I provide below a schedule for the upcoming InnovationWell and eCheminfo Community of Practice meetings at Bryn Mawr.
I also include a location map here which may be useful upon arrival:
Download bryn_mawr_campus_map_douglas_connect_meeting.pdf
[Please follow continuation here to view schedule.]
Current advances in computer-based predictive toxicology offer the potential to create more advanced environments for the screening and prediction of safety issues due to chemical and drug adverse side effects, drug-drug and chemical-system interactions, and chemical and drug toxicologies in the environment and the human body. Advances in this growing field also offer the potential to replace or reduce the need for animal testing and to reduce later stage clinical trial failures or new product development rejection. Acceleration of progress in practical applications requires the creation of interoperable environments, knowledge sharing, data integration, algorithm development, and extensive validation and testing.
Numerous opportunities exist in this field for scientific advances, but also for innovation, service and product development, and value creation. Additionally, significant collaboration approaches are a scientific, industry and society imperative to advance this field and the safety of new products and all society members. Collaborative approaches need to support the multidisciplinary networking and collaboration between computer scientists, biologists, chemists, toxicologists, product development and clinical and environmental researchers, and to network groups, centers, initiatives, projects and data into interoperable semantic frameworks, systems, knowledge bases and virtual organisations.
At our Predictive Toxicology session chaired by Artem Cherkasov (University of British Columbia) running 17 October 2008 at Bryn Mawr recent developments in the field of predictive toxicology will be presented and discussed.
The session will be preceded the evening of October 16 by a Knowledge Café to discuss Collaboration Opportunities in Predictive ADME & Predictive Toxicology.
A description of the session with presentation abstracts follows. Please add your comments, discussion or questions at the end of the post.
Predictive Toxicology
(Please follow continuation here to read abstracts. Comments can be made at the end.)
On the 17-19 October 2007 we will host a joint
eCheminfo and InnovationWell Community of Practice Workshop and Forum at Bryn Mawr College, Philadelphia to discuss and advance
best practices in predictive ADME and Toxicology, to develop best practices for
comparison studies and validation, to review latest developments in method
development and applications related to drug discovery and development, and to
discuss the potential for collaborations between initiatives and international
cooperation.
This conference, forum and
workshop activity will consist of the following parts:
1. Workshops to discuss developments, challenges and potential for collaborations. (afternoons of October
17-19).
2. Conference
sessions on latest ADMET methods and application developments with
presentations and panel discussions. (mornings of October 18 and 19)
3. Hands-on Workshop
sessions with drug discovery informatics software (running during afternoons throughout week)
4. Evening Poster Sessions on latest modelling
developments (evenings of October 17 and 18)
Workshop
Facilitators
Joseph Tomaszewski (NCI), Artem Cherkasov
(University of British Columbia), Dennis Pelletier (Pfizer), Richard Beger
(FDA), Anthony Klon (Pharmacopeia Drug Discovery), Tony Hopfinger (University
of New Mexico College of Pharmacy), Joseph Contrera (FDA), Christoph
Helma (University of Freiburg and in silico toxicology), Vladimir Poroikov
(Russian Academy of Sciences), Judith Madden (Liverpool John Moores
University), Ann Richard (EPA)
(continued ...)
Continue reading "Advancing best practices in predictive ADME and Toxicology" »
A variety of initiatives of relevance to the development of ADME/Toxicology resources of value to supporting improved productivity in drug discovery and development are in progress in different organisations and countries. There is potential for great benefits for collaboration and alignment between such initiatives so as to support the robust development of the emerging field of predictive toxicology and to advance goals related to heathcare safety and development as expressed in the FDA's Critical Path Initiative in the USA and the EU's Innovative Medicines Initiative in Europe.
To further development and progress in this area we are scheduling the following activity:
International Forum & Workshop on Cooperation on ADME/Tox
18-19 October 2007
to take place at the Community of Practice Meeting, Autumn 2007
a joint InnovationWell and eCheminfo InterAction Meeting
Bryn Mawr College, Philadelphia
http://www.echeminfo.com/COMTY_conferences
This forum and workshop will have an agenda developed by workshop leaders to address ways forward for international cooperation and including discussion of the following topics:
The agenda of the forum and workshop will be designed by a set of workshop leaders so as to maximise interaction, discussion, issue resolution, and action plans for cooperation. In addition to presentations on latest developments, workshop activities will address specific challenges to progress in the field and areas where collaboration can support integration and alignment of programs and resources and reduction of duplication. An Innovation Cafe format will be used in which the group will define a scenario in which optimum confidence in predictive toxicology methods has been reached and will then prioritize steps for achieving that goal. The resulting roadmap should provide action plans where cooperation between initiatives can accelerate the contribution of predictive toxicology methods to enhanced confidence in safety of new healthcare products and progressing the goal of reduction and replacement of animal testing by computational methods. Virtual communication and collaboration approaches will be used pre- and post-event to maximise the benefit of the workshop.
