Transcript An Introduction to PK/PD Models

An Introduction to PK/PD Models
Part 2
Yaming Hang
Biogen
Sep. 16, 2015
FDA/Industry Workshop 2015
1
Learning Objectives for Part 2
After finishing this lecture, the attendees are expected to:
• Obtain general understanding of the cascade of
pharmacological events between drug administration and
outcome
• Recognize different types of pharmacodynamic endpoints
• Distinguish different temporal relationships between
pharmacokinetics and pharmacodynamics
• Explain common causes for delay in drug effect
• Able to identify proper class of PK/PD models to describe
different PK/PD relationships
• Give a few examples on the application of PK/PD analysis in
drug development
2
Outline for Part 2
•
•
•
•
Why PD Models are Important
Cascade of Pharmacological Events
Different Types of PD Endpoints
Different Types of PD Models
– Direct link vs. indirect link
– Direct response vs. indirect response
• Case Studies
3
Changes that Potentially Lead to
Different PK Profiles
•
•
•
•
Route of administration, delivery technology
Dosing Regimen (dose amount and frequency)
Formulation or manufacturing process
Population
–
–
–
–
–
–
Race
Pediatric, geriatric
Light vs. heavy subjects
Renal impairment, liver impairment
Drug-drug interaction
HV vs. Diseased population
4
Why PD models are important
• Population PK models aim to characterize and
identify important intrinsic and extrinsic
factors that influence pharmacokinetics
• Only with a pharmacodynamic model, we can
assess the clinical significance of difference in
PK under different circumstances, therefore
decide whether the dose regimen should be
adjusted accordingly
5
Example of Changing From Intravenous (IV)
to Subcutaneous (SC) Administration
• Frequently, biologics are delivered intravenously (IV)
and dosage is body weight based, which complicates
the drug administration process and leads to drug
product waste
• It will bring significant convenience to patients as well
as cost saving associated with reduced drug product
waste/clinical site visit if drug can be self-administered
(e.g. SC) and at a fixed dose amount
• However, variability in PK has to be evaluated and
ultimately what matters is whether the different
regimen can deliver similar efficacy/safety profile
6
PK/PD Modeling Facilitated
Abatacept SC Program
• Weight-tiered IV regimen approved for
treatment of rheumatoid arthritis in 2005
• Flat SC dosing regimen subsequently tested and
approved in 2011
• Knowledge in the IV program was utilized to
design a bridging program:
– Pop PK and PK/PD models developed for simulation
– Dose-ranging study was not needed
– A PK study with SC route was followed directly by a
Phase 3 study
7
Cascade of Pharmacological Events
Blood
Site of
Action
Target
Engagement
…
8
TYSABRI®: MoA, Target and Biomarker
↑ Nat ↑ α4 Sat ↓ Total α4 ↑ Lymphocyte
Questions to be addressed by PK/PD modeling:
• Extent of receptor occupancy
• Lymphocyte elevation
• Relationship between receptor occupancy and clinical efficacy
• …
https://www.youtube.com/watch?v=9zLYxr2Tv7I
9
Pharmacokinetics/Pharmacodynamics (PK/PD):
description of time-course and factors
controlling drug effects on the body
10
H. Derendorf, B. Meibohm, Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD) Relationships: Concepts and Perspectives, Pharmaceutical Research, Vol. 16, No.2, 176-185, 1999
Biological Turnover Rates of Structure or Functions
Fast
Slow
Electrical Signals (msec)
Neurotransmitters (msec)
Chemical Signals (min)
Mediators, Electrolytes
(min)
Hormones (hr)
mRNA (hr)
Proteins / Enzymes (hr)
Cells (days)
Tissues (mo)
Organs (year)
Person (.8 Century)
William J. Jusko, PK-PD Modeling Workshop
B
I
O
M
A
R
K
E
R
S
CLINICAL
EFFECTS
11
Different PD Outcomes:
by Role in Pharmacology Cascade
• Biomarker
– Measurable physiological or biochemical parameters that
reflect some pharmacodynamic activity of the drug
– E.g. Alpha-4 Integrin Saturation
• Surrogate marker
– Observed earlier than clinical outcome, easily quantified,
predicts clinical outcome
– Does not change as fast as biomarker
– E.g. MRI Gd enhancing lesions
• Clinical outcome
– E.g. Relapse Rate, EDSS
12
Different PD Outcomes:
by Accessibility
• Readily accessible, e.g.
