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Dissolution coupled with oral absorption modeling to predict clinically relevant performance David Sperry, Nikolay Zaborenko and Kaoutar Abbou Oucherif Eli Lilly Third FDA/PQRI Conference on Advancing Product Quality March 22-24, 2017
© 2017 Eli Lilly and Company
Outline
Biorelevant tools useful for drug development
Integrating in vitro and in silico models to predict in vivo performance
Three case studies
22 March 2017 © 2017 Eli Lilly and Company 2
Approach to biorelevant methods
22 March 2017 © 2017 Eli Lilly and Company 3
In Vitro Apps • Biorelevant fluids • Phenomenological
measurements
Models • Mechanistic
description
Mechanisms • Parameterize
models • Help translate in
vitro → in vivo
Biorelevant product performance predictions
Best predictions
In Vivo Absorption Model
Biorelevant tools • In vitro apparatuses
– USP-type paddle methods – One-step and two-step – Artificial Stomach Duodenum (ASD) model – Scaled-down two-step dissolution – Microcentrifuge test Biorelevant fluids – SGF (FaSSGF, FeSSGF) – FaSSIF, FeSSIF – Simulated saliva
22 March 2017 © 2017 Eli Lilly and Company 4
See, for example: E. Jantratid and J. Dressman, “Biorelevant Dissolution Media Simulating the Proximal Human Gastrointestinal Tract: An Update,” Diss. Tech., (2009) 16(3), 21.
Two-Step Dissolution • 300 mL 0.01 N HCl • 75 rpm paddle
→ 20 min • 300 mL FaSSIF • 75 rpm paddle
→ 260 min
USP-type methods: Example
22 March 2017 © 2017 Eli Lilly and Company 5
One-Step Dissolution • 900 mL FaSSIF • 75 rpm paddle
ASD model
22 March 2017 © 2017 Eli Lilly and Company 6
Stomach Secretion Rate = 2 mL/min
Duodenum Secretion Rate = 2 mL/min
Resting Volume = 50 mL Dosing Volume = 200 mL
Stomach Emptying: First order decay (w.r.t. Volume) Emptying Half-life = 15 min
Duodenum: Constant volume = 30 mL
Stomach
Duodenum
ASD measurements
22 March 2017 © 2017 Eli Lilly and Company 7
Stomach
Duodenum
Spec.
Light
Spec.
Light
Drug Dissolution Dilution and Transfer Out
Duodenum Data – Key Output Upper intestinal drug concentrations can be used as a surrogate for drug Absorption.
Stomach Data – Context Additional information can be learned from stomach profiles.
Biorelevant tools
• Mechanisms – Rotating disk dissolution (RDD, intrinsic
dissolution) – Flow-through imaging – NMR – Focused beam reflectance measurement
(FBRM)
22 March 2017 © 2017 Eli Lilly and Company 8
Diffusion coefficient
• Intrinsic dissolution rate: Solve Navier-Stokes for a rotating disk (Levich equation)
• Stokes-Einstein: laminar viscous drag on a spherical molecule
22 March 2017 © 2017 Eli Lilly and Company 9
Diffusion coefficient by other methods
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♦ Flow-through UV Imaging (SDi300)
http://www.sirius-analytical.com/system/files/Sirius%20Application%20 Note%20304-13%20UK%20web.pdf
♦ Diffusion-Ordered Spectroscopy 2D NMR
Lasentec® FBRM® experiments
22 March 2017 © 2017 Eli Lilly and Company 11
half way btw wall and shaft
Front view Side view
~20o angle
~ 1 cm from top of paddle
High pH
Low pH
Intermediate pH
Slide courtesy of Carrie Coutant
Biorelevant tools
• Dissolution Models
22 March 2017 © 2017 Eli Lilly and Company 12
Granules Tablet Powder Disintegration Disintegration
Very Limited Dissolution
Limited Dissolution
Best Dissolution
Limited physical models -> simple 1st-order rate
Limited physical models -> simple 1st-order rate
Good physical models
Hydrodynamics
Dissolution model • Noyes-Whitney dissolution model,
with several enhancements.
