Download Modelling and simulation to help define MABEL and Starting

Modelling and simulation to help
define MABEL and Starting dose in
FIH studies
B Laurijssens, BEL Pharm Consulting.
Steven W Martin, Pharmacometrics
Group, Dept Clinical Pharmacology,
Pfizer, Sandwich Labs, Kent, UK.
How should we select a starting dose ?
Base the starting
dose on
toxicology in
animals
Base the starting
dose on
Pharmacology
in animals
Base the starting dose
on toxicology in animals
and expected
pharmacology in humans
Slide 2
What is the right thing to do ?
1. More accurately predict exposure in humans
–
Scaling to man should use state of the art approaches
– PK scaling to humans
2. Prediction of Pharmacology in Humans (MABEL)
–
Inter-species differences in binding, relative time (e.g.
normalized to lifespan), signalling or pathway differences
should be taken into account
3. Exposure-Toxicity relationship in animals
–
Need to take account of NOAEL
• To estimate a safety margin
– All predictions are relative to observed toxicology
– Pharmacology usually evident prior to toxicology
4. Apply a Safety Margin/Factor to predicted dose
–
To ensure the safety and wellbeing of subjects in the trial
Slide 3
4. What drives the “safety factor”?
Allow a safety adjustment based on level of risk
1.
Safety Margin depends upon patient population
¾
2.
Higher risk molecules would be those that:
¾
¾
¾
¾
¾
¾
3.
In oncology patients willing to accept smaller safety
margin than in healthy volunteers
Are novel
Are very potent
Are agonists
Have low species cross-reactivity
Have steep dose-response curves
Have a high degree of uncertainty in the calculation of the MABEL.
Additional consideration should be given to molecules
with pleiotropic effect
•
Biological cascade or cytokine release leading to
amplification of an effect that might not be sufficiently
controlled by a physiologic feedback mechanism (eg in
Slide 4
the immune system or blood coagulation system)
How do we determine safe starting dose ?
Pharmacokinetics
1. Prediction in humans
• Allometric Scaling
• Mechanistic Models
• PBPK Models
1.
2.
3.
4.
Pharmacology
1. Prediction in humans
• Receptor occupancy (Kd)
• PK model + RO
• Mechanistic PKPD models
Calculate MABEL
Perform risk assessment of drug
Potentially apply safety factor
Quote FIH dose relative to NOAEL
Slide 5
Modelling is vital when the concentration or doseresponse relationships are not straight forward
1. Time delays between pharmacokinetic and
Pharmacodynamics
–
–
–
Active metabolites
Equilibrium delays (e.g. Oxycodone)
indirect response models
2. Complex Pharmacodynamics Response
–
Biological systems that develop tolerance or rebound
(Benzodiazepine & exaggerated anxiety)
3. Complex Pharmacological System
•
–
Transduction delays (e.g. AMG531, EPO, GNRH agonists)
Drug response is the results of the interaction on many
physiological pathways; e.g. inflammation, bone
remodelling, Endocrine pathways.
4. Complexities in translation of PD effect across species
Slide 6
When would we want to use
Modelling to estimate MABEL ?
• When the drug response takes time to develop and is
not directly related to plasma concentrations.
– May be due to an active metabolite or equilibrium delay between
plasma concentration and biophase (eg Oxycodone) causing Hysteresis.
Dose
Disposition
Direct PD
Response
Ce
Biophase
Distribution
Pharmacokinetics
(mm from Baseline)
Equilibrium
Delay
Pupil Diam
Cp
Anticlockwise Hysteresis
Elapsed time
after dosing
Lalovic Clin Pharm Ther. 2006
Pharmacodynamics
Oxycodone ng/mL
Slide 7
When would we want to use
Modelling to estimate MABEL ?