– In circulation
• Receptor saturation, cell count, enzyme/protein level/activity
– Electrical signal
• Electroencephalography (EEG), Electrocardiography (ECG)
– Clinical measurement/assessment
– Intensive sampling feasible
• Less accessible, e.g.
– Imaging technique for brain lesions, Amyloid plaque, receptor
binding outside blood, tumor size
– CSF fluid
– Invasive tissue biopsy
– Infrequent sampling
13
Different PD Outcomes:
by Data Type
• Types of variables
– Continuous: e.g. blood pressure
– Categorical: e.g. AE Occurrence, AE severity, Pain
Likert Score, Sleep State
– Count data: e.g. number of MRI lesions in Multiple
Sclerosis
– Time-to-event: e.g. repeated time to bleeding in
treatment of hemophilia A with ELOCTATE®
• Longitudinal vs. cross-sectional
14
Different PK/PD Model Types
• Empirical Models
– Models that describe the data well but without biological meaning
– Interpretation of parameters can be challenging
– E.g., polynomial function to describe an exposure-response
relationship
• Mechanistic Models
– Reflecting underlying physiological process
– Preferred due to better predictive power
– Reversible
• Direct link/response model
• Indirect link/response model
– Irreversible
• Chemotherapy
• Enzyme Inactivation
15
Model Components
• Structure Model
– The underlying relationship between PK, time and
PD response
– For mechanistic models, understanding of
Mechanism of Action is required
• Stochastic Model
– Inter-subject variation
– Intra-subject variation
– Residual error
16
Direct Link Model
• Appropriate to visually assess
the relationship between
concentration and response
collected at the same time
• PK model can be used to predict
missing concentration where PD
is available but not PK
• Examples:
 heart rate change
 receptor binding
 some acute pain medication
17
H. Derendorf, B. Meibohm, Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD) Relationships: Concepts and Perspectives, Pharmaceutical Research, Vol. 16, No.2, 176-185, 1999
Hysteresis: Concept
PK
PK vs. PD
15
PD
15
1.0
5
0.5
0.0
0
20
40
60
Time (hr)
80
100
0
0
10
10
5
1.5
QTc Prolongation (msec)
QTc Prolongation (msec)
Concentration (ng/ml)
2.0
0
20
40
60
Time (hr)
80
100
0.0
0.5
1.0
1.5
2.0
Concentration (ng/ml)
18
Hysteresis: Real Example
Three subjects showing different
degree of hysteresis between
plasma drug concentration and
QTc interval
19 (1997)
Salazar et al, A Pharmacokinetic-Pharmacodynamic Model of d-Sotalol Q-Tc Prolongation During Intravenous Administration to Healthy Subjects, J. Clin Pharmacol. 37: 799-809
Indirect Link Model
• Hysteresis due to DISTRIBUTION DELAY TO SITE OF ACTION
• Also called Effect Compartment Model or Biophase Distribution Model
Blood
20
H. Derendorf, B. Meibohm, Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD) Relationships: Concepts and Perspectives, Pharmaceutical Research, Vol. 16, No.2, 176-185, 1999
Extent of Hysteresis Under Different
Doses or Distribution Rate Constants
Effect under Different Doses
21
