Approaches to FirstInMan and Beyond: Early Evidence of Target Engagement with Biomarkers and Innovat

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Approaches to First-In-Man and Beyond Early
Evidence of Target Engagement with Biomarkers and
Innovative Clinical Trial Designs

• Rajesh Krishna, PhD, FCP
• Clinical Pharmacology

AGAH-ACCP Annual Meeting 2006 Transatlantic
Strategies in Early Development Düsseldorf,
Germany
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Overview

• Part I
• Experimental medicine and biomarkers for early
  evidence of concept and target engagement
• Part II
• Adaptive designs to maximize dose-response
  information and select the winners

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Part I Experimental Medicine and Biomarkers
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Experimental Medicine

• Scope
• Designed to provide a preliminary assessment of
  pharmacologic activity, efficacy, and/or safety
  of new compounds in early clinical development
• Predictive of Phase III clinical efficacy /
  clinical outcomes
• Approaches
• Experimental medicine tools
• Biomarkers and surrogate endpoints
• Experimental models
• Imaging
• Molecular profiling
• Unique role as experimental medicine tool and in
  biomarker discovery

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Experimental Medicine

• Goals
• Increase efficiency of drug development
• Accelerate and improve quality of drug
  development decisions
• Augment understanding of test drugs, dose
  response, biology, and mechanisms of action
• Aid in regulatory evaluation and, where possible,
  regulatory approval of test drugs

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DPP-IV Inhibitor Biomarkers
Disease or distal biomarkers
Meal bolus
GI tract
Skeletal muscle
GLP-1 neuroendocrine cells in ileum
Delayed gastric emptying
Neural innervation
Glucose
Insulin (? cell)
Active-GLP1 ?inactive GLP-1
DPP-IV
Pancreatic islet
Target engagement or proximal biomarkers
Glucagon (? cell)
CNS
Food intake/body weight
Hepatic glucoseproduction
GIgastrointestinal CNScentral nervous system
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DPP-IV and Active GLP-1 levels
2-fold increases in active GLP-1 levels
gt80 DPP-IV inhibition
Placebo MK-0431 25 mg MK-0431 200 mg
Placebo MK-0431 25 mg MK-0431 200 mg
Herman et al., Diabetes 53(Suppl. 2) A82, 2004
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Insulin and glucose levels post-OGTT
MK-0431 Enhanced Insulin Levels by 22-23
MK-0431 Reduced Glycemic Excursion by 22-26
OGTT
Herman et al., Diabetes 53(Suppl. 2) A82, 2004
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DPP-IV Inhibitor Biomarkers Tie Mechanism of
Action Together
Disease or distal biomarkers
Meal bolus
GI tract
Skeletal muscle
GLP-1 neuroendocrine cells in ileum
Delayed gastric emptying
Neural innervation
Glucose
Insulin (? cell)
Active-GLP1 ?inactive GLP-1
DPP-IV
Pancreatic islet
Target engagement or proximal biomarkers
Glucagon (? cell)
CNS
Food intake/body weight
Hepatic glucoseproduction
GIgastrointestinal CNScentral nervous system
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DPP-IV Biomarkers Allow Assessment of Target
Engagement
EC50 26 nM EC80 100 nM
Herman et al. Clin Pharmacol Ther 78675-88, 2005
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DPP-IV Biomarkers Allow Assessment of Target
Engagement
Herman et al. Clin Pharmacol Ther 78675-88, 2005
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Biomarker PPARg MOA
? FFAs
? FA uptake ? FA release
?? specific gene expression in adipocytes
? insulin sensitizing factor(s) Acrp30 ?
expression / action of insulin resistance
factor(s) TNFa
PPARg ligand

• Selection strategy
• Examine gene expression data
• Select significantly up and down regulated genes
• Select putative secreted proteins (derived from a
  search of databases containing annotation of
  "secreted or extracellular")
• Derive MOA hypotheses for further testing

Small-insulin sensitive adipocytes ? visceral
adiposity
? insulin action in muscle / liver ? hyperglycemia
Reviewed in Wagner, 2002
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• WAT gene expression in lean and db/db mice
• Adiponectin is up regulated in lean mice by PPARg
  agonist treatment
• Adiponectin is down regulated in db/db mice
  relative to lean, but not regulated by PPARg
  agonist treatment as assessed by microarray
• Adiponectin is up regulated in db/db mice by
  RT-PCR

C57B/6
db/db
Lean vs db/db
Rosi Gamma
Rosi Gamma
Rosi
Gamma
Alpha
ACRP30
Reviewed in Wagner, J Clin Endocrinol Metab.
875362-6, 2002
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Biomarker Adiponectin

• Expression is correlated with glucose lowering in
  db/db mice
• Recombinant ACRP30 has glucose lowering
  properties