• When there is a lag time in drug response even after the
drug reaches the biophase (e.g. AMG531, EPO, INFa)
AMG531
PK
Dose
Transduction
delays
Cp
Biosignal
PD
Response
0
1
Time 2(days)
3
4
PD
Disposition
Pk
PD
Slide 8
Outline:
Starting Dose Calculation for FIH
• Predicting PK in humans
• Predicting human pharmacology (MABEL)
• Pulling it all together
• Working example
• Conclusions
Slide 9
Methods for predicting human PK
1. Allometric scaling
– Simplest approach
– Very reliable for some small molecules and biotherapeutics
(not TMD)
– Relates PK parameter to body weight
2. (Semi-)mechanistic-based PK models
– Takes into account species and disease state differences
– Better for extrapolation beyond dose-range
– Simcyp Simulator for small molecules
3. Physiologically-based PK models
– May be most accurate method to predict across species
– [not being covered in this course]
Slide 10
1. Allometric Scaling for PK Prediction
X = a * Wb
b
a: coefficient, :allometric scaling
exponent
•
•
Usually performed on log-log scale
Exponent tends to be similar across
molecules
– CL: b= 0.75
[α BSA]
– Vd: b = 1
[α BW]
– t½: b = 0.25
[1-0.75]
Human Predicted
4
Log [Vss (mL)]
Allometric scaling relates a variable to
size
• Variable: Vd, CL,
• Size: Body weight, maximum life
span, brain weight, body surface
area
Rhesus Monkey
2
Mice
Vss = 188*Wt(0.95)
0
-2
-2
-1
0
1
2
Log[Bwt (Kg)]
Slide 11
3
Methods for predicting human PK
1. Allometric scaling
–
–
–
Simplest approach
Very reliable for some small molecules and biotherapeutics (not
TMD)
Relates PK parameter to body weight
2. (Semi-)mechanistic-based PK models
–
–
–
–
More reliable for target-mediated disposition (membrane bound
targets)
Takes into account species and disease state differences
Better for extrapolation beyond dose-range
Simcyp Simulator for small molecules
3. Physiologically-based PK models
–
–
May be most accurate method to predict across species
[not being covered in this course]
Slide 12
2. Mechanistic PK models:
why are they needed?
Membrane bound, internalizing antigen which binds MAb and undergoes target
mediated disposition (TMD)
1000000
100000
Humans Predicted
Human Observed
2
1
10
Note: Non-linearity
occurs where Cp
>>> Kd (3x10-12M)
10
5
100
Concentration (ug/mL)
1000
20
50
10000
100
Monkeys
Predicted concentrations
Observed concentrations
0.1
1
Serum Concentration (ng/mL)
Parameters Scaled to Humans
•Inter-compartmental Cl and VoL
•Production and elimination of antigeN
Human parameters
•Linear component cl and vo
0.003mg/kg
0.01mg/kg
0.1mg/kg
1.0mg/kg
3.0mg/kg
0
7
14
21
28
35
42
0
Time(Days)
100
200
300
Time (h)
400
Slide 13
500
Outline:
Starting Dose Calculation for FIH
• Predicting PK in humans
• Predicting human pharmacology (MABEL)
• Pulling it all together
• Working example
• Conclusions
Slide 14
Methods used for predicting human
pharmacology
• Predicted receptor occupancy (Kd)
• PK model coupled with receptor occupancy
• PK model coupled with ex vivo assay
• PK model as empirical “PK/PD” model in absence
of biomarker or other in vivo PD data
• Mechanistic PKPD models
Slide 15
Why is receptor occupancy not straight
forward, especially for Biotherapeutics?
1. [MAb] is similar to target levels
– More complex equation needed
2. Limited distribution leads to Cplasma>> Cbiophase
– Target Cplasma needs to be much higher
3. On and off-rates at receptor quite slow for Mab
– Equilibrium calculations (e.g. Kd based) may over predict
receptor occupancy.
