Bai Xue, PhD

Institute for Clinical Pharmacodynamics

43 British American Blvd. | Latham, NY 12110

Telephone: 518-429-2600

E-mail: BXue@ICPD.com

A Method to Define a Single Optimal Sampling Strategy for Three Drugs (Delafloxacin, Linezolid, and Vancomycin) to Facilitate

PK-PD Analyses in a Blinded, Phase 2 Study

Bai Xue1,2, Ridhi Parasrampuria2, Christopher Rubino1,2, Eric Burak3, Alan Forrest1,2

1Institute for Clinical Pharmacodynamics (ICPD), Latham, NY, USA2SUNY Schools of Pharmacy and of Public Health, Buffalo, NY

3Rib-X Pharmaceuticals, Inc., New Haven, CT

Given that the PD driver for efficacy is AUC:MIC ratio for all three drugs, the primary objective of this analysis was to develop a single optimal sampling scheme (OSS) that

could be used to estimate overall exposure (AUC) with maximum accuracy and precision for patients with a range of PK values, regardless of treatment assignment.

INTRODUCTION

OBJECTIVE

METHODS

• The population had a wide range of creatinine clearance (ml/min/1.73m2) from 35 to

178 with median of 73.4; body weight (kg) ranged from 37 to 182 with median of 80.4.

• Although the results (Table 2) suggested that the schemes would be inadequate for

AUC48-60 for VAN (0.378 as best-case adj. r2), MLR was able to identify several schemes

predictive of AUC48-60 for DFX and LZD (0.819 and 0.983 as best-case adj. r2).

• MLR analysis also indicated that all the schemes would be expected to perform

similarly.

RESULTS

• Delafloxacin (DFX) is an investigational fluoroquinolone with activity against methicillin-resistant Staphylococcus aureus (MRSA) being developed for the treatment of acute

bacterial skin and skin-structure infections (ABSSSI).

• During the drug development process, a phase 2, double-blind study comparing the effects of DFX to that of linezolid (LZD) and vancomycin (VAN) for the treatment of

ABSSSI required an informative, blinded PK sampling strategy, adequate for all 3 drugs.

• The primary constraints on the possible sampling schemes were: 1) samples would be drawn on day 3 of treatment; 2) the dosing intervals were fixed at 12 hours so no

sampling times could exceed 12 hours; 3) the scheme had to be the same for each drug due to blinding issues.

[1] Rubino CM, Forrest A, Bhavnani SM, et al. Delafloxacin Population Pharmacokinetics and Covariate Exploration (A1-682). 50th Interscience Conference on Antimicrobial Agents and Chemotherapy, Boston, MA,

September 12-15, 2010.

[2] Meagher AK, Forrest A, Rayner CR, et al. Population pharmacokinetics of linezolid in patients treated in a compassionate-use program. Antimicrob Agents Chemother 2003;47:548-53.

REFERENCES

• A relatively simple MLR procedure was able to identify potential OSSs for unbiased and reasonably accurate prediction of overall exposure (AUC) of 3 different drugs,

delafloxacin, linezolid, and vancomycin, simultaneously.

• A bootstrap re-estimation procedure provided a more robust evaluation of the sampling schemes performance and allowed for incorporation of the Bayesian priors.

• The protocol-specified scheme was chosen as it had previously been deemed most feasible for the investigators and is expected to perform nearly as well as the more

complex schemes.

• The lack of accuracy and precision in population PK parameters is expected but of secondary concern given that the primary goal of PK sampling in this analysis is to

obtain prediction of AUC exposure to DFX in each patient. However, these results suggest that it might be prudent to pool the Phase 2 data with the Phase 1 DFX data in

order to obtain more reliable population PK parameter estimates.

CONCLUSIONS

Monte Carlo Simulation of Data• A Monte Carlo simulation of 1000 subjects was first performed to generate a plasma

PK dataset for all three drugs after multiple dosing (ADAPT5 Version 5.0.35,

Population Simulation with Output Error method). PK concentrations were simulated

in the sampling window from 48 to 60 hours at 0.1 hour intervals.

