Nicotine abstinence genotyping: assessing the impact on smoking cessation clinical trials

Abstract

Twin studies document substantial heritability for successful abstinence from smoking. A genome-wide association study has identified markers whose allele frequencies differ with nominal P<0.005 in nicotine-dependent clinical trial participants who were successful vs unsuccessful in abstaining from smoking; many of these results are also supported by data from two additional samples. More study is required to precisely determine the variance in quitting success that can be accounted for by the single-nucleotide polymorphisms that are currently identified and to precisely classify individuals who may display varying degrees of genetic vs environmental effects into quitters or nonquitters. However, the data at hand do allow us to model the effects of genotypic stratification in smoking cessation trials. We identify relationships between the costs of identifying and genotyping prospective trial participants vs the costs of performing the clinical trials. We quantitate the increasing savings that result from genetically stratified designs as recruiting/genotyping costs go down and trial costs increase. This model helps to define the circumstances in which genetically stratified designs may enhance power and reduce costs for smoking cessation clinical trials.

Figure 1. Distributions of χ2 values for n = 200 clinical trials for smoking cessation success

The distributions of the significance value for placebo (0.1 quit success) vs treatment (0.2 quit success) group effects in 100,000 simulation trials of subject groups selected in three ways are shown. Dotted line: Random assignment (90% genetic nonquitters). Dashed line: half-maximal genetic stratification (75% genetic nonquitters), Solid line: total genetic stratification (50% genetic nonquitters) (p < 0.05 threshold corresponds to χ2 >3.84)

Figure 2. Costdifferences in clinical trials: half-max genetic stratification vs random assignment

Cost differences (Y axis, in millions of US dollars) with 0.9 power to achieve p < 0.05 that are stratified by genotypes (half max genetic influences, n = 1019 genotyped, n= 200 participants) vs conventional design (p < 0.05, n = 450) as recruitment costs (Z axis, in US dollars) vary from $25 – $500 subject and trial costs (X axis, in US dollars) vary from $1,000 – $25,000/subject. We assume genotyping costs of $150 for data on the main surface of the plot. If genotyping costs rise to $300/subject, and trial/recruiting costs are1000/25 or 25,000/500 savings from half-max genetic stratification are $ −69,925 and $5,659,800, respectively. If genotyping costs rise to $500/subject, and trial/recruiting costs are1000/25 or 25,000/500 savings from half-max genetic stratification are $ −273,725 and $5,456,000, respectively.