Introduction:
Evidence from pre-clinical and human studies suggests that higher serum progesterone concentrations may be protective against drug-induced QT interval lengthening.
Hypothesis:
Oral progesterone administration attenuates QT interval response to I
Kr
inhibition.
Methods:
In this prospective, double-blind, crossover study, 15 healthy female volunteers ages 21-40 years were randomized to receive progesterone 400 mg or matching placebo orally once daily for 7 days timed to the menses phase of the menstrual cycle (between-phase washout period = 52 days). On day 7, a low dose of ibutilide (0.003 mg/kg) was infused over 10 minutes, after which QT intervals were recorded and blood samples serially collected for 12 hours post-infusion. Prior to the treatment phases, subjects underwent ECG monitoring for 12 hours to calculate individualized heart rate-corrected QT intervals (QT
c
I).
Results:
There was no significant difference in maximum serum ibutilide concentrations between the progesterone and placebo phases. (1291±910 vs 1328±718 pg/mL, p=0.87). ECG data are reported from lead II; results are similar from leads V
1
and V
5
. There was no significant difference in pre-ibutilide QT
c
I between the progesterone and placebo phases (Table). Maximum ibutilide-associated QT
c
I, maximum % change in QT
c
I, and % change in area under the effect (QT
c
I) curve during the first hour following ibutilide administration (AUEC
0-1
, s-hr) were significantly lower during the progesterone phase. Progesterone-associated adverse effects were mild, predominantly fatigue/general malaise. Ibutilide-associated adverse effects during the progesterone and placebo phases included bradycardia [3/15 (20%) vs 2/15 (13%)], burning at infusion site [1/15 (7%) vs 1/15 (7%)], and transient (< 1 minute) Bazett’s-corrected QT
c
> 500 ms [0/15 vs 1/15 (7%).
Conclusions:
Oral progesterone administration attenuates QT
c
I lengthening associated with I
Kr
inhibition
Quantitative Assessment of the Physiological Parameters Influencing QT Interval Response to Medication: Application of Computational Intelligence Tools