Transcript Slide 1

Population Pharmacokinetics and Exposure-Response Modeling and Simulation to Support Quinolone Phase IIa Dose Selection
P.K. Wickremasingha1, S. Rohatagi1, C. Falcoz2, S. Kshirsagar2, T. Khariton2, ,H. Kastrissios2, T.J. Carrothers2, J. Kuwabara-Wagg2
1 – Daiichi Sankyo Pharma Development, Edison, NJ; 2 – Pharsight Corporation, Mountain View, CA
ABSTRACT
Objective: Establish a quantitative framework to support Phase IIa dose selection
for a novel quinolone, Q. Methods: Preclinical and Phase I Q data were used to
develop: (i) a population PK model; (ii) an exposure-efficacy response model based
on preclinical data scaled to humans to predict the cumulative fraction of response
(CFR); (iii) exposure-tolerability response models for QTc prolongation and liver
function test (LFT) and serum creatinine elevations. Models were used to simulate
likely efficacy and tolerability outcomes for dosing regimens of interest. Results:
The final Q PK model was a three-compartment model consisting of a single central
compartment and two peripheral compartments. QTc prolongation was modeled as
an additive combination of baseline, placebo, active treatment and residual
variability (ε) effects: QTcF = Base + PCB + Effq + ε. LFT elevation was modeled
using logistic regression equations: Logit(Pr(Day 13 LFT Elevation => 1)) = K*EM +
Int. A physiological, non-linear time dependent model of creatinine dynamics was
used to model serum creatinine elevations. For Gram-positive infections, Q 400 mg
QD IV was identified as the target dose. The predicted CFR was 90.4%, 88.3%, and
85.2% for a bacteriostatic, 1 log and 2 log kill, respectively. This dose was
predicted to have an acceptable safety profile. Conclusions: Given the safety and
efficacy profile, an optimal dose range for IV Q administered once a day for Gram
positive infections was identified.
BACKGROUND
Q is a new quinolone antibacterial agent
Marked activity against multidrug-resistant staphylococci, streptococci, and
enterococci including strains resistant to other marketed quinolones1
These pathogens are frequently associated with respiratory diseases and skin
infections
Organisms show a low mutation rate to Q1
OBJECTIVES
MODELING & SIMULATION PLAN
Model Development
Establish a quantitative framework to support Phase IIa dose selection for Q
EFFICACY MODEL
Efficacy methodology was based on an established approach outlined in Ref 2-5.
Simulation
• Likely Q efficacious doses for IV dosing were predicted by simulation based on 4 data sets
Specific objectives:
Develop a population PK model for Q administered IV
Provide dose recommendation for future Phase II/III studies based on Q exposureresponse analyses for
Exposure-efficacy response
Exposure-tolerability responses
Exposure-QTc prolongation response
Exposure-LFT elevation response
Exposure-serum creatinine elevation response
To support the above analyses, develop respective exposure-response models and
utilize these to simulate predicted responses for Q dosing regimens of interest
Preclinical data
Simulate tolerability & efficacy
outcomes as function of Q dosing
Phase I data
Covariates
Tolerability
Models
1.
2.
3.
LFT elevation probability
QTc interval prolongation
Serum creatinine elevation
IV infusion
Systemic
PK
Q
Covariates
PO
Phase I PK
data
Efficacy
Model
Efficacy in patients for infections
caused by bacterial species of
interest
Preclinical data
• Q plasma protein binding characteristics in mouse and protein binding in man
• The distribution of the PD target, Q AUC/MIC, (the pharmacodynamic index (PDI)) which was identified as the best
predictor of efficacy in the mouse model of infection
• 24-hour steady-state Q AUC distribution for the human population of interest
• Clinical isolate MIC distributions
• Three pharmacodynamic index effects were simulated: stasis, 1 and 2 Log CFU killing
• Q exposures in patients are expected to be higher than in healthy volunteers. However, patient Q PK
was assumed to be equivalent to that of healthy volunteers. As a result, patient Q AUC estimates were
conservative.
• The probability of target effect (PTA) was calculated for each PDI effect, dose and fixed MICs.
