Pharmacokinetic-pharmacodynamic integration in drug...
Transcript of Pharmacokinetic-pharmacodynamic integration in drug...
Pharmacokinetic-pharmacodynamic
integration in drug development
1
Pierre-Louis Toutain,
Ecole Nationale Vétérinaire
INRA & National veterinary School of Toulouse, France
Wuhan 10/10/2015
The campus of the national
Veterinary school at Toulouse
1-What is PK/PD?
• PK-PD modeling is a scientific tool to
quantify, in vivo, the key PD
parameters of a drug, which allows to
predict the time course of drug effects
under physiological and pathological
conditions (intensity and duration)
What is the main goal of a PK/PD trial
It is an alternative to dose-titration
studies to discover an optimal
dosage regimen
PK/PD to support decision making
and strategic applications during
drug development
Objectives of the presentation
• Overview on the concept of PK/PD
• PK/PD and extrapolation from in vitro to in vivo
• The dose-titration approach for dose determination and its limits
• The case of NSAID
2-An overview on the
concept of PK/PD
Clinical trials (Dose titration)
vs.
PK/PD trials
Dose titration
Dose Response Parasite killing
Black box
PK/PD
Dose
PK PD
Plasma
concentration
Surrogate
Response
9
PK/PD: mechanistic approach
PK/PD
Dose Response
PK PD
Plasma
concentration
Plasma
concentration
Drug receptor
interaction Transduction
Dose Response
Drug specificity, affinity &
intrinsic efficacy
System specificity
Kineticists can be viewed
as the first “engineers” of
clinicians
3-Why is plasma concentration
profile a better explicative
(independent) variable than dose for
determining a dosage regimen ?
Dose vs. plasma concentration profile
as independent variable
Dose
Mass (no biological
information)
Dose
Concentration profile (biological information)
X F%
Clearance
Time
4-Why to prefer a PK/PD
approach to a classical
dose-titration?
Why to prefer a PK/PD approach to
a classical dose-titration?
To separate PK and PD
variability
Dose effect vs. concentration
effect relationship
15
DOSE AUC = (Dose/Cl)
EFFECT EFFECT
Less variance must be expected in the AUC/effect
than in the dose/effect relationship
External dose Internal dose
Dose-effect vs. exposure-
effect relationship for GnRH
A
0
10
20
30
40
50
60
70
0 100 200 300
GnRH dose (µg in toto)
AU
C L
H (
ng.h
.mL
-1)
B
0
10
20
30
40
50
60
70
0 50 100 150 200 250 300
AUC GnRH (pg.h.mL-1
)
AU
C L
H (n
g.h
.mL-1
)
Monnoyer et a.l J vet pharmaco ther 2004
LH response
PK and PD variability
well documented – species
– food
– age
– sex
– diseases
PK PD
Dose
Plasma
concentration
Effect
BODY Receptor
Generally ignored
but usually more pronounced than
PK variabilities (for a given species)
PK/PD variability
• Consequence for dosage adjustment
PK PD
Dose
Plasma
concentration
Effect BODY
Receptor
Parasite
Kidney function
Liver function
...
Clinical covariables
• disease severity or duration
• Parasites susceptibility (MEC)
PK/PD population approach
5-When to use PK/PD in drug
development
Preclinical drug development Clinical drug development
Learning
Dru
g d
isc
ove
ry
Ap
pro
va
l
Confirming
1. To acquire basic knowledge
on drug
2. Extrapolation from in vitro to
in vivo
3. To be an alternative to dose-
titration studies to discover
an optimal dosage regimen
• To adjust dosage regimen to
different subgroups of animals
(age, sex, breed, disease)
Predictive PK/PD • Simulations
• Trial forecasting
• Bioequivalence
Preclinical PK/PD •Integrated information supporting go/no go
decision
Predicting
Clinical PK/PD
Population PK/PD
PK/PD applications
1. identify key PD parameters (efficacy,
potency, selectivity, affinity…)
2. predict dosage regimen
3. in vitro to in vivo extrapolation
4. interspecies extrapolation
5. sources (PK or PD) of intra- and inter-
individual variability in drug response
6. drug-drug interactions
7. influence of pathological conditions
• etc.
6-PK/PD and extrapolation
from in vitro to in vivo
Antiparastic (tick) efficacy of
fipronil
Question: how to quickly and roughly
predict a antiparastic (tick) dose from in
vitro or from in vivo test system?
PK/PD: in vitro vs. in vivo
Response Plasma
concentration Body
Medium
concentration Test
system
Response
In vivo
In vitro
Extrapolation
in vitro in vivo
Mechanism-
based PK/PD
The tick blood meal: From a living animal or from a
silicone membrane?
Cumulative mortality of Ixodes ricinus females
feeding on bovine blood through a silicone
membrane with dimethylsulfoxide (DMSO)
added (placebo) and with increasing doses of
fipronil in DMSO.
Extrapolation from In vitro by directly
incorporating the in vivo efficacious
concentration in the equation giving the dose
Dose =
Dose - is a hybrid parameter (PK and PD)
-
Clearance x target concentration
Bioavailability
PD
PK
What is a Fipronil dose in
sheep
• In vitro efficacious concentration : about 100 ng/mL over 7 days
• Plasma clearance : about 2 mL/kg/min or 2.88 L/kg/day
• Daily dose : 2.8L/kg/day x 0.1 µg/L= 288 µg/kg /day
• Total dose: Daily dose x 7 days≈2mg/kg
7-PK/PD applications
1. in vitro to in vivo extrapolation
2. Estimate key PD parameters
(efficacy, potency, selectivity,
affinity…)
3. predict dosage regimen
4. sources (PK or PD) variability in
drug response (antibiotics)
The 3 PD parameters of a dose-
effect relationship
Emax ED50 Slope
stiff
ED502
Emax 1
Efficacy Potency
• Sensitivity
• Range of useful
concentrations
• Selectivity
Emax 2
1
2
1 2
ED501
Emax/2
Slope of the dose-effect
relationship & selectivity
ECVPT Toulouse 2009 -
30
therapeutic effect
side effect
A more potent than B
A = B for efficacy
B is preferable to A in a clinical context for its selectivity
A B
Concentration
Eff
ec
t 100
80
The most useful drug is not
necessarily the most potent one
8-PK/PD and the discovery of
an optimal dosage regimen
Components of an optimal
dosage regimen
Components Tools for investigation
Dose dose-titration or PK/PD
Interval of adm. PK/PD
Duration of adm. Clinics
Route, site and
conditions of adm. PK (bioavailability)
The discovery of an (optimal)
dosage regimen
• Should be acquired early in the
development process (preclinical studies)
for an efficient drug development
– to ensure succesful clinical trials
9-The dose-effect relationship
The two kinds of Dose/Effet
relationship
• There are 2 fundamental types of
endpoints that can be used to quantify the
dose-effect relationship, graded and
quantal.
35
Dose-Effect Endpoints
Graded
Quantal
• Continuous scale
(blood pressure, body temperature..)
• Measured in a single biologic unit
• Relates dose to intensity of effect
• All-or-none pharmacologic effect
dead/alive; pain/no pain
• Population studies
• Relates dose to frequency of effect
Anemia and erythropietin (a graded dose/effect relationship)
Erythropoietin Dose [units/kg]
[Peak hematocrit
increment %]
Eschbach et al. NEJM 316:73-8, 1987
Graded Dose-Effect Curve
% of
Maximal
Effect
[Concentration] EC50
Maximal effect
Potency
Efficacy
Log Dose-Effect Curve
% of
Maximal
Effect
[Drug]
EC50
Quantal Dose-Effect
relationship
Quantal Dose-Effect Distribution
• Dose is related to the frequency (probability) of the
all-or-none effect, such as the % of subjects who
survived.
