While in Zululand in December working on conservation monitoring work in the bush, I learned about an interesting tree called the Tamboti Tree. The leaves of this tree were traditionally used to cure toothache. But it seemed like ingredients in this tree can cause severe nerve damage. For example, we were told that it was quite dangerous to use its wood in a fire, as inhaling the fumes could cause brain damage, if not death. Without such local knowledge, how would I have known that? I could easily have just assumed this wood was like any other wood and burnt it!
And so this provided the seed to me for what I will call The Tamboti Tree Use Case. In this case the individual moving about in the environment will be provided advice on such risks. Such advice could be provided from the future distributed semantic knowledge base we would have in predictive toxicology that we have been working to develop on OpenTox. The advice could be delivered to the individual's mobile device which would recognise this biological object and then query the knowledge base and return the risk warning. This could apply to many other situations in the bush e.g., if you encounter a lion "don't run" or indeed many contexts in society e.g., "don't buy this product, you are allergic to it" to shoppers in the supermarket.
So this Tamboti Tree Use Case can serve as an inspiration for us to work towards in our efforts to create this future semantic web of predictive toxicology knowledge and services.
What can we do now? Once back at my computer I searched on google, found information on the tree on wikipedia and even some sentences on its toxicity. However the active ingredient chemcial structure mentioned, the diterpene excoecarin, had no chemical information or structure linked.
I go back to google and search around and finally find a chemical string as a SMILES. I paste this into the new Bioclipse application, see the structure, and click play. Bioclipse then starts running local predictive models on the toxicity of the molecule which I can start examing. BUT it also goes out on the web and starts to bring in predictions and alerts for the distributed set of OpenTox services available. I can also edit the structure, click play again, and the models and predictions are recalculated.
You can see a Bioclipse-OpenTox movie of my activities on this "early version of the Tamboti Tree Use Case" at:
We still have lots of work to do, but you can see the directions for progress.
This promising interoperation between Bioclipse and OpenTox was achieved in Autumn 2010 by Ola Spjuth (blogging at http://bioclipse.blogspot.com/), Egon Willighagen, and OpenTox developers, and first demoed at the OpenTox-EBI industry forum workshop on ontology and interoperability at Hinxton (16, 17 November). It is I think a good early practical example of the value of ontology and interoperability and the applications it enables linked with the nascent semantic toxicology web, and has much promise for further development in the months and years ahead.
You can download the Bioclipse application that interoperates with OpenTox and try it out yourself using one of the following downloads for PC, Mac or linux:
http://pele.farmbio.uu.se/dl/bioclipse2.4.1.OT-linux.gtk.x86_64.zip
http://pele.farmbio.uu.se/dl/bioclipse2.4.1.OT-linux.gtk.x86.zip
http://pele.farmbio.uu.se/dl/bioclipse2.4.1.OT-macosx.cocoa.x86_64.zip
http://pele.farmbio.uu.se/dl/bioclipse2.4.1.OT-win32.win32.x86_2.zip
http://pele.farmbio.uu.se/dl/bioclipse2.4.1.OT-win32.win32.x86.tar.gz
Last year we formed the “Scientists Against Malaria” (SAM) collaboration to apply modern drug design and modelling techniques in combination with industry standard infrastructure and interdisciplinary science to help develop new treatments against Malaria. The group’s first project assembles a number of leading academic researchers together with smaller innovative companies who are collaborating to develop novel inhibitors active against the Plasmodium parasite.
This is our first step in creating a collaboration learning machine for our community to enable and accelerate knowledge flow to progress the scientific research needed to develop new treatments against neglected diseases, which include other parasitic and tropical diseases, and diseases such as ALS which devastate people's health and are currently without any available treatment solutions.
Our drug design project involve situations when a number of partners collaborate to jointly solve molecular design problems as an early stage step in a drug discovery situation. The partners may involve commercial organisations, academic labs, and individual consultants who form a Virtual Organisation (VO) to collaborate on running the project, that typically has been historically carried out at a pharmaceutical organisation. The knowledge and experience of the partners involved is a critical resource and success factor for the project as is the ability to collaborate effectively. Additional resources include computer software and machinery for molecular design, modelling and virtual screening, experimental lab facilities for running assays and experiments on predicted hits for the problem studied, and supporting Information and Communications Technology (ICT) infrastructure. A significant amount of activity involving analysis, interpretation of results, synthesis and discussion is involved in many steps of the research process.
