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

Abstract

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.

A Framework for Assessing Scientific Merit in Ethical Review of Clinical Research

“…A new approach to data visualization of research programs could be particularly informative here. For example, some recent analyses of drug development, involving interventions in cancer 33 and cardiovascular medicine, 34 have elucidated the potential research waste and harms to participants that may arise when many trials of new treatments are conducted in parallel with each other. These analyses employ a technique called “accumulating evidence and research organization” (“AERO”) graphing, which visually depicts the volume and patterns of research activity for a given scientific domain 35 …”

“…Figure S1 (available online under “Supporting Information”) shows an example AERO graph from a recent analysis of the cholesteryl ester transfer protein (CETP) inhibitor class of drugs conducted by Spencer Hey, Jessica Franklin, Jerry Avorn, and Aaron Kesselheim 36 . In this figure, each clinical trial of a CETP inhibitor is represented by a node, arranged by the date of study publication (or the date the study was completed if it is unpublished) along the x‐axis and stratified by drug and primary study end point along the y‐axis.…”

“…35 Figure S1 (available online under "Supporting Information") shows an example AERO graph from a recent analysis of the cholesteryl ester transfer protein (CETP) inhibitor class of drugs conducted by Spencer Hey, Jessica Franklin, Jerry Avorn, and Aaron Kesselheim. 36 In this figure, each clinical trial of a CETP inhibitor is represented by a node, arranged by the date of study publication (or the date the study was completed if it is unpublished) along the x-axis and stratified by drug and primary study end point along the y-axis. Green nodes represent positive studies; red nodes represent negative or terminated studies; yellow nodes represent studies that had mixed or ambiguous results; grey nodes represent studies that were completed (according to their Clinicatrials.gov registration record) but for which no study outcome information is available.…”

Assessment of the Role of Niacin in Managing Cardiovascular Disease Outcomes

“…If surrogate measures are not known to correlate with clinical outcomes, we should be wary about using them to guide prescribing decisions or as end points in clinical trials. In line with previous analyses on niacin and on other HDL-C level–increasing agents, such as cholesteryl ester transfer protein inhibitors, evidence is accumulating that the HDL-C level is not a sensitive indicator of cardiovascular risk modification, clouding its use as a surrogate measure in clinical research or clinical practice. With our stratified analysis, we were able to show that when the LDL-C level is corrected using statins, there is no evidence that adding niacin provides incremental clinical benefit, which in such a clinical scenario should be mainly because of its ability to increase HDL-C levels.…”

Ethical Challenges in Biomarker‐Driven Drug Development

“…A recent analysis of the cholesteryl ester transfer protein (CETP) inhibitor class of drugs illustrates the ethical challenge for evaluating efficiency in biomarker‐driven research . Development of this class was driven in part by the “HDL hypothesis”—the mechanistic rationale that CETP transfers cholesterol from high‐density lipoproteins (HDL or “good cholesterol”) to low‐density lipoproteins (LDL or “bad cholesterol”), and therefore, CETP inhibition should raise the concentration of good cholesterol, lower the concentration of bad cholesterol, and thereby reduce the risk of cardiovascular disease …”