrospray ionization in positive ion mode). The individual metabolites were then collected and reformatted into separate wells in a microtitre plates. A freeze drying process was employed to remove organic solvents such as acetonitrile and formic acid which were required by the liquid chromatography, and the metabolites were re-solubilised in DMSO. One concern the authors did address is obtaining a full solubilisation of the freeze dried metabolite in the DMSO solution, however the metabolites that they were using had a number of protonated nitrogen atoms and were relatively polar so poor solubility was not an issue in this case.  The authors however suggested if dealing with very non-polar compounds, 10% DMSO could be added before the freeze drying step, which would dry into a DMSO film which would aid with re-solubilisation step. Another suggestion would be to use further analytical techniques such as ELSD (evaporative light scattering detection), to determine the true concentration of the metabolite preparation and therefore correct any activity measurements determine the true concentration curve for the metabolite

Once the metabolites were re-solubilised in DMSO, the author’s re- tested them in the 384 well cell based reporter gene assay.  This allowed determination of the functional response of the metabolites in comparison to parent compounds. With the optimisation of the fraction collection procure, two individual compounds, with a full profiling run from the LS/MS can be screened on a 384 plate. This allows key compounds from structure activity profiled in a timely manner to be profiled.

The results from this work were quite interesting, the first finding was that there was a contaminant in all four of the preps of the compounds, and this contaminant was a histamine receptor antagonist underlining the importance of QC on compounds that you are testing in any drug discovery programme, otherwise structure activity relationships could be mislead. When the individual metabolites were tested, one was shown to be a competitive antagonist compared to its parent compound being an inverse agonist. Again this is important to determine to drive further optimisation of your lead compound.  Other metabolites appeared to be inactive or still have the same functional response as their parent compound.

The key from this study is the process development which allows a fast turnaround of key series in the same assay format used for SAR studies and can be integrated into a screening cascade. That can only help in assisting the drug design process.