4. Target (receptors or ligands) have inherent turnover rates
5. Binding of target by antibody changes kinetics of target
(i.e. internalization)
– resulting in RO that is not predictable by pharmacological
equilibrium approaches
Slide 16
1. Estimating Receptor Occupancy: MAb
Standard Receptor Occupancy Calculation for
[MAb]>>target (Also termed the Kd of Duff calculation)
What do we need to
calculate RO
Only KD
Slide 17
2. Diffusion barrier to biophase
Panitumumab (Anti-EGF MAb)
Plasma concentration 100- fold >> Tissue Concentration
Slide 18
3. Impact of changing Koff rate
on RO calculations
PKPD Model (TMD)
Dose
Ab
+
Rin
Ag
kon
koff
kdeg
kel
Ab
Ag
kint
Duff Formula
Dose
Ab + Ag
kon
koff
Ab
Ag
Slide 19
4. Impact of Target Turnover
on RO Estimation
PKPD Model (TMD)
Dose
Ab
+
Rin
Ag
kon
koff
kdeg
kel
Ab
Ag
kint
Duff Formula
Dose
Ab + Ag
kon
koff
Dose For 10% Receptor Occupancy
• At high receptor turnover rates, higher molar excess of MAb is
required (Not accounted for in simple Kd model)
-1
10
PKPD prediction
-2
10
Duff formula
-3
10
Ab
Ag
1.000
10.000
100.000
Receptor turnover half- life (hours)
Slide 20
4. Impact of target/receptor turnover:
variability across species and target
Slide 21
Methods used for predicting human
pharmacology
• Predicted receptor occupancy (Kd)
• PK model coupled with receptor occupancy
• PK model coupled with ex vivo assay
• PK model as empirical “PK/PD” model in absence
of biomarker or other in vivo PD data
• Mechanistic PKPD models
Slide 22
4. Impact of target turnover:
Impact on FIH dose selection
Subcutaneous Dosing
Slide 23
PKPD model-based approach to MABEL
• Establish a mechanism-based model in a relevant animal
species to demonstrate the relationship between dose and
RO.
• Determine RO and pharmacological effect relationship.
• Extrapolate model to humans using all relevant data (literature,
in vitro human etc)
• Perform simulations considering both uncertainty in model
parameters and in scale-up
• This approach should help select a more rational starting dose
for FIH within the minimum anticipated biological effect level
(MABEL) principles using all relevant literature and project level
data
Slide 24
What is an appropriate level of
receptor occupancy for first dose?
90% RO may be appropriate
for certain ANTAGONISTS
Receptor Occupancy (%)
100
80
60
Will also depend on:
• target (novel? )
• therapeutic area (oncology?)
• preclinical toxicology profile
• pharmacology
(immunomodulatory?)
•Effector Mechanisms ADCC or
ADC are NOT directly related to
RO
~10% RO may be more
appropriate for most
ANTAGONIST
40
20
0
0.0001
0.001
0.01
0.1
1
10
Dose (mg/kg)
<10% RO is more
appropriate for an AGONIST
Slide 25
FIH Starting Dose Working Example
• Molecule: IgG1 Fusion Protein (Peptide) NPLATE (AMG531)
• Action:
Agonist
• Mechanism:
– Promotes the viability and growth of megakaryocyte colonyforming cells, increasing platelet production
• Target:
– Binds the TPO receptor (peptide is distinctly different from TPO)
• Competitor info:
– Similar mechanism Genentech rhu-TPO, Amgen PEG-MGDF
• Background information:
–
–
–
–
–
Molecular weight = 59,000 Da
Platelets 2.3 x 1011 L
TPO Receptors/ Platelet 56
(Baseline) TPO Receptors 0.043 nM
Kd = 14 nM
Slide 26
AMG-531 Preclinical Data Available
In vitro studies
• Platelet binding relative to TPO
– Rabbit, mice, rat, monkey, human
• MK-CFU proliferation assays
– Monkey and human
In vivo studies
• PK FcRn knock-out and wild type (single dose)
• 1 mth tox in rat and monkey
Slide 27
In vitro results:
Platelet binding across species
Competition assay with TPO
• number of receptors/platelet
• human : ~60
• monkey : ~30
• rat :
~6
• rabbit :
~2
MK-CFU proliferation assays
•Similar results in both monkeys
and humans for AMG531 and
MGDF
What can you conclude from this information?
• species specificity?
•AMG-531 competes at least as effectively as TPO, for the same
binding sites on Mpl receptor.
•Monkeys appear the most similar to humans for binding and in
proliferation assay, Also observed with first generation compounds
Slide 28
In vivo results:
AMG-531 PK in FcRn KO & WT Mice
What can you conclude from this information?
•PK characteristics in wild type mouse?
•Slower Clearance and smaller volume of distribution
• Role of FcRn receptor?
