Breast Cancer Coalition talk on ONS and Taxol solubility

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:

NBCC Open Notebook Science Talk

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Drexel Chemistry Mini-Symposium on Bradley Lab

Every year the chemistry department at Drexel gives faculty the opportunity to present their research to incoming students in 10 minutes slots. On September 30, 2010 I presented on "Open Notebook Science for Malaria Drug Discovery and Solubility Modeling". I think such a short format is good for keeping student attention. Recording it also provides a handy link to use for other purposes. Most people just don't have time for 30-60 minute presentations.

Bradley Drexel MiniSymp 2010

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Open Notebook Science in Drug Discovery at Opal Event

I presented on "Open Notebook Science in Drug Discovery" on August 24, 2010 at a panel on Industry and Academia part of the Opal Event "Drug Discovery: Easing the Bottleneck".

Open Notebook Science in Drug Discovery

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I only had about 15 minutes to present so I could not go into much detail but I did want to highlight the most recent work Andrew Lang and I (also with Peter Li from ChemTaverna) carried out involving solubility prediction and web services. Most of the attendees were from industry and I appropriately used the recent GSK malaria data sharing to introduce the talk. It is clear that there is a role for Open Science in drug discovery and I think that industry involvement will continue to increase in this area.

My co-panelist Rathindra Bose from Ohio University presented on his group's development of a novel cancer treatment compound based on platinum. He made the point that academic research complements that from industry by being able to explore more speculative hypotheses. The dominant hypothesis for the mechanism of action of platinum based drugs is binding with DNA. By exploring alternative scenarios, his group found an active platinum drug that does not bind with DNA.

During the preceding session on the Emergence of Biologics in Drug Discovery, Albert Giovanella from the University of Pennsylvania School of Medicine gave a particularly enlightening talk about comparing biologics with small molecule drugs. Although biological drugs tend to have less toxicity, the overall cost to bring them to market is still quite high and their cost to the consumer may be so high as to limit their impact. It looks like it will not be generally easy to translate new biomedical knowledge to a widespread impact on human health.

InkSpot and Open Drug Discovery

I had a nice long chat with David Leahy from InkSpot yesterday. There is actually a good discussion in the comments of David's most recent post.

What I gathered from our conversation is that InkSpot will be providing transparency on the computational side of the drug discovery process. This is something that could be very valuable to the Open Science community in general and to our group in particular as we look for new anti-malarial agents.

We've tried to record the docking results we've obtained from Rajarshi Guha using a wet-lab style report (for example D-EXP014). Hopefully there is enough information there for the docking experiment to be replicated but clearly it would be better if a workflow system were in place to automatically record the steps and make those records public and easily searchable.

David said he would start with a QSAR-style exploration of our Ugi product precipitate prediction problem. It will be interesting to see how this plays out.

Internet-based tools for communication and collaboration in chemistry

Antony Williams just published an article in Drug Discovery Today:

Internet-based tools for communication and collaboration in chemistry

Web-based technologies, coupled with a drive for improved communication between scientists, have resulted in the proliferation of scientific opinion, data and knowledge at an ever-increasing rate. The availability of tools to host wikis and blogs has provided the necessary building blocks for scientists with only a rudimentary understanding of computer software science to communicate to the masses. This newfound freedom has the ability to speed up research and sharing of results, develop extensive collaborations, conduct science in public, and in near-real time. The technologies supporting chemistry, while immature, are fast developing to support chemical structures and reactions, analytical data support and integration to related data sources via supporting software technologies. Communication in chemistry is already witnessing a new revolution.

There is a detailed description of UsefulChem with a screenshot of EXP148 on Figure 2, where one of our Ugi products is characterized. This is another fine example of extensive interlinking between the worlds of peer-reviewed literature and Web2.0 content, including making use of Nature Precedings in the references.

Cell Article on Open Drug Discovery

Seema Singh wrote a review "India Takes an Open Source Approach to Drug Discovery" which just appeared in Cell: Volume 133, Issue 2, 18 April 2008, Pages 201-203. (The doi doesn't work yet but try this link in the meantime). You'll need a subscription to view it, an increasingly familiar irony of much of the Open Science discussion these days.

UsefulChem and our collaborators got a nice mention:

A related initiative is UsefulChem (http://usefulchem.wikispaces.com/), set up by Drexel University chemist Jean-Claude Bradley. Bradley has pioneered Open Notebook Science in which lab notebooks and raw research data are posted on the web for anyone to see and respond to (http://usefulchem.wikispaces.com/All+Reactions). As for success, Bradley says, “Probably the best example of a positive outcome from UsefulChem is finding two compounds that are somewhat active against malaria [in vitro],” blocking the activity of falcipain-2, a Plasmodium falciparum cysteine protease. “This demonstrates that a team of researchers can work together in the open—Rajarshi Guha from Indiana University did the docking calculations, my group at Drexel did the syntheses and Phil Rosenthal's group at UCSF did the testing.”

