On May 1, 2011 I presented "Accelerating Discovery by Sharing: a case for Open Notebook Science" at the National Breast Cancer Coalition Annual Advocacy Conference in Arlington, VA. This was the first year where they had a session on an Open Science related theme and the organizers invited me to highlight some of the tools and practices in chemistry which might be applicable to cancer research.

I was really touched by the passion from those in the audience as well as the other speakers and conference participants I met afterward. For many, their deep connection with the cause was strongly rooted in a personal experience as breast cancer survivors themselves or their loved ones. Several expressed a frustration with the current system of sharing results from scientific studies. They felt that knowledge sharing is much slower than it needs to be and that potentially useful "negative" results are generally not disclosed at all.

The NBCC has ambitiously set 2020 as the deadline to end breast cancer (including a countdown clock). It seems reasonable to me that encouraging transparency in research is a good strategy to accelerate progress. Of course, great care must be exercised wherever patient confidentiality is a factor. But health care researchers are already experienced with following protocols to anonymize datasets for publication. Opting to work more openly would not change that but it might affect when and how results are shared. Also there is a great deal of science related to breast cancer that does not directly involve human subjects.

One initiative that particularly impressed me was The Susan G. Komen for the Cure Tissue Bank, presented by Susan Clare from Indiana University and moderated by Virginia Mason from the Inflammatory Breast Cancer Research Foundation. As a result of this effort, thousands of women have donated healthy breast tissue to create a comprehensive database richly annotated with donor genetics and medical history. The idea of trying to tackle a disease state by first understanding normal functioning in great detail was apparently somewhat of a paradigm shift for the cancer research community and it was challenging to implement. According to Dr. Clare, data from the Tissue Bank have shown that the common practice of using apparently unaffected tissue adjacent to a tumor as a control may not be valid.

This example highlights one of the key principles of Open Science: there is value in everyone knowing more - even if it isn't immediately clear how that knowledge will prove to be useful.

In my experience, this is a fundamental point that distinguishes those who are likely to favor Open Science from those who reject its value. If two researchers are discussing Open Science and only one of them views this philosophy as being self-evident the conversation will likely be about why someone would want (or not want) to share more and the focus will fall on extrinsic motivators such as academic credit, intellectual property, etc. If both researchers view this philosophy as self-evident the conversation will probably gravitate towards how and what to share.

I refer to this philosophy as being self-evident because I don't think people can become convinced through argumentation (I've never seen that happen). Within the realm of Open Notebook Science I have been involved in countless discussions about the value of sharing all experimental details - even when errors are discovered. I can think of a few ways in which this is useful - for example telegraphing a research direction to those in the field or providing data for researchers who study how science is actually done (such as Don Pellegrino). But even if I couldn't think of a single application I believe that there is value in sharing all available data.

A good example of this philosophy at work is the Spectral Game. Researchers who uploaded spectral data to ChemSpider as Open Data did not anticipate how their contribution would be used. They didn't do it for extrinsic motives such as traditional academic credit. Assuming that their motivation was similar to our group's, they did it because they believed it was an obviously useful thing to do. It is only much later - after a critical mass of open spectra were collected - that the idea arose to create a game from the dataset.

With this mindset, I explored what contribution we might make to breast cancer research by performing a phrase search strategy. Doing a simple Google search for "breast cancer" solubility generated mainly two types of results.

The first set involve the solubility behavior of biomolecules within the cellular environment. An example would be the observed increased solubility of gamma-tubulin in cancerous cells.
The second type of results address the difficulty in preparing formulations for cancer drugs due to solubility problems. A good example of this is Taxol (paclitaxel), where existing excipients are not completely satisfactory - in the case of Cremophor EL some patients experience a hypersensitivity.
Since our modeling efforts thus far have focused on non-aqueous solubility, there is possibly an opportunity to contribute by exploring the solubility behavior of paclitaxel. By inputting solubility data from a paper by Singla 2002 into our solubility database, Abraham descriptors for paclitaxel are automatically calculated and the solubilities in over 70 solvents are predicted.

In addition, by simply adding the melting point of paclitaxel, we automatically predict its solubility at any temperature where these solvents are liquids (see for example water).

Because of the way we expose our results to the web, a Google search for "paclitaxel solubility acetonitrile" now returns the actual value in the Google summary on the first page of results (currently 7th on the first page). The other hits have all 3 keywords somewhere in the document but one has to click on each link then perform a search within the document to find out if the acetonitrile solubility for paclitaxel is actually reported. (Note that clicking on our link ultimately takes you to the peer-reviewed paper with the original measurement.)

To be clear about what we are doing here - we are not claiming to be the first to predict the solubility of paclitaxel in these solvents using Abraham descriptors or any other method. Nor are we claiming that we have directly made a dent in the formulation problem of paclitaxel. We are not even indicating that we have done a thorough search of the literature - that would take a lot more time than we have had given the enormous amount of work on paclitaxel and its derivatives.

All we are doing is fleshing out the natural interface between the knowledge space of the UsefulChem/ONS Challenge projects and that of breast cancer research - AND - we are exposing the results of that intersection through easily discoverable channels. By design, these results are exposed as self-contained "smallest publishable units" and they are shared as quickly (and as automatically) as possible. The traditional publication system does not have mechanism to disseminate this type of information. (Of course when enough of these are collected and woven into a narrative that fits the criteria for a traditional paper they can and should be submitted for peer-reviewed publication).

Here is a scenario for how this could work in this specific instance. A graduate student (who has never heard of Open Science or UsefulChem, the ONS Challenge, etc.) is asked to look for new formulations for paclitaxel (or other difficult to solubilize anti-cancer agents). They do a search on commercial databases offered by their university for various solubilities of paclitaxel and cannot find a measurement for acetonitrile. They then do a search on Google and find a hit directly answering their query, as I detailed above. This leads them to our prediction services and they start using those numbers in their own models.

That is a good outcome - and that is exactly what has been happening (see the gold nanodot paper and the phenanthrene soil contamination study as examples). But the real paydirt would come from the graduate student recognizing that we've done a lot of work collecting measurements and building models for solubility and melting points, and contact us about a collaboration. As long as they are comfortable with working openly we would be happy actively work together.

I'm using the formulation of paclitaxel as an example but I'm sure that there are many more intersections between solubility and breast cancer research. With a bit of luck I hope we can find a few researchers who are open to this type of collaboration.

As another twist to this story, I will briefly mention here too that Andrew Lang has started to screen our Ugi product virtual library for docking with the site where paclitaxel binds to gamma-tubulin (D-EXP018). This might shed some light on some much cheaper alternatives to the extremely expensive paclitaxel and derivatives. The drug binds through 3 hydrogen bonds, shown below - rendered in 2D and 3D representations (obtained from the PDB ligand viewer)


The slides and recording of my talk are embedded below: