Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D

Smoking Cessation Pharmacotherapy Based on Genetically-Informed Biomarkers: What is the Evidence?

INTRODUCTION

Pharmacogenomic studies have used genetic variants to identify smokers likely to respond to pharmacological treatments for smoking cessation.

METHODS

We performed a systematic review and meta-analysis of primary and secondary analyses of trials of smoking cessation pharmacotherapies. Eligible were trials with data on a priori selected single nucleotide polymorphisms, replicated non-single nucleotide polymorphisms, and/or the nicotine metabolite ratio. We estimated the genotype × treatment interaction as the ratio of risk ratios (RRR) for treatment effects across genotype groups.

RESULTS

We identified 18 trials (N = 9017 participants), including 40 active (bupropion, nicotine replacement therapy [NRT], varenicline, or combination therapies) versus placebo comparisons and 16 active versus active comparisons. There was statistical evidence of heterogeneity across rs16969968 genotypes in CHRNA5 with regard to both 6-month abstinence and end-of-treatment abstinence in non-Hispanic black smokers and end-of-treatment abstinence in non-Hispanic white smokers. There was also heterogeneity across rs1051730 genotypes in CHRNA3 with regard to end-of-treatment abstinence in non-Hispanic white smokers. There was no clear statistical evidence for other genotype-by-treatment combinations. Compared with placebo, NRT was more effective among non-Hispanic black smokers with rs16969968-GG with regard to both 6-month abstinence (RRR for GG vs. GA or AA, 3.51; 95% confidence interval [CI] = 1.19 to 10.30) and end-of-treatment abstinence (RRR for GG vs. GA or AA, 5.84; 95% CI = 1.89 to 18.10). Among non-Hispanic white smokers, NRT effectiveness relative to placebo was comparable across rs1051730 and rs169969960 genotypes.

CONCLUSIONS

We did not identify widespread differential effects of smoking cessation pharmacotherapies based on genotype. The quality of the evidence is generally moderate.

IMPLICATIONS

Although we identified some evidence of genotype × treatment interactions, the vast majority of analyses did not provide evidence of differential treatment response by genotype. Where we find some evidence, these results should be considered preliminary and interpreted with caution because of the small number of contributing trials per genotype comparison, the wide confidence intervals, and the moderate quality of evidence. Prospective trials and individual-patient data meta-analyses accounting for heterogeneity of treatment effects through modeling are needed to assess the clinical utility of genetically informed biomarkers to guide pharmacotherapy choice for smoking cessation.

A computational algorithm for personalized medicine in schizophrenia

Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine.

Current pharmacotherapy for obesity

More than one-third of adults in the USA have obesity, which causes, exacerbates or adversely impacts numerous medical comorbidities, including diabetes mellitus and cardiovascular disease. Despite intensive lifestyle modifications, the disease severity warrants further aggressive intervention, including pharmacotherapy, medical devices and bariatric surgery. Noninvasive anti-obesity drugs have thus now resurfaced as targeted adjunctive therapeutic approaches to intensive lifestyle intervention, bridging the gap between lifestyle and bariatric surgery. In this Review, we discuss FDA-approved anti-obesity drugs in terms of safety and efficacy. As most of these drugs have a mean percentage weight loss reported in clinical trials but individual variations in response rates, a future direction of obesity pharmacotherapy research might include the potential for personalized medicine to target early responders to these anti-obesity drugs.

Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers

BACKGROUND

Smoking cessation therapies are not effective for all smokers, and researchers are interested in identifying those subgroups of individuals (e.g. based on genotype) who respond best to specific treatments.

OBJECTIVES

To assess whether quit rates vary by genetically informed biomarkers within pharmacotherapy treatment arms and as compared with placebo. To assess the effects of pharmacotherapies for smoking cessation in subgroups of smokers defined by genotype for identified genome-wide significant polymorphisms.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group specialised register, clinical trial registries, and genetics databases for trials of pharmacotherapies for smoking cessation from inception until 16 August 2016.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) that recruited adult smokers and reported pharmacogenomic analyses from trials of smoking cessation pharmacotherapies versus controls. Eligible trials included those with data on a priori genome-wide significant (P < 5 × 10-8) single-nucleotide polymorphisms (SNPs), replicated non-SNPs, and/or the nicotine metabolite ratio (NMR), hereafter collectively described as biomarkers.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. The primary outcome was smoking abstinence at six months after treatment. The secondary outcome was abstinence at end of treatment (EOT). We conducted two types of meta-analyses- one in which we assessed smoking cessation of active treatment versus placebo within genotype groups, and another in which we compared smoking cessation across genotype groups within treatment arms. We carried out analyses separately in non-Hispanic whites (NHWs) and non-Hispanic blacks (NHBs). We assessed heterogeneity between genotype groups using T², I², and Cochrane Q statistics.

