Biomarkers for smoking cessation

Abstract

One way to enhance therapeutic development is through the identification and development of evaluative tools such as biomarkers. This review focuses on putative diagnostic, pharmacodynamic, and predictive biomarkers for smoking cessation. These types of biomarkers may be used to more accurately diagnose a disease, personalize treatment, identify novel targets for drug discovery, and enhance the efficiency of drug development. Promising biomarkers are presented across a range of approaches including metabolism, genetics, and neuroimaging. A preclinical viewpoint is also offered, as are analytical considerations and a regulatory perspective summarizing a pathway toward biomarker qualification.

Figure 1

Biomarker development is interdependent. Initially, a measure is “analytically validated” for its precision and accuracy. An optimal measure would be practical (e.g., robust and cost effective) and easy to collect (e.g., noninvasive, measured within a surrogate tissue (e.g., saliva)). A validated test is required before “qualification,” which is the evidentiary linkage of the measure with its clinical outcome. Finally, independently corroborated qualification of biomarkers can be “utilized” for more general purposes. Data from concurrent biomarker discovery efforts (e.g., “omic”-type projects) can be used to hone further efforts along each step of development (Table 2). Adapted from ref. .

Figure 2

US Food and Drug Administration (FDA) drug development tools (DDTs). The DDT qualification program, including biomarkers, was created by the Center for Drug Evaluation and Research to guide and prepare drug development efforts for rigorous safety and efficacy testing and eventual regulatory evaluation.

Figure 3

Proximal vs. distal biomarkers. Proximal markers are those biomarkers closely associated with the underlying molecular mechanisms of a disease; proximal biomarkers are not likely to capture the disease phenotype in its entirety (e.g., nicotine dependence). Distal markers, by comparison, are those biomarkers that reflect the endophenotypes further up the phenotypic “tree”; they are more likely to capture more features of an underlying pathophysiology or a biological response to a therapeutic intervention. NMR, nicotine metabolic ratio.

Figure 4

A biomarker is an objective measure of a clinical outcome. A biomarker is an objectively measured indicator of a normal biological process, pathogenic process, or biological response to a therapeutic intervention. As opposed to other more subjective measures related to nicotine dependence (e.g., craving, mood) (top circle), an objective biomarker for nicotine dependence might include a measure collected via a metabolic (e.g., NMR), genetic (e.g., quit-success score), physiologic (e.g., precessation response to NRT), or neuroimaging approach (e.g., rsFC) (see text for details). However, these data may reflect only a single aspect of a disease phenotype or pharmacotherapeutic response. For example, if combined as a “biosignature,” two or more biomarkers (e.g., neuroimages + genetics; see text) are more likely to capture key processes underlying efficacy and/or toxicity along the causal pathway(s) of a pharmacological process (large “Biosignature” box) that might have otherwise been missed if a single measure were applied (small “Biomarker” box). In terms of drug development, this type of data could help assess the overall risk (i.e., off-target, nonpathway effects) to benefit (i.e., intended targeted pathway activation/repression) ratio and improve the efficiency by which clinical trials are conducted. NMR, nicotine metabolic ratio; NRT, nicotine-replacement therapy; rsFC, resting-state functional connectivity.