•Fc receptor binding activates a salvage pathway that prolongs serum levels
Slide 29
AMG531: Prediction of human PK
Predicted human Vss = 122 mL/kg
Predicted human CL = 7.8 mL/hr/kg
Human Predicted
3
Human Predicted
2
Log [ CL(mL/hr)]
Log [Vss (mL)]
4
Rhesus Monkey
2
Mice
Vss = 380*Wt(0.89)
0
Rhesus Monkey
1
0
Cl = 9.53*Wt(0.97)
Mice
-1
-2
-2
-2
-1
0
1
2
3
-2
Log[Bwt (Kg)]
-1
0
1
2
Log[Bwt (Kg)]
Is allometric scaling appropriate for this type of molecule?
Are the parameter estimates and exponents reasonable?
Slide 30
3
AMG-531 In vivo pharmacology studies
• Mice
– Single dose 100 to 1000 µg/kg
– Platelet numbers increased 4.7 to 8.4 fold
– MABEL dose <10 µg/kg
• Monkeys
– Single dose
– Doses 2000 µg/kg
– Platelet numbers increased 2-fold
Does this tell you anything about species sensitivity?
Mice appear to be a more sensitive species than monkeys
Slide 31
Toxicology Information
Monkeys
–
–
–
–
Dosing 3x week (4 wks)
Doses 500, 1000 and 5000 µg/kg
NOAEL = 5000 µg/kg
Platelet count increased 4-6- fold at NOAEL
Rats
–
–
–
–
Dosing 3x/week (4 weeks)
Doses 10, 30 and 100 µg/kg
LOAEL = 10 µg/kg myelofibrosis of bone marrow
Platelet count markedly (>6-fold) increased at all doses
Does this tell you anything about species sensitivity?
•Rodents appear to be the more sensitive species
Slide 32
PKPD Approach:
Platelet kinetics
PKPD Model developed for PEG-rHu-MGDF
Effects of PEG-rHu-MGDF
PKPD model of platelet kinetics was used to
predict the effects of AMG-531 in humans
• allometric scaling of PK
• in vitro potency scaling
Predicted no effect dose in humans
for AMG-531 was 10 µg/kg
Slide 33
Pharmacology Approach
Estimate Receptor Occupancy
DRUG
AMG-531
Total Number
TPO Receptors
On Platelets
+
Drug-receptor
Complex
Kd = 14 nM
•
•
•
•
Dose 0.01 mg/kg
(i.e. 10ug/kg)
Mwt ~ 59,000
Plasma Volume 2.5 L
AMG-531 = 11.86 nM
(IV Cmax )
•
Platelets 2.3 x 1011 L
•
TPO Receptors/
Platelet = 56
•
(Baseline) TPO
Receptors = 0.043 nM
•
AMG-531-TPO
Receptor Complex
• 0.011 nM
(At Equilibrium)
• 25% Receptor
Occupancy
See Excel Spreadsheet Calculator to estimate receptor occupancy
Slide 34
Predicting Pharmacology in Humans
for AMG-531
Rats
Cynomolgus Monkeys
• LOAEL = 10 µg/kg
• NOAEL = 5000 µg/kg
– Cmax = ~200 ng/mL
– AUC =
~80 ng*hr/mL
– Cmax = ~100 µg/mL
– AUC = ~ 1000 µg*hr/mL
Human Equivalent (HED) Human Equivalent (HED)
• Dose§ 1.61 µg/kg
– Cmax# = ~32 ng/mL
– AUC# = ~2.1 ng*hr/mL
• Dose § 1600 µg/kg
– Cmax# = ~ 32 µg/mL
– AUC# = ~ 230 µg*hr/mL
Apply Safety Margin 10 fold
Apply Safety Margin 10 fold
MRSD = 0.16 µg/kg
MRSD = 160 µg/kg
# Based upon Allometric Scaling of CL and Volume (slide 40)
35
§ Based upon FDA Guidance on HED; divide animal dose by 6.2 rat, 3.1Slide
monkey.
AMG-531 What is a safe starting dose ?
• Which species do you think is most predictive of human
– rat, monkey ?
• How should we predict human PK ?
– Allometric or TMD PKPD modelling ?
• Compare the starting doses from various methods?
