impact of prior knowledge on drug development decisions: case studies across companies jaap w...
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Impact of Prior Knowledge on Drug Impact of Prior Knowledge on Drug Development Decisions: Development Decisions:
Case studies across companies Case studies across companies
Jaap W Mandema, PhD
Quantitative Solutions Inc.
845 Oak Grove Ave, Suite #100
Menlo Park, CA 94025
Ph: 650-743-9790
Email: [email protected]
ACPS 10-19-2006
October 19, 2006
2 10/19/2006
Prior Information is always used for Prior Information is always used for decision makingdecision making
Topic of today
The use of mathematical models to formally (quantitatively) use prior information to enhance decision making
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What do models provide?What do models provide?
Enhanced Data analysis
More effective use of the available data, resulting in increased knowledge and better (more precise) decision making
Enhanced Trial design
Better understanding of the data we need and how best to obtain it to inform future decisions.
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Models improve decision making by combining Models improve decision making by combining multiple pieces of informationmultiple pieces of information
Include information across time points– Understanding of the time course of response
Include information across doses– Understanding of the shape of the dose response relationship (e.g.
Emax model)
Include information across trials– Accounting for differences in patient populations (e.g. disease
severity)
Include information across drugs– Understanding similarities in dose response (e.g. similar Emax for
analogues)
Include information across endpoints– Understanding of link between preclinical, biomarker and clinical
endpoints (e.g. similar relative potency/ efficacy)
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Trade-off between improved decisions and Trade-off between improved decisions and validity of assumptionsvalidity of assumptions
AdvantageBetter decisions
DisadvantageValidity of assumptions
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Scope of data integrationScope of data integration
Several to ~500 clinical trials
Several to ~15 endpoints– Preclinical, biomarker, clinical efficacy and
tolerability
Summary level data +/- individual patient level data– Better understanding of impact of patient
level covariates such as disease severity
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Scope of applicationScope of application
Investment of several large pharma companies
All therapeutic areas
From late pre-clinical through post approval– Models are continuously updated as new
information is obtained
Close collaboration between clinical pharmacology, statistics and medical specialties
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Example: Example: Importance of accounting for Importance of accounting for differences between patient populationsdifferences between patient populations
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One of the conclusions of the meta-analysisOne of the conclusions of the meta-analysis
The net change in LDL-C is– Bezafibrate 8% (p=0.04)– Fenofibrate 11% (p=0.01)– Ciprofibrate 8% (p=0.005)– Clofibrate 3% (p=0.53)– Gemfibrozil 1% (p=0.68)
However, the LDL-C response is dependent on the baseline Lipid profile, which is quite different from trial-to-trial
Very different relative effects are calculated when the differences in baseline lipids are accounted for
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Dependency of LDL effect of Fibrates on baseline Dependency of LDL effect of Fibrates on baseline triglyceridestriglycerides
Baseline Triglycerides
LD
L %
ch
an
ge
fro
m p
re-t
rea
tme
nt
100 500 1000
-20
02
0
mean LDL effect in trial normalized for dose and fibrate(size ~ sample size)
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Example: value of pharmacological assumptionExample: value of pharmacological assumption
Meta-analysis of Statins, Ezetimibe, Fibrates, and Niacin to compare effectiveness/ tolerability profile as function of dose
Focus on combination products
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Atorvastatin eq. Dose (mg)
LD
L %
ch
an
ge
fro
m p
re-t
rea
tme
nt
0 20 40 60 80 100 120 140
-60
-50
-40
-30
-20
-10
0
AtorvastatinCerivastatinFluvastatinLovastatinPitavastatinPravastatinRosuvastatinSimvastatin
