Navigating Trials of Personalized Pain Treatments

We're Going to Need a Bigger Boat

Jennifer S. Gewandter; Michael P. McDermott; Omar Mbowe; Robert R. Edwards; Nathaniel P. Katz; Dennis C. Turk; Robert H. Dworkin

Disclosures

Pain. 2019;160(6):1235-1239. 

In This Article

Abstract and Introduction

Introduction

Personalized or precision treatments are targeted therapies that are particularly effective or have reduced harms for a subgroup of patients within a given condition.[3] This type of targeted therapy has considerable potential benefits, including reduced exposure of patients and study participants to treatments from which they are not likely to benefit and reduced financial resources spent on treatments that are ineffective or potentially harmful for certain individuals. Personalized treatment may be potentially beneficial for chronic pain conditions, in which the percentages of patients who meaningfully improve after initiation of efficacious pharmacologic therapies have been modest.[19] However, the ability of a particular patient characteristic to predict treatment response must be demonstrated in clinical trials before such predictors can be implemented in clinical practice.

Significant advances have occurred in developing a foundation for personalized pain treatment.[10,22] Phenotypic or genotypic characteristics (eg, predictive biomarkers[13]) that potentially identify groups of patients that are more likely to respond to a particular treatment have been suggested in multiple studies. For example, quantitative sensory testing can be used to group patients with similar sensory phenotypes within neuropathic pain conditions and has been shown to predict response to oxcarbazepine.[1,5,25] Patients with specific genetic mutations in receptors that are targeted by certain drugs (eg, sodium channels[2]) may respond more robustly to those drugs. Psychological characteristics associated with negative affect and pain catastrophizing have been associated with worse outcomes in uncontrolled, prospective studies of single treatments and in studies that qualitatively compare subgroup differences in treatment effect (active vs placebo) groups.[9,26,27] Other potential phenotypic predictors include pain qualities (eg, neuropathic vs nociceptive) and sleep quality.[10] The evidence derived from uncontrolled studies is very difficult to interpret because it is possible that the observation of better outcomes in a subgroup might reflect an enhanced placebo effect in that subgroup. For example, increased brain connectivity identified using functional magnetic resonance imaging has been shown to predict placebo response in recent studies.[17,18,21] Also, increased variability in baseline pain ratings was shown to predict placebo response in retrospective analyses of data from multiple randomized clinical trials (RCTs).[12,16]

Much of the current literature regarding potential predictors of treatment response, including the studies referenced above, is based on secondary or retrospective analyses of RCT data that demonstrate a treatment effect in only 1 subgroup.[7,10] However, the finding that a treatment effect (vs placebo) yields a significant result in 1 subgroup but not the other is not sufficient to demonstrate that the treatment is more efficacious in 1 subgroup than the other. Concluding that the treatment effect in 1 subgroup is larger than that in another subgroup because the effect is what some authors term "more significant" (ie, associated with a smaller P value) in 1 subgroup (eg, P= 0.001 vs P = 0.045) is also not appropriate.[15] In such a case, differences in statistical significance could be simply due to differences in subgroup sample sizes rather than the magnitude of the treatment effect. In addition, even if the difference in statistical significance is partially due to differences in the magnitudes of the treatment effect, it does not follow that the magnitudes of the treatment effects for the 2 subgroups are significantly different from each another.[15] To demonstrate rigorously the ability of a phenotype or genotype to predict response to active treatment, a RCT with a prespecified primary analysis that tests the significance of the difference between the effect sizes in the 2 subgroups must be conducted. This is accomplished by testing what is termed the treatment-by-phenotype/genotype interaction[11,24] (Figure 1). Demant et al.[5] recently reported such a trial in which the primary analysis was a test of significance of the treatment-by-"irritable nociceptor" phenotype interaction. The trial successfully demonstrated that an "irritable nociceptor" phenotype in patients with peripheral neuropathic pain detected using quantitative sensory testing predicted greater response to oxcarbazepine vs placebo compared with patients who did not have the irritable nociceptor phenotype. Specifically, the estimated standardized effect size (SES) (ie, (meanactive − meanplacebo)/pooled SD) was 0.73 in the irritable nociceptor group and 0.38 in the nonirritable nociceptor group, and the treatment-by-phenotype interaction was statistically significant (P = 0.047). However, a second trial with a similar design and primary analysis that compared the effects of lidocaine on neuropathic pain between patients with and without the "irritable nociceptor" phenotype did not successfully demonstrate a treatment-by-phenotype interaction.[4] The remainder of this article will discuss sample size determination for prospective RCTs designed specifically to evaluate treatment-by-phenotype/genotype interactions and the interpretation of such trials. These methods apply to all RCTs, including those that test pharmacologic, psychological, physical, surgical, and device interventions. Of course, those with the most rigorous blinding possible for the particular intervention will produce the highest quality data.

Figure 1.

Illustrative treatment-by-phenotype/genotype interaction trial schema.

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