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
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