D. Mager, E. Wyska, W. Jusko, Diversity of Mechanism-based Pharmacodynamic Models, Drug Metabolism and Disposition, 31: 510-519, 2003
Indirect Response Model
22
H. Derendorf, B. Meibohm, Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD) Relationships: Concepts and Perspectives, Pharmaceutical Research, Vol. 16, No.2, 176-185, 1999
Indirect Response Model (cont’d)
23
D. Mager, E. Wyska, W. Jusko, Diversity of Mechanism-based Pharmacodynamic Models, Drug Metabolism and Disposition, 31: 510-519, 2003
Indirect Response Model (cont’d)
• Type I (inhibition of production)
– Inhibition of BACE1 enzyme leads to reduced
production of amyloid-β peptide
• Type II (inhibition of clearance)
– Tysabri® hinders the migration of lymphocyte out of
blood
• Type III (stimulation of production)
– Epogen® stimulate the growth of red blood cell
• Type IV (stimulation of clearance)
– Aducanumab ® stimulate the clearance of amyloid-β
24
Case Study One:
PK/PD Modeling to Support Q2W Regimen vs. Q4W
Regimen in Label for Plegridy®
Highlight
• An example of Empirical Model
• Both PK and PD samples are sparse
• PD endpoint, a clinical endpoint, changes much
slower than PK
• Modeling results used to support labeling claim
Y Hang et al, Pharmacokinetic and Pharmacodynamic Analysis of Longitudinal Gd-Enhanced Lesion Count in Subjects with Relapsing Remitting Multiple Sclerosis
Treated with Peginterferon beta-1a, Population Approach Group in Europe 2014 Annual Conference
25
Background
• Plegridy® is a PEGylated form of human IFN beta-1a; it increases
half-life and exposure to IFN beta-1a compared with non-pegylated,
intramuscular IFN
• A pivotal Phase 3 study for Plegridy® compared
– Plegridy® 125 ug SC every 2 weeks (Q2W)
– Plegridy® 125 ug SC every 4 weeks (Q4W)
– Placebo
• Both Plegridy® regimens are better than placebo, but difference
between them were not statistically significant in some of the key
efficacy endpoints (e.g. annual relapse rate)
• Regulatory agency proposed to include both regimens in the label
in the review process
• PK/PD analysis on Relapse and Gd+ Lesion Count were performed
to demonstrate Q2W provides better exposure coverage than
Q4W
26
Endpoint
• Gadolinium-enhanced lesions are associated with blood-brain
barrier disruption and inflammation, an informative
biomarker for disease progression
Objective
• To develop a PK and PD model to assess the effect of monthly
exposure of Plegridy® on the reduction of Gd+ lesion count
over time in patients with relapsing-remitting multiple
sclerosis
Gd+ = gadolinium-enhancing; MRI = magnetic resonance imaging; MS = multiple sclerosis; PD =
pharmacodynamic; PK = pharmacokinetic
1Hu
27
X, et al. J Clin Pharmacol 2012;52(6):798‒808
Study Design

Study design: 2-year, multicenter, randomized, double-blind, parallel-group Phase
3 study in RRMS patients, with a 1-year placebo-controlled period (ADVANCE;
NCT00906399)1
Year 1
Year 2
Peginterferon beta-1a 125 μg Q4W SC
Peginterferon beta-1a 125 μg Q2W SC
Placebo (n=500)
1512 patients
randomized (1:1:1)
