Drug Research and Translational Bioinformatics

Abstract

Setting the stage Drug research relies on classical scienti!c methods and statistical inferences across the spectrum of preclinical discovery to clinical practice: formulate a hypothesis, test the hypothesis experimentally, analyze the data, and make informed decisions to accept, reject, or re!ne the hypothesis. Drug discovery and development is iterative, collaborative, and multidisciplinary, culminating in pivotal clinical trials that provide evidence of ecacy and safety to support market access. Through the mid-1990s, this process worked remarkably well in some disease areas with the rapid identi!cation of validated drug targets, such as with antiretroviral drugs for treating HIV and statins for managing high cholesterol in cardiovascular diseases, leading to the launch of many new medicines that reduced morbidity and mortality and increased life expectancy. But in other diseases, such as cancer, progress has been more modest. Over the past 15 years, this success story has been threatened by expiring patents, poorly understood disease mechanisms, a high rate of attrition, and burgeoning drug development costs. As a result, the pace of new medicines coming out of the pharmaceutical industry pipelines has slowed considerably. Translational bioinformatics (TBI) has recently emerged as an important technology to address these challenges. At the risk of being oversold, TBI is expected to help bridge the gap between pathogenic pathways and disease phenotypes and guide molecular measurements that can improve target identi!cation, drug selection, and clinical trial design. #e quotation by Einstein above emphasizes the importance of “thinking about thinking” and the need to better understand human cognition. In the context of scienti!c thinking, cognition is the thought process that describes how data acquired from drug research are transformed into information and stored as knowledge for future decision making. In today’s world of rapid technological and computational advances, it is easy to get lost in the ubiquitous and dicult problem of cognitive overload and workload timelines. TBI is a novel approach to solving these problems and is designed to avoid getting “lost in the data.” It can help decision makers answer important questions by integrating pertinent information beyond that which could be achieved by human memory, intuition, and pattern thinking alone. Today, pharmaceutical companies, academia, and others involved in drug discovery and development have access to a far more sophisticated understanding of disease pathways and biological networks to decode complex clinical disease phenotypes. #is has changed the way drug research is conducted. It also has led to recent breakthroughs in targeted therapies for cancer such as vemurafenib, a B-Raf enzyme inhibitor for the treatment of late-stage melanoma, and crizotinib, an anaplastic lymphoma kinase inhibitor for treating some types of non–small cell lung carcinomas. Federal agencies have become part of the solution to problems in drug discovery and development. #e US Food and Drug Administration (FDA) had taken notice of the trend toward lower productivity when it launched the Critical Path Initiative in 2004, which was intended to frame a national strategy for driving innovation in scienti!c tools and processes that would foster a turnaround in the success of drug development. More recently, the FDA published a strategic plan for advancing regulatory science and emphasized the importance of TBI in several priority areas and implementation strategies.1 #e Obama administration started a government drug development center, called the National Center for Advancing Translational Sciences, to partner with pharmaceutical companies and other organizations to apply scienti!c advances and develop new tools for drug research. Both initiatives acknowledge the rapid growth of biomedical knowledge and the urgent need for new predictive bioinformatics tools in drug discovery, development, and postmarketing clinical practice. This article provides a macroscopic view of TBI and a perspective on the future of knowledge development in the drug discovery, development, regulatory, and clinical practice continuum.