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Feature Articles : Feb 15, 2006 ( )
Target Identification Methods Advance
Reflection on the Next Wave of R&D!--h2>
Spring is coming, and GENs thoughts have turned to target identification and the IBC confabs that will coincide with the vernal equinox. Screening to Analysis will offer talks and panels about advancing targets from discovery to lead optimization to preclinical development. Targeting Metabolic Syndrome will examine new developments in drug targets, disease phenomena, and clinical intervention. More information about the conferences can be found at www.ibclifesciences.com, but read on for a preview of some of the presentations.
Antisense is an important technology for drug discovery and development. It is broadly used by the pharmaceutical industry as a tool for functional genomics to discover and develop highly specific drugs for a wide range of diseases. Cumulative clinical and preclinical data suggest that antisense technology has the potential to create a new sector of the pharmaceutical industry.
Using advanced antisense chemistries, Isis Pharmaceuticals (www.isispharm.com) has evaluated more than 15 targets involved in intermediary lipid metabolism, of which DGAT2 has emerged as an exciting therapeutic target. Antisense reduction of DGAT2 expression in liver and fat, reduced hyperlipidemia, hepatic steatosis, and also caused a marked improvement in insulin sensitivity in obese, insulin-resistant rodents. Thus, DGAT2 antisense therapy may provide a novel approach for treatment of metabolic disorders.
We look for targets that are not readily amenable to the small molecule approach, says Sanjay Bhanot, M.D., Ph.D., program leader, metabolic diseases program, and executive director, antisense drug discovery. Beyond in vitro validation, our antisense drugs also allow us to evaluate pathways in vivo, an approach that is attractive for drug discovery and development.
Antisense inhibitors can be used to identify what a gene does and whether a specific gene is a good drug target. The characteristics of our antisense technology make it an ideal tool for functional genomics, Dr. Bhanot explains. It is rapid, highly specific in that it reduces the expression of a single gene, broadly applicable in that it works in cultured cells, animals, and man, and versatile in that it can reduce any gene of interest including transcription factors and adaptor proteins.
The use of antisense technology to identify the role of genes is simply the first step in Isis drug discovery process, Dr. Bhanot adds. Because our antisense drugs have similar pharmacokinetic properties, we can rapidly progress our drugs from lead identification to early clinical trials, an advantage that is very valuable in drug development.
RNAi-based screens on cell types relevant to metabolic disease have been highly productive in identifying potential drug targets. siRNA reagents used in high-throughput mode have been particularly useful in screening large sets of genes. This approach provided numerous interesting hits and in combination with recently developed in vivo validation techniques, revealed novel drug targets for diabetes and obesity at CytRx Laboratories (www.cytrx.com).
We focus mostly on using RNAi technologies to identify new targets and develop small molecule and RNAi therapeutics against novel targets, notes Harold M. Wright, Ph.D., director of biology.
CytRx Labs uses a genomic- and proteomic-based drug discovery approach that leverages RNAi to swiftly screen and identify key drug targets and pathways in obesity and type 2 diabetes, Dr. Wright says. This approach has really streamlined the drug development process for us. But, we are a small company and we are not going to be able to pursue every hit we get.
Iterative Focused Screening
An alternative approach to high-throughput screening (HTS) is to apply iterative focused screening (IFS) for instances where targets do not permit an HTS campaign, a method practiced by Merck Frosst Canada (www.merckfrosst.com).
The rationale is to use a small subset of postulated actives, enriched with inactives and Random Forest, a statistical modeling tool, to select a biased test set of molecules from a large compound library. This results in higher quality data by testing samples at three doses followed by confirmation of actives before the next round of IFS.
Were one of the smallest Merck basic research sites in the world but we are still 200 scientists strong, notes Christine Brideau, senior research fellow, biochemistry and molecular biology. We have our own internal drug discovery programs here. We develop HTS assays and perform most of the screening except for ultrahigh-throughput screening, as this is centralized in the U.S. At IBC we will explain how our methodology came about. When you are doing high-throughput screening, you are randomly screening compounds. The more you do, the more likely you are to find a hit. But, how many do you need to test to get results?
Our method rapidly identifies true hits and is more successful than others to reduce the number of screening iterations and test samples. Results from two examples of IFS will be shown at IBC and compared with real HTS data to demonstrate how our approach increases the hit rate and is successful in identifying novel lead structures.