Workshop leaders are being invited from the US and Europe and will include representatives from industry, government and academia.
Barry Hardy
Community of Practice Manager
eCheminfo InnovationWell cheminformatics chemoinformatics bioinformatics Medicinal Chemistry Computational Chemistry Virtual Screening ADME ADMET toxicology Molecular Modelling Molecular Modeling pharmaceutical pharma meeting workshop training Oxford Critical Path toxicology Bursary Life Sciences Pharma Drug Discovery Research and Development Drug Development Healthcare Innovation Knowledge Management events
During November I will be planning the program for the following InnovationWell workshop in Oxford (cross-industry sector) and InterAction Meeting in Bryn Mawr (life science/pharma/healthcare sector). Please contact me with your interests and proposals!
Knowledge Assessment & Performance Improvement of Collaborative Work and Innovation Activities
InnovationWell Workshop & Innovation Café, accompanied by pre-meeting
2 day hands-on workshop activities on collaborative systems and ELNs
20-22 June 2007, Oxford University, Oxford UK
Innovation in Life Science & Healthcare Research & Product Development
InnovationWell Community of Practice InterAction Meeting
15-18 October 2007, Bryn Mawr College, Philadelphia, PA, USA
Themes: FDA Critical Path Themes, Knowledge Management, Translational Research, Biomedical Informatics, Metabolomics, Biomarkers, Toxicology, Patient & Drug Safety
InnovationWell Gold membership
This entitles members to access InnovationWell meeting proceedings including audio, access to our Executive Insights reports from meeting and community of practice activities, and additional member discounts on meeting and training registration fees.
Download InnovationWell2007MembershipForm.pdf
Barry Hardy
Email: barry.hardy *[at]* douglasconnect.com
InnovationWell meeting workshop conference management executive Bryn Mawr Philadelphia Critical Pathbiomarkers metabolomics toxicology personalised medicine KM Life Sciences Pharma Drug Discovery Drug Safety Research and Development Drug Development Healthcare Innovation Knowledge Management events
Metabolomics is an FDA-identified Critical Path Opportunity (1) offering a toolkit which can be potentially applied to identification of safety biomarkers, diagnostic monitoring of patient response to drug treatment, lead optimization through toxicity assessment in non-clinical drug development, biochemical pathway studies in cells, animals and humans, patient stratification, and insights on and tracking of mechanisms associated with the onset of disease or following therapeutic intervention.
However, despite its above promise, the challenges in the complexity of the biological systems studies, the experimental spectroscopy methods used and the datasets generated has restricted to this point the full commercial application of metabolomics methods in the pharmaceutical industry and in healthcare.
The complexity of the interpretation of metabolomics data points to the need for improved data analytics and visualisation methods in decision making processes and situations (2). The importance of the value-added of the integration of metabolomics data with other proteomic and genomic data to provide a more integrated, accurate and broader view of the state of the biological system studied points to the importance of explicit knowledge management techniques in critical path areas of clinical research and drug development (3) to the application of semantic web, ontology and web service approaches (4) and to the adoption of these approaches in the day-to-day scientific research activity as supported by electronic laboratory notebooks and collaboration systems (5). It also points again to the importance of co-operation on the always difficult agreement area of the definition of standards and data integration.
I also find it quite interesting that two different fields (knowledge management and metabolomics) share a common critical concept: that of context. Context is critical in the human area of knowledge management and transfer in social ecosystems and its poor treatment a reason why many early IT and informatics approaches to knowledge management worked poorly. In the biological situation of a cell or animal, metabolomics provides the critical biochemical context and data to match it, and can do so in a dynamic way over time and can track perturbations in the behaviour of such a complex system.
Recent progress in the metabolomics field includes new advances in spectroscopic and statistical and analytical techniques that strengthen and expand the accuracy and scope of the analysis possible. But the progress also increasingly includes significant application experience which already has included a significant role in the 2005 Nobel Prize in Medicine award for assigning the causative role of H. pylori bacterium in peptic ulcers and gastritis, and more recently has been applied to patient stratification in Lou Gehrig’s disease, identification of off-targeted side effects for several drugs and new chemical entities, has been applied in diagnostic roles on serum samples of diabetics and non-diabetics, the development of biomarkers for prediction of drug-induced liver injury and to insight into toxic effects from urine analysis.