• Notion of threshold
– This is the minimal concentration to observe an
effect
– This threshold concentration is different amongst
different subjects and a distribution exist in the
population
– Thus it will be possible to associate a given
concentration to the probability of an effect
• E.g : adverse effect of drugs
Quantal Dose-Effect Distribution
Threshold dose to trigger the
effect of interest
# of
Subjects
ED5
0
Cumulative Dose-Effect Curve
Dose
Cumulative %
of Subjects
Doxorubicin Cardiotoxicity
Total Doxorubicin Dose [mg/m2]
Probability
of CHF
0
0.20
0.40
0.60
0.80
1.0
0 200 400 600 800 1000
von Hoff et al. Ann Intern Med 91:710-7, 1979
Modeling quantal dose-effect
relationship:
The logistic model
45
Probability of cure (POC)
• Logistic regression can be used to link measures of drug exposure to the probability of a clinical success
MICAUCbfaePOC
1
1
Dependent variable
(from 0 to 1) Placebo
effect
sensitivity Independent
continuous
variable
(here for a
for antibiotic
) 2 parameters:
a (placebo effect) & b (slope of the exposure-effect
curve)
11-The dose-titration
Dose ranging designs
• Parallel randomized dose design
– generally recommended by Regulatory
Authorities
• Cross-over design
The parallel design: Statistical model
• The null hypothesis
– placebo = D1 = D2 = D3
• The statistical linear model
– Yj = wj + j
• Conclusion
– D3 = D2 > D1 > Placebo
ECVPT Toulouse 2009 -
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Placebo Dose
Response
1 2 3
*
*
NS
Selected dose
The parallel design
• Advantages
– easy to execute
– total study lasts over one period
– approved by Authorities
• Disadvantages
– "local information" (response at a given dose does not
provide any information about another dose)
– no information about the distribution of the individual
patient's dose response.
Parallel design: a population analysis
• The structural model
•Conclusion – Evaluation of 3 parameters:
– Emax (maximum response): efficacy
– Dose50 (dose producing half Emax): potency
– n (shape factor): selectivity
nn
n
DoseED
DoseEE
50
max
ECVPT Toulouse 2009 - 51
0 1 2 3
Dose, AUC
i (dose)
Re
sp
on
se
3 Subjects per dose
What is exactly an ED50 ?
What is exactly an ED50 ?
ED50 - is a hybrid parameter (PK and PD)
- is not a genuine PD drug parameter
𝑫𝒐𝒔𝒆 =𝑪𝒍𝒆𝒂𝒓𝒂𝒏𝒄𝒆 × 𝒆𝒇𝒇𝒊𝒄𝒂𝒄𝒊𝒐𝒖𝒔 𝒑𝒍𝒂𝒔𝒎𝒂 𝒄𝒐𝒏𝒄𝒆𝒏𝒕𝒓𝒂𝒕𝒊𝒐𝒏
𝑩𝒊𝒐𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚
PK PD
PK
ED50 vs EC50
A variable vs. a parameter
ED50 - is a hybrid variable (PK and PD)
- is not a genuine PD drug parameter
ECVPT Toulouse 2009 -
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PD
ilityBioavailab
ECclearancePlasmaED 50
50
_
PK
EC50 is a PD parameter allowing extrapolation
•Between formulations
•Between physiological status (renal failure)
•Between species
12-Measuring exposure and
response in PK/PD trial
Measuring variables in PK/PD
trial
• Full concentration time curve
• AUC
• Cmax , Cmin
• Biomarkers
• Surrogate
• Clinical outcomes
Measuring response Measuring exposure
Independent variable for
PK/PD modelling • Any concentration
in any matrix can
be used for
PK/PD modeling
The case
of diuretics
Clinical endpoint vs.
surrogate/biomarkers
• True clinical endpoints are patient
feeling, wellbeing, survival rate etc.