• The cumulative fraction of response (CFR) for each effect, dose and MIC distribution of interest was
based on the PTA calculated for each MIC and the proportion of that MIC in that distribution
• Minimum doses achieving a CFR ≥ 80% were considered representative of microbiological and clinical
cure and thus likely efficacious doses
Clinical Isolate
MIC data
PHARMACOKINETIC MODEL
Models developed from IV & oral phase I study data, respectively
Final models were used to simulate subject level Q exposure measures for dosing
regimens of interest
The final Q PK model was a three compartment
model consisting of a single central compartment
and two peripheral compartments with zero-order
absorption and first order elimination to and from
the central compartment, respectively. Clearance
was affected by sex and age. Peripheral volume V2
was affected by weight and age. Peripheral volume
V3 was affected by weight, age and gender.
Overview of Efficacy
M&S Methodology
Effect is saturable
and reversible
upon cessation of
dosing
PK data from 5 distinct phase I studies in which Q was administered via IV infusion,
QD
2 of these studies were single dose studies spanning the following doses: 10, 25,
50, 100, 200, 400, 600 & 800 mg
The remaining studies were multiple dose studies of 14, 4 and 15 days duration
spanning the following respective doses: 400 and 800 mg; 800 mg; and 800,
1200 and 1600 mg
PK data from 1 phase I study in which Q was administered orally
Multiple oral dosing for 12 days at doses of 400, 800 or 1200 mg
Exposure-efficacy response analysis was based on
Protein-binding in man and mouse and PDI values derived from a mouse thigh
infection model
Pooled demographic data from 5 phase I clinical studies (IV dosing)
Clinical isolate MICs from a Q susceptibility survey
Exposure-QTc prolongation response analysis was based on
Serum creatinine data from 4 of the 5 phase I IV dosing studies
Exposure-LFT elevation response analysis was based on
Data from the phase I oral dosing study
Distribution varies by
indication and
geographic region
AUC Distribution
MIC Distribution
Dose/CL where CL is
from population pk
model
Gram positive
(n=2017)
• Q is predicted to have a narrow therapeutic window with an estimated minimum
efficacious dose of 400 mg IV QD against Gram-positive organisms
• Increasing Q dose provides relatively small efficacy benefits BUT at the expense of
relatively large increases in QTc prolongation and the likelihood of LFT elevation
• Serum creatinine elevations are saturated at estimated therapeutic doses and above
Gram negative
(n=564)
Anaerobes
(n=426)
CONCLUSIONS
For gram-positive infections, 400 mg QD IV is predicted to be the lowest efficacious dose
Typical Plots for
Assessing Simulated
Dose-Efficacy
Relationships
Median change of serum creatinine from baseline as a function
of IV QD dose. Note that the serum creatinine elevation effect
appears to saturate at around 200 mg.
EXPOSURE-LFT ELEVATION MODEL
• Predicted maximum mean QTc prolongation at 400 mg is 9.8 msec with a 90%
confidence interval of 7.1 to 13 msec
• Modest risk of mild to moderate ALT/AST elevations
• Serum creatinine elevations predicted to be saturable and near maximal and not to
change with dose increases above 400 mg
However, Q therapeutic window is likely to be narrow such that increasing Q dose from
400 mg to 800 mg is predicted to:
EXPOSURE-QTc MODEL
Focused on modeling the relationship between
individually predicted Q plasma concentrations
and, QTcF, Fridericia corrected QT interval
measurements.
Based on exposure-response analysis, the 400 mg dose is predicted to have an acceptable
tolerability profile
• Provide minimal improvements in efficacy against Gram-positive causative organisms
• Almost double mean QTc prolongation resulting in borderline to unacceptable QTc
outcomes
• Approximately double the likelihood of ALT/AST elevation
REFERENCES
• Phase I oral dosing data
1. Daiichi Medical Research, 2006. Investigator’s Brochure, Version 3.0. March 24th, 2006
• Mild ALT elevations observed in the 400 and 800
mg treatment groups
2. Drusano GL, Preston, SL, Hardalo C, Hare, R, Banfield C, Andes D, Vesga O and Craig WA (2001). Use of
preclinical data for selection of a Phase II/III dose for evernimicin and identification of a preclinical MIC
breakpoint. Antimicrob. Agents Chemother. 45: 13-22.
