Transcript L-410198: Go/No-Go to Phase III and Dose Selection (I)

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
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”
Part I:
Experimental Medicine and
Biomarkers
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
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
DPP-IV Inhibitor Biomarkers
Disease or distal biomarkers
Meal bolus
GI tract
GLP-1 neuroendocrine
cells in ileum
Delayed
gastric
emptying
DPP-IV
Skeletal muscle
Neural
innervation
Glucose
Active-GLP1
inactive GLP-1
Insulin ( cell)
Pancreatic islet
Target
engagement
or proximal
biomarkers CNS
Glucagon ( cell)
Food intake/body weight
GI=gastrointestinal; CNS=central nervous system
Hepatic glucose
production
6
DPP-IV and Active GLP-1 levels
>80%Plasma
DPP-IV
inhibition
DP-IV Inhibition
Percent Inhibition† From Baseline
100
90
80
70
60
50
40
30
20
10
0
-10
20
Active GLP-1 (pM)
Percent Inhibition
From Baseline
~2-fold increases in
active GLP-1 levels
25
15
10
5
0
0 1 2
4
6
8
10
Placebo(n=56)
Placebo
MK-04312525mg
mg(n=56)
L-224715
L-224715
MK-0431200
200mg
mg(n=56)
† Back-transformed from the log scale
12 14
Hour
16
18
20
22
24
0
1
2
3
4
5
6
Hour
Placebo(n=56)
Placebo
L-224715
MK-043125
25mg
mg(n=56)
L-224715
MK-0431200
200mg
mg(n=56)
7
Herman et al., Diabetes 53(Suppl. 2): A82, 2004
Insulin and glucose levels post-OGTT
MK-0431 Enhanced
Insulin Levels by ~22-23%
MK-0431 Reduced Glycemic
Excursion by ~22-26%
Plasma Glucose (mg/dL)
Plasma Insulin (mcIU/mL)
40
30
20
10
0
0
1
2
3
4
5
320
310
300
290
280
270
260
250
240
230
220
210
200
190
180
170
160
150
OGTT
06
1
3
4
5
6
Hour
Hour
Placebo (n=56)
MK-0431 25 mg (n=56)
MK-0431 200 mg (n=56)
2
Placebo (n=56)
MK-0431 25 mg (n=56)
MK-0431 200 mg (n=56)
8
Herman et al., Diabetes 53(Suppl. 2): A82, 2004
DPP-IV Inhibitor Biomarkers
Tie Mechanism of Action Together
Disease or distal biomarkers
Meal bolus
GI tract
GLP-1 neuroendocrine
cells in ileum
Delayed
gastric
emptying
DPP-IV
Skeletal muscle
Neural
innervation
Glucose
Active-GLP1
inactive GLP-1
Insulin ( cell)
Pancreatic islet
Target
engagement
or proximal
biomarkers CNS
Glucagon ( cell)
Food intake/body weight
GI=gastrointestinal; CNS=central nervous system
Hepatic glucose
production
9
DPP-IV Biomarkers Allow Assessment of
Target Engagement
EC50 ~26 nM
EC80 ~100 nM
10
Herman et al. Clin Pharmacol Ther 78:675-88, 2005
DPP-IV Biomarkers Allow Assessment of
Target Engagement
11
Herman et al. Clin Pharmacol Ther 78:675-88, 2005
Biomarker: PPARg MOA
 specific
gene
expression
in adipocytes
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
 FFA’s
 FA uptake
 FA release
 insulin sensitizing
factor(s): Acrp30
 expression / action
of insulin resistance
factor(s): TNF
Smallinsulin
sensitive
adipocytes
 visceral
adiposity
 insulin action
in muscle / liver
 hyperglycemia
12
Reviewed in Wagner, 2002
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
Rosi
Gamma
Alpha
db/db
Rosi
Gamma
Lean
Rosi
vs
Gamma db/db
ACRP30
13
Reviewed in Wagner, J Clin Endocrinol Metab. 87:5362-6, 2002
Biomarker: Adiponectin
• Expression is correlated with
100
ACRP30 ( ug/ml)
glucose lowering in db/db
mice
• Recombinant ACRP30 has
glucose lowering properties
75
50
25
0
0
2
4
6
8
100
80
80
60
60
40
40
20
20
Glucose
Acrp30
0
(mg/dl)
100
GLUCOSE
120
Plasma Acrp30 (% Max Increase)
Plasma Glucose (% of Day 0 level)
350
120
300
250
200
*
150
*
0
0
2
4
6
8
Days of TZD dosing
10
12
100
0
2
4
6
8
14
Reviewed in Wagner, J Clin Endocrinol Metab. 87:5362-6, 2002
Biomarker: Adiponectin
At the protein level, ACRP30 is robustly regulated by PPARg treatment in db/db mice
800
Glucose, mg/dl
Glucose, mg/dl
800
600
400
200
600
400
200
0
0
0
50
100
150
Acrp30, g/ml
200
250
PPARg
Agonist
0
50
100
150
Acrp30, g/ml
Full
(Rosi)
Reviewed in Wagner, J Clin Endocrinol Metab. 87:5362-6, 2002
200
15
250
Biomarker: Adiponectin
• Pilot Study