MAIN RESULTS

Analyses included 18 trials including 9017 participants, of whom 6924 were NHW and 2093 NHB participants. Data were available for the following biomarkers: nine SNPs (rs1051730 (CHRNA3); rs16969968, rs588765, and rs2036527 (CHRNA5); rs3733829 and rs7937 (in EGLN2, near CYP2A6); rs1329650 and rs1028936 (LOC100188947); and rs215605 (PDE1C)), two variable number tandem repeats (VNTRs; DRD4 and SLC6A4), and the NMR. Included data produced a total of 40 active versus placebo comparisons, 16 active versus active comparisons, and 64 between-genotype comparisons within treatment arms.For those meta-analyses showing statistically significant heterogeneity between genotype groups, we found the quality of evidence (GRADE) to be generally moderate. We downgraded quality most often because of imprecision or risk of bias due to potential selection bias in genotyping trial participants. Comparisons of relative treatment effects by genotypeFor six-month abstinence, we found statistically significant heterogeneity between genotypes (rs16969968) for nicotine replacement therapy (NRT) versus placebo at six months for NHB participants (P = 0.03; n = 2 trials), but not for other biomarkers or treatment comparisons. Six-month abstinence was increased in the active NRT group as compared to placebo among participants with a GG genotype (risk ratio (RR) 1.47, 95% confidence interval (CI) 1.07 to 2.03), but not in the combined group of participants with a GA or AA genotype (RR 0.43, 95% CI 0.15 to 1.26; ratio of risk ratios (RRR) GG vs GA or AA of 3.51, 95% CI 1.19 to 10.3). Comparisons of treatment effects between genotype groups within pharmacotherapy randomisation armsFor those receiving active NRT, treatment was more effective in achieving six-month abstinence among individuals with a slow NMR than among those with a normal NMR among NHW and NHB combined participants (normal NMR vs slow NMR: RR 0.54, 95% CI 0.37 to 0.78; n = 2 trials). We found no such differences in treatment effects between genotypes at six months for any of the other biomarkers among individuals who received pharmacotherapy or placebo.

AUTHORS' CONCLUSIONS

We did not identify widespread differential treatment effects of pharmacotherapy based on genotype. Some genotype groups within certain ethnic groups may benefit more from NRT or may benefit less from the combination of bupropion with NRT. The reader should interpret these results with caution because none of the statistically significant meta-analyses included more than two trials per genotype comparison, many confidence intervals were wide, and the quality of this evidence (GRADE) was generally moderate. Although we found evidence of superior NRT efficacy for NMR slow versus normal metabolisers, because of the lack of heterogeneity between NMR groups, we cannot conclude that NRT is more effective for slow metabolisers. Access to additional data from multiple trials is needed, particularly for comparisons of different pharmacotherapies.

Pharmacogenomic Testing for Psychotropic Medication Selection: A Systematic Review of the Assurex GeneSight Psychotropic Test

BACKGROUND

A large proportion of the Ontario population lives with a diagnosed mental illness. Nearly 5% of Ontarians have major depressive disorder, and another 5% have another type of depressive disorder, bipolar disorder, schizophrenia, anxiety, or some other disorder not otherwise specified. Medications are commonly used to treat mental illness, but choosing the right medication for each patient is challenging, and more than 40% of patients discontinue their medication within 90 days because of adverse effects or lack of response. The Assurex GeneSight Psychotropic test is a pharmacogenomic panel that provides clinicians with a report to guide medication selection that is unique to each patient based on their individual genetic profile. However, it is uncertain whether guided treatment using GeneSight is effective compared with unguided treatment (usual care).

METHODS

We performed a systematic review to identify English-language studies published before February 22, 2016, that compared GeneSight-guided care and usual care among people with mood disorders, anxiety, or schizophrenia. Primary outcomes of interest were prevention of suicide, remission of depression symptoms, response to depression therapy, depression score, and quality of life. Secondary outcomes of interest were impact on therapeutic decisions and patient and clinician satisfaction. Risk of bias was evaluated, and the quality of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group criteria.

RESULTS

Four studies met the inclusion criteria. These studies used a version of GeneSight that included the CYP2D6, CYP2C19, CYP1A2, SLC6A4, and HTR2A genes; one of the studies also included CYP2C9. Patients who received the GeneSight test to guide psychotropic medication selection had improved response to depression treatment, greater improvements in measures of depression, and greater patient and clinician satisfaction compared with patients who received treatment as usual. We observed no differences in rates of complete remission from depression. The findings were based on GRADE assessment of low to very low quality evidence, and the body of evidence had several limitations: the included studies used an older version of GeneSight and were limited to a population with major depression, so results may not be generalizable to other versions of the test or different populations such as patients with anxiety or schizophrenia.

CONCLUSIONS

There is uncertainty about the use of GeneSight Psychotropic pharmacogenomic genetic panel to guide medication selection. It was associated with improvements in some patient outcomes, but not others. As well, our confidence in these findings is low because of limitations in the body of evidence.