Screening technology has moved forward so much that we can screen millions of compounds in a short time. The only way you can do that is to miniaturize and automate the assays. But, not every assay is compatible to miniaturization or automation. Another issue is that certain targets are unattainable in that not enough information is available about the biology of some targets, limiting the tools available to develop HTS assays.
Using IFS allows you to kickstart the process of filtering through, first finding the molecules that do interact with the target, explains Brideau. And it allows you to eliminate from the beginning the molecules that have a lower probability of interacting with the target. The first time wont necessarily give you the best leads, but youll get a smaller number of actives to initiate the next round of IFS, until new leads are found. And you can adjust the algorithm accordingly. Using IFS we can test fewer compounds but in replicates, where in HTS you can only afford to test 1 million compounds in singlet. IFS is more robust than HTS.
Over one-half of the protein-encoding genes in the human genome have no known function. These molecules represent an unprecedented opportunity for novel target identification. In conjuction with genetic analysis in mammalian cells, the Genomics Institute of the Novartis Research Foundation (GNF; www.gnf.org) has employed high-content imaging to interrogate complex molecular phenotypes, such as cell migration, nuclear translocation, and cell cycle at the level of the genome.
GNF is undertaking the first industrial and comprehensive attempt to unveil the function of each gene in the genome, using both cDNA and siRNA to assess the affect of overexpression and gene knockout, respectively, in phenotypic cellular assays.
Were actually doing genetics in mammalian cells, says Sumit K.Chanda group leader, genomics. Were looking at one gene at a time, and our challenge is modulating one particular cell phenotype at a time.
To this end, GNF, with the aid of Novartis, has compiled and arrayed in excess of 20,000, unique, full-length human cDNAs in mammalian expression vectors in 384-well plates, each well containing a unique gene. Custom-built plate and liquid-handling robotics screen for effects in a variety of complex cellular assays. Current capabilities allow screening of more than 150,000 genes per day.
A lot of the latest technology is siRNAs, how we set up and how we analyze data becomes an automated process, Chanda notes. The advantages are tremendous and obvious. It allows us to systematically knock out every gene in the genome. At the end of the assays, what you get is a list of genes, you take that list and triage it. What you do with this information will make or break the target identification project at this point. It is an incredibly powerful methodology, but as a tool, it is only as good as the person using it. And it is a tool, not the final answer.
Readouts include luminescence, fluorescence, and recently through collaborative efforts with local biotech and academia, GNF is performing high-content imaging screens for distinct cellular events including differentiation, cell cycle arrest, apoptosis, protein translocation, membrane ruffling, neurite outgrowth, and others. These efforts have enabled the identification of many novel effectors in even the most well-studied signaling pathways, as well as ascribing novel function to old genes.
One thing plaguing the field right now is off-target activity, notes Chanda. This is the greatest source of false positives, and Ill discuss strategies to minimize false positives.
Physiologically Relevant Context
The screening of cell-based assays for the study of pathways and biological systems has enabled the analysis of drug targets in a physiologically relevant context. By conducting successful pathway analysis, BioImage (www.bioimage.com) and its partners have been able to identify and quantify relationships between target molecules, test compounds, and other cellular elements.
We dont consider ourselves focused on target identification but more on target validation, as well as compound screening and profiling, points out Len Pagliaro, vp of business development. And that is where combining our Redistribution Assay technology with RNAi approaches comes into play.
Redistribution assay technology relies on the imaging and quantification of intracellular protein translocation. For development of high-quality, robust, and automation-friendly redistribution assays, BioImage uses Aequorea victoria GFP and other fluorescent proteins, fused to translocating targets of interest.
We have studied the Akt-isoform dependency of translocation response of the Forkhead (FKHR) transcription factor using RNAi reagents at BioImage. Akt regulates FKHR activity, and all Akt isoforms are expressed in the FKHR Redistribution Assay cell line. The Akt-isoform specific siRNAs allowed us to investigate whether our FKHR redistribution assay is dependent on endogenous Akt function, and further which Akt isoforms are most important for FKHR regulation, Pagliaro adds.
BioImage is extending this work via a collaboration with David Sabatini, M.D., Ph.D., at the Whitehead Institute at MIT. Dr. Sabatinis lab will probe the BioImage FKHR cell line with a broad knockdown array.
Potentially, new upstream targets will be identified and validated. If successful, small molecule screens against these targets could then be performed in efforts to identify new therapeutics. The data should be interesting scientifically and perhaps fruitful from a drug discovery perspective as well, notes Pagliaro. We will be keen to see the results.
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