Richard Beger (FDA) has pointed out (6,7) that the development of NMR- based multi-dimensional quantitative spectrometric data-activity relationships (QSDAR) provides models which could be useful for estimating chemical toxicity, risk assessment of environmental contaminants and drug-lead identifications, and that such models of biological activity should be more objective and overcome some of the unreliabilities of traditional Quantitative Structure-Activity Relationship (QSAR) approaches (8). He also indicates that Metabolomics can play an important role in the creation of better evaluation tools and models for diseases, better identification and quantification of safety biomarkers, and improving the measurement of patient response. The voluntary submission of genomics data (VGDS) to the FDA is now accepting both proteomics and metabolomics data sets (9).
In response to such demand and interest, and in addition to significant activity in academic research, a number of companies including Metabolon, Leco, Blue Gnome, Bio-Rad and Chenomx are increasingly offering commercial solutions and services in metabolomics to industry.
I provide below a description of the presentation, discussion and workshop activity for the InnovationWell Session on Application of Metabolomics to Drug Discovery & Development which will take place on Wednesday 18th October ’06 at the InnovationWell meeting at Bryn Mawr. (Follow the Continuation…)
Barry Hardy
References
1. FDA Critical Path Opportunities Report, http://www.fda.gov/oc/initiatives/criticalpath
2. Decision Support in Drug Discovery & Development, http://barryhardy.blogs.com/theferryman/2006/09/decision_suppor.html
3. Knowledge Management in Translational Research, http://barryhardy.blogs.com/theferryman/2006/09/utilising_knowl.html
4. Semantic Web & Drug Development, http://www.innovationwell.net/COMTY_semweb/
5. KM in R&D and ELNs, http://www.innovationwell.net/COMTY_conferenceopenevent/
6. Richard D. Beger, Drug Discovery Today, Vol. 11, pp 429-435, May (2006).
7. R. D. Beger, D. A. Buzata, J.G. Wilkes, Drug Discovery Handbook, Ed. Shayne C. Gad, John Wiley & Sons, pp 227-285 (2005)
8. Predictive Toxicology, http://barryhardy.blogs.com/cheminfostream/2006/09/appyling_predic.html
9. FDA’s Critical Path Initiative: Opportunities for Metabolomics http://www.innovationwell.net/COMTY_mebegerr/
(continued…)
I summarise here what I think are three interesting questions & discussion topics for progress in current chemistry and informatics research in Drug Discovery & Development.
Computational chemistry methods have now been applied with success and significant contributions to drug discovery research and development. Nevertheless current methods are not yet able to comprehensively or easily meet the needs of medicinal chemists, or on certain kinds of problems are failing or have challenges or complications. We ask the question:
Where do we currently stand with cheminformatics-driven medicinal chemistry?
More at http://barryhardy.blogs.com/cheminfostream/2006/10/where_do_we_cur.html
(This includes a presentation and discussion of the discovery and development of low molecular weight non-peptidic beta-secretase inhibitors as potential new therapeutics against Alzheimer's Disease.)
After twenty years of undeniable progress, molecular docking seems to have plateaued. A recent paper by Tirado-Rives and Jorgensen [1] dashes some of the few hopes we had left by showing that conformational energetics alone make it impossible to rank order diverse compounds in high throughput virtual screening. In a Perspective in the same issue [2], Leach, Shoichet and Pieshoff summarize the stagnating state of the art that is docking, and suggest a pragmatic way forward, through measurement and benchmarking. Again in the same issue, a laborious evaluation of 10 docking programs, using 37 scoring functions was applied to seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization [3]. Among some encouraging results and upbeat analysis, the paper makes a number of worrying observations, including that "high fidelity in the reproduction of observed binding poses did not automatically impart success in virtual screening". Moreover, for eight diverse systems, "no statistically significant relationship existed between docking scores and ligand affinity."
John Irwin and I are asking the following question:
Could we take a Community Approach to Comparing Virtual Screening Methods?
More at http://barryhardy.blogs.com/cheminfostream/2006/10/could_we_take_a.html
The ability to make informed decisions during the early phases of drug discovery is the key to decreasing hit-to-lead and lead optimization cycle times. The motto “fail early, and fail cheap” represents the need to identify problematic chemotypes early in the development process so that more productive lines of inquiry may be followed. The field of predictive modeling has reached a point where there is a realistic expectation that troublesome moieties can be flagged through computational virtual screening. The next major step in the development of predictive methods is to be able to also use these modeling techniques to suggest productive courses of action to identify and correct ADME and PK problems during lead compound optimization. Thus, the use of validated, interpretable models may serve both as a way of identifying ADME/Tox and PK failures, and also provide a means for correcting them. Predictive Toxicology is therefore one area that potentially can contribute significantly more in the future than it has in the past to improving our confidence in the safety of new drug candidates in clinical development [6].
We ask the question:
How can we improve our Confidence in the Drug Safety of potential new Therapeutics through Predictive Toxicology?