22 March 2017 © 2017 Eli Lilly and Company 13
0 10 20 300
0.5
1
t
Dissolved Coated tablet
Tablet
Granules
Aggregates
API
32134 rrrV π=
𝜕𝑟1,2,3
𝜕𝜕�min 𝑟1,2,3 >0
= −𝑘𝑡𝑡𝑡𝑡𝑡𝑡
𝜕𝑟𝑖𝜕𝜕�𝑟𝑖>0
= −𝑘𝑔𝑟𝑡𝑔
Tablet and granule disintegration
Henderson-Hasselbalch, pH and buffer capacity
API↔API+
HPO42-
H2PO4-
PO43- pH
Sol
parti
cle
Surface concentration
0.1 1 10 1000
0.05
0.1Particle size distribution
mi
r0i
Population balance
In vitro-in silico approach
22 March 2017 © 2017 Eli Lilly and Company 14
Biorelevant product performance predictions In Vitro Apps
Models Mechanisms
In Vivo Absorption Model
Johnson model
Input CR profile z-factor Fitted PSD Fitted D Mechanistic
parameters
Case 1 – Modified release formulation • Overall development challenge
– Increase the throughput of the process by a simple formulation change: increase the drug-containing layer thickness thereby increasing the drug load.
22 March 2017 © 2017 Eli Lilly and Company 15
• Formulation A – Spray-coated bead – Bead enteric coated
• “New” high-drug load formulation B – Drug load increased ~50% by spray-
coating more drug on bead – Other aspects remain the same
Core
Drug layer
Enteric coat * Some details of formulation omitted for clarity
Sperry et al., Molecular Pharmaceutics, 2010, 7, 1450–1457.
In vitro dissolution • Different strengths tested in vitro by different
methods
• Many method conditions explored – e.g. Both paddles and baskets specifically
investigated.
• Dissolution is different between formulations • And difference is not an artifact of the test
22 March 2017 © 2017 Eli Lilly and Company 16
A
B
Sperry et al., Molecular Pharmaceutics, 2010, 7, 1450–1457.
In vitro modeling
• Agreement suggests differences in release rate observed in vitro are due purely to the difference in surface area.
22 March 2017 © 2017 Eli Lilly and Company 17
Sperry et al., Molecular Pharmaceutics, 2010, 7, 1450–1457.
( ) ( )
−⋅
+−⋅⋅
−⋅⋅⋅⋅−
=VXCrrr
XX
rrhXD
dtdX d
sccs
c
s3
2
3330
033
0
03ρ
0.1 1 10 1000
0.05
0.1Particle size distribution
Mas
s in
bin
mi
r0i
rc
r0
Bioequivalence study • Met bioequivalence criteria
of 0.8 and 1.25
In this case, simple buffers and USP II method produced adequate biorelevant predictions.
22 March 2017 © 2017 Eli Lilly and Company 18
Sperry et al., Molecular Pharmaceutics, 2010, 7, 1450–1457.
Case 2 – Free base conversion • Solid forms: free base & a salt • Properties: Low solubility, pka ~7
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FaSSIF Salt Dissolution
Conversion to free base
Rotating Disk Dissolution
Free base
Salt
ASD: Salt supersaturation and precipitation
Stomach concentrations Duodenum concentrations
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Low dose
High dose D0=450
D0=120
D0=0.1
D0=0.03
Distribution of gastric conditions
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Normal
With PPI population
Map of Gastric conditions
In Vivo Absorption Model
Dissolution input
Systems-based Pharmaceutics
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- Linking manufacturing excellence with patient performance
Alliance
Case 3 – Integrating ASD data • Low solubility base
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ASD Duodenum Concentration
In Vivo Absorption
Model
In vitro ASD
In silico ASD
Dissolution/precipitation parameters
Precipitation rate parameters
22 March 2017 24
Model parameter Final value Initial guess 95% C.I Standard deviation
Growth constant 0.55 0.36 1.42 0.70 Growth order 1.60 0.81 2.29 1.13 Nucleation coefficient 14.01 13.85 974.9 481.2 Nucleation order 2.61 2.77 399.3 197.1
© 2017 Eli Lilly and Company
Duodenal Concentration Profile
Incorporating ASD results in gCOAS GI model
• 15 mg dose – Precipitation is predicted to be negligble. Fa =1
• 50 mg dose – Fa =0.82 due to precipitation in the
small intestine 22 March 2017 25
50 mg dose
15 mg dose
© 2017 Eli Lilly and Company
Acknowledgements
• Lee Burns • Carrie Coutant • Todd Gillespie • Brian Winger
22 March 2017 © 2017 Eli Lilly and Company 26