# Presentation: 257765

## 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/pharmacodynamics, 
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/pharmacodynamics,
- 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

## Definition

- E.g.: A simple Pk model
- Ri = infusion rate
- Cl = drug clearance
- k =elimination rate constant
-  = measurement error, intra-individual error

- Drug Conc

- Time

-   N(0,)

- 

**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

## Definition

- Drug Conc

- Time

**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)

## 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 28

## Slide 29

## 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 31

## 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

## Clinical Trial Simulation

- Used to assess how different design and drug factors may affect trial performance.
- May be viewed as an extension of statistical design evaluation.

## Planning Phase 2 POC for Alzheimer's Disease Drug

- Because the mechanism of action of CI-1017 was untested clinically, the principle objective of the clinical study was to ascertain whether CI-1017 improved cognitive performance at least as fast and as well as tacrine.
- This would be considered proof of concept (POC).

## Typical Effectiveness Trials (AD)

- Parallel group design
- Two to four treatment groups + placebo
- Powered to detect 3 point improvement in ADAS-Cog
- Minimum 12 weeks of treatment
- Require about 80 subjects per dose group to have 90% power (2 sided 50% sig. Level)

## Simulation Model

## Drug effect models considered in simulations study. Parameters characterizing the model are displayed in the individual panels (Lockwood et al.) 

## TRIAL DESIGN

| Design number | Design description | Number of sequences | Subjects per sequence | Number of treatments periods | Period length (weeks) | Measurements per period |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 6X6 Latin Square | 6 | 10 | 6 | 2 | 1 |
| 2 | 6X3 Incomplete block | 6 | 10 | 3 | 4 | 2 |
| 3 | Parallel group | 6 | 10 | 1 | 12 | 6 |
| 4 | 6X4 Incomplete block | 6 | 10 | 4 | 3 | 1 |
| 5 | 6X3 Incomplete block with 2 parallel groups | 8 | 8 | Seq 1-6: 3
Seq 7-8: 1 | Seq 1-6: 4
Seq 7-8:12 | 2
6 |
| 6 | 4X4 Latin Square | 4 | 15 | 4 | 3 | 1 |
| 7 | 4X4 Latin Square with 2 parallel groups | 6 | 10 | Seq 1-4: 4
Seq  5-6: 1 | 3
12 | 1
6 |
| 8 | 4X4 Latin Square | 4 | 15 | 4 | 4 | 2 |

## DATA EVALUATION

- DOES THE DRUG WORK?
- AOV to test null hypothesis of no drug effect
- Rejection of null hypothesis judged correct
- Dose trend test
- IS THE SHAPE MONOTONIC OR U-SHAPED?
- Similar to the above two steps
- Non-positive trial pattern classified as flat
- Inference between monotonic and u-shaped based on highest dose having best mean outcome.

## SIMULATION

- 100 Trial simulations
- Pharsight trial simulator (TS2)
- Data from each trial analyzed
- Conclusions scored

## DRUG EFFECT

| Design  number  | 8 | 7 | 6 |
| --- | --- | --- | --- |
| Dose response shape |  |  |  |
| Linear | 84 | 41 | 51 |
| Emax | 88 | 58 | 67 |
| Smax | 96 | 75 | 85 |
| U-shape | 57 | 40 | 49 |
| AVERAGE | 81 | 54 | 63 |

- Design number 6: 4X4 Latin Square, 3 weeks per treatment.
- Design number 7: 4X4 Latin Square with 2 parallel groups,
- Design number 8, 4X4 Latin Square, 4 weeks per treatment

**Percent of 100 trials (power) that detected a drug effect for design number 6, 7 and 8.**

## SHAPE

**Percent of 100 trials (power) that correctly identified dose-response shape for design number 6, 7 and 8**

| Design number | 8 | 7 | 6 |
| --- | --- | --- | --- |
| Dose response shape |  |  |  |
| Linear | 96 | 69 | 72 |
| Emax | 84 | 62 | 74 |
| Smax | 96 | 83 | 89 |
| U-shape | 45 | 34 | 39 |
| AVERAGE | 80 | 62 | 69 |

## Simulation ConclusionsDesign 

- 4x4 LS with 4-week periods using bi-weekly measurements
- Was best among alternatives considered for detecting activity and identifying DR shape
- Met minimum design criteria (80% average power)

##   Results

- 4x4 LS design was accepted, conducted, and analyzed more-or-less as recommended
- Unfortunately, drug didn’t work
- But we were able to find this out more quickly and with less resources than with conventional design

## Gabapentin – Neuropathic PainNDA

- Two adequate and well controlled clinical trials submitted.
- Indication – post-herpetic neuralgia
- Trials used different dose levels
- 1800 mg/day and 2400 mg/day
- 3600 mg/day
- The clinical trial data was not replicated for each of the dose levels sought in the drug application

## FDAMA 1997

- FDA review staff decided to explore whether PK/PD analyses could provide the confirmatory evidence of efficacy.
- “—based on relevant science, that data from one adequate and well controlled clinical investigation and confirmatory evidence (obtained prior to or after such investigation) are sufficient to establish effectiveness.”

## SIMULATION

- 100 Trial simulations
- Pharsight trial simulator (TS2)
- Data from each trial analyzed
- Conclusions scored

## Gabapentin Study Designs for PHN

- Used all daily pain scores (27,678 observations)
- Exposure-response analysis included titration data for within-subject dose response

## Gabapentin Response in PHN

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

## Results

- Summary statistics showed pain relief for both studies at different doses concur.
- M & S showed pain scores for both studies can be predicted with confidence from the comparative pivotal study (cross confirming).

## Conclusion

- The use of PK/PD modeling and simulation confirmed efficacy across the three studied doses, obviating the need for additional clinical trials.
- Gabapentin was subsequently approved by FDA for post-herpetic neuralgia
- The package insert states “pharmacokinetic/pharmacodynamic modeling provided confirmatory evidence of efficacy across all doses”