More at: http://barryhardy.blogs.com/cheminfostream/2006/09/appyling_predic.html
Barry Hardy
References
[1] Tirado-Rives & Jorgensen, Contribution of Conformer Focusing to the Uncertainty in Predicting Free Energies for Protein-Ligand Binding, J. Med. Chem, 2006, 59,5880-5884.
[2] Leach, Shoichet, Pieshoff, Prediction of Protein-Ligand Interactions. Docking and Scoring: Successes and Gaps., J Med Chem, 2006, 49, 5851-5855.
[3] Warren et al, A Critical Assessment of Docking Programs and Scoring Functions, J Med Chem, 2006, 49, 5912-5931.
[4] http://www.nigms.nih.gov/News/Reports/DockingMeeting022406.htm
[5] Huang, Shoichet, Irwin, Benchmarking Sets for Molecular Docking, J. Med. Chem, 2006, in press.
[6] B. Hardy, P. Elkin, J. Averback, A.L. Fontaine, S. Kahn, Improving Confidence in Safety in Clinical Drug Development: The Science of Knowledge Management, The Monitor, Association of Clinical Research Professionals, p. 37-41, October 2006.
InnovationWell eCheminfo cheminformatics chemoinformatics bioinformatics Medicinal Chemistry Computational Chemistry Virtual Screening Docking Molecular Modelling Molecular Modeling pharmaceutical pharma meeting workshop conference management Bryn Mawr Philadelphia Critical Path toxicology personalised medicine Life Sciences Pharma Drug DiscoveryResearch and Development Drug Development Healthcare Innovation Knowledge Management events
The FDA has identified the development of new biomarkers as one of the key opportunities to increase efficiency, predictability, and productivity in drug development. I provide below a description of the presentation, discussion and workshop activity for the session on Biomarker Discovery & Applications in Drug Development which will take place on Thursday 19th October ’06 at the InnovationWell meeting at Bryn Mawr.
The session on the application of biomarkers in drug development will explore new advances and challenges in this area and will include a keynote from Keith Elliston, CEO (Genstruct), with Zentam Tsuchihashi (BMS) discussing Biomarker Roles in Tumor Immunotherapy, Darius Dziuda (CCSU) will cover the topic of Multivariate BioMarkers, Michael Jones (Novartis) will discuss Proteomics applications whereas Bernd Bonnekoh (Otto-von-Guericke University) and Ansgar J. Pommer (SkinSysTec) will provide perspectives for Multi Epitope Ligand Kartography (MELK) and skin disease applications. This latter work is being published in Nature Biotechnlogy and is featured on its mid-October cover.
Full abstracts are provided below.
Barry Hardy
(continued…)
Continue reading "Biomarker Discovery & Applications in Drug Development" »
The data created during drug discovery and development and from clinical research programs is great in volume and complicated in diversity. Additionally, both biological systems and chemical interventions in them are complex, as are the human, medical, industry and regulatory environments in which programs are created and carried out. Hence gathering together and processing all relevant sources of knowledge at any time in which a decision needs to be made is by its nature very difficult.
Selection and visualisation of the most relevant data required for insights and decisions is a challenging area to which increasingly sophisticated analytics tools are being applied. Concept mapping, knowledge maps, natural language processing of unstructured information, semantic classification and ontologies, and sophisticated visualisation tools all offer approaches which can be helpful in this area. Nevertheless, many knowledge management issues remain challenging in both the management and availability of information and in the selection and use of processing tools.
Systems biology approaches attempt to unify diverse data and models about complex cell behaviour so as to enable the study of a complex subject such as cell or organ toxicity. Modern analytics tools and data mining provide researchers tools to interactively explore such biological information and to test hypotheses about their data, and to ask new and interesting questions arising from it.
Risk management has been seen by regulators and industry as an increasingly important methodology and the incorporation of risk-based approaches into decision making around product safety issues and clinical trial management is a current vital area of practice development.
Electronic lab notebooks systems are currently offering a better start in how we initially record our experimental scientific information and such improved initial record keeping and semantic frameworks offer the potential for improved knowledge transfer and re-use at subsequent decision making points.
On Tuesday 17th October 2006 we will discuss these important current topics in decision making as applied to drug discovery and development and clinical research at the InnovationWell Community of Practice meeting at Bryn Mawr College, Philadelphia. In the addition to the morning conference session, workshops will run in the afternoons and from 4pm we will runs a series of demonstrations, a panel discussion and a knowledge café into the evening hours to share insights on problems and solutions in this complex but important area.
I provide below a description of some of the presentations and workshops.