With respect to LDL the only difference between With respect to LDL the only difference between Statins is doseStatins is dose
After adjusting for differences in potency (ED50) all statins share a common dose response relationship for LDL
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0 20 40 60 80
-60
-40
-20
0
Atorvastatin
0 20 40 60 80
-60
-40
-20
0
Lovastatin
0 20 40 60 80
-60
-40
-20
0
Pravastatin
0 20 40 60 80
-60
-40
-20
0
Simvastatin
Statin Dose (mg)
LD
L %
ch
an
ge
fro
m p
re-t
rea
tme
nt
Statin alone+10 mg Ezetimibe
Interaction between statins and ezetimibe is Interaction between statins and ezetimibe is characterized by simple interaction modelcharacterized by simple interaction model
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A simple interaction model for ezetimibe and A simple interaction model for ezetimibe and statinsstatins
The interaction for lipid effects could be described by a simple interaction model
Only 1 additional parameter, required to characterize the magnitude of interaction; > 0 means that the combined effect is greater than the
sum of the effects of the drugs when given alone. of 0 means that the combined effect is the sum of the
effects of the drugs when given alone. of -1 indicates a pharmacologically independent
interaction. < -1 indicates a reduced benefit
statinnonstatinstatinnonstatin EEEEELDL 01.0% 0
nn
n
drugEDDose
EDoseE
50
max
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Interaction model also characterized statin Interaction model also characterized statin gemcabene combinationgemcabene combination
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Interaction between Atorvastatin and Interaction between Atorvastatin and gemcabene (600 mg) and ezetimibe (10 mg)gemcabene (600 mg) and ezetimibe (10 mg)
0 20 40 60 80
-60
-40
-20
0gemcabene
0 20 40 60 80
-60
-40
-20
0
ezetimibe
Atorvastatin Dose (mg)
LD
L %
ch
an
ge
fro
m b
ase
line
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Value of model for development of novel lipid Value of model for development of novel lipid altering agentaltering agent
Validated methodology of response-surface analysis
Significantly increased power of phase II design
Enabled assessment of the competitive clinical profile of a new lipid altering agent when given alone or in combination with a statin.– Precise quantitative assessment of benefit of gemcabene/
atorvastatin vs. ezetimibe/ atorvastatin combination
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Example: accounting for random Example: accounting for random differences in patient populationsdifferences in patient populations
Meta analysis of 19 trials that evaluate Eletriptan and/ or Sumatriptan
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Fraction of patients with pain relief at 2 hours
0.2 0.4 0.6 0.8
Placebo
Eletriptan 20 mg
Sumatriptan 25 mg
Eletriptan 40 mg
Sumatriptan 50 mg
Eletriptan 80 mg
Sumatriptan 100 mg
Pain relief at 2 hoursPain relief at 2 hoursObserved response (mean, 95% CI)Observed response (mean, 95% CI)
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Fraction of patients with pain relief at 2 hours
0.2 0.4 0.6 0.8
Placebo
Eletriptan 20 mg
Sumatriptan 25 mg
Eletriptan 40 mg
Sumatriptan 50 mg
Eletriptan 80 mg
Sumatriptan 100 mg
Pain relief at 2 hoursPain relief at 2 hoursResponse adjusted for differences in placebo effectResponse adjusted for differences in placebo effect
Fraction of patients with pain relief at 2 hours
0.2 0.4 0.6 0.8
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Placebo
Eletriptan 20 mg
Sumatriptan 25 mg
Eletriptan 40 mg
Sumatriptan 50 mg
Eletriptan 80 mg
Sumatriptan 100 mg
Fraction of patients with pain relief at 2 hours
0.2 0.4 0.6 0.8
Placebo
Eletriptan 20 mg
Sumatriptan 25 mg
Eletriptan 40 mg
Sumatriptan 50 mg
Eletriptan 80 mg
Sumatriptan 100 mg
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Trial specific Random effects logistic Trial specific Random effects logistic regression model regression model
iij
ijij EDDose
DoseEEliefPainPit
50
max0})Re({log
P(Pain Relief)i represents the probability of a patient achieving pain relief in the jth treatment arm of the ith trial.
E0 represents the Placebo response; Emax is the maximum response; ED50 is the dose required to get 50% of maximum response.
i is a trial specific random effect with mean 0 and variance 2 to account for the heterogeneity among the trials.
