# Presentation: 389006

## POPULATION PHARMACOKINETICS

- RAYMOND MILLER, D.Sc.
- Pfizer Global Research and Development

## Population Pharmacokinetics

- Definition
- Advantages/Disadvantages
- Objectives of Population Analyses
- Impact in Drug Development

## 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

- 	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

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## E.g.: A simple Pk model  Ri = Cl·Cpss

Cpss = Rate in / Rate out  
Rate in = infusion rate
Rate out = drug clearance
 = measurement error, intra-individual error

- Cpss = Rate in / Rate out ****** ******
- Rate in = infusion rate
- Rate out = drug clearance
-  = measurement error, intra-individual error

- Drug Conc

- Time

- Definition

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## E.g.: A simple Pk model
Cpss = Rate in / Rate out  
Rate in = infusion rate
Rate out = drug clearance
 = measurement error, intra-individual error

- Cpss = Rate in / Rate out ****** ******
- Rate in = infusion rate
- Rate out = drug clearance
-  = measurement error, intra-individual error

- Drug Conc

- Time

-   N(0,)

- Definition

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## Cpss = Infusion rate / Cl
CL = Infusion rate / Cpss

- CL = Infusion rate / Cpss

- Drug Conc

- Time

- Definition

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## Cl = metabolic clearance + renal clearance
Cl = 1 + 2• CCr   

- Cl = 1 + 2• CCr  ****** **

- Drug Clearance

- Creatinine Clearance

- Drug Conc

- Time

- Definition

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## Cl = metabolic clearance + renal clearance
Cl = 1 + 2• CCr   

- Cl = 1 + 2• CCr  ****** **

- Drug Clearance

- Creatinine Clearance

-   N(0,)

- Definition

**Notes:**
This slide provides a background for where we currently are.

Philosophy points to our PDM best practices document (This is what we need CRD buy in on)

Method Sheet, is an internal PDM document which will improve the consistency of PDM approaches to this type of analysis.

Integration: deals with work ongoing to assure ready access to data, and standardization of methods used to collect the necessary information

## 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

- Variability among subjects around that mean:

- Residual (unexplained) variability 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 			User has to supply code 		Classified as inter- and intra-individual	None, but must 	conform to 								covariates SAS conventions
- 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 			Yes (specified by the routine PREDPP)	Variables in the data set 		None (some dimensions are
- 	initially set but these may be
- 	changed by the user)
- NPEM 	ASCII via USC*PACK program		Yes 			Either linked to a pharmacokinetic	99 days of time, 99 doses,					or numerical value. Interpolation
- 	99 values of dependent					between covariate values is possible
- 	variables (maximum of 6)
- 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

- Time

- Models are critical in sparse sampling situations:

## Drug Conc

- Time

- Models are critical in sparse sampling situations:

## Drug Conc

- Time

- Models are critical in sparse sampling situations:

## Drug Conc

- Time

- Models are critical in sparse sampling situations:

## Drug Conc

- Time

- Models are critical in sparse sampling situations:

## Drug Conc

- Time

- Models are critical in sparse sampling situations:

## Study Objectives

- To evaluate the efficacy of drug treatment or placebo as add on treatment in patients with partial seizures.

## Data Structure

## Baseline

- Placebo

## Count Model

-  represents the expected number of events per unit time
- 	E(Yij)=itij
- The natural estimator of  is the overall observed rate for the group.

## Suppose there are typically 5 occurrences per month in a group of patients:- =5

- Suppose there are typically 5 occurrences per month in a group of patients:- =5

## 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 = 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 sub-populations.

## Mixture Model

_***Population B (1-p)***_

- 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

## Final Model

## Slide 31

## Slide 32

## 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

## Slide 34

## Results

## Conclusions

- A comparison of the dose-response relationship for gabapentin and pregabalin reveals that pregabalin was 2.5 times more potent, as measured by the dose that reduced seizure frequency by 50% (ED50).
- Pregabalin was more effective than gabapentin based on the magnitude of the reduction in seizure frequency (Emax)
- Three hundred clinical trials for each drug were simulated conditioned on the original study designs. Each simulated trial was analyzed to estimate % median change in seizure frequency. The observed and model-predicted treatment effects of median reduction in seizure frequency for gabapentin and pregabalin are illustrated for all subjects and for responders. Data points represent median percentage change from baseline in seizure frequency for each treatment group (including placebo). The shaded area corresponds to predicted 10th and 90th percentiles for median change from baseline in seizure frequency.

## Relationship Between %Change in Seizure Frequency (Relative to Baseline) and Daily Dosage of Gabapentin and Pregabalin

*Dose-response model in epilepsy using pooled analysis of 4 gabapentin studies + 3 pregabalin studies*

## Relationship Between %Change in Seizure Frequency (Relative to Baseline) and Daily Dosage of Gabapentin and Pregabalin in Responders to Treatment

*Dose-response model in epilepsy using pooled analysis of 4 gabapentin studies + 3 pregabalin studies*

## Impact in Drug Development

- Gabapentin was subsequently approved by FDA for post-herpetic neuralgia
- Approved label states under clinical studies: “Pharmacokinetic-pharmacodynamic modeling provided confirmatory evidence of efficacy across all doses”

## PHN Study Designs

- Used all daily pain scores
- Exposure-Response analysis utilized titration data for within-subject dose response

## Fits to Data

- Time Dependent Placebo Response, Emax Drug Response and Saturable Absorption,

## 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