Barry Hardy
Decision Support for Research & Development
http://www.innovationwell.net/COMTY_decisionsupport/
InterAction Meeting Session, Bryn Mawr, Philadelphia, USA,
Tuesday, 17 October 2006
chaired by Dennis Underwood (Praxeon)
The Role of Systems Biology and Knowledge Management in Advancing Toxicology Knowledge in Big Pharma
Peter V. Henstock, Pfizer
This talk will focus on two key research areas of the Systems Biology group at the Pfizer Research Technology Center in Cambridge, Massachusetts. The first area involves efforts to identify indications of hepatic injury using a set of cell-based assays. Panels of compounds with known toxic endpoints have been assembled and screened to characterize hepatic toxicity using a variety of assays. The second area is an effort to model the p38 signaling pathway associated with rheumatoid arthritis. A combination of literature mining, cell-based assays, and mathematical modeling of the pathway has been used with the goal of better understanding the associated toxicities. The approaches and challenges of both areas will be presented.
Case Studies in Using Interactive Visual Analytics to Accelerate Drug Development
David Mosenkis, Spotfire
Pharmaceutical R&D is a data-intensive process. While computer-assisted instruments and analysis tools are ubiquitous at all phases, research teams are often compelled to make key decisions based on limited analysis and reports that reflect only discrete islands of information in a vast sea of data. By integrating access to disparate types of data, and by applying interactive visualization and exploration of information, decision makers can gain the insights needed to make better-informed decisions. Visual analytics is an approach that allows users to interactively explore information, form and test hypotheses, and get immediate answers to questions about their data.
We present examples from several stages of the R&D process that illustrate how visual analytics can help researchers make better decisions earlier in the process:
* High-throughput screening: diagnosing systematic quality problems
* Lead optimization: discovering correlations between structure, biological activity and ADME properties
* Clinical trials: uncovering early indicators of drug safety issues
We conclude with an overview of the Spotfire DecisionSite platform, and show how it enables improved decision-making at all levels of the enterprise.
Searching for Answers: the game of twenty questions
Dennis Underwood, Praxeon
Curing human disease is one of the most challenging of human endeavors. Despite advances in drug development and our deeper understanding of medicine it has become increasingly difficult to bring new drugs to market. From discovery to market, drug development is an odyssey through an information maze; every path to success is obscured by dead-ends. Selecting a new disease therapy or extending current indications is a difficult process involving decisions about the likelihood of success, competition challenges and the value of the opportunity to the current portfolio.
Despite the dramatic increase in data and information generation, it has become increasingly challenging to bring new drugs to market. Paradoxically, the mass of new information has served to confound rather than to enlighten. In other industries, the use of sophisticated information technology has transformed processes, increased efficiency and created innovation. Pharma and Biotech are knowledge industries and yet there are very few tools available that increase the efficiency of the transformation of data to information to knowledge.
Even with sophisticated new technologies, the challenges of the complexity of data-types and problems associated with building models of understanding disease therapies that are based on inherently complex and variable biological systems remain. I will describe some of these challenges and highlight new methods that have the potential to revolutionize the way in which data and information are used within the industry. This is the beginning of a new era in which searching for answers in the information that surrounds us becomes as facile and as enlightening as dialog with a mentor.
Management Reporting of Clinical Trial Programs, Portfolios, and Studies: Managing Risks / Managing Projects
Joel Hoffman, Insightful
Managing a clinical trial is managing risk. In this presentation, the types of risk that companies and academic institutions face are reported from a study of 11 companies. Next, a best practice for managing risk in clinical trials will be described and discussed. The importance of metrics will also be described and best practices for establishing and using metrics in organizations. Finally, example uses of technology for managing risk in clinical trials will be shown.
