Post on 18-Aug-2020
Supersaturation Maintenance & Drug Precipitation Inhibition: In-Vitro
Characterization
Dhaval Patel
Bristol-Myers Squibb, Inc.
Sunrise Session
AAPS Annual Meeting 2016
Acknowledgement
Amy Saari
Yan Xu
Christoph Gesenberg
Neil Mathias
Madhushree Gokhale
Umesh Kestur
Balvinder Vig
John Crison
David Good
Krishnaswamy Raghavan
Munir Hussain
2
Jatin Patel
Manisha Desai
Nancy Barbour
Roy Haskell
John Morrison
Maria Vincent
Ajit Narang
Sharif Badawy
Bradley Anderson
Des O’Grady
Outline
Supersaturating drug delivery systems (SDDS)
Key elements of SDDS quality risk assessment
SDDS characterization tools
Case Study: Mechanism of supersaturation generation
Case Study: Mechanism of precipitation inhibition
Conclusions
3
Supersaturating Drug Delivery Systems (SDDS)
Matthews & Sugano, Drug Delivery System, 25-4, 2010
Biorelevant Supersaturation:
Higher drug concentration in GI
tract than its equilibrium
solubility
Degree of
supersaturation
Precipitation
Inhibitors
(PIs)
Brouwers et al., JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 98, NO. 8, 2009
4
Salts, Cocrystals, Prodrugs…
Amorphous dispersion, lipid-based formulations, SEDDS, SMEDDS..
Drug Precipitation Process
Degree of Supersaturation
Equilibrium Solubility
Critical Nuclei
Fre
e E
nerg
y (
ΔG
)
Time
Three major steps:
1) Amorphous
phase separation
2) Nucleation
3) Crystal Growth
Precipitation
inhibitors could
impact one, two or
all three steps
McCoy AJ. 1999. Energy Diagram for Crystallization. University of Cambridge.5
SDDS: Major Elements for Quality Risk Assessment
How is supersaturation generated?
Mechanisms: well understood or complex
Higher dissolution rate
Higher energy phases
How is supersaturation maintained?
Drug alone (self-supersaturation)
Precipitation inhibitor
How do precipitation inhibitors work?
Dissolved drug molecules
Amorphous & crystalline phase (adsorption)
Can in-vitro supersaturation assessment help predict in-vivo outcomes?
In-vivo conditions (pH, hydrodynamics, bile salts, enzymes, fluid volumes)
Dosage form dissolution: dissolution rates of drug and PIs
6
What tools are available to address these
questions?
Matthews & Sugano, Drug Delivery System, 25-4, 2010
Key attributes:
(1) Supersaturation generation
(2) Supersaturation maintenance
(1)(2)
SDDS Characterization Tools
In-vitro tools:
– Microdissolution (fiber-optic UV-Vis)
– Online FBRM, PVM and Raman Probes
– High-throughput technology (plate readers)
– Particle size analyzers
– Polymer adsorption techniques (QCMD)
– Fluorescence spectroscopy
In-silico tools:
– GastroPlus
– Precipitation kinetic modeling
In-vivo models:
– Preclinical and clinical studies
– In-vivo supersaturation 7
Microdissolution & FBRM Tools
Benefits:
– Small volumes
– Online measurements
• Concentration
• Visible absorbance
• Particle Size
• Raman spectra
Limitations
– UV detector saturation
– Undissolved solid interference
– Nanoparticle detection (FBRM)
– Low drug concentrations (Raman)
FBRM
Temperature
G400 #/sec 0-20µm
Microdissolution apparatus
FBRM Technology
Mettler Toledo
FBRM Technology
9
FBRM measures a chord length distribution – a precise measurement sensitive to changing…
Particle size
Particle count
Particle shape
2-3 CLDs from a key experiment
– don’t overcrowd
2 trends from same experiment – showing a
particle mechanism
Key statistics from the measured chord
length distributions can then be trended
over time to quantify how particles are
changing…
- Particle count in different size classes
- Mean
- Median
Increase
in fine
counts
Reduction in
dimensionIncrease
in fine
counts
Decrease in
dimension
Granule dispersion
kinetics
Quartz Crystal Microbalance with Dissipation monitoring (QCMD)
10
Q-Sense E4 Channel set up with sensors
www.biolinscientific.com
In-Silico Tool: GastroPlus
Prediction of
– Particle size effect
– Food effect
– pH effect
– Human plasma profiles
Select optimum API forms and formulations
www.simulations-plus.com
ACAT model
Physiological models
GI absorption and permeability
pH dependant and biorelevant solubility
In vivo drug precipitation
GI degradation
11
Fluorescence Spectroscopy: Environmentally Sensitive Probe
12
http://www.unk.edu/academics/chemistry/faculty-staff/haishi_cao.php
Probe: Pyrene
Ratio of fluorescence
intensity indicates level
of hydrophobicity
Jackson et al., Mol. Pharmaceutics, 2014, 11 (9), pp 3027–3038
Danazol Supersaturation
13Jackson et al., Mol. Pharmaceutics, 2014, 11 (9), pp 3027–3038
Supersaturation generated by
Liquid-liquid phase separation
prior to nucleation &
crystallization
Danazol Precipitation Inhibition
Jackson et al. Pharm. Res. 2016
PIs maintain supersaturation by stabilizing LLPS
Stabilizing effect varies with the chemistry of PIs
Precipitation Inhibitor (PI) Effectiveness: PVP < HPMC < HPMC-AS
14
Mechanism of Supersaturation Generation
Model Drug: BMS-582664
Question: How is supersaturation generated?