and dosed
Follow-up
Peginterferon beta-1a 125 μg Q4W SC (n=500)
Peginterferon beta-1a 125 μg Q2W SC (n=512)
MRI scans
Blood sampling
Week
†Intensive

4†
12
24†
48
56
84
96
blood sampling in a subset of 25 patients who provided additional consent
Population PK model: A one-compartment model described the peginterferon
beta-1a PK profiles well2, no exposure accumulation was observed with both
Calabresi PA. et al. Lancet Neurol 2014:
dose regimens
1
MRI = magnetic resonance imaging PD = pharmacodynamic; PK =
pharmacokinetic; Q2W = every 2 weeks; Q4W = every 4 weeks; SC
= subcutaneous
2Hu
doi:10.1016/S1474-4422(14)70068-7
X, et al. Poster presentation at AAN 2014, April 26–3 May,
28
Philadelphia, PA, USA (P3.194)
Gd+ Lesion Count Over Time
Placebo-treated patients
0
ID : 240309
10
20 30 40
Q4W
50
ID : 121301
ID : 241303
60
30
40
10
0
ID : 101307
ID : 450305
ID : 137304
30
20
10
0
ID : 251303
ID : 430302
ID : 303302
30
Observed Gd+ Lesion Count
Observed Gd+ Lesion Count
20
20
~ 40% of patients had data
at Week 96
0
Placebo
Q2W
60
20
10
40
0
ID : 317306
ID : 441302
ID : 437325
30
20
20
10
0
0



10 20 30
40 50
Week
0
10 20 30
40 50
0
-400 -200
0
200 400 600
800
Time Since First Active Dose (day)
Large inter-subject variation was observed
There was a significant proportion of patients without Gd+ lesions throughout the trial
Distribution shifted toward 0 while on treatment
Gd+ = gadolinium-enhancing; Q2W = every 2 weeks; Q4W = every 4 weeks
29
Relationship between Steady State 4Week AUC and Gd+ Lesion Count
Placebo
Q2W
Q4W
Placebo->Q2W
Placebo->Q4W
Observed Gd+ Lesion Count
60
40
20
0
0
50
100
150
Estimated Individual Cumulative AUC Over 4 Weeks (ng/mL*hr)

What is the proper statistical distribution to describe these data?

How can we quantify the effect of exposure on the distribution of Gd+ lesion count?
AUC = area under the curve; Gd+ = gadolinium-enhancing; Q2W = every 2 weeks; Q4W = every 4 weeks
30
Some Key Features of Data
Large Proportion of Zero Lesion Count
Large over-dispersion
31
Candidate Models
• Poisson, Zero-inflated Poisson
𝒑𝟎 + 𝟏 − 𝒑𝟎 ∗ 𝐞𝐱𝐩 −λ ,
– 𝑷 𝒙 = 𝒎|λ, 𝒑𝟎 =
𝟏 − 𝒑𝟎 ∗
𝝀𝒎
𝒎!
∗ 𝒆𝒙𝒑(−λ),
𝒎=𝟎
𝒎>𝟎
– 𝐸 𝑋 = 1 − 𝑝0 ∗ λ, Var X = 1 − 𝑝0 ∗ (λ + 𝑝0 ∗ λ2 )
• Negative Binomial (NB), Zero-inflated NB
– 𝑷 𝒙 = 𝒎|λ, 𝑶𝑽𝑫𝑷, 𝒑𝟎 =
𝟏
𝟏
𝑶𝑽𝑫𝑷
,
𝟏+𝑶𝑽𝑫𝑷∗𝝀
𝒑𝟎 + 𝟏 − 𝒑𝟎 ∗
1 − 𝑝0 ∗
𝟏
𝑶𝑽𝑫𝑷
𝟏
∗𝜞
𝑶𝑽𝑫𝑷
𝜞 𝒎+
𝜞 𝒎+𝟏
𝒎=𝟎
𝟏
∗
𝟏
𝑶𝑽𝑫𝑷
𝟏+𝑶𝑽𝑫𝑷∗𝝀
∗
𝝀
𝝀+
𝟏
𝑶𝑽𝑫𝑷
𝒎
, 𝒎>𝟎
– OVDP is overdispersion parameter
– 𝐸 𝑋 = 1 − 𝑝0 ∗ λ, 𝑉𝑎𝑟 𝑋 = 1 − 𝑝0 ∗ λ ∗ 1 + λ ∗ 𝑂𝑉𝐷𝑃 + 𝑝0 ∗ λ2
32
Candidate Models (cont’d)
• Marginal (Naïve Pooled) Model
– 𝝀𝒊𝒋 = 𝝀𝟎 ∗ 𝐞𝐱𝐩 𝜷 ∗ 𝑨𝑼𝑪𝒊𝒋 ∗ (𝟏 − 𝒆𝒙𝒑 −𝒌 ∗ 𝒕𝒊𝒋 )
– 𝑙𝑜𝑔𝑖𝑡 𝑝0 = 𝛼0 + 𝛼1 ∗ 𝐴𝑈𝐶𝑖𝑗
• Mixed Effect Model
⁻
𝝀𝒊𝒋 = 𝝀𝒊𝟎 ∗ 𝐞𝐱𝐩 𝜷 ∗ 𝑨𝑼𝑪𝒊𝒋 ∗ (𝟏 − 𝒆𝒙𝒑 −𝒌 ∗ 𝒕𝒊𝒋 )
• Mixed Effect Negative Binomial Model
– λ𝑖0 ~𝐿𝑁(μ, ω2 ), OVDP constant
• Mixture Negative Binomial Model
–
–
–
–
λ𝑖0 = λ𝑖0,1 ∗ 𝐼 𝑌 = 1 + λ𝑖0,2 ∗ 𝐼 𝑌 = 0
𝑌~𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖(1, 𝑝)
λ𝑖0,1 ~𝐿𝑁(μ1 , 𝜔12 ), λ𝑖0,2 ~𝐿𝑁(μ2 , 𝜔22 ),
OVDP1 and OVDP2 for two subpopulations†
