CLINICAL pHARMACOLOGY & THERAPEU I'ICS P 2 4 American Society for Clinical Pharmacology and Therapeutics IVEBRUARY2003

PI-82

COMPARISON OF CENSORED REGRESSION (CR) VS STANDARD REGRESSION (SR) ANALYSES FOR MODELING RELATIONSHIPS BETWEEN MINIMUM INHIBITORY CON- CENTRATIONS (MIC) AND PATIENT- AND INSTITUTION- SPECIFIC VARIABLES. J. P. Hammel, MS, S. M. Bhavnani, PharmD, P. G. Ambrose, PharmD, A. Forrest, PharmD, R. N. Jones, MD, M. R. Piedmonte, MA, Cognigen Corporation, SUNY at Buf- falo, The JONES Group/JMI Laboratories, Buffalo, NY.

Background: A challenge in the treatment of resistant bacteria has been the difficulty in identifying patients likely to be infected with such pathogens. Novel methods may be applied to surveillance data to determine patient- and institution-specific factors predictive of increased MIC. The censored nature of some MIC values (e.g. MIC~<0.5 or MIC>8) is a difficulty for SR analyses. Simulations were performed to compare CR versus SR in which MICs of the form MIC-<2 L or MIC>2 R (left- or right-censored) were replaced with specific values or excluded.

Methods: Using a model relating MIC of piperacitlin-tazobactam for Enterobacter species to categories of patient age and hospital bed count, 200 simulations of 500 isolates were performed. Various MIC censoring patterns were imposed using 26 (L, R) pairs. Data were fit with CR, and with SR using 3 procedures: (1) censored MIC replaced by 2 L or 2 R, (2) censored MIC excluded, and (3) censored MIC replaced by 2 L4 or 2 R+I.

Results: Censoring for the 26 pairs ranged from 7-86%. CR showed no estimation bias across pairs, whereas procedures (1), (2), and (3) tended toward greater bias as censoring increased. For all model parameters, the bias in (1) and (2) was over 50% of the parameter value for at least 10 pairs. Procedure (3) yielded smaller bias than (1) and (2), but had larger standard errors than CR.

Conclusion: When modeling censored outcomes such as MIC, CR is preferable to SR analyses to avoid biased parameter estimates.

PI-83

NOVEL MATHEMATICAL MODELS FOR BACTERIAL GROWTH, DEATH & PHARMACODYNAMICS. B .M . Booker= P t ~ A. Forrest, PharmD, P. F. Smith, PharmD, University at Buffalo, Buffalo, NY.

Time-kill curve (KC) studies were performed with vancomycin (V) 0-20x MIC & rifampin (R) 0-50x MIC vs. 2 strains of Staph (MRSA, MSSA). All KC data, for each drag-isolate pair, were fit simultaneously (ADAPT); model discrimination was by AIC. Two models assuming capacity-limited bacterial growth & drug effect as a Hi/l-type function either inhibiting replication (REP) or enhancing 1 st order death were applied to the KC data. The total initial inoculum (TII) was modeled as a mixture of 1-3 subpopulations (POP); each differing in % of TII & drug sensitivity (SENS). V was best fit as drug effect inhibiting REP. In MRSA, 2 POP were needed, with POP l& 2 ~99.9 & 0.1% of TII, with SENS ~0.7 & 1.8 xMIC, In MSSA, 3 POP were need with POP 1, 2 & 3 ~6, 80 & 14% of TII & SENS ~0.3, 1 & 6 xMIC. R was best fit in both MRSA & MSSA as drug enhancing death with 2 POP. In MRSA, POP1 & 2 ~99 & 1% of TII, with SENS ~ 6 & >50 xMIC. In MSSA, POP1 & 2 ~99.9 & 0.1% of TII, with SENS ~ 6 & 36 xMICs. The fit of both models to the KC data was excellent, median (range) r 2 0.985 (0.81-1). These models with capacity-limited growth & drug effect on either growth or death of bacterial POPs are biologically realistic, can capture the full spectrum shapes seen in KC, may lend insight into bug-drug inter- actions & be applied to describe/predict results in larger PK/PD studies.

PI-84 COMPARISON OF CONFIDENCE INTERVALS DERIVED

USING ASYMPTOTIC STANDARD ERRORS AND BOOT- STRAPPING. E~ V. Mishina, P ~ B. P. Booth, PhD, J. V. Gob- buru, PhD, US FDA, Rockvitte, MD.

Purpose. The objective of the analysis was to compare the 90% confidence intervals (CI) determined using the asymptotic standard error (SE) estimated by NONMEM to that calculated using nonpara- metric bootstrap technique. Bootstrap method is a gold standard method to estimate CI.

Methods. Population pharmacokinetic (PK) modeling was per- formed lbr the three drugs (A, B, and C) using NONMEM (ver 5.0, level 1.1). The 90% CI was determined using the asymptotic SE (NMCI) as well as empirically using 1000 bootstrap data sets (BSCI). Relative errors for the lower (REL) and upper (REu) boundaries of the CI for each parameter were calculated.

Results. BSCIs were generally asymmetric while always symmet- ric for NMCI. Figure 1. Left panel: RE u, right panel: RE b. Circles are for fixed effects, squares are for between-subject variability, and triangles are for within-subject variability.

~00 ~0o o 30 ,..a 30

$ -.~ -ao o ,,% o g~. -ao

.~-~o $ -~o

5 10 15 20 25 5 10 15 20 25 P A R A M E T E R P A R A M E T E R

RE u values ranged from - 4 3 % to 71%, and RE L values ranged from - 8 2 % to 89%

Conclusion. Both REL and RE u indicate that NMCI is biased compared to the BSCI. By and large, NMCI are narrower than BSCI. The construction of the CI for the parameters based on bootstrap method is recommended,

PI-85 UNDERSTANDING SCHEDULE DEPENDENCE OF AN IR-

REVERSIBLE AROMATASE INHIBITOR VIA SIMULATIONS. J. Z. Duan, PhD, J. V. Gobburu, PhD, FDA, Rockville, MD.

Purpose The objective of the study was to employ a pharmaco- kinetic (PK) and pharmacodynamic (PD) model of an irreversible aromatase (Ar) inhibitor (IArI) for exploring the impact of various dosing strategies. Methods IArI concentrations (Cp) could be de- scribed using a 2-compartment model. A model for the kinetics of endogenous Ar enzyme and its role in the production of eslradiol (E2) was described. The effect of Cp on Ar was modeled using a bimo- lecular interaction PD model. The IArI PKPD model was used to conduct deterministic simulations of Cp and E2. Dosing strategies varying in dose and schedule were tested without varying the total number of doses (=9). The time-course of effect, time to maximal effect, area under the effect curve (AUEC), duration of effect and efficiency (effect per unit exposure) were determined. Results AUEC increased monotonically with doses, above a certain threshold, Dos- ing q96h offered higher efficiency over others. Following table shows the AUEC values at different doses and schedules for a drug with a

Schedule [

\ \ N Dose q24h q48h q72h q96h

3.5 mg 8.85 10.17 10.31 9.87 mg 86.72 91.50 92.41 91.76 mg 456 515 532 534

10 mg 673 802 846 856 )-5 mg 983 1282 1403 1440

Conclusions Taking schedule dependence into consideration leads to more efficient dosing regimens. Replenishing Ar is the rate limiting step. Simulations may serve to optimize dosing of aromatase inhib- itors with chemoprevention potential.

tl/2 Of 16 h.