• Mild and moderate ALT elevations observed in
the 1200 mg group
• Logistic regression used to model the likelihood
of ALT elevation as a function of Q exposure
measures
3. Drusano GL, Preston SL, Fowler C, Corrado M, Weisinger B & Kahn, J. (2004). Relationship between
Fluoroquinolone Area under the Curve: Minimum Inhibitory Concentration Ratio and the Probability of
Eradication of the Infecting Pathogen, in Patients with Nosocomial Pneumonia. The Journal of
Infectious Diseases, 189:1590–7
• ALT elevations found to be well described by
logistic regression functions of dose, steadystate Cmax, AUC or Cavg
4. Mouton JW, Dudley MN, Cars O, Derendorf H, and Drusano GL (2005). Standardization of
pharmacokinetic/pharmacodynamic (PK/PD) terminology for anti-infective drugs: an update. J.
Antimicrob. Chemother. 55:601-607.
ECG data derived from 5 phase I studies (IV dosing)
Exposure-serum creatinine elevation response analysis was based on
PDI Distribution
Gram neg. (4 strains,
MIC 0.06-0.25)
Simulated serum creatinine vs time for QD IV dosing regimens over 14 days. Simulations
based on a physiological model of serum creatinine dynamics6 in which Q mediates a
competitive inhibition of creatinine renal tubular secretion7.
AST/ALT levels from oral dosing study graded by the NCI Common Terminology
Criteria and day 13 levels encoded as 0 (no elevation) or 1 (grade 1 or greater)
Logistic regression then used to model likelihood of day 13 elevation as a function
of Q exposure measures
Final model used to simulate AST/ALT elevation likelihood for dosing regimens
In-vitro testing of clinical
isolates
Distribution varies by
subject population and
treatment regimen
VRE (7 strains, MIC
0.12-2)
To date, 6 phase I clinical studies of Q have been completed in healthy
volunteers1
PK models were developed for IV and oral dosing:
DX619 popPK model
Distribution of AUC/MIC
varies by effect,
Static, 1 log Kill, etc..
MRSA (6 strains, MIC
0.03-0.25)
Pooled phase I IV dosing data was used to calculate and plot the median % change from baseline
of serum creatinine as a function of dose and an Emax function of dose fitted to this data
An established model of creatinine dynamics was implemented and linked to Q plasma levels
under the assumption of Q mediated competitive inhibition of creatinine renal tubular
secretion
Resulting PK/PD model was then used to simulate the time course of serum creatinine elevation
as a function of Q dosing regimen
Median changes from placebo of % change of serum creatinine
for 400 and 800 mg IV QD for 14 days. Note that the two
doses elicit comparable elevations of serum creatinine.
Based on these observation, Q may be an effective antibacterial agent in the
treatment of gram-positive infections1
Mouse neutropenic thigh
model of infection
EXPOSURE-SERUM CREATININE MODEL
Pharmacokinetic properties of Q facilitate human dosing1
PHASE I DATA OVERVIEW
OVERVIEW OF SIMULATED CLINICAL PROFILE
Note: Solid lines denote lines of best fit, the shaded
area the 90% confidence interval, symbols the
observed mean incidence of ALT elevation and
the error bars the standard error of the mean.
All model parameter estimates and associated
standard errors with corresponding t-values are
listed in the table inserts.
5. Schentag JJ, Meagher AK, and Forrest A (2003). Fluoroquinolone AUIC break points and the link to
bacterial killing rates. Part 2: human trials. Ann. Pharmacother. 37:1478-1488.
6. Kirschner D, Buchman T (2000). Mathematical modeling in surgical research. In: Surgical Research,
Editors: Souba WW and Wilmore, D. Academic Press, San Diego, pp1105-1115.
7. Urakami Y, Kimura N, Okuda M, and Inui K-I (2004). Creatinine Transport by Basolateral Organic
Transporter hOCT2 in the Human Kidney. Pharmaceutical Research, Volume 21, Number 6, June 2004,
pp976-981(6).