Success, Failure, and Transparency in Biomarker-Based Drug Development

Biomarkers are frequently proposed as a way of identifying targets and facilitating the evaluation of new drugs. When these chemical or genetic indicators are used as surrogate measures, the effects of drugs can be observed using smaller and shorter trials. However, even though biomarkers are based on plausible biological hypotheses, many end up failing to predict which drugs will later succeed in trials using clinical outcomes. Relying on biomarkers for drug development without sufficient validation of their connection to actual clinical end points can lead to patient–subject harms and research waste. However, the US Food and Drug Administration (FDA) already approves about half of all new drugs on the basis of changes to surrogate measures, some patient advocates and pharmaceutical manufacturers have argued for greater reliance on biomarkers in new drug approval. One biomarker that has continued to disappoint in clinical testing is the raising of high-density lipoprotein (HDL) cholesterol by the cholesteryl ester transfer protein (CETP) inhibitors. The plausible biological hypothesis-driving research into this class is that CETP transfers cholesterol from HDL to very low-density lipoprotein or low-density lipoprotein (LDL). Therefore, the hope is that CETP inhibition should raise HDL, lower LDL, and reduce the risk of cardiovascular disease. However, this hope has remained unfulfilled. In October 2015, Eli Lilly announced that it was abandoning development of its CETP inhibitor, evacetrapib, after an interim analysis of an ongoing phase 3 trial showed that it was unlikely to conclude effectiveness. Evacetrapib is now the third CETP inhibitor to fail in clinical development. Pfizer abandoned torcetrapib in 2006 because it increased the risk of death and Roche abandoned dalcetrapib in 2012 because of lack of effectiveness. Despite these failures, it was recently reported that Merck is continuing to develop its CETP inhibitor, anacetrapib, as is Amgen, which recently acquired Dezima Pharmaceuticals and its agent, TA-8995. Background—Although biomarkers are used as surrogate measures for drug targeting and approval and are generally based on plausible biological hypotheses, some are found to not correlate well with clinical outcomes. Over-reliance on inadequately validated biomarkers in drug development can lead to harm to trial subjects and patients and to research waste. To shed greater light on the process and ethics of biomarker-based drug development, we conducted a systematic portfolio analysis of cholesterol ester transfer protein inhibitors, a drug class designed to improve lipid profiles and prevent cardiovascular events. Despite years of development, no cholesterol ester transfer protein inhibitor has yet been approved for clinical use. Methods and Results—We searched PubMed and Clinicaltrials.gov for clinical studies of 5 known cholesterol ester transfer protein inhibitors: anacetrapib, dalcetrapib, evacetrapib, TA-8995, and torcetrapib. Published reports and registration records were extracted for patient demographic characteristics and study authors’ recommendations of clinical usage or further testing. We used Accumulating Evidence and Research Organization graphing to depict the portfolio of research activities and a Poisson model to examine trends. We identified 100 studies for analysis that involved 96 944 human subjects. The data from only 41 201 (42%) of the human subjects had been presented in a published report. For the 3 discontinued cholesterol ester transfer protein inhibitors, we found a pattern of consistently positive results on lipidmodification end points followed by negative results using clinical end points. Conclusions—Inefficiencies and harms can arise if a biomarker hypothesis continues to drive trials despite successive failures. Regulators, research funding bodies, and public policy makers may need to play a greater role in evaluating and coordinating biomarker-driven research programs. (Circ Cardiovasc Qual Outcomes. 2017;10:e003121. DOI: 10.1161/ CIRCOUTCOMES.116.003121.)