At the front end of most drug discovery programs lies a step called Target Identification, and a few months ago I sat down with a colleague to discuss their approach to target identification. In particular, “how do you characterize a target”? I was surprised at how much that process can vary from company to company.
As I set out to describe my workflow for this blog post, I was reminded of this cartoon, and how much work goes on between the starting point and the end point when researching the function of genes.
I should preface what I’m about to say, with the words “this is the way I work” your goals and tools might be different, and I’m always curious about the way people work. So please feel free to comment.
At a macroscopic level (regardless of your ultimate research goals) there are three levels of research:
- Foundational Research: where you familiarize yourself with the general “landscape” of a particular research topic.
- Deep Dive Research: where you examine certain concepts exposed in step 1 in-depth.
- Current Research: where you create a “surveillance” program to keep yourself up-to-date with the latest developments in a particular area of research.
In the examples which follow, I’ll be showing you the steps that I take and the tools that I use to learn more about the target space for pancreatic cancer.
My goal at this stage in the game is to answer the following questions:
- What is the etiology of the disease? (What syndromes predispose people to the disease and what percentage of the patient population do they account for?)
- How does the disease progress? (What are the clinical stages?)
- What genes & proteins are involved in the progression of the disease?
- What pathways & disease processes do they participate in?
- What is the current standard of care, and what genes are targeted by that standard of care?
- Who are the thought leaders in this area?
- Is research in this area heating up?
Since my workflow is very disease-centric, I usually start by searching the OMIM database. OMIM provides a good overview of the disease, with information on the genes involved, and relevant literature. Recently, I’ve also added Wikipedia to the list. I’ve been pleasantly surprised with the depth of information available on Wikipedia, both for diseases and for genes. In addition, to these more general sources, the National Cancer Institute’s PDQ site provides a good overview of the clinical stages of the disease, and the standards of care applied at each stage. This information is critical for two reasons. It gives discovery scientists insight into the clinical presentation of the disease, and makes it possible to design a drug or cocktail that targets a particular patient population.
My usual starting point for most research projects is PubMed. And I start by looking for review papers on a topic of interest. In this case my query looks like this:
(pancreatic cancer) AND “review”[Publication Type]
You can further restrict the results by limiting hits to the last few years. Sorting by publication date also helps focus your attention on the latest developments. You’ll find more tips and tricks for using PubMed here.
As I read through the review papers, I compile a list of genes which I keep in two “piles” — targets and biomarkers. I also compile a list of pathways, and attempt to connect those to specific biological processes involved in the disease.
Gene-centric vs Pathway Centric vs Disease Centric Workflows
When I first started out in this industry, I thought, perhaps rather naively, that drug discovery research always followed the same path, and consequently that every company used the same approach to identify new drug candidates. However, I quickly learned that this wasn’t the case.
Some companies used a traditional compound-centric approach to drug discovery. They would screen a compound through a particular target panel, find some interesting binding characteristics for a target, and then back-track to an indication or set of indications.
In a gene-centric approach, the process starts with a gene. The function of the gene is determined (at least initially) by the Gene Ontology terms, by literature, by sequence homology, by protein domain, etc. Depending on the drug class (small molecule vs peptide or antibody, siRNA, etc) certain types of genes/transcripts/proteins may be more or less amenable to being addressed. For example, antibodies may be more appropriate for targets that have extracellular domains to which the antibody can attach.
A few years ago, Novartis espoused a more pathway-centric approach to drug discovery. The aim of which was to use the signaling pathways to help identify new targets, either for monotherapies, or collections of targets for drug cocktails, or for repurposing existing drugs.
In a disease-centric approach, the disease biology, and the genes that drive that biology are used to drive the strategy for therapeutic development. This approach, originally pioneered by organizations with a vested interest in research in particular disease areas, appears to be the most promising. These organizations, that I loosely classify as “Translational Medicine Companies”, have a great deal of knowledge and experience in a particular indication, and thus tend to take a systems biology approach to identifying potential targets and drug candidates. Organizations like the Michael J. Fox Foundation, and globalCure (an initiative of Translational Genomics Institute to find new treatments for Pancreatic Cancer) spring to mind.