The Rosania Lab Open Notebook Science Wiki

I recently reported on a new collaborator who agreed to work with us in the open on modelling subcellular drug transport.

I am very pleased to report that Gus Rosania has now created an entire wiki (1CellPK) for his lab to use as an open notebook. From the home page of the wiki:

Open Notebook Science is ideally suited for community-wide collaborative research projects involving mathematical modeling and computer simulation work, as it allows researchers to document model development in a step-by-step fashion, then link model prediction to experiments that test the model, and in turn, use feedback from experiments to evolve the model. By making our laboratory notebooks public, the evolutionary process of a model can be followed in its totality by the interested reader. Researchers from laboratories around the world can now follow the progress of our research day-to-day, borrow models at various stages of development, comment or advice on model developments, discuss experiments, ask questions, provide feedback, or otherwise contribute to the progress of science in any manner possible.

How's that for a Christmas present to the Open Science community?

Subcellular Drug Transport UsefulChem Collaborator

Rajarshi Guha has yet again made a key contribution to our UsefulChem project by connecting us with Gus Rosania at the University of Michigan. Gus is interested in a fully open collaboration to help us further prioritize our drug targets based on predicted subcellular drug transport:

It is the first time I hear about Open Notebook Science, but it sounds like a fantastic idea!

My research group studies the subcellular transport of small molecules. We are interested in combining cellular distribution of small molecules together with systems biology, to analyze the pharmacological activity of small molecules in a cellular (and organismic) context. For more information about our subcellular transport lab, you can visit us at http://www-personal.umich.edu/~grosania/

Indeed, one of our objectives is to make all our cellular pharmacokinetic models open source, so that they can be modified, evolved and used for educational purposes and as virtual drug discovery tools, throughout the world. We are porting all our models to Virtual Cell (http://vcell.org/), where they are in a form that can be freely accessed and distributed on-line, though a simple graphical user interface Plus, Virtual Cell sponsors courses in computational modeling and systems biology as well as conferences, so it is more than just a modeling tool. As we work on this, we would like to explain what we are doing step-by-step, for everyone else to follow.

Gus provides more details about what his group can do:

We can readily calculate on-target lysosomal drug concentrations, vs. off-target mitochondrial or cytosolic drug concentrations. Question: are the inhibitors small "drug-like" molecules, peptides(or some other awful thing)? If they are small drug-like molecules, then we are good.

With 1cellPK we can identify the inhibitors that would lead to greatest accumulation in lysosomes of a cell surrounded by a homogeneous extracellular drug concentration (the parasite) while minimizing the accumulation in lysosomes of an off-target cell (ie the intestinal epithelial cell mediating absorption in the pesence of a transcellular concentration gradient). With 1CellPk we should also able to select those molecules with the highest transcellular permeability (for oral administration) while at the same time accumulating minimally in the cytosol of cells in the presence of a transcellular concentration gradient (to minimize metabolism andtoxicity in intestinal epithelial cells and hepatocytes, while maximizing intestinal absorption and systemic bioavailability).

There are several ways we can actually execute the collaboration. At the most basic level, if you give me the compounds' chemical structures, I can run them through 1cellPK (in Virtual Cell), and estimate their permeability, intracellular accumulations and distribution in the parasite, as well as in non-target intestinal epithelial cells. Once we give you the 1CellPK calculations, we can sit down together and figure out the best way to combine docking predictions with 1cellpK predictions.

Our falcipain-2 project is an obvious place to start.

Drug Design on the Open Web

A few months ago I started working with Mesa Analytics and Computing as a consultant on an SBIR project aiming to provide new tools for drug design. Although the business model component is confidential, a large part of the project involves the use and creation of freely available online tools for educational or other purposes.

This is a great example of a for-profit company aiming to provide significant value in the form of free services for the chemistry and biology communities. ChemSpider is another example.

What we would like to do is provide an intuitive interface for someone to perform some QSAR and docking work.

Mitch Chapman has provided a detailed description of a test dataset we'll use for the QSAR example. The advantage of using this source is that Rajarshi Guha has already created a publicly available service that we can use for performance comparison.

We are updating a "working scenario" to think about how this could all work and identify which pieces are missing. Hopefully we can put together a prototype for Phase I and find the right partners to get something robust constructed during Phase II.

This is all taking place on the UsefulChem wiki and we welcome contributions and suggestions from everyone.