Drug-Disease Modeling Applied to Drug
Development and Regulatory Decision
Making in the Type 2 Diabetes Mellitus Arena
Tokyo, Japan, December 8, 2015
Stephan Schmidt, Ph.D. Assistant Professor
Center for Pharmacometrics and Systems Pharmacology
University of Florida at Lake Nona
Overall Trend in R&D Efficiency
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Scannel et al. (2012) Nat Rev Drug Disc 11: 191-200
Current Trends in Drug Development: Costs
3
* Adapted from ‘Biopharmaceutical Research Industry 2013 Profile.’ Pharmaceutical
Research and Manufacturers of America. 2013.
• Average cost to
develop a new drug is
~ $1.2 Billion
The Learn/Confirm/Apply Paradigm
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Discovery Preclinic Phase I Phase II Phase III Phase IV
From today’s phased approach:
Discovery Learn Confirm
Optimize execution Optimize understanding
to maximize medical value
To Learn & Confirm:
Quantitative Pharmacology (M&S)
5
Visser et al. (2014) CPT Pharmaco Sys Pharmacol
Keys to Successful Use of M&S For Strategy and Decision-Making
in Drug Development
6
Who is the patient?
Disease biomarkers
Understand pathophysiology
Identify the right target
Define the shape of D/R or PK/PD
curves
3 Key Questions that Define the Context for M&S
7
What do we want to know?
How certain do we need to be?
What are we willing to assume?
ALL MODELS ARE BUILT “FIT FOR PURPOSE”
What Do We Want to Know?
EFFECTS
PATIENT
REGIMEN
DRUG RESPONSE SURFACE OR USERS MANUAL
8
What Do Clinicians Need to Know?
What is a reasonable initial
dosage regimen?
How to adjust for intrinsic
and extrinsic factors?
When will effect be seen?
When will effect plateau?
How to know when to
change dose?
What happens when dose is
skipped? 9
How Certain Do we Need to Be?
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What Are We Willing to Assume?
Barrett, ASCPT, 2014
Lung
Drug in
Drug out
Fat
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Application: Clinical Trial Simulations
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Adapted from: Garhyan et al. In: Applied Pharmacometrics (2014) 139-159
What Impact Does This Have on Regulatory Decision Making?
Critical Part of Regulatory Decision-Making
In theory, any and all clinical situations where physicians
need information about dosing can be tested during drug
development.
However, ethical and practical limit the number of studies
that a sponsor can conduct.
CONDUCT LEARN CONFIRM PREDICT WAIVE
13
Challenge: Relating Knowledge & Data
14 Aronow, AAPS, 2014
Challenge: Different Time Scales for PK&PD
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Implications for Drug-Disease Models
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OUTCOME
(YEARS)
Slow biomarker(s)
(MONTHS)
Fast biomarker(s)
(MINUTES to HOURS)
EFFECT
PHARMACODYNAMICS
DOSE (PK)
(MINUTES to HOURS)
PHARMACOKINETICS
Drug-Disease Model Setup
Drug Effect Drug
Conc.
Bio-
marker
Pharmacokinetics (PK) Pharmacodynamics (PD)
PK/PD Models
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Case Study: Diabetes Mellitus
Is a chronic progressive disease
One of the top 10 leading causes of death
Type 2 is the most common form
To date, >8% of the global adult population (>380 million people) are
affected (T2DM)
Expected rise to ~600million people worldwide by 2035
Cost: 11% of the global healthcare budget in 2013 ($US 548 billion)
and rising ($US 612 billion)
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Key Processes in Glucose Homeostasis
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Biomarkers
Lesko and Atkinson; Ann Rev Pharmacol Toxicol. (2001) 41:347-66
Biomarkers are generally defined as:
“Characteristics that are
objectively measured and evaluated as indicators of
normal biological processes, pathogenic processes, or
pharmacologic responses to a therapeutic intervention”
Fasting Plasma Glucose
• Biomarker for diabetes
• Normal level 4.0 – 6.0 mmol/L
• t1/2 dependent on insulin
concentrations
HbA1c
• Biomarker for sustained glycemic
control
• Non-enzymatic glycation of hemoglobin
• Normal level 3.6 – 5.4%
• t1/2 ≈ 100 – 120 days (cf. RBCs)
Fasting Serum Insulin
• Biomarker for diabetes
and insulin tolerance
• Normal level < 11 mU/L
• t1/2 ≈ 4 – 6 min
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Pharmacometric (Drug-Centric) Models
Study in 624 Type 2 Diabetes
Mellitus patients evaluating the
long-term effect of modified
release gliclazide on fasting
plasma glucose (FPG) levels
TftFPGtFPG 0
α is a hybrid constant that does not
provide any information about the
underlying physiological parameters
and may also change over time
Modified from: Frey et al., Br J Clin Pharmacol (2003) 55:147-157
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Application of Pharmacometric Models
• Pharmacometric models are currently used to:
1) Quantify treatment response (Δ from placebo)
2) Support dose selection
3) Inform clinical trial design
• However, they face limitations with characterizing:
1) Complex, multilevel (disease) processes
2) The impact of the patient’s disease status on treatment response
Need for more mechanistic modeling approaches to explain the dynamic interaction between drug, biological system and underlying disease processes
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Mechanism-Centric Models
Drug EFFECT Target
exposure
Physiologically-
based
pharmacokinetic
modeling