– No additional heterogeneity was found for Emax
),)((~# ijijij NeventPbinomialevents
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Key question: Encapsulation does not impact the Key question: Encapsulation does not impact the time course of response to Sumatriptantime course of response to Sumatriptan
o Commercial SumatriptanΔ Encapsulated Sumatriptan
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But so much more was learned about the differences But so much more was learned about the differences in speed of onset and magnitude of response in speed of onset and magnitude of response between Eletriptan and Sumatriptanbetween Eletriptan and Sumatriptan
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Eletriptan
0 20 40 60 80 100
0.0
0.2
0.4
0.6
0.8
1.0
Sumatriptan
Dose (mg)
Fra
ctio
n of
pat
ient
s w
ith p
ain
relie
f
4 h2 h1 h0.5 h
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Benefit of Eletriptan 40 mg over Benefit of Eletriptan 40 mg over Sumatriptan 100 mgSumatriptan 100 mg
Time (hours)
Pa
in r
elie
f (%
pa
tien
ts),
diff
ere
nce
fro
m S
um
atr
ipta
n
0 1 2 3 4
05
10
Eletriptan 40 mg vs. Sumatriptan 100 mg
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Example: value of understanding comparative Example: value of understanding comparative clinical profile of anti epileptic drugs (AEDs)clinical profile of anti epileptic drugs (AEDs)
Comparative trials are limited because of large sample sizes required
Meta-analysis of 8 newer AEDs to compare effectiveness/ tolerability profile as function of dose– Literature data– FDA/ EMEA websites
Summary level data on almost 7000 patients with refractory partial seizures
Efficacy endpoints: – reduction in seizure frequency– proportion of patients with 50% or greater reduction in seizure
frequency (responders)
Tolerability endpoint: – proportion of patients withdrawing from trial due to AEs
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Dose response relationship for seizure frequencyDose response relationship for seizure frequency
Dose (mg)
Sei
zure
fre
quen
cy (
% c
hang
e)
0 100 200 300 400 500 600
-50
-40
-30
-20
-10
0
o
o
o
oo
o
o
o
o
o
o
Pregabalin
Dose (mg)
Sei
zure
fre
quen
cy (
% c
hang
e)
0 10 20 30 40 50 60
-50
-40
-30
-20
-10
0 o
oo
o o
oo
o
o
Tiagabine
Dose (mg)
Sei
zure
fre
quen
cy (
% c
hang
e)
0 200 400 600 800
-50
-40
-30
-20
-10
0
o
o
o
o
o
o
o
o o
o
o o o
o
oo
o
o
Topiramate
Dose (mg)
Sei
zure
fre
quen
cy (
% c
hang
e)
0 100 200 300 400 500
-50
-40
-30
-20
-10
0 o
o
o
o
o
o
o
o
o
o
Zonisamide
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Dose response analysis major findingsDose response analysis major findings
Significant random trial effect (heterogeneity) on mean response but not on treatment effect, validating placebo as an internal reference
Significant dose response relationship for each compound and each endpoint– High correlation between potency estimates for seizure
frequency and responder endpoints
Significant differences between the AEDs in potency and selectivity for each endpoint, i.e. – Therapeutic window is significantly different between
compounds
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Dropouts due to AEs (%)
Sei
zure
freq
uenc
y (m
edia
n %
cha
nge)
0 5 10 15 20 25 30
-50
-45
-40
-35
-30
-25
-20
Gab
Lam
Lev
Oxc
Pre
Tia
Top
Zon
Gabapentin 1800 mg
Lamotrigine 500 mg
Levetiracetam 3000 mg
Oxcarbazepine 1200 mg
Pregabalin 450 mg
Tiagabine 32 mg
Topiramate 400 mg
Zonisamide 400 mg
Comparison of Efficacy and Tolerability of AEDsComparison of Efficacy and Tolerability of AEDs
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Dropouts due to AEs (%)
% P
atie
nts
with
> 5
0% r
educ
tion
in s
eizu
re fr
eque
ncy
0 5 10 15 20 25 30
2025
3035
4045
50
Gab
Lam
Lev
Oxc
Pre
Tia
Top
Zon
Gabapentin 1800 mg
Lamotrigine 500 mg
Levetiracetam 3000 mg
Oxcarbazepine 1200 mg
Pregabalin 450 mg
Tiagabine 32 mg
Topiramate 400 mg
Zonisamide 400 mg
Comparison of Efficacy and Tolerability of AEDsComparison of Efficacy and Tolerability of AEDs
30 10/19/2006
Value of model for novel AED developmentValue of model for novel AED development
Provided understanding of competitive landscape and product opportunities