Computer-based models of Protein targets, Protein-Ligand and Protein-Protein interactions are built based on existing knowledge from crystal structures, physical chemistry and applications of bioinformatics and cheminformatics methods. A variety of methods including virtual screening, docking, pharmacophore-based design and free energy simulation methods are applied to the design of drug candidate molecules and their affinity for the target based on interactions such as involving specific hydrogen bonding and hydrophobic interactions with the active site of an enzyme. Holistic approaches to design also take into account specificity, cross-target interactions, Lipinski’s rule of 5 on druglikedness, ADME and toxicity properties of candidate molecules. Predictions are tested in the laboratory using a variety of experimental screening methods. High Throughput Screening (HTS) can be used to examine the activities of libraries of molecules against a target, whereas High Content Assays may probe a specific toxicity mechanism and property of a molecule.
A Lessons Learned process is run at the end of every significant process in the collaborative research workflow and prioritised lessons are documented into the VO knowledge base. Best Practices are agreed and documented at the start of the project. If best or better practices are discovered during the Lessons Learned process (e.g., on discussing “what went well”), they are documented into the VO knowledge base for future reference.
A complex event-driven engine is used to track all significant events occuring during the collaborative work and to provide recommendations with regards to traffic light situations (e.g., green: positive, red: negative, yellow: uncertain) where yellow situations may trigger discussion and further actions. The combination of people and infrastructure may evolve and improve as activity expands, thus becoming a Collaboration Learning Machine for Drug Discovery and Neglected Diseases Research.
I will discuss the activities of the Scientists Against Malaria (SAM) consortium at the BIO-IT conference in Boston, taking place 12 – 14 April 2011 in its collaborative drug discovery session (http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=101305). SAM was formed in 2010 from the InnovationWell Neglected Diseases Collaboration Pool as a virtual drug discovery organization to collaborate on the design of kinase inhibitors against the Plasmodium Malarial parasite. Work activities have included target selection and modelling, protein expression and assay development, computational drug design, and screening. Supported by developments on the EU FP7 funded SYNERGY and OpenTox projects, a combination of interoperable information systems, ontologies and web services were designed and deployed to manage the data, documents, computational and assay results, activity and toxicology predictions, as well as dashboards to track project progress and to support decision making. We will discuss our results, experiences and lessons learned to date, and future directions and opportunities for collaborative drug design based on our virtual organization approach.
The ToxBank project was launched in January 2011. ToxBank establishes a dedicated web-based warehouse for toxicity data management and modelling, a "gold standards" compound database and repository of selected test compounds, and a reference resource for cells, cell lines and tissues of relevance for in vitro systemic toxicity research carried out across the FP7 HEALTH.2010.4.2.9 Alternative Testing Strategies SEURAT-1 program. The project develops infrastructure and service functions to create a sustainable predictive toxicology support resource going beyond the lifetime of the program.
ToxBank should provide a unique opportunity to create an interoperable linked resource infrastructure supporting many diverse scientific research activities in the development of alternative testing methods whose ultimate goal is to replace animal testing. Developments on OpenTox should provide a strong foundation on interfaces, components, standards, and ontology that will enable and accelerate development of a modern infrastructure servicing the needs of many researchers including cell biologists, assay developers, omics technology developers, device engineers, molecular biologists, chemists, computational scientists and systems biologists.
The Seurat-1 research cluster that ToxBank is supporting had its Kick-Off meeting activities 1-3 March 2011 in Cascais, Portugal. Seurat-1 is a public-private partnership inititative between the EU's FP7 program and Colipa. The cluster involves seven projects: SCR&TOX, HeMiBio, Detective, COSMOS, NOTOX, ToxBank (infrastructure) and COACH (coordination).
This project will be jointly funded by COLIPA and the EC. Any opinions expressed in this post are those of the author. COLIPA is not liable for any use that may be made of the information contained therein
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