g

14 day treatments
– Placebo,
– Fenofibrate
– Fenofibrate +
rosiglitazone
– Rosiglitazone


Plasma levels
increased in healthy
volunteers treated with
PPARg but not PPAR
agonists
Supports use as
biomarker
16
Wagner et al, J Clin Pharmacol. 45:504-13, 2005
% Change in insulin sensitivity (Dsi)
Biomarker: Adiponectin
500
In patients with type 2 diabetes,
ACRP30 rises with PPARg treatment
TRIPOD Study, Tom Buchanan,UCLA
400
300
200
100
0
-100
-100
0
100
200
300
% Change in total Adiponectin
Pajvani et al. JBC 279:12152-62, 2004
400
17
% Change in insulin sensitivity (Dsi)
Biomarker: Adiponectin
500
But, some patients will:
400
•Increase ACRP30
Without Concomitant
Increase in Insulin
Sensitivity
300
200
100
0
-100
-100
0
100
200
300
400
•Improve Insulin
Sensitivity Without
Concomitant
Increase in
ACRP30
% Change in total Adiponectin
18
Pajvani et al. JBC 279:12152-62, 2004
19
Pajvani et al. JBC 279:12152-62, 2004
% Change in insulin sensitivity (Dsi)
Change in Insulin Sensitivity vs. Change in HMW/Total Adiponectin
Pre- vs. Post-TZD Treatment (TRIPOD Study, Tom Buchanan)
500
n=40
400
300
200
100
0
-100
-50
0
50
100
% Change in HMW/Total
20
Pajvani et al. JBC 279:12152-62, 2004
Imaging as a Biomarker
Target Engagement and Dose of Aprepitant
Blockade of
NK1 receptors
after aprepitant
dosing
Brain NK1 Receptor Occupancy (%)
Binding of PET
tracer to NK1
receptors
Mean (± SE) Plasma Trough Concentrations of
Aprepitant
40/25
125/80 375/125
100
90
80
70
60
50
40
30
20
10
0
0
1
10
100
1000 10000
Aprepitant Plasma Trough Concentration (ng/mL)
Tracer Binding
Low
High
Hargreaves J Clin Psych 63: (suppl 11): 18-24, 2003
21
Imaging as a Biomarker
Percent of Patients
with Complete Response
Aprepitant: CINV Dose Finding Study
Time to First Emesis or Rescue
100
APR 375/250
80
APR 125/80
60
40
0
APR 40/25
Control
0
24
48
72
Hours
96
120
869-acm 40-42 Time Cr3 Feb. 15, 2003
22
Part II:
Novel Clinical Trial Designs
Response
Issues in Dose Selection
Standard Parallel Group Design
Dose
Response
Issues in Dose Selection
Increased Number of Doses to Confirm ED95
ED95
Wasted
Doses
Wasted
Doses
Dose
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
Response
Issues in Dose Selection
Increased Number of Doses and Adaptation
ED95
Dose
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
Up & Down Design
Simulated from Past Trial Results
• Single-dose dental pain study (total 399 patients)
–
–
–
–
–
–
51
75
76
74
76
47
placebo patients
Dose 1 patients
Dose 2 patients
Dose 3 patients
Dose 4 patients
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.
Simulated Up & Down Design
Completed 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 > 0
• Algorithm continues until all ibuprofen data exhausted
– originally planned precision for ibuprofen vs placebo
– (16 groups = 191 total patients)
Dental Pain Randomized Design
vs Up & Down Design Results
Number of Patients Studied
Dental Pain Randomized Design
vs Up & Down Design Results
60
40
76
75
80
51 48
76
74
47 47
42
24
24
6
20
0
placebo
Dose 1
Dose 2
Dose 3
Dose 4
Treatment Group
parallel group design
up&down
Active
Control
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
Acknowledgements
• John Wagner
• James Bolognese
• Gary Herman