Transcript Clinical PK/PD Short Course - Population Pharmacokinetics

POPULATION
PHARMACOKINETICS
RAYMOND MILLER, D.Sc.
Pfizer Global Research and Development
Population Pharmacokinetics
Definition
Advantages/Disadvantages
Objectives of Population Analyses
Impact in Drug Development
Definition
Population pharmacokinetics describe
The typical relationships between physiology
(both normal and disease altered) and
pharmacokinetics,
The interindividual variability in these
relationships, and
Their residual intraindividual variability.
Sheiner-LB
Drug-Metab-Rev. 1984; 15(1-2): 153-71
Definition
E.g.: A simple Pk model Ri = Cl·Cpss
Cpss = Rate in / Rate out  
Rate in = infusion rate
Rate out = drug clearance
Drug Conc
 = measurement error, intra-individual error
Time
Definition
E.g.: A simple Pk model
Cpss = Rate in / Rate out  
Rate in = infusion rate
Rate out = drug clearance
  N(0,)
Drug Conc
 = measurement error, intra-individual error
Time
Definition
Cpss = Infusion rate / Cl
Drug Conc
CL = Infusion rate / Cpss
Time
Definition
Cl = metabolic clearance + renal clearance
Drug Conc
Drug Clearance
Cl = 1 + 2• CCr  
Time
Creatinine Clearance
Definition
Cl = metabolic clearance + renal clearance
  N(0,)
Drug Clearance
Cl = 1 + 2• CCr  
Creatinine Clearance
Graphical illustration of the statistical model used in NONMEM for the special
case of a one compartment model with first order absorption. (Vozeh et al. Eur J
Clin Pharmacol 1982;23:445-451)
Definition
Mean, expected value, or
some other point estimate:
  1 2 3 4
Variability among subjects
around that mean:
11  21  31
   23  22  32
13  32  33
Residual (unexplained) variability
 11  21  31
   12  22  32
 13  23  33
and/or model misspecification:
Responses on data input requirements from a questionnaire survey of producers of software for population pharmacokinetic-pharmacodynamic analysis
Program
Nature of input, Constraints
Dosing histories specified in a flexible manner
How is covariate information specified?
BUGS
ASCII, S-Plus data set
User has to supply code
Variable in data set
MIXNLIN
SAS data set
None, but must conform to
covariates SAS conventions
User has to supply code
Classified as inter- and intra-individual
NLINMIX
SAS data set
User has to supply code
Variables in the SAS data set
NLME
ASCII, spreadsheets and data bases
User has to supply code
Variables in the data set
NLMIX
ASCII, user responsible for writing input routine User has to supply code
As for input
NONMEM
ASCII
None (some dimensions are
initially set but these may be
changed by the user)
Yes (specified by the routine PREDPP)
Variables in the data set
NPEM
ASCII via USC*PACK program
99 days of time, 99 doses,
99 values of dependent
variables (maximum of 6)
Yes
Either linked to a pharmacokinetic
or numerical value. Interpolation
between covariate values is possible
NPML
ASCII
User has to supply code
Variables in the data set
PPHARM
Dedicated data base ASCII
Yes
Variables in data base or in ASCII file
Objectives
1. Provide Estimates of Population PK
Parameters (CL, V) - Fixed Effects
2. Provide Estimates of Variability - Random
Effects
• Intersubject Variability
• Interoccasion Variability (Day to Day Variability)
• Residual Variability (Intrasubject Variability,
Measurement Error, Model Misspecification)
Objectives
3. Identify Factors that are Important
Determinants of Intersubject Variability
• Demographic: Age, Body Weight or Surface Area,
gender, race
• Genetic: CYP2D6, CYP2C19
• Environmental: Smoking, Diet
• Physiological/Pathophysiological: Renal (Creatinine
Clearance) or Hepatic impairment, Disease State
• Concomitant Drugs
• Other Factors: Meals, Circadian Variation,
Formulations
Advantages
•Sparse Sampling Strategy (2-3
concentrations/subject)
–Routine Sampling in Phase II/III Studies
–Special Populations (Pediatrics, Elderly)
•Large Number of Patients
–Fewer restrictions on inclusion/exclusion criteria
•Unbalanced Design
–Different number of samples/subject
•Target Patient Population
–Representative of the Population to be Treated
Disadvantages
•Quality Control of Data
–Dose and Sample Times/Sample Handling/
Inexperienced Clinical Staff
•Timing of Analytical Results/Data
Analyses
•Complex Methodology
–Optimal Study Design (Simulations)
–Data Analysis
•Resource Allocation
•Unclear Cost/Benefit Ratio
Drug Conc
Models are critical in sparse sampling situations:
Time
Drug Conc
Models are critical in sparse sampling situations:
Time
Drug Conc
Models are critical in sparse sampling situations:
Time
Drug Conc
Models are critical in sparse sampling situations:
Time
Drug Conc
Models are critical in sparse sampling situations:
Time
Drug Conc
Models are critical in sparse sampling situations:
Time
Study Objectives
 To evaluate the efficacy of drug treatment or
placebo as add on treatment in patients with
partial seizures.
Data Structure
Study N
Doses Explored
1
308 0, 600 mg/day (bid & tid)
2
287 0, 150, 600 mg/day (tid)
3
447 0,50,150,300,600 mg/day (bid)
Total 1092
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Placebo
5
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Count Model
P(Yi  x)  e


x
x!
 represents the expected number of events per unit time
E(Yij)=itij
The natural estimator of  is the overall observed rate for
the group.
Total counts

