Applying Semantic Web Technologies to Drug Safety Determination

Abstract

Ensuring drug safety is of paramount importance to the life sciences industry. It's critical that drugs are able not only to achieve the desired result but also to do so without harmful side effects. By identifying problems as early as possible in the drug discovery and development process, life sciences companies can avoid drug safety sagas, such as a recent example that concerned COX-2 inhibitors. Unfortunately, drug safety problems are often revealed only during clinical trials or occasionally after marketing. These challenges are becoming more acute as medicines are targeted to defined patient populations. The life sciences industry can use semantic Web technologies to integrate data more effectively across all drug discovery and development business units, thereby providing a more supportive environment for the early detection of safety-related issues. Effective integration would enable genomic data and patient profiles to be more easily related to safety, thus providing: 1) a simpler framework for determining risk-benefit for individual patients in particular treatment regimens, 2) a better mechanism to distribute new data relating to safety throughout the organization, and 3) a better decision-making environment to determine which drugs to pursue. Furthermore, semantic Web inferencing capabilities enable an intelligent decision support system or autonomous agent to reason about combined domain-specific and industry-specific knowledge and act on the conclusions drawn from this inferencing process.