• Drug administration for all three drugs was simulated as 1 hour i.v. infusions given

every 12 hours for 5 doses.

• DFX dose: 300 mg

• LZD dose: 600 mg

• VAN dose: individualized to achieve a target steady state concentration of

17.5 mg/L using CLcr and BW (encoded in the model)

• Two-compartment (2-CMT) models with mixed linear and non-linear Michaelis-

Menten (MM) elimination were used for DFX [1] and LZD [2].

• For VAN, a 2-CMT model with linear renal and non-renal clearance pathways was

used [data on file, University at Buffalo].

• All the volume and clearance parameters in LZD and VAN models were

proportional to body weight (BW). The linear portion of LZD clearance and the renal

clearance of VAN were both proportional to creatinine clearance (CLcr).

• The population means and variances of PK parameter values are presented in

Table 1, assuming log-normal distribution.

Optimal Sampling Strategy Identification• Initial screening of potential schemes was conducted using stepwise multiple linear

regression (MLR) in SYSTAT 11. The dependent variable was area under the plasma

concentration-time curve from time 48 to 60 hours (AUC48-60) and the independent

variables were the plasma concentrations at each simulated time point.

• Based on adjusted r2 value, the most influential time points (maximum of 7) were first

selected by MLR algorithm for each drug and represented a theoretical best-case

scenario.

• After discarding duplicate time points and balancing regimens between different

drugs, OSSs for 5 (best5), 6 (best6) and 7 (best7) time points were selected.

• The preliminary scheme from the protocol and one empirical scheme were also

evaluated using MLR.

Bootstrap Re-estimation• The ability of selected OSS to provide unbiased and reasonably precise estimates of

AUC48-60 was then assessed using a bootstrap re-estimation procedure (SADAPT

Version 1.56).

o Subsets of reduced data for the full population (1000 subjects) were created for

each OSS by including only the observed sampling times identified by MLR.

o The population PK models were then re-fit to these reduced data to obtain

Bayesian PK parameter estimates for each subject.

o Individual predicted concentrations after re-estimation were used to calculate

“fitted” AUCs. These were compared to the “true” AUCs from the original full

simulated data.

o The OSSs from MLR were evaluated based on the accuracy (percent error, PE)

and precision (root mean square error, RMSE) of the fitted AUC48-60 as

compared to the true AUC48-60 from the simulation.

• To assess the capability of the selected OSS to characterize the accuracy and

precision of population PK parameter estimates for DFX, a second bootstrap re-

estimation was performed using Monte Carlo Parametric Expectation Maximization

(MC-PEM) in SADAPT (Version 1.56).

o Fifty (50) datasets with 80 patients in each were simulated for this purpose, using

the same PK parameter inputs as before (Table 1) with concentrations

simulated at the sparse times from the chosen OSS.

o Population PK parameter estimates for each dataset were obtained.

o The distributions of resultant PK parameters and their inter-individual variability

(IIV) were examined visually and compared to the original simulation inputs.

Figure 1. Scatter Plots of Percent Error vs AUC48-60 for Delafloxacin and Linezolid or vs Creatinine

Clearance for Vancomycin, for Each of the OSS Strategies Solid lines: Lowess smoothers through the data

Linezolid

Vancomycin

DelafloxacinFigure 2. Histograms of the Mean and the Inter-Individual Variability (%CV) of

Population PK Parameters Resulting from Validation Re-estimationVertical line: simulation inputs for population mean or inter-individual variability; percentiles relative to the distribution are shown