The following topics in Clinical Development & Risk Management will be discussed:
1) Survey on Risk Dimensions in Clinical Development
- Background to the survey
- Dimensions of Risk Identified
Results, Safety, Efficacy, Recruitment, Timelines (other than recruitment), Budgets, Resources, Systems
- Summary
2) Processes for Managing Risk – A Best Practice
- Process Overview
- Basic Tools
- The Study Plan
* Project/Study Risks: Examples
* Study Status Review Meeting
* Tracking Tools
- Getting a Plan Pre-Approved
- Linking Team Members
- Summary: Managing a Trial is Managing Risks
3) Managing Risk with Metrics
- Approach to Developing Metrics
- Clinical Milestones and Deliverables
- Clinical Milestones and Metrics Used by Life Science and Academic Institutions
4) Example uses of Technology
5) Summary and Conclusions
WORKSHOPS
* Electronic Laboratory Notebook Workshops – Rescentris, Symyx
* Applying Roadmap processes to the Clinical Trials Project Process, Joel Hoffman (Insightful)
* Innovation Management in R&D – an Enterprizer Briefing and Case Study, Joseph Bitran (Enterprizer)
* Using Interactive Visual Analytics to Accelerate Drug Development, David Mosenkis (Spotfire)
InnovationWell meeting workshop conference management executive Bryn Mawr Philadelphia Critical Pathbiomarkers metabolomics toxicology personalised medicine KM Life Sciences Pharma Drug DiscoveryResearch and Development Drug Development Healthcare Innovation Knowledge Management events
InnovationWell Press Release (2 October 2006): http://www.prweb.com/releases/2006/10/prweb444155.htm
eCheminfo Press Release (29 September 2006): http://www.prweb.com/releases/2006/9/prweb443727.htm
The FDA Critical Path to new Medical Products report (http://www.fda.gov/oc/initiatives/criticalpath/) recognised that improvements in knowledge management and transfer between research and development could provide critical productivity improvements and have significant impact on the successful development of new healthcare treatments. Translational research involving multi-directional knowledge flows between basic science and clinical research are hence a primary area for both future improvement and new approaches. Development and evaluation have not kept pace in recent years with advances in R&D technologies (e.g., genomics, microarrays, in silico) so as to enable better and earlier identification of novel targets, innovative therapies, safety risks, benefits or target sub-populations. It appears that new technology has been put into place in the R&D part of the pipeline without the ability to interpret and understand it in later decision-making.
Knowledge management approaches can help support improved performance in Critical Path areas of development and can help unite advances and diverse knowledge flows from genomics, systems biology, biomarkers, computational modelling, toxicology, pharmacology, diagnostics etc. with clinical trial design, operation and decision-making. New approaches to the use of information and communications technology and resources, in addition to increasing adoption of emerging knowledge management practices, both in explicit knowledge transfer and organisational development, could be increasingly applied to aid progress of drug, diagnostic and device development objectives.
On Monday 16th October 2006 a number of leading experts and practitioners will meet at the InnovationWell Community of Practice meeting at Bryn Mawr College, Philadelphia to discuss new applications of knowledge management and assessment in life science research and clinical development. I hope you can join the presentations and workshops followed by good conversations around the bbq!
I provide below a summary of the presentations and workshops.
Barry Hardy
Utilising Knowledge Management to increase R&D Productivity along Critical Paths
http://www.innovationwell.net/COMTY_kmrandproductivity/
InterAction Meeting Session, Bryn Mawr, Philadelphia, USA,
Monday, 16 October 2006
chaired by Michael Liebman (Windber Research Institute)
What a Quality Assurance Program can do to Facilitate Clinical Research and Development Process
Delia Y. Wolf, Harvard Medical School
The key to an efficient clinical research and development process is not only having a well-designed protocol, but also having a well-run clinical trial. As the majority of these clinical trials are conducted by investigators in an academic medical center, it is important to ensure that each participating investigator is well-informed about both scientific and regulatory requirements. In fact, a recent FDA report found that investigator noncompliance accounted for more than 70% of the deficiencies noted in the agency’s audit findings. One way to improve investigators’ understanding of and compliance with clinical research regulations and guidelines is implementing an effective quality assurance (QA) program that aims to improve investigator compliance with federal regulations, Good Clinical Practice (GCP) guidelines, and institutional requirements by providing on-site audit and assisting with investigator self-assessment.
The morning presentation will provide an overview of two practical QA approaches, namely onsite audit and investigator self-assessment, as well as their effectiveness in measuring compliance. The afternoon workshop will focus on a step–by-step guide to conducting an onsite audit and assisting investigators with conducting a self-assessment. Common findings of regulatory non-compliance in investigator-initiated study, as well as industry sponsored/CRO managed study will be covered. Practical tips on how to identify and avoid potential violations throughout clinical trials will also be discussed.
A Framework for Research Informatics
Peter Gates, Johnson & Johnson PR&D
We consider the question “What is research informatics?” We assert that the conceptual invariant across all research informatics is the scientific method. We define the scientific method as the interplay between observation and model construction. We take the position that model construction is generally not amenable to software automation. This we claim lies on the human side of the human-computer boundary. With that in mind we consider the following sequence of steps:
1. The mining of existing data
2. The planning of experiments
3. The execution of experiments
4. The reduction of observations to data
We propose the automation of these activities to be the program of research informatics. We focus on steps 2 though 4 and consider two specific strategies. The first is the use of partial evaluation of a function (f(b,x) = fb(x)) as a conceptual framework for combining convenience with flexibility in software. The second is the use of transitive closure (e.g. a->b and b->c => a->c) together with recursion as a convenient way of generating rich structures from a small set of simple building blocks. This last concept has been described as a free construction in the mathematics literature. These ideas will be illustrated with a design pattern for managing mixtures associated with biological assays used in pharmaceutical research.