– because therapeutic endpoints may be
unavailable, impossible to evaluate, time
taking…
biomarkers & surrogates
Relation of serum cholesterol to coronary heart disease death*
* From Gotto AM Jr, et al. Circulation 81:1721-1733, 1990
Measuring response
e.g.: ACE inhibitors
biomarker
surrogate
Clinical outcome
Binding affinity
ACE inhibition
Renin/angiotensin
aldosterone
modulation
Blood pressure
Survival time
Well-being
Continuity Objectivity
Sensitivity
reproducibility
Validity +++
13-PK/PD modeling
Modelling issues:
Need professional skill
14-A working example: the
case of NSAIDs
Step 1: selection of an
appropriate inflammatory
model
As for a conventional dose titration,
PK/PD investigations generally require a
relevant experimental model (here a
kaolin inflammation model)
Possibility to perform PK/PD in patient
Step 2: selection of
endpoints
• As for a conventional dose
titration, PK/PD
investigations require to
measure some relevant
endpoints
• To measure the vertical forces, a corridor of walk is used with a force plate placed in its center.
• The cat walks on the force plate on leach.
Video
Measure of vertical forces exerted on force
plate
• The measure of vertical force and video control are recorded
Vertical forces (Kg)
Video
Measure of vertical forces
exerted on force plate
Measure of pain with
analgesiometer
• Cat is placed in a Plexiglas
box.
• A light ray is directed to its
paw to create a thermal
stimulus.
• The time for the cat to
withdraw its paw of the ray is
measured.
withdrawal time of the
paws (second) Video
0
1
2
3
4
5
0.0 0.6 1.5 2.5 3.5 4.6 6.1 8.1 10.1 12.1 23.5
Lo
co
mo
tio
n s
co
re
Time after robenacoxib administration (h)
Follow-up of mean locomotion score
Results: locomotion score
2 mg/kg
38.0
38.5
39.0
39.5
40.0
40.5
41.0
0.0 0.5 1.5 2.5 3.5 4.5 6.0 7.0 8.0 10.0 12.0 23.4
Re
cta
l te
mp
era
ture
(°C
)
Time after robenacoxib administration (h)
Follow-up of mean rectal temperature
Results: body temperature
2 mg/kg Robenacoxib
0
2
4
6
8
10
12
14
16
18
20
22
0.0 0.8 1.7 2.7 3.7 4.8 6.3 8.3 10.3 12.3 23.7
Wit
hd
raw
al ti
me
(s
)
Time after robenacoxib administration (h)
Follow-up of mean paw withdrawal time
Results: Pain (withdrawal time)
2 mg/kg
Step 5: modelling
IC50 40.0 ng/mL
ID50 = 0.59
mg/kg/24h
Robenacoxb : analgesic effect
0
200
400
600
800
1000
1200
1400
1600
1800
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12C
on
ce
ntr
ati
on
s (n
g/m
L)
Pa
in (
%)
Time (h)
𝒅𝑹
𝒅𝑻= 𝑲𝒊𝒏 𝟏 −
𝑰𝒎𝒂𝒙+𝑪𝒏
𝑰𝑪𝟓𝟎𝒏 +𝑪𝒏 -𝑲𝒐𝒖𝒕 × 𝑹
Step 6 : simulations
Simulated dose-response:
Robenacoxib: analgesic
effect
-250
-200
-150
-100
-50
0
50
100
0 4 8 12 16 20 24
Time (h)
Pain
sco
re (
%)
0.1 mg/kg
0.2 mg/kg
0.3 mg/kg
0.4 mg/kg
0.5 mg/kg
1 mg/kg
Simulations Robenacoxib: once vs. twice a day
Mean effect 32
% Mean effect 52
%
Simulated time course of pain
0
10
20
30
40
50
60
70
80
90
100
0 4 8 12 16 20 24
Time (h)
Pa
in (
%)
5 mg/kg
2 x 2.5 mg/kg
5 mg/kg split in 12
Mean effect 96 %
PK / PD modeling
CONCLUSION
• A powerful tool for many applications
• Requires clear understanding of
theoretical background and computer
software
• Veterinary pharmacologists should be
encouraged to consider PD, and not only
PK.