The online version of this article (doi:10.1007/s13300-014-0090-y) contains supplementary material, which is available to authorized users.
This study aimed to determine if data mining methodologies could identify reproducible predictors of dapagliflozin-specific treatment response in the phase 3 clinical program dataset.
Baseline and early treatment response variables were selected and data mining used to identify/rank all variables associated with reduction in glycated hemoglobin (HbA1c) at week 26. Generalized linear modeling was then employed using an independent dataset to identify which (if any) variables were predictive of dapagliflozin-specific treatment response as compared with treatment response in the study’s control arm. The most parsimonious (i.e., simplest) model was validated by meta-analysis of nine other trials. This staged approach was used to minimize risk of type I errors.
From the large dataset, 22 variables were selected for model generation as potentially predictive for dapagliflozin-specific reduction in HbA1c. Although baseline HbA1c was the variable most strongly associated with reduction in HbA1c at study end (i.e., the best prognostic variable), baseline fasting plasma glucose (FPG) was the only predictive dapagliflozin-specific variable in the model. Placebo-adjusted treatment effect of dapagliflozin plus metformin vs. metformin alone for change in HbA1c from baseline was −0.65% at the average baseline FPG of 192.3 mg/dL (10.7 mmol/L). This response changed by −0.32% for every SD [57.2 mg/dL (3.2 mmol/L)] increase in baseline FPG. Effect of baseline FPG was confirmed in the meta-analysis of nine studies, but the magnitude was smaller. No other variable was independently predictive of a dapagliflozin-specific reduction in HbA1c.
This methodology successfully identified a reproducible baseline predictor of differential response to dapagliflozin. Although baseline FPG was shown to be a predictor, the effect size was not of sufficient magnitude to suggest clinical usefulness in identifying patients who would uniquely benefit from dapagliflozin treatment. The findings do support potential benefit for dapagliflozin treatment that is consistent with current recommended use.
Nauck MA, Del PS, Meier JJ, et al. Dapagliflozin versus glipizide as add-on therapy in patients with type 2 diabetes who have inadequate glycemic control with metformin: a randomized, 52-week, double-blind, active-controlled noninferiority trial. Diabetes Care. 2011;34:2015–22. PubMedCentralPubMedCrossRef
Cefalu WT, Leiter LA, Debruin TW, Gause-Nilsson I, Sugg JE, Parikh SJ. Dapagliflozin treatment for type 2 diabetes mellitus patients with comorbid cardiovascular disease and hypertension. Diabetes. 2012;61(suppl 1):A271. (Abstract 1056-P).
Jabbour S, Hardy E, Sugg JE, Parikh SJ. Dapagliflozin as add-on therapy to sitagliptin with or without metformin: a randomized, double-blind, placebo-controlled study. Diabetes. 2012;61(suppl 1):A275–6. (Abstract 1071-P).
Leiter LA, Cefalu WT, Debruin TW, Gause-Nilsson I, Sugg JE, Parikh SJ. Efficacy and safety of dapagliflozin for type 2 diabetes mellitus patients with a history of cardiovascular disease. Diabetes. 2012;61(suppl 1):A287. (Abstract 1114-P).
Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: Saitta L, editor. Machine Learning: Proceedings of the Thirteenth International Conference (ICML ‘96). Burlington: Morgan Kaufmann Publishers; 1996. p. 156–8.
Friedman JH. Greedy functions approximation: a gradient boosting machine. Ann Stat. 2001;29:1189–232. CrossRef
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B Stat Methodol. 2005;67:301–20. CrossRef
Rossi PE, Alenby GM, McCulloch R. Bayesian statistics and marketing. Chichester: Wiley; 2005. CrossRef
Bristol-Myers Squibb, AstraZeneca. Forxiga Product Information. Australian Government Department of Health and Ageing-Therapeutic Goods Administration website. http://www.tga.gov.au/pdf/auspar/auspar-dapagliflozin-propanediol-monohydrate-130114-pi.pdf. Accessed July 8, 2013.
- Patient Characteristics are not Associated with Clinically Important Differential Response to Dapagliflozin: a Staged Analysis of Phase 3 Data
Angelo Del Parigi
- Springer Healthcare
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