Building an informatics infrastructure for translational research
Jonathan Sheldon, InforSense
Workflow has become a key technology for discovery informatics in life science research. Workflow technology enables scientists to dynamically construct their own research protocols for scientific analytics and decision making by connecting together various information resources and software applications in an intuitive manner. Workflow is not just a visual way of writing scripts for a vertical application. Workflow can form the foundation of an enterprise informatics environment enabling organizations to optimize their important data-driven processes and deliver information, expertise and derived knowledge effectively and quickly to decision-makers throughout the organization from scientist to the board.
Over the last couple of years we have also seen increasing investment in approaches that enable cross domain research both within major pharma and biomedical research centers. Strategies such as systems biology, pharmacogenomics, and biomarker discovery all have in common the requirement to access and integrate data and methods that cut across traditional silos: biology and chemistry, biology and clinical, chemistry and pre-clinical.
InforSense has combined advanced workflow technology with cross-domain analytics. Our integrative analytics platform has been engineered to effectively support translational research and addresses the needs of a diverse user community which ranges from biomedical informaticians to clinicians. In this presentation a variety of case studies will be presented with emphasis on examples which have a patient centric, clinically driven focus. In particular, we will describe in detail our work with Windber Research Institute to build a decision support environment that enables scientists to translate research into decisions that impact patient care. Specifically, we will describe how we have used workflow as a tool to:
* build a non disease specific clinical data warehouse
* provide a mechanism to browse this data (>600 fields per patient) and stratify into patient subsets e.g. responders vs non responders
* drill down into comprehensive views of all data collected for an individual patient
* integrate this clinical data in a dynamic manner with experimental data such as gene expression
* provide a powerful analytical environment to build predictive models of say treatment response which can reside in an Oracle database
* produce a simple web interface deployed into a customized portal to interpret the output of complex analysis for decision support
Why the “Top-Down Approach” to Knowledge and Content Management has failed the United Sates Intelligence Community – Implications for Healthcare Research
Duane Shugars, Concentia Digital
For the last 5 years we have witnessed hundreds of millions of dollars spent on large Enterprise Content Management (ECM) projects most of which have failed because too much time was spent on figuring out how a single piece of ECM technology was going to solve all the content management problems for the enterprise. The commercial space has long known that “Content” is king but the Government has been slow to understand this basic tenant of Content Management.
After millions of dollars and 4-5 years of effort, they look back on their ECM projects and realize that they have nothing more then a lofty architecture and an impressive search engine. What they lack, however, is the one thing that their ECM project was originally intended to help find ~Metadata and Content.
The owners of the content are now bound to using their local folders to manage their content, using tools such as MS Excel and MS Access as their primary tools to search and analyze data, and utilizing e-mail to share and collaborate. While each has a place in the organization’s value chain, all fall short of doing what they need to do to make the content manageable and thus the enterprise successful.
What went wrong and why?
What needs to be done to fix it?
Case Studies of success using the Bottom up Approach to enable the enterprise
What can the life science/healthcare sector learn from these efforts?
Research Information Management Systems for Translational Research
Jian Wang, BioFortis
The emergence of translational research as the paradigm for rapid conversion of knowledge attained from basic research through to clinical practice (and back) necessitates the development of flexible platforms to communicate across information silos. This talk will focus on the role of Research Information Management Systems (RIMS) in supporting translational research. Specifically, I will use our experience at the National Institutes of Health as a case study, supplemented by anecdotes from other academic institutions, hospitals and the private sector, to illustrate the unique challenges as well as lessons learned in this arena. I will discuss:
* How RIMS can fit into and support the translational research workflow, at the lab, project and enterprise levels
* Translational research must overcome a significant information gap – the gap between what is traditionally supported by Laboratory Information Management Systems (LIMS) and Electronic Medical Records (EMR)
* The participants in translational research and their different needs for and rights to information
* Translational research, by its very nature, demands collaboration and the need to manage institutional knowledge
* Regulatory compliance (patient privacy, data security etc) is a unique challenge given the different types of people and activities involved in a translational research program – I will use de-identification of Protected Health Information (PHI) as an example for discussion.
* The need for cultural changes in order to break down walls that hinder progress in translational research.
Applying Knowledge Assessment Techniques to Improving Productivity in Life Science Research
Barry Hardy, Douglas Connect & Jeff Spitzner, Rescentris
This presentation will discuss steps to be taken in the Knowledge Assessment of an R&D organization. Such an assessment initially obtains an overview of the current state of knowledge management in the organization and an identification of existing problem areas where actions could bring performance improvement. Areas to be investigated include knowledge gaps, bottlenecks, absence or under-utilisation of knowledge, lack of communication or collaboration, lack of access to or re-use of existing knowledge, difficulty in storing or retrieving knowledge, organizational or cultural issues, and barriers to knowledge sharing and innovation. The initial assessment can be subsequently followed by the introduction of new processes and tools as required, and a subsequent repetition of the assessment at periodic intervals as part of an ongoing performance improvement and support program.