Total tim e
Suppose there are typically 5 occurrences per
month in a group of patients:- =5
P(Yi  x)  e
X=
0
1
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1
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Pr(Yi=x)
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
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Pr(Y=x)
0.007
0.034
0.084
0.140
0.175
0.180
0.150
0.104
0.065
0.036
0.018
P(Yi  x)  e


x
x!
The mean number of seizure episodes per month (λ) was
modeled using NONMEM as a function of drug dose, placebo,
baseline and subject specific random effects.
  Baseline placebo drug  
Baseline = estimated number of seizures reported during
baseline period
Placebo = function describing placebo response
Drug = function describing the drug effect
 = random effect
Sub-population analysis
 Some patients are refractory to any particular
drug at any dose.
 Interest is in dose-response in patients that
respond
 Useful in adjusting dose in patients who would
benefit from treatment
 Investigate the possibility of at least two subpopulations.
Mixture Model
A model that implicitly assumes that some fraction p of the
population has one set of typical values of response, and that the
remaining fraction 1-p has another set of typical values
Population A (p)
1  Baseline1  placebo1  drug1  1
Population B (1-p)
2  Baseline2  placebo2  drug2  2
Final Model
PopulationA  75%
1 Dose

 1
  11.1 1 
 D1  0.11 D0   e
 186 Dose

PopulationB  25%
  15.1 1  0.26 D1  1.44 D0  e
2
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Expected percent reduction in
seizure frequency
 Monte Carlo simulation using parameters and
variance for Subgroup A
 8852 individuals (51% female)
 % reduction from baseline seizure frequency
calculated
 Percentiles calculated for % reduction in
seizure frequency at each dose
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Impact in Drug Development
 Gabapentin was recently approved
by FDA for post-herpetic neuralgia
 Approved label states under clinical
studies: “Pharmacokineticpharmacodynamic modeling provided
confirmatory evidence of efficacy
across all doses”
PHN Study Designs
able 1. Overview of PHN Controlled Studies: Double-Blind Randomized/Target Dose and ITT Population
Duration of Double-Blind Phase
Fixed
Titration
Dose
4 Weeks 4 Weeks
Number of Patients
Final Gabapentin Dose, mg/day
Overall
Duration
8 Weeks
Placebo
116
600
--
1200
--
1800
--
2400
--
All
Any
3600 Gabapentin Patients
113
113
229
-430) 3 Weeks
4 Weeks
7 Weeks
111
--
--
115
108
--
223
334
4 Weeks
4 Weeks
8 Weeks
152
379
-0
-0
-115
153
261
-113
153
489
305
868
ents
oup not included in study design
ation = All randomized patients who received at least one dose of study medication.
 Used all daily pain scores
 Exposure-Response analysis utilized titration data for
within-subject dose response
Fits to Data
945-211
0.0
0.0
Placebo (Observed)
1800 mg Daily (Observed)
2400 mg Daily (Observed)
Placebo (Predicted)
1800 mg Daily (Predicted)
2400 mg Daily (Predicted)
-0.2
-0.2
-0.4
-0.4
-0.6
-0.8
Placebo (Observed)
Placebo (Predicted)
3600 mg Daily (Observed)
3600 mg Daily (Predicted)
-1.0
-1.2
-1.4
-1.6
Mean Pain Score
-0.6
Mean Pain Score
945-295
-0.8
-1.0
-1.2
-1.4
-1.6
-1.8
-1.8
-2.0
-2.0
-2.2
-2.2
-2.4
-2.4
-2.6
-2.6
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
Time (Days)
Time (Days)
Time Dependent Placebo Response, Emax Drug Response
and Saturable Absorption,
Model Predicted Gabapentin Effect (Less Placebo)
Plot of Model Predicted Gabapentin Effect by
Total Daily Dose and Estimated Dose Absorbed
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
Total Daily Dose
Estimated Dose Absorbed
0.4
0.3
0.2
0.1
0.0
0
500
1000
1500
2000
2500
3000
3500
Gabapentin Dose (Total Daily or Total Daily Absorbed)
4000
Outcomes
 Model and Data Provided with Submission
• FDA reviewers used model to test various scenarios
• Supported doses and conclusions of Pfizer
• Provided confidence to eliminate need for replicate
doses
• FDA proposed language in the label on PK-PD
modeling and clinical trials
 FDA/Pfizer publication to discuss modeling and
impact on regulatory decision-making
• clinical endpoints
• similar study design
• familiarity with drug class