Reviewed in Wagner, J Clin Endocrinol Metab.
875362-6, 2002
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Biomarker Adiponectin
At the protein level, ACRP30 is robustly
regulated by PPARg treatment in db/db mice
PPAR? Agonist
Full (Rosi)
Reviewed in Wagner, J Clin Endocrinol Metab.
875362-6, 2002
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Biomarker Adiponectin

• Pilot Study
• 14 day treatments
• Placebo,
• Fenofibrate
• Fenofibrate rosiglitazone
• Rosiglitazone
• Plasma levels increased in healthy volunteers
  treated with PPARg but not PPARa agonists
• Supports use as biomarker

Wagner et al, J Clin Pharmacol. 45504-13, 2005
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Biomarker Adiponectin
500
In patients with type 2 diabetes, ACRP30 rises
with PPARg treatment TRIPOD Study, Tom
Buchanan,UCLA
400
300
200
Change in insulin sensitivity (Dsi)
100
0
-100
-100
0
100
200
300
400
Change in total Adiponectin
Pajvani et al. JBC 27912152-62, 2004
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Biomarker Adiponectin

• But, some patients will
• Increase ACRP30
• Without Concomitant
• Increase in Insulin
• Sensitivity
• Improve Insulin
• Sensitivity Without
• Concomitant
• Increase in
• ACRP30

Pajvani et al. JBC 27912152-62, 2004
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Pajvani et al. JBC 27912152-62, 2004
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Change in Insulin Sensitivity vs. Change in
HMW/Total Adiponectin
Pre- vs. Post-TZD Treatment (TRIPOD Study, Tom
Buchanan)
500
n40
400
300
Change in insulin sensitivity (Dsi)
200
100
0
-100
-50
0
50
100
Change in HMW/Total
Pajvani et al. JBC 27912152-62, 2004
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Imaging as a BiomarkerTarget Engagement and Dose
of Aprepitant
Mean ( SE) Plasma Trough Concentrations of
Aprepitant
Binding of PET tracer to NK1 receptors
Brain NK1 Receptor Occupancy ()
Blockade of NK1 receptorsafter aprepitant dosing
Aprepitant Plasma Trough Concentration (ng/mL)
Hargreaves J Clin Psych 63 (suppl 11) 18-24,
2003
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Imaging as a Biomarker Aprepitant CINV Dose
Finding Study
Time to First Emesis or Rescue
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Part II Novel Clinical Trial Designs
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Issues in Dose SelectionStandard Parallel Group
Design
Response
Dose
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Issues in Dose SelectionIncreased Number of
Doses to Confirm ED95
ED95
Response
Wasted Doses
Wasted Doses
Dose
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Bayesian Adaptive Designs

• Increase number of doses
• placebo a large number of actives
• Adaptive learning about dose response
• Prevent allocating patients to ineffective doses
• Borrowing strength from neighbouring doses and
  insuring continuity of response
• Stop dose-ranging trial when response at ED95 is
  known reasonably well

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Issues in Dose SelectionIncreased Number of
Doses and Adaptation
ED95
Response
Dose
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Up and Down Design

• Yields distribution of doses clustered around
  dose with 50 responders (ED50)
• 1st subject receives dose chosen based on prior
  information
• Subsequent subjects receive next lower dose if
  previous subject responded, next higher dose if
  no response
• Inference based on conditional distribution of
  response given the doses yielded by the dosing
  scheme

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Up Down DesignSimulated from Past Trial Results

• Single-dose dental pain study (total 399
  patients)
• 51 placebo patients
• 75 Dose 1 patients
• 76 Dose 2 patients
• 74 Dose 3 patients
• 76 Dose 4 patients
• 47 ibuprofen patients
• Primary endpoint is Total Pain Relief (AUC)
  during 0-8 hours post dose (TOPAR8)
• Up Down design in sequential groups of 12
  patients sampled from study results.

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Simulated Up Down DesignCompleted Dental Pain
Study

• Sequential groups of 12 patients (3 placebo, 6
  test drug, 3 ibuprofen)
• First group receives Dose 2
• Subsequent group receives next higher dose if
  previous group is non-response, next lower dose
  if response
• Response (both conditions satisfied)
• Mean test drug mean placebo 15 units TOPAR8
• Mean test drug mean ibuprofen gt 0
• Algorithm continues until all ibuprofen data
  exhausted
• originally planned precision for ibuprofen vs
  placebo
• (16 groups 191 total patients)

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Dental Pain Randomized Design vs Up Down
Design Results
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Dental Pain Randomized Design vs Up Down
Design Results
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Key Conclusions Simulated Up Down Design in
Dental Pain

• Up Down design is viable for dose-ranging in
  dental pain
• yields similar dose-response information as
  parallel group design
• Can use substantially fewer patients than
  parallel group design
• Logistics of implementation more complicated than
  usual parallel group design
• Can be accomplished in single center or small
  number of centers

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Acknowledgements

• John Wagner
• James Bolognese
• Gary Herman