Abstract
To the Editor:
We thank Dr Srinivas for the thought‐provoking comments and the opportunity to clarify a number of aspects of our work.
With reference to Figure 2 from RUN8, the residual error model has been weighted by the inverse square root of the number of patients within each study so as to prioritize the impact of studies with higher subject numbers. For example, although the studies that included multiple sclerosis individuals were repeat dose studies (concentrations were therefore higher and the inherent variability was represented across time), given the larger subject numbers, the curve fits were equal or better to those seen with the shorter single‐dose studies. Despite this, the effect of poly‐pharmacy and varying disease states on teriflunomide concentrations should be explored further, particularly for known inducers or inhibitors of ABCG2, CYP1A2, or CYP2C19.
We thank Dr Srinivas for the thought‐provoking comments and the opportunity to clarify a number of aspects of our work.
With reference to Figure 2 from RUN8, the residual error model has been weighted by the inverse square root of the number of patients within each study so as to prioritize the impact of studies with higher subject numbers. For example, although the studies that included multiple sclerosis individuals were repeat dose studies (concentrations were therefore higher and the inherent variability was represented across time), given the larger subject numbers, the curve fits were equal or better to those seen with the shorter single‐dose studies. Despite this, the effect of poly‐pharmacy and varying disease states on teriflunomide concentrations should be explored further, particularly for known inducers or inhibitors of ABCG2, CYP1A2, or CYP2C19.
Original language | English |
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Pages (from-to) | 564 |
Number of pages | 1 |
Journal | CPT: Pharmacometrics and Systems Pharmacology |
Volume | 4 |
Issue number | 10 |
Early online date | 4 Sept 2015 |
DOIs | |
Publication status | Published - Oct 2015 |