New Drug Discovery Process Underscores Value of Collaboration

Biologics

DNA ligase repairing a double strand break, which was the subject of a new drug discovery collaborative effort. By Tom Ellenberger, Washington University School of Medicine in St. Louis [Public domain], via Wikimedia Commons.

DNA ligase repairing a double strand break, which was the subject of a new drug discovery collaborative effort. By Tom Ellenberger, Washington University School of Medicine in St. Louis [Public domain], via Wikimedia Commons.
As all scientists know, collaboration makes the lab function. Without collaboration, science itself is impossible. This is true in every field of science, including oncology drug target finding, according to a presentation issued at the AACR Special Conference on DNA Repair.1 In the presentation, researchers unveiled a new piece of software designed for collaboration between laboratories who may be interested in finding potential targets within the same oncological niche: double strand breaks.

By allowing researchers from different groups to combine interests, data and screens, the new software’s immediate results are clear: researchers who may be interested in finding a problem to fit to a solution can get connected to researchers with information about that problem. In the initial use cases of the software, repair of double strand breaks in DNA in two notorious oncogenes–BRCA1 and Rad51–were the first targets for the software based collaboration.2 It’s clear that future collaborations enabled by generative model building software will need a parallel collaboration platform which can enable researchers to share their experimental data in addition to their theoretical insights.

Collaboratively Understanding Double Strand Breaks in Disease

The creators of the new software had a very specific problem in mind: researchers examining the causes of double strand breaks in oncogenes often do so in total isolation from each other despite each investigation generating an abundance of generally useful data, which could be shared with other, outside researchers. Each research group approaches their specific interest on DNA repair of double strand breaks, runs a few experiments, learning generally useful information–which then falls to the wayside relative to other groups who may be investigating something slightly different.

In essence, the researchers build a piece of software so that findings involving inhibited DNA repair could developed collaboratively. Even if two research groups aren’t examining DNA repair for the same reason, they can still share relevant theoretical tidbits which may be broadly useful.

What’s the Point of Sharing?

For non-geneticists, this entire concept is a bit confusing. Aren’t DNA damage, DNA repair and DNA repair inhibition essentially “solved” areas of scientific understanding, beyond minute molecular dynamic details?

In the abstract sense, yes–DNA damage and repair are well-characterized scientific hypotheses, and have been for quite some time.3 But for hypotheses to move beyond theory and contribute to generating actual drug targets within the process of DNA damage and repair, the particulars of any given gene in question’s molecular repair process in a specific disease context must be hashed out, as they could be impacted in bizarre and non-canonical ways.4 5   In a nutshell, the canonical DNA repair hypothesis isn’t detailed enough when it comes to the conditions of drug target location.

Thus the utility of collaboration software that allows for piecemeal building on prior information. By providing a platform for pinning related information to potential drug targets of interest, researchers can contribute the esoteric knowledge they have regarding DNA repair under certain very specific circumstances to help other researchers hone their own efforts to develop the same target within the repair process. In effect, the new drug target hunting software is helping researchers looking for drug targets to find where the canonical hypothesis breaks down or paints a misleading picture which would cause researchers to disregard an essential element.

One such example, though not one cited by the creators of the new software, might be the redundancy of repair proteins in the context of excision repair.6 If researchers were trying to find a drug target within the process of ribonucleotide excision repair, information left by prior collaborators would inform them that the scale of any hypothetical intervention would have to take into account a protein rich genetic repair environment rather than the sparse efficiency suggested by the textbooks and commonly understood hypotheses. This knowledge would save researchers time and effort, and might convince them to pick another target altogether if the obstacles seemed too daunting.     

Target Finding Collaboration Isn’t Enough To Make A Cure

If researchers collaborate to find targets with areas of overlapping expertise, as suggested by the authors of the new software, there will still be a lot of ground to cover before the collaboration between the two research groups is robust enough to take an experiment to the lab.

After collaborating to find a potential drug target, collaborators still need to:

  • Exchange substantive information regarding the target
  • Exchange technical expertise for the experiments used to develop the target
  • Come to a consensus on terminology and methodologies that may differ
  • Determine whether a division of labor across the collaboration makes sense
  • Determine whether both collaborators can easily share data, personnel, inventory, and others
  • Squabble over what order people’s names are on any papers that are collaboratively published

Though the last bullet point is (barely) in jest, basic logistical issues frequently hamper collaborations that could be immensely scientifically fruitful. Most laboratories in the same institution aren’t organized enough internally for one lab group to coordinate cleanly with another, nevermind collaborating with an outsider who may be in a different timezone or speak a different language. Collaborative software needs to go beyond target finding and hypothesis modification; collaborative software has to knock down the walls that prevent labs from actually chasing down the targets located with joint data.

This means that the drug discovery and the drug development efforts of future laboratories using target finding software will need the most powerful collaborative software available if they want to avoid the massive slowdown caused by imperfect coordination. Luckily, there is such a software platform designed specifically to be used for biological researchers who must thrive in a high-coordination environment.

BIOVIA’s Collaborative Science Solutions is the collaboration, data management and data sharing platform that your team can use to develop a new drug targets hand in hand with outside groups. Using Collaborative Science Solutions, harnessing the power of software tools designed to help you locate potential vectors of attack and passing the information off to the groups that can make use of it is easier than ever. Contact us today to find out how BIOVIA can help you get the most out of you and your collaborators’ drug discovery efforts and finally start knocking down cancers caused by genetic damage.

  1. “TargetDBR”—A DNA repair drug and target discovery collaboration: Exploiting synthetic lethal, high content, and functional cellular reporter assays to accelerate DNA repair targeted drug discovery.”
  2. “Targeting DNA Repair In Cancer: Beyond PARP Inhibitors.” January 2017, http://cancerdiscovery.aacrjournals.org/content/7/1/20.
  3. “Mechanisms Of DNA Repair By Photolyase And Excision Nuclease (Nobel Lecture).” June 2016, http://onlinelibrary.wiley.com/doi/10.1002/anie.201601524/full.
  4. “DNA-Repair Defects and Olaparib In Metastatic Prostate Cancer.” November 2015, http://www.nejm.org/doi/full/10.1056/NEJMoa1506859#t=article.
  5. “The Relationship Between Seven Common Polymorphisms From Five DNA Repair Genes And The Risk For Breast Cancer In Northern Chinese Women.” March 2014, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092083.
  6. “Redundancy In Ribonucleotide Excision Repair: Competition, Compensation, And Cooperation.” May 2015, http://www.sciencedirect.com/science/article/pii/S1568786415000440.