Abstract
Prediction of treatment responses is essential to move forward translational science. Our question was to identify patient-based variables that predicted responses to treatments. We conducted secondary analyses on pooled data from two randomized phase III clinical trials (NCT02697773 and NCT02709486) conducted in participants with moderate to severe osteoarthritis randomized to subcutaneous placebo (n = 514) or tanezumab 2.5 mg (n = 514). We used gradient boosted regression trees to identify variables that predicted Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) Pain subscale scores at Week 16 and marginal plots to determine the directional relationship between each variable category and responses to placebo or tanezumab within the models. We also used Virtual Twins models to identify potential subgroups of response to the active treatment vs. placebo. We found that responses to placebo were predicted by baseline WOMAC Physical Function, baseline WOMAC Pain, the radiographic classification of the index joint, and the standard deviation of diary pain scores at baseline. In contrast, baseline WOMAC Pain along with failure of prior medications, duration of disease, and standard deviation of diary pain scores at baseline were predictive of tanezumab responses as expressed by the WOMAC Pain scores at Week 16. Those who responded to tanezumab vs. placebo were identified based on the radiographic classification of the index joint and either age or smoking status. These secondary-data analyses identified distinct and common patient-based variables to predict response to placebo or tanezumab. These findings will inform the design of future clinical trials, helping to move forward clinical pharmacology and translational science.