Scientists Against Malaria - our first step to creating a Collaborative Learning Machine against Neglected Diseases

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.

 

Comments

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