Pharmacogenomics and Placebo Response in a Randomized Clinical Trial in Asthma

Wang, Rui-Sheng; Croteau‐Chonka, Damien C.; Silverman, Edwin K.; Loscalzo, Joseph; Weiss, Scott T.; Hall, Kathryn T.

doi:10.1002/cpt.1646

Cited by 9 publications

(13 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, duloxetine may perturb a genuinely STAC1 ‐related placebo response, which would suggest a gene–disease/drug/placebo or gene–treatment interaction, again undermining the assumption of additivity. In a recent GWAS of subjective and objective outcomes in the placebo arm of an asthma RCT, the effect estimates of the lead genome‐wide suggestive single nucleotide polymorphisms (SNPs) were attenuated in the budesonide and nedocromil drug treatment arms . The lead placebo SNPs mapped to genes associated with cilia function.…”

Section: Genetics and Nonadditivity In Rctsmentioning

confidence: 99%

Drug‐Placebo Additivity in Randomized Clinical Trials

Hall

Loscalzo

2019

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

In randomized clinical trials (RCTs), it is assumed that nonspecific effects beyond action of pharmacological agents are roughly equivalent in drug and placebo treatment groups. Hence, since the inception of RCTs, drug efficacy is determined by comparing outcomes in active to those in placebo control arms. However, quantitation of efficacy is based on an unproven assumption, that drug and placebo responses are always additive. Response to treatment in RCTs can be differentially influenced by the perturbing effects of patient expectations, side effects, and pharmacogenomic interactions in both drug and placebo arms. Ability to control for these effects requires understanding of when and where they arise, how to mitigate, analyze, and even leverage their impact. Here, we examine three factors that influence additivity: expectation, side effects, and pharmacogenomics. Furthermore, to provide novel insights into nonadditivity and solutions for managing it, we introduce systems pharmacogenomics, a network approach to integrating and analyzing the effects of the numerous interacting perturbations to which a patient is exposed in RCTs.In randomized placebo controlled clinical trials (RCTs), the placebo treatment arm is designed to capture and control for the nonspecific variables that can influence clinical outcomes. These include regression to the mean, spontaneous remission, changes due to the natural course of the disease, and placebo effects. Placebo effects refer to clinical improvement derived from the patient's interactions with the clinician, the information they receive with regard to their condition and treatment, and the therapeutic ritual performed in the clinical setting. 1 Hence, factors that influence clinical outcomes in RCTs derive from the "players" engaged in the therapeutic encounter and their emergent properties. From the patient's characteristics, the physician's appearance and manner, and the information communicated, to the mise-en-scène, and the pharmacological agent's properties, this complexity of factors can make predicting that there will be a statistically significant drug-placebo difference difficult. It is this complexity that placebo controls, along with randomization and double-blinding, were designed to reduce, allowing us to use simple arithmetic to subtract the nonspecific effects captured in the placebo control arm to reveal the efficacy of the drug. Hence, determination of drug efficacy relies on the assumption that the placebo and drug responses are additive (i.e., drug efficacy = drug response -placebo response).For drugs like antimicrobials or chemotherapeutics that target well-defined exogenous pathogens or cellular processes not under the influence of placebo effects, the additivity assumption seems to hold, allowing us to establish successfully the efficacy of numerous medications. [2][3][4] In contrast, treatments for conditions such as pain, anxiety, and depression, and a wide variety of functional and central sensitization disorders defined less by pathophysiology and more...

show abstract

Section: Genetics and Nonadditivity In Rctsmentioning

confidence: 99%

Drug‐Placebo Additivity in Randomized Clinical Trials

Hall

Loscalzo

2019

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Altogether, eleven candidate-gene studies, [24][25][26][27][28][55][56][57][58][59] a meta-analysis of a candidate gene, 60 and six pharmacogenomic studies (one of them in adults but including replication in children) have been performed. 32,36,40,73,74,79 During this period, candidate-gene studies reported the association of genes previously described in pharmacogenetic studies of pediatric asthma with treatment response (ADRB2, GSDMB, FCER2, and VEGFA). 13,[16][17][18] Additionally, other candidate genes have been related for the first time to this phenotype in children (SPATS2L, ASB3, IL1RL1, MMP9, and COL2A1).…”

