Impact of Prior Knowledge on Drug Development Decisions: Case studies across companies

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Impact of Prior Knowledge on Drug Development
Decisions 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 jmandema_at_wequantify.com
• ACPS 10-19-2006

October 19, 2006
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Prior Information is always used for decision
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?

• 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
multiple 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 validity
of assumptions

• Advantage
• Better decisions

• Disadvantage
• Validity of assumptions

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Scope 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 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 Importance of accounting for differences
between patient populations
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One of the conclusions of the meta-analysis

• The net change in LDL-C is
• Bezafibrate 8 (p0.04)
• Fenofibrate 11 (p0.01)
• Ciprofibrate 8 (p0.005)
• Clofibrate 3 (p0.53)
• Gemfibrozil 1 (p0.68)
• 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
triglycerides

• mean LDL effect in trial normalized for dose and
  fibrate
• (size sample size)

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Example 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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With respect to LDL the only difference between
Statins is dose
After adjusting for differences in potency (ED50)
all statins share a common dose response
relationship for LDL
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Interaction between statins and ezetimibe is
characterized by simple interaction model
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A simple interaction model for ezetimibe and
statins

• The interaction for lipid effects could be
  described by a simple interaction model
• Only 1 additional parameter, ? required to
  characterize the magnitude of interaction
• ? gt 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.
• ? lt -1 indicates a reduced benefit

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Interaction model also characterized statin
gemcabene combination
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Interaction between Atorvastatin and gemcabene
(600 mg) and ezetimibe (10 mg)
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Value of model for development of novel lipid
altering 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 differences in
patient populations

• Meta analysis of 19 trials that evaluate
  Eletriptan and/ or Sumatriptan

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Pain relief at 2 hoursObserved response (mean,
95 CI)
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Pain relief at 2 hoursResponse adjusted for
differences in placebo effect
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Trial specific Random effects logistic regression
model

• 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

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Key question Encapsulation does not impact the
time course of response to Sumatriptan
o Commercial Sumatriptan ? Encapsulated
Sumatriptan
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But so much more was learned about the
differences in speed of onset and magnitude of
response between Eletriptan and Sumatriptan
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Benefit of Eletriptan 40 mg over Sumatriptan 100
mg
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Example value of understanding comparative
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 frequency
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Dose 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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Comparison of Efficacy and Tolerability of AEDs
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Comparison of Efficacy and Tolerability of AEDs
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Value 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

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Example value of biomarker-endpoint models

• 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

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Example value of biomarker-endpoint models

• 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 models

• 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 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 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 towardsModel-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 compounds product
  profile in relation to the compounds
  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