†The
two subpopulations in the model were patients with lower Gd+ lesion
activity and patients with higher Gd+ lesion activity at baseline.
Gd+ = gadolinium-enhancing; OVDP = over dispersion parameter
33
Model Comparison



Model
-2LL
β
SE
Poisson
21792.2
-0.0248
0.0036
ZIP
15804.0
-0.0111
0.0156
0.0041
0.0014
NB
11112.5
-0.0197
0.0016
ZINB
11105.0
-0.025
-0.455
Model unstable
Mixed NB
10552.8
-0.0269
0.0024
Mixture NB
10238.8
-0.0257
0.0028
AUC in zero-inflated models may be related to both probability of zero as well as
the mean of the non-zero part, its effect estimate cannot be compared with other
models directly
Naïve NB model yielded a different AUC effect parameter estimate
Slope parameter β were estimated similarly across different models, but the
uncertainty estimation could be very different
AUC = area under the curve; NB = negative binomial, SE = standard error; ZINB = zero-inflated NB; ZIP = Zero-inflated Poisson
34
Goodness-of-Fit Assessed by
Marginal Probabilities
Below 10
0
Naive Poisson
Above 10
2
4
6
8
10
10
Naive NB
40
50
Naive Poisson
Naive NB
ZIP
ZINB
60
70
0.002
Model Prediction
Observed
0.4
30
0.003
0.8
0.6
20
0.001
0.2
0.000
0.0
Marginal Probability
ZIP
ZINB
0.8
0.003
0.6
0.002
0.4
0.001
0.2
0.000
0.0
Mixed NB
Mixture NB
0.8
0.6
Mixed NB
Mixture NB
0.003
0.002
0.4
0.001
0.2
0.000
0.0
0
2
4
6
8
10
Gd+ Lesion Count
NB = negative binomial; ZINB = zero-inflated NB; ZIP = Zero-inflated Poisson
10
20
30
40
50
60
70
Gd+ Lesion Count
35
Final Model Parameter Estimates
Model
Parameter
λ0_1
λ0_2
σ2
r1
r2
P
β
t1/2
Description
Point
Estimate
(RSE %)
Non-parametric bootstrap
(500 replicates)
Median (RSE %)
Baseline mean Gd+ lesion count for a
0.546
0.543 (12.7%)
typical subject in lower lesion activity
(13.2%)
subpopulation
Baseline mean Gd+ lesion count for a
1.615
typical subject in higher lesion activity
1.624
subpopulation
Variance of random effect on baseline λ in
log scale for the higher lesion activity
1.26 (9.5%)
1.25 (9.6%)
subpopulation
Dispersion parameter for baseline λ in the
44.26 (6.5%)
44.6 (6.7%)
lower lesion activity group
Dispersion parameter for baseline λ in the
0.452
0.446 (10.0%)
higher lesion activity group
(9.9%)
Proportion of lower lesion activity
0.594
0.593
subpopulation
-0.026
-0.0259 (10.7%)
Slope of AUC effect on log(λ)
(11.0%)
Half-life of drug effect onset time (day)
111 (25.5%)
112.3 (25.0%)
AUC = area under the curve; CI = confidence interval; Gd+ = gadolinium-enhancing; RSE = relative standard error
95% CI
(0.428, 0.693)
(1.02, 1.51)
(38.5, 50.9)
(0.357, 0.541)
(0.550, 0.641)
(-0.033, -0.021)
(69.2, 207.6)
36
More Reduction in Gd+ Lesion Count
was Driven by Greater Exposure
• Observed data aligned with model
predicted data
• Correlation between cumulative
monthly AUC and Gd+ lesion data
• Steep Gd+ decline in the AUC range of
Q4W, vs. a more flat curve in the AUC
range of Q2W
37
Conclusions for Case Study One
• An example of Empirical Model
• Multiple models were compared and quantified the
