Poster Session I (PI 1-106)Displayed 8:00 am – 3:00 ... - Nature

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subjects after coadministration of NER+RIF. The most common
on-treatment AEs were diarrhea (n=8 [33%], oropharyngeal pain
(6 [25%]), and seborrhoeic dermatitis (2 [8%]). There were no AErelated
discontinuations. As a result of rifampin’s induction effect, during
coadministration, NER C max and AUC substantially decreased to
24.1% and 12.7%, respectively, of values observed with NER alone;
correspondingly, NER metabolite C max was significantly increased by
about 2.3-fold for M3, 1.5-fold for M6, and 1.4-fold for M7 compared
with values after administration of NER alone.
CONCLUSION: The results indicate that NER, a substrate
of CYP3A4, is susceptible to interaction with potent CYP3A4
inducers.
PI-92
USE OF A PHARMACOKINETIC-PHARMACODYNAMIC
(PKPD) MODEL FRAMEWORK IN THE DESIGN OF A DOSING
REGIMEN FOCUSED ON RESPONSE. A. Grover, L. Z. Benet; University
of California, San Francisco, San Francisco, CA. A. Grover:
None. L.Z. Benet: None.
BACKGROUND: Grover and Benet (J Pharmacokinet Pharmacodyn
(2011)) argue that pharmacokinetic dosing interval predictors
will be more relevant for direct PKPD model drugs than they will be
for indirect PKPD model drugs. As the direct-indirect distinction is
actually a continuum, we sought to determine if a pattern in PKPD
model parameters (k e0 and k out for the indirect link (IDL) and indirect
response (IDR) models, respectively) can be discerned to be used as
additional guidance in determining a dosing regimen.
METHODS: The relevance of pharmacokinetic dosing interval predictors
was determined as the ratio of the longest recommended dosing
interval to the time plasma concentrations are over an efficacy EC 50 .
Using a number of case studies from the literature, we simulated the
time plasma concentrations are above the EC 50 during multiple dosing
steady state at the second to lowest approved dose and at the longest
recommended dosing interval.
RESULTS: The relationship between the ratio of the dosing interval
to time above EC 50 and k e0 or k out is approximately log-linear (r 2 =
0.750, p < 0.001, n = 15), and a k e0 or k out value greater than 2 hr -1 (e.g.
terbutaline, atropine, ranitidine) provides a dosing interval predicted
by the pharmacokinetics. A finding that k e0 or k out is below 0.5 hr -1
(e.g. terazosin, dexamethasone) indicates that pharmacokinetic dosing
interval predictors will not be clinically relevant. By combining efficacy
and toxicity PKPD models for a single drug (here, levodopa), we
show that this regression can be used to design a dosing regimen such
that efficacy falls along the regression and toxicities appear underdosed.
CONCLUSION: As a basic PKPD model should be derived early
in clinical trials, the log-linear regression can be used as a framework
within which to understand the relevance of pharmacokinetic dosing
interval predictors and as a guide for determining dosing regimens that
are considerate of efficacious and toxic response for both IDL and IDR
model drugs.
PI-93
ORAL MIDAZOLAM (MDZ) PARTIAL AREA-UNDER CURVE
(AUC) DOES NOT RELIABLY PREDICT CYTOCHROME P450
(CYP) 3A BASELINE ACTIVITY IN HEALTHY SUBJECTS. W.
Tai, 1 S. L. Gong, 1 S. M. Tsunoda, 1 H. E. Greenberg, 2 J. C. Gorski, 3
S. R. Penzak, 4 S. A. Stoch, 5 J. D. Ma 1 ; 1 UCSD, Skaggs School of
Pharmacy & Pharmaceutical Sciences, La Jolla, CA, 2 Department of
Pharmacology and Experimental Therapeutics, Thomas Jefferson University,
Philadelphia, PA, 3 Mylan Pharmaceuticals, Morgantown, WV,
4 Pharmacy Department, National Institutes of Health, Bethesda, MD,
5 Merck, Rahway, NJ. W. Tai: None. S.L. Gong: None. S.M. Tsunoda:
None. H.E. Greenberg: None. J.C. Gorski: None. S.R. Penzak:
None. S.A. Stoch: None. J.D. Ma: None.
BACKGROUND: MDZ clearance (CL) and AUC INF are used as
biomarkers to predict CYP3A activity. A recent study recommended
a MDZ partial AUC from 2 to 4 hr to predict MDZ metabolic CL. We
evaluated a partial AUC approach to predict MDZ CL and thus CYP3A
activity during baseline conditions.
METHODS: MDZ plasma concentrations from 116 healthy adults
were obtained from seven published studies. Observed CL, AUC, and
partial AUCs over several intervals were determined via noncompartmental
analysis. Subject data were randomly divided into a training
set (n=30) and a validation set (n=86). Linear regression analyses of
log-transformed partial AUCs were performed using the training set.
MDZ predicted CL was determined from the validation set. Backtransformed
predicted CL was compared to observed CL. Bias and
precision were evaluated by percent mean precision error (%MPE),
percent mean absolute error (%MAE), and percent root mean square
error (%RMSE).
RESULTS:
Model
equation
AUC 0-2 AUC 0-6 AUC 2-4
-0.33*
[log(AUC 0-2 )] +5.45
-0.43*
[log(AUC 0-6 )] +5.68
-0.44*
[log(AUC 2-4 )] +5.45
r2 0.26 0.40 0.47
Mean
predicted
CL
113.04 L/hr 114.29 L/hr 113.27 L/hr
Mean
observed
CL
115.34 L/hr 115.34 L/hr 115.34 L/hr
%MPE
(±5%)
-1.7 -0.7 -1.7
%MAE
(