Section: Discussionmentioning

confidence: 99%

“…77, 78 Interestingly, Wang et al conducted a pharmacogenomic study of placebo response in non-Hispanic white children with asthma and attempted to evaluate the top hits on patients under pharmacological treatment (budesonide or nedocromil). 79 A significant interaction was found between the SNP rs2392165 near BBS9 with ICS use on wheezing/ coughing (p-value interaction = 1.48 × 10 −7 ), being associated with better ICS response (β for the G allele: −0.53, p-value=0.02). BBS9 is implicated in lung development and ciliary function.…”

Section: Pharmacogenomic Studies Of Ics Responsementioning

confidence: 95%

<p>Pharmacogenetics of Pediatric Asthma: Current Perspectives</p>

Pérez-García¹,

Espuela‐Ortiz²,

Lorenzo-Díaz³

et al. 2020

PGPM

View full text Add to dashboard Cite

Asthma is a chronic respiratory disease that affects 339 million people worldwide and has a considerable impact on the pediatric population. Asthma symptoms can be controlled by pharmacological treatment. However, some patients do not respond to therapy and continue suffering from symptoms, which impair the quality of life of patients and limit their daily activity. Genetic variation has been shown to have a role in treatment response. The aim of this review is to update the main findings described in pharmacogenetic studies of pediatric asthma published from

show abstract

“…Further, neurological placebo response pathways are thought to induce downstream immune function effects 51,52 . Genetic variation in putative placebo response pathway molecules has been shown to differentially influence placebo and drug arm outcomes 70–75 . Without concurrent placebo controls, it is not possible to identify any effects of study drug on the placebo response pathways or drug‐placebo interactions.…”

Section: Patient‐level Sources Of Heterogeneitymentioning

confidence: 99%

“…51,52 Genetic variation in putative placebo response pathway molecules has been shown to differentially influence placebo and drug arm outcomes. [70][71][72][73][74][75] Without concurrent placebo controls, it is not possible to identify any effects of study drug on the placebo response pathways or drug-placebo interactions. Hence, a major limitation in the use of historical controls is that it limits the direct comparison of a specific drug to placebo, which is a challenge for evaluating adverse and off-target effects and potential placebo-drug interaction effects.…”

Section: Pharmacogenomics Of Drug and Placebo Responsementioning

confidence: 99%

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges

Hall

Vase

Tobias

et al. 2020

Clin Pharma and Therapeutics

Self Cite

View full text Add to dashboard Cite

Randomized control trials (RCTs) with placebo are the gold standard for determining efficacy of novel pharmaceutical treatments. Since their inception, over 75 years ago, researchers have amassed a large body of underutilized data on outcomes in the placebo control arms of these trials. Although rare disease indications have used these historical placebo data as synthetic controls to reduce burden on patients and accelerate drug discovery, broad use of historical controls is in its infancy. Large‐scale historical placebo data could be leveraged to benefit both drug developers and patients if warehoused and made more available to guide trial design and analysis. Here, we examine challenges in utilizing historical controls related to heterogeneity in trial design, outcome ascertainment, patient characteristics, and unmeasured pharmacogenomic effects. We then discuss the advantages and disadvantages of current approaches and propose a path forward to broader use of historical controls in RCTs.

show abstract

Pharmacogenomics and Placebo Response in a Randomized Clinical Trial in Asthma

Cited by 9 publications

References 43 publications

Drug‐Placebo Additivity in Randomized Clinical Trials

Drug‐Placebo Additivity in Randomized Clinical Trials

<p>Pharmacogenetics of Pediatric Asthma: Current Perspectives</p>

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges

Contact Info

Product

Resources

About