relationship between Plegridy® AUC and Gd+ lesion
count
• Demonstrated that Q4W regimen is more likely to
result in sub-optimal exposure
• Only Q2W regimen was approved in the label
38
Case Study Two:
PK/PD Analysis to Identify Reason for Study
Failure and Supporting Dose Selection
Highlight
• An example of Direct Link/Response Model
• Intensive PK and PD samples
• Modeling results used to
– identify reason for trial failure
– predict outcome for new formulation
– facilitate dose selection
KG Kowalski, S Olson, AE Remmers and MM Hutmacher, Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective
Cox-2 Inhibitor for the Treatment of Acute and Chronic Pain, Clinical Pharmacology & Therapeutics, Vol83, 857-866, 2008
39
Background
• A selective COX-2 Inhibitor
• Preclinical potency estimates and PK model from
HV suggests 60 mg SC-75416 should provide pain
relief (PR) similar to 50 mg rofecoxib (Vioxx)
• In a dose-ranging study for pain relief in postsurgical dental patients:
– Single oral dose of placebo, 3, 10, and 60 mg SC-75416
CAPSULES were compared with 50 mg rofecoxib
– 10 and 60 mg doses were better than placebo, but did
not achieve PR comparable to 50 mg rofecoxib
– Drop out rate was higher in SC-75416 groups than
rofecoxib
40
Formulation Difference
was Behind PK Difference
capsule formulation had slower and more erratic absorption at critical
early time points compared to oral solution data in Phase I, which is believed
41
to be the reason for poor pain relief response
PK/PD Analyses for
Pain Relief and Drop Out
• A PK/PD model was developed to predict how a 60
mg ORAL SOLUTION dose may have performed in
the post-oral surgery pain study
• A nonlinear mixed effects logistic-normal model
related plasma concentration of SC-75416 and
rofecoxib to the PR scores on a 5-point Likert scale
(0=no PR, 4=complete PR)
• Survival model was fit to time of dropout (time of
rescue)
42
PK/PD Models for
Pain Relief and Drop Out
• PR Model to describe the distribution of Pain
Reduction (PR) at each time point tj for individual i:
𝑙𝑜𝑔𝑖𝑡 Pr 𝑃𝑅𝑖𝑗 ≥ 𝑚 η𝑖
= 𝑓𝑝 𝑡𝑗 , 𝑚 + 𝑓𝑑 𝑐𝑖𝑗 + (𝑡𝑗 )𝑥 𝜂𝑖
𝑓𝑝 𝑡𝑗 , 𝑚 : placebo effect; 𝑓𝑑 𝑐𝑖𝑗 : drug effect; 𝑐𝑖𝑗 : plasma concentration
• Drop-out Model to describe the probability of an
individual dropout in the time interval (tj, tj+1) given
he/she was still in the study in the previous time
interval (tj-1, tj):
𝑡𝑗+1
Pr 𝑇𝑖 = 𝑡𝑗+1 𝑇𝑖 ≥ 𝑡𝑗 , 𝑃𝑅𝑖𝑗 = 𝑚 = 1 − exp(−
𝜆 𝑡, 𝑚 𝑑𝑡)
𝑡𝑗
43
Goodness of Fit for Capsule PR and
Drop-out Model
Solid line represent the mean of predicted pain reduction for 50000 hypothetical subjects
44
based on both PR and drop-out model, and LOCF imputation method applied
Predicted Outcomes for Oral Solution
at Different Doses
• Dashed lines are predicted profiles
• Solid lines and squares are
for 50 mg rofecoxib as reference
45
Results from a Subsequent Clinical Study
Comparing Oral Solution SC-75416 and
Ibuprofen
Vioxx was withdrawn by the time they conducted the next study
46
Conclusions for Case Study Two
• An example of Direct Link/Response Model