Target
binding
&
activation
Receptor
theory
Trans-
duction
Dynamical
systems
analysis
Homeostatic
feedback
Modified from: Danhof et al.; Annu Rev Pharmacol Toxicol (2007) 47:357-400
PK PD 24
Link to Biomarkers
Modified from: Danhof et al.; Pharm Res. (2005) 22:1432-7
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Identification of Clinically Relevant Covariate Relationships
Drug
Dose EFFECT
Modified from: Danhof et al.; Pharm Res. (2005) 22:1432-7
Covariates
PK PD
Modified from: Swiss Med Wkly. (2012)142:w13629
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Physiological Response to Food Intake
GI-Tract
GLP-1 Glucagon-like peptide 1 • Stimulates insulin • Inhibits glucagon, somatostatin, gastric emptying and food-intake • Serum conc. ≈ 6 ng/mL • t1/2 ≈ 2 min
GIP Gastric inhibitory polypeptide •Stimulates insulin and fat storage •Inhibits somatostatin •Serum conc. ≈ 16 ng/mL •t1/2 ≈ 7 min
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Integrated Incretin-Glucose-Insulin Model
Jauslin-Stetina et al., J Clin Pharmacol (2011) 51:153-164
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Application of Mechanism-Centric Models
• Mechanism-centric models are currently used to:
1) Characterize the dynamic interaction between drug, biological system and disease at multiple (biomarker) levels
2) Evaluate the effect of combination therapy
3) Distinguish between treatment effects (given an appropriate study design)
• However, they face limitations with characterizing:
1) Multiple (disease) pathways contributing to the clinical condition
2) T2DM patients are treated as a homogeneous patient population
3) Primarily focused on evaluation of efficacy
Network-centric models may be needed to sufficiently characterize on- as well as off-target effects
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Systems Pharmacology (Network-Centric) Models
Modified from: Kohl et al., Clin Pharmacol Ther. (2010) 88:25-33
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Application of Network-Centric Models
• Network-centric models are currently used to:
1) Characterize the pertinent physiology that comprise the key pathways or targets of interest
2) Quantitatively integrate relevant biology across systems
3) Explore the impact of (novel) therapeutic interventions on the system
• However, they face limitations with characterizing:
1) Clinical data due to the inherent complexity of the model
2) Parameter values obtained from the literature for informing these models can be highly variable between settings
3) Link to long-term clinical outcome is frequently missing
Simplified version of these network-centric models may have to be developed that conserve key dynamic properties
32
Wait a Second!
Aren’t physiologically-based pharmacokinetic (PBPK) models using information on metabolic and transporter networks?
Yes.
So could one call them network-centric (Systems Pharmacology) models?
Yes. They provide the pharmacokinetic front-end to systems pharmacology models.
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Blo
od
Lung
Rapidly perfused
organs
Slowly perfused
organs
Kidney
Liver
Intestines
Blo
od
System component
(drug-independent)
Predict, Learn, Confirm, Apply
Intrinsic/extrinsic Factors
Huang and Temple, 2008
Individual or combined effects
on human physiology
Zhao et al. (2011) Clin Pharmacol Ther 89: 259-67
PBPK Model Elimination
Dosing
ADME, PK, PD and
MOA
Metabolism
Active transport
Passive diffusion
Protein binding
Drug-drug interactions
Receptor binding
Drug-dependent
component
PBPK Model components
Physiologically-Based Pharmacokinetic Models
How is PBPK Being Utilized by Sponsors?
• Increased use of PBPK by drug developers
• Majority of the cases were related to drug-drug interactions (~ 60%); pediatrics ranks the second
35
Huang et al, J Pharm Sci, 2013 Pan, ASCPT Annual Meeting, 2014, Atlanta, GA
Extension to a PBPK/PD Model for Diabetes Mellitus
36 Schaller et al. (2013) CPT Pharmacometrics Syst Pharmacol. Aug 14;2:e65. doi: 10.1038/psp.2013.40
Resimulation of Clinical Trial Selected Individual
0 2 4 6 8 10 12 14 16 0
2
4
6
8
10
12
14
16
Fitte
d C
once
ntr
ation
[m
mo
l/L
]
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
0 2 4 6 8 10 12 14 16 0
2
4
6
8
10
12
14
16
Reference Concentration [mmol/L] P
redic
ted C
once
ntr
ation
[m
mo
l/L
]
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
A
D
E C
C E
D
B
B
0
5
10
15
20
Meal Meal Meal
Fit Subject 117-1
Glu
co
se
[m
mo
l/L
]
Model Data
0
20
40
60
80
100
Insu
lin[m
U/L
]
0 3 6 12 16 21 240
20
40
60
80
100
Time [h]
Glu
ca
go
n[p
mo
l/L
]
Meal Meal Meal
Prediction Subject 117-2
0 3 6 12 16 21 24
Time [h]Reference Concentration [mmol/L]
37 Schaller et al. (2013) CPT Pharmacometrics Syst Pharmacol. Aug 14;2:e65. doi: 10.1038/psp.2013.40
Model Applications: T1DM - Automatic Blood Glucose Control
– Initialization with patient data (physiological parameters, e.g. weight, height, gender)
– Blood glucose measurements taken frequently, stored and delivered to the controller
– The process works on two time scales:
– Short: online calculation of optimal insulin dose based on recent glucose measurements
– Long: offline “model adaptation” based on full measurement data history
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Schaller et al. (2015) IEEE Trans Biomed Eng. 2015 Nov 2. [Epub ahead of print]
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