– 1% RO = Dose of 0.3 ug/kg
– PKPD Approach = Dose 10 ug/kg
– HED Approach Rat = Dose 0.16 ug/kg
– HED Approach Cyno = Dose = 160 ug/kg
• What should be the FIH starting dose ?
• What is the predicted pharmacological response at that dose?
Slide 36
Decisions made prior to FIH for AMG-531
• Which species is most predictive of human
– monkey (based upon MGDF scaling)
• How should we predict human PK ?
– Allometric (provided good prediction)
• What is a safe starting dose for FIH?
– 10 µg/kg based upon mechanistic PKPD model
• What is the predicted pharmacological response at that
dose?
– 10 µg/kg no effect dose
Slide 37
AMG-531 FIH results: First cohort
Unanticipated Robust Pharmacology
First Dose 10µg/kg
Platelet Count (109/L)
1000
Normal
Placebo
0
5
10
15
20
Study Day
25
30
35
Slide 38
AMG531: Findings after 1st Cohort
• New In vitro binding studies showed
human cell lines to have a much higher
affinity (slower off rate) than monkeys –
If they had more robust Koff data then
FIH starting dose would have been 0.3
ug/kg
Subsequent in vitro studies indicate order
of species sensitivity
Mouse>Human>Rat>Monkey>Rabbit
Slide 39
Pharmacology Approach
Estimate Receptor Occupancy
DRUG
AMG-531
Total Number
TPO Receptors
On Platelets
+
Drug-receptor
Complex
Kd = 0.6 nM
•
•
•
•
Dose 0.01 mg/kg
(i.e. 10ug/kg)
Mwt ~ 59,000
Plasma Volume 2.5 L
AMG-531 = 1.87 nM
(IV Cmax )
•
Platelets 2.3 x 1011 L
•
TPO Receptors/
Platelet = 56
•
(Baseline) TPO
Receptors = 0.043 nM
•
AMG-531-TPO
Receptor Complex
• 0.032 nM
(At Equilibrium)
• 89% Receptor
Occupancy
Additional in vitro study indicates AMG-531 binds more tightly
(slower off rate) to human cells than monkey to give a lower Kd
(0.6 vs 14 nM)
Slide 40
AMG531: FIH dose de-escalation
Bing Wang et al. Clinical Pharmacology & Therapeutics 2004 76(6) 628-38
Slide 41
Conclusions
• Know your Human! Pharmacology
• Be clear and transparent about the underlying assumptions
– quality of the data
– risks
– strength of assumptions made
• Manage uncertainty
– Sensitivity analysis, worst case scenarios, and robust designs
– safety margins, proper monitoring can address these
• Merely “calculating” RO is not sufficient you need to predict a
pharmacological response in humans to estimate MABEL.
• Case by Case
• Try the new web based software from Implore Foal Labs
http://implore.glenavy.com/
Slide 42
Slide 43
Steady-State calculation of 1:1 binding of a drug to a target
Based on A+BC where Kd = [A][B]/[C]
Assuming i) no turnover (elimination) of target, ii) no loss of drug to
distribution & clearance and iii) instantaneous equilibrium
This is useful for estimation of a dose of a biotech product in man for the first
minutes to hours of exposure, before significant distribution and clearance of
drug take place. To take distribution, clearance and competing entities into
account requires far more extensive differential equation systems together with
supporting biological literature.
Philip Lowe, June 2007.
Inputs
Dose
0,0003 mg/kg
Kd
0,6 nM
Molecular wt
59000 g/mole
Cell count Bas 2,00E+11 cells per L blood
Target per cell
59 receptors per cell
Body weight
70
Volume plasm
2,5
Volume blood
5
Avogadro
6,02E+23
kg
L
L
per mole
(Initial estimate nM)
(IgG)
(Hospital laboratory ranges)
2.5 L for standard 70 kg human
5 L for standard 70 kg human
Calculations: with expression of receptor
Dose_nmoles
0,36 nmoles
TDc
0,14 nM
2,8 mg/L or µg/mL Total Drug concentration
TTc
0,039 nM
Total Target concentration
DTc
0,007 nM
Drug-Target complex concentration
Occupancy
18,4 percent
Quick calculation: assume receptor
Occupancy
19,2 percent