The objectives of such a knowledge assessment and continuous improvement exercise are:
a) to assess the organization’s current situation and performance in a number of areas relating to the management and use of scientific knowledge and technical know-how;
b) to identify and prioritise areas for improvement in knowledge discovery, knowledge use and transfer and innovation;
c) to evaluate the effect that Electronic Lab Notebooks, Collaboration Support Systems and other IT and Knowledge Management tools is having, or is expected to have, on improving knowledge and innovation processes.
The benefits of such an approach are to:
* Allign R&D activities and knowledge processes with business strategies
* Improve productivity of R&D individuals and organisation
* Improve efficiency and reduce waste in R&D through knowledge loss or under-utilisation
* Increase knowledge sharing and innovation of R&D organisation
* Increase valuation of organisation through improved management of intellectual property and knowledge workers
* Improve quality of information captured from R&D processes
* Improve access to information captured from R&D processes
WORKSHOPS
Carrying out an Onsite Audit and Self-assessment in Clinical Trial Management
Delia Y. Wolf, Harvard Medical School
This workshop will focus on a step–by-step guide to conducting an onsite audit and assisting investigators with conducting a self-assessment. Common findings of regulatory non-compliance in investigator-initiated study, as well as industry sponsored/CRO managed study will be covered. Practical tips on how to identify and avoid potential violations throughout clinical trials will also be discussed.
Information Management Needs by Translational Researchers
Jian Wang, BioFortis
The emergence of translational research as the paradigm for rapid conversion of knowledge attained from basic research through to clinical practice (and back) necessitates the development of flexible platforms to communicate across information silos. In this workshop, we will re-trace the adoption process of Labmatrix™ by several institutes at the National Institutes of Health to support their translational research initiatives. These case studies demonstrate a set of unique information management needs by translational researchers as well as scientific administrators. The following will be discussed:
* The translational research process and its information management challenges at the lab, project and enterprise levels
* How is translational research different from basic research and clinical practice from an information management perspective
* How is translational research information managed today
* Who are the participants in translational research and what are their different needs for and rights to information
* Translational research, by its very nature, demands collaboration and the need to manage institutional knowledge
* What is a Research Information Management System (RIMS) and how does it differ from Laboratory Information Management Systems (LIMS) and Electronic Medical Records (EMR)
* Regulatory compliance (patient privacy, data security etc) is a unique challenge given the different types of people and activities involved in a translational research program
* The need for cultural changes in order to bridge the information gap that hinders progress in translational research
In addition to open discussions, workshop participants will have an opportunity to interact with a demonstration of Labmatrix™ and gain direct understanding of many challenges that NIH Labmatrix™ users encountered during their adoption of translational research information systems. Some examples are:
* Protecting patient privacy without stymieing research productivity
* Collaboration while maintaining oversight of your data
* Managing and querying patient specimen information, together with clinical information as well as research data (“-omics” etc) generated on the specimens
* Every lab/project is unique, how can a system serve them all
* Security Certification and Accreditation: software, network and human behavioral issues
* What does it take to get started
* System integration – fitting into the existing environment
* Training, support and maintenance
Knowledge Assessment in R&D – Impact on Project Management & Research Productivity
Barry Hardy, Douglas Connect & Jeff Spitzner, Rescentris
We will discuss Knowledge Assessment techniques and their impact on Project Management & Research Productivity from the following perspectives:
* Assessing organisational performance in the categories of Managing Records & Information, Finding Information, Finding Expertise, Organisational Development, Collaboration, Leadership, Knowledge Utilisation, Knowledge Transfer, Innovation, Project Management, Training, IT, and Support
* Methods for performing a knowledge assessment (e.g., surveying, interviews, knowledge mapping, benchmarking, organisational structure analysis, culture measurement, social network analysis, IT systems analysis, etc.)
* Measures for tracking performance improvement in organisational performance and innovation
* How to identify key areas of weakness, priority and importance identified for next improvement steps
* Processes, change, modifications, initiatives, training etc. required to implement improvement steps
* Selection of technologies, software systems, tools, ontologies, controlled vocabularies, etc., - and including both new approaches and modifications to existing systems - to enhance organisational performance, innovation and knowledge management
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InnovationWell Press Release (2 October 2006): http://www.prweb.com/releases/2006/10/prweb444155.htm
eCheminfo Press Release (29 September 2006): http://www.prweb.com/releases/2006/9/prweb443727.htm
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