• Identified formulation as cause for not
achieving anticipated PR effect size
• PK/PD analysis predicted dose levels which
will yield intended effect size using a different
formulation
• PK/PD prediction guided dose selection for a
subsequent dose-ranging study and outcome
was consistent with prediction
47
Take Home Message for Statisticians
• Improve understanding on
– Basic pharmacology principles
– Mechanistic components of the PD models
– The role of Dose and Time in PK/PD relationship
• Involve
– Provide constructive suggestions on analysis method
of non-trivial data types
– Perform hands-on analysis
– Contribute to methodology development
• Engage with pharmacometricians one-on-one
48
Learning Objectives for Part 2
After finishing this lecture, the attendees are expected to:
• Obtain general understanding of the cascade of
pharmacological events between drug administration and
outcome
• Recognize different types of pharmacodynamic endpoints
• Distinguish different temporal relationships between
pharmacokinetics and pharmacodynamics
• Explain common causes for delay in drug effect
• Able to identify proper class of PK/PD models to describe
different PK/PD relationships
• Give a few examples on the application of PK/PD analysis in
drug development
49
References for Parts 1 and 2
• Davidian, M. and D. Giltinan, Nonlinear Models for Repeated Measurement Data, Chapman
and Hall, New York, 1995.
• Gabrielsson, J. and D. Weiner, Pharmacokinetic and Pharmacodynamic Data Analysis:
Concepts and Applications, Swedish Pharmaceutic, 2007.
• Pinheiro, J.C. and D.M. Bates, Approximations to the log-likelihood function in the nonlinear
effects model, J. Comput. Graph. Statist., 4 (1995) 12-35.
• Pinheiro, J.C. and D.M. Bates, Mixed-Effects Models in S and S-Plus, Springer, New York,
2004.
• The Comprehensive R Network, http://cran.r-project.org/
• Pharma Stat Sci, http://www.pharmastatsci.com/
• H. Derendorf, B. Meibohm, Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD)
Relationships: Concepts and Perspectives, Pharmaceutical Research, Vol. 16, No.2, 176-185,
1999
• Salazar et al, A Pharmacokinetic-Pharmacodynamic Model of d-Sotalol Q-Tc Prolongation
During Intravenous Administration to Healthy Subjects, J. Clin Pharmacol. 37: 799-809
(1997)
• D. Mager, E. Wyska, W. Jusko, Diversity of Mechanism-based Pharmacodynamic Models,
Drug Metabolism and Disposition, 31: 510-519, 2003
• Y Hang et al, Pharmacokinetic and Pharmacodynamic Analysis of Longitudinal Gd-Enhanced
Lesion Count in Subjects with Relapsing Remitting Multiple Sclerosis Treated with
Peginterferon beta-1a, Population Approach Group in Europe 2014 Annual Conference
• KG Kowalski, S Olson, AE Remmers and MM Hutmacher, Modeling and Simulation to
Support Dose Selection and Clinical Development of SC-75416, a Selective Cox-2 Inhibitor
for the Treatment of Acute and Chronic Pain, Clinical Pharmacology & Therapeutics, Vol83,
857-866, 2008
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