Inter-Subject Variability of PK ParametersMean of Population PK Parameters

Table1. Initial Simulation Input Population PK Parameters Values for

Different Drugs

a. Unit of those parameters is L

b. Unit of those parameters is L/hr

PK Parameter Description Parameter SymbolDFX LZD VAN

Mean SD Mean SD Mean SD

Central volume of distribution Vc (L/65kg) 26.6a 3.68a 39.6 8.99 14.3 6.0

Peripheral volume of distribution Vp (L/65kg) 33.1a 12.0a 26.3 10.99 23.0 8.0

Distribution clearance CLd (L/hr/65kg) 6.77b 6.0b 9.09 1.35 11.0 3.0

Maximum intrinsic clearance CLi (L/hr/65kg) 29.4b 15.0b 43.5 22.84 - -

Concentration generating 50% of maximum intrinsic clearance

Km (µg/ml) 0.506 0.2 1.46 0.99 - -

Linear clearance CLl (L/hr) 6.78 1.10 - - - -

Slope of creatinine clearance for linear renal clearance

SL - - 0.269 0.092 0.75 0.07

Linear non-renal clearance CLnr(ml/min/65kg) - - - - 7.0 3.0

Creatinine clearance CLcr(ml/min/1.73m2) - - 75.0 15.0 75.0 15.0

Body weight BW (kg) - - 82.1 19.9 82.1 19.9

Intercept of standard deviation of residual variability

SDint 0.1 - 0.1 - 1.0 -

Slope of standard deviation of residual variability

SDslp 0.15 - 0.1 - 0.1 -

Table 2: Summary of MLR Results

SchemeDFX LZD VAN

Time Points (hr)adj. r2 ratioa adj. r2 ratioa adj. r2 ratioa

Best 5 0.757 0.924 0.976 0.993 0.279 0.738 50, 51, 53, 56, 60

Best 6 0.759 0.927 0.976 0.993 0.288 0.762 48, 50, 51, 53, 56, 60

Best 7 0.77 0.940 0.977 0.994 0.346 0.915 48, 49, 50, 51, 53, 56, 60

Empirical 0.768 0.938 0.977 0.994 0.329 0.870 49, 50, 51, 53, 56, 60

Protocol 0.759 0.927 0.976 0.993 0.330 0.873 48, 49, 50, 51, 53, 60

Best-case 0.819 0.983 0.378aRatio of adjusted r2 for that sampling scheme to the adjusted r2 for the best possible regimen for that drug. This provides a means

of comparing schemes across drugs

Table 3: Summary of Bootstrap Results

Scheme

DFX LZD VAN

PEMean

PEMedian

RMSE r2 PEMean

PEMedian

RMSE r2 PEMean

PEMedian

RMSE r2

Best 5 -0.594 -2.14 14.0 0.74 1.61 0.197 12.8 0.98 2.12 2.03 6.94 0.96

Best 6 -1.85 -3.05 14.5 0.71 1.89 0.471 12.9 0.98 2.03 1.93 6.77 0.96

Best 7 -1.54 -3.22 13.4 0.74 1.34 -0.0151 11.9 0.98 -0.450 -0.522 5.27 0.97

Empirical -0.351 -1.88 13.1 0.76 1.20 -0.0438 11.9 0.98 -0.424 -0.543 5.60 0.97

Protocol -1.51 -3.02 13.6 0.74 1.41 0.315 12.2 0.98 -0.494 -0.821 5.55 0.97

Note: r2 is for the correlation between true AUC and fitted AUC

• The bootstrap re-estimation procedure (Table 3) confirmed the results of the MLR analysis in that

all the schemes would be expected to perform similarly and with relatively little bias and

acceptable precision, overall (Figure 1).

• The protocol-specified scheme was then chosen as it had previously been deemed most

feasible for the investigators and performs as well as the more complex schemes.

• The DFX schemes exhibited significant bias (approaching 20% mean PE) at each extreme of

the exposure spectrum (Figure 1, left panel), likely due to the lack of covariate effects in the DFX

population PK model.

• No significant bias was seen in the LZD and VAN schemes. Incorporation of the Bayesian prior

information into the evaluation of the VAN schemes greatly increased their predicted utility (i.e.,

compared to the MLR results).

• As shown in Figure 2, the protocol-specified

sampling scheme, applied to DFX, resulted in

somewhat biased estimates of all of the

population mean PK parameters and IIV

terms, with the exception of Vc.

• The overall ranges of mean parameter

estimates were relatively tight, indicating

that the precision was acceptable, whereas

the ranges of IIV were relatively wide.