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

2

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

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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

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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

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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

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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

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Fitte

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once

ntr

ation

[m

mo

l/L

]

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Reference Concentration [mmol/L] P

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ted C

once

ntr

ation

[m

mo

l/L

]

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0

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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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