Biomarkers of COPD

Abstract

Chronic obstructive pulmonary disease (COPD) is a major health burden across the world. Globally, more than 300 million people suffer from COPD [1] and nearly three million die each year from this disease [2]. COPD mortality continues to climb at an alarming rate, such that by 2030, nearly nine million people will die annually from COPD [3]. The economic burden of COPD is also enormous. In the United States alone, COPD accounted for $20.9 billion USD in direct and $7.4 billion USD in indirect costs in 2004 [4]. Regrettably, the pipeline for new drugs for COPD is relatively dry compared to other major causes of mortality such as HIV/AIDs, cancer, and diabetes [5]. One major barrier to drug discovery in COPD is the paucity of well-accepted and well-validated biomarkers. Currently, the only “marker” that is widely accepted by regulatory agencies for new drug approval in COPD is FEV1 (forced expiratory volume in 1 s), which is a robust measure of lung function. However, COPD is defined operationally as a chronic respiratory condition that results in airflow limitation which is progressive and not fully reversible [6]. In other words, COPD is defined by limited reversibility of FEV1, making this endpoint unsuitable for drug discovery in COPD. The discovery of a validated, reliable, robust, and reproducible blood biomarker would provide a major boost to the development of novel compounds because it would allow investigators (and companies) to demonstrate the therapeutic promise of a drug in small (usually phase II) trials before proceeding to a much more expensive and logistically difficult phase III trials. Without such data, pharmaceutical companies are hesitant to invest millions of dollars on large phase III studies to bring compounds to market. For this reason, some international companies have recently abandoned COPD drug development altogether, while many others have scaled back their efforts significantly. The purpose of this chapter is to review potential biomarkers of COPD, especially those that could be used in predicting treatment responses.