A New Approach To Cancer Drug Development

Dr. Nick recently posted a blog entry describing the need for a change to the approach used to develop to new cancer therapeutics.  Echoing a point made by Dr. Stephen Neidle (Dir. of Cancer Drug Development at the University of London), he says that most of the low-hanging fruit has been harvested.  During the early part of the last decade drug discovery companies were able to use the knowledge gained from the Human Genome Project to understand drug targets better, and develop more targeted therapeutics.  Companies doing Structure-Based Drug Discovery, crystalize the target protein, design a small molecule that best fits the binding pocket and after lots of screening and preclinical assays, release the results to a development partner to take it through clinical trials. 

The problem with this approach is that most cancers are multigenic in nature.  Hematopoeitic cancers like Chronic Myelogenous Leukemia are the exceptions and constitute the low-hanging fruit mentioned earlier because you can modulate a single target and get an effective outcome.  Most solid tumor-forming cancers though are the result of mutations in a number of different key cellular processes: Cell-cycle disruption, apoptosis, proliferation, and metastasis

It’s like having a chorus of singers, where a few key choristers are singing off-key and leading neighboring choristers astray.   As Dr. Nick’s article (and other similar articles) have indicated, the pharmaceutical industry needs to focus on treating the disease, not simply identifying and addressing single targets.  Ultimately, as a physician you want to modify the behaviour of those choristers and get the patient back on track.  And thus single-target therapeutic approaches aren’t going to be very successful, especially in cancers that are asymptomatic until their latter stages.  On average, 97% of pancreatic cancer patients diagnosed within a given year will die within a few months of diagnosis.

The current standard of care is Gemcitabine (which is as ineffective now, as it was 13 years ago when my mother and grandmother were both diagnosed).  If we are to see some real changes in the way pancreatic cancer is treated, it will be because companies are willing to change the way in which new cancer drugs are developed.  Imagine for a moment that you are a drug developer working on new cancer therapeutics.  You’ve found a compound that could help address metastasis.  Using deep sequencing, you know what variations of you target are addressed by the compound, and therefore what patients would benefit most from the compound.  You also know what other mutations are common in DNA repair, proliferation and other cellular processes.  And along with those mutations, you have the screening data for those compounds.  So you know the best combination of drugs that will help the patient.  You can then design smaller more focused trials around a particular set of common mutations.  Mutations that you can easily test for with a SNP panel.  This increases your chance of success, and also improves the chances of survival for the patient.

And this brings us to another problem with drug trials.  The purpose of Phase II and Phase III trials are to establish the efficacy of the new compound.  And the way this is currently measured is by analyzing the compound in case-control studies, or in comparative drug studies.  But as we’ve seen, there are multiple processes at work in cancer, and these processes are not being addressed during the course of the trials.  These trials help answer whether a compound is more or less effective than the current standard of care but they don’t help us establish is what combination of drugs is most effective.  These types of combinatorial studies need to be standard operating procedure when creating new trials.  Because at the end of the trial you don’t want to have to tell the patient’s family that “we fixed those off key sopranos, but the patient still died.”

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About aspenbio

I write software for scientists. I'm interested in Java/Groovy/Grails, the Semantic Web and Cancer Biology.
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