Aided in quick assessment of potential of new AED– It is possible to get a good understanding of the competitive
profile of the NCE with limited phase II data, i.e. small number of doses and limited sample size
31 10/19/2006
Example: value of biomarker-endpoint Example: value of biomarker-endpoint modelsmodels
Novel anti-coagulant for VTE prophylaxis
Analyzed dose response data for VTE and bleeding risk for Heparin, LMWH, Thrombin inhibitors, and FXa inhibitors after hip and knee surgery– Set targets and identify opportunity
Scale to NCE on basis of bio-marker data– Generated biomarker data internally because of inconsistency
of methods– Used to optimize Phase II design for prophylaxis
Established link between efficacy and safety for prophylaxis of VTE and treatment of VTE– Acute and chronic treatment period– Used to select dose for VTE treatment
32 10/19/2006
Example: value of biomarker-endpoint Example: value of biomarker-endpoint modelsmodels
Novel PDE-5 inhibitor intended for the treatment of male erectile dysfunction – Scale clinical profile of PDE5 inhibitors to NCE on basis of relative potency
(and efficacy) estimates from preclinical studies and Biomarker studies (efficacy) and first in man dose escalation studies (tolerability)
Model identified dose range to study– Wider instead of narrow range because of differences among “bio-
markers”
Model allowed for scaling to moderate/mild patient population to set appropriate targets and expectations in that patient population.
Model enhanced power of phase II design– Analysis of prior data jointly with NCE data reduced sample size from 350
to 200 for equal decision making power
Model put trial in decision context of ability to identify dose and competitive positioning for phase III and not solely showing statistical benefit vs. placebo.– Better tolerability predicted by biomarker was confirmed in clinical trial
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Example: value of biomarker-endpoint Example: value of biomarker-endpoint modelsmodels
Preclinical and biomarker data show increased selectivity for beneficial effect vs. AEs for NCE
Biomarker-endpoint model put potency and selectivity from the biomarker study in a clinical context– How much more effect can we expect at similar AEs
Short and directed phase II study can quickly answer key development uncertainties:– Does biomarker selectivity translate into clinical
selectivity?
– Is Emax for clinical efficacy large enough to allow for a meaningful benefit
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Opportunities at FDAOpportunities at FDA
Important to engage with Industry
Wealth of Information to mine that can be used for patient benefit– Understanding of trial-to-trial variability in response
• Explanatory covariates (disease severity)• Magnitude of random (non-explained) variability
– Safety modeling• Therapeutic index across drugs: is reduced safety a drug
effect or dose effect.
– Biomarker linking• Predictive power of biomarkers (QTc)
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Summary of ValueSummary of Value
Better understanding of competitive landscape and targets
Better understanding of NCE earlier in development– Learn from other compounds, endpoints, and species
Enabling major improvements in clinical trial design
Better understanding of impact of patient and disease characteristics– Disease severity– Special populations
Objective quantitative assessment of information– as long as we state our assumptions
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The current trend towardsThe current trend towardsModel-Based Drug DevelopmentModel-Based Drug Development
There is a tremendous opportunity to integrate the wealth of public and proprietary data spanning discovery and clinical into a probabilistic model of the compound’s product profile in relation to the compound’s competitors.
Utilize the smooth relationship across time, dose patient characteristics, and endpoints from our understanding of the underlying pharmacology and (patho)-physiology.
Models become knowledge repository and provide a quantitative basis for certain drug development and regulatory decisions