In-vitro tools: 2nd derivative UV, visible absorbance, DLS, surface tension
In-silico tools: GastroPlus
BCS II (Low Solubility & High Permeability)
Free base with pKa around 7
Case Study: BMS-582664
Significant
deviation of
experimental
solubility at
lower pH
Self
association
Narang et. al., Pharm. Res. 201516
BMS-582664: Surface Tension Measurements
Lower surface tension at higher concentrations--BMS-582664 self associates at higher
concentrations
Question: Does self-association influence supersaturation generation?
50
55
60
65
70
75
0 2 4 6 8 10
Su
rface T
en
sio
n (
dyn
e/c
m2)
Concentration (mg/ml)
pH 3pH 4.5pH 3.5pH 4.0
Method: Surface tension measurement at varying pH using Pendant Drop method
Narang et. al., Pharm. Res. 201517
In-Vitro Supersaturation Characterization Method
pION microdissolution
Online UV spectroscopy
Non-invasive supersaturation measurement
18
Step 1
• Start with a dissolution medium (simple buffers or biorelevant media)
Step 2
• Create supersaturation using a concentrated drug solution
Step 3
• Obtain concentration vs. time profile (second derivative UV method
• Characterize solids by size or form (online FBRM & Raman probes)
Time
Co
ncen
trati
on
Solubility
Supersaturation
BMS-582664: In-Vitro Supersaturation Characterization
0
10
20
30
40
50
60
70
80
0 1000 2000 3000 4000 5000
Co
nc
en
tra
tio
n (
µg
/mL
)
Time (min)
35
30
25
12
9
Degree of
Supersaturation (DS):
Secondary precipitation event @ high DS
Supersaturation maintenance was shorter at high degrees of supersaturation (DS)
Hypothesis: Precipitation of higher energy form before the secondary precipitation
event
Method: Supersaturation created by adding high concentration drug solution; Final pH: 6.8
Narang et. al., Pharm. Res. 2015 19
BMS-582664: In-Vitro Supersaturation Assessment (low DS)
Co
ncen
trati
on
or
Ab
so
rban
ce (
a.u
.)
-0.05
0.00
0.05
0.10
0.15
0.20
0 1000 2000 3000 4000 5000
Time (minutes)
Dissolution profile
(normalized concentration vs. time)
Visible absorbance profile
(A500 vs. time)
Dissolution
of high energy
precipitates
Supersaturation maintenance
Method: Visible absorbance used to detect precipitation in visually clear supersaturated solution
Higher energy species dissolve and help maintain supersaturation
Narang et. al., Pharm. Res. 2015 20
pH 6.8
Phosphate
buffer
BMS-582664: Dynamic Light Scattering
21
DLS indicated particles in 0.5 to 2 nm size range at different
Solution concentrations (high energy species)
Narang et. al., Pharm. Res. 2015
BMS-582664: Self-association & Supersaturation
Narang et. al., Pharm. Res. 2015 22
Hypothesis further supported by NMR & Isothermal titration calorimetry
BMS-582664: Biorelevance of In-Vitro Supersaturation (GastroPlus Modeling)
Effect of in-vitro supersaturation maintenance on oral exposure
Human clinical data
GastroPlus in-silico modeling
Optimum drug
precipitation time:
9000 sec or 150
min
In-vitro supersaturation characterization was relevant to model clinical
exposuresNarang et. al., Pharm. Res. 2015
23
Mechanism of Precipitation Inhibition
Model Drug: Indomethacin
Question: How do PIs maintain supersaturation?
In-vitro tools: 2nd derivative UV
In-silico tools: crystal growth kinetic modeling
Precipitation Inhibitor Screening
Precipitation Inhibition
Amorphous (LLPS) Phase Stabilization
Nucleation Inhibition
Clear solution of API+PPI
Induced supersaturation
Crystal Growth Inhibition
Suspension of API in PPI solution
Powder dissolution/
Induced supersaturation
Solution-state Interactions Solid-liquid Interface Interactions
25LLPS: Liquid-Liquid Phase Separation
Precipitation Inhibitor Screening
pION microdissolution
Online UV spectroscopy
Non-invasive supersaturation measurement
26
Step 1
• Start with a clear drug solution or suspension containing a precipitation inhibitor(s)
Step 2
• Create supersaturation using a concentrated drug solution
Step 3
• Obtain concentration vs. time profile (second derivative UV method
• Characterize solids by size or form (online FBRM & Raman probes)
Time
Co
ncen
trati
on
Solubility
Supersaturation
Indomethacin: Effect of PI on Crystal Growth Inhibition
0
2
4
6
8
10
12
14
0.0E+00 5.0E+04 1.0E+05 1.5E+05 2.0E+05 2.5E+05 3.0E+05
Deg
ree
of
Su
per
satu
rati
on
(S
)
Time (seconds)
HPMC (0.2% w/w)
PVP (0.2% w/w)
HPCD (0.2% w/w)
Order of indomethacin crystal growth inhibitory effect: HP-β-CD < HPMC < PVP
0.91
0.02 0.04
0.78
0.01 0.02
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
HPCD PVP HPMC
Ind
om
eth
aci
n C
ryst
al
Gro
wth
Inh
ibit
ion
Fact
or
(R)
0.05% w/w
0.2% w/w
Patel et al, Molecular Pharmaceutics 2014 27
Possible Mechanisms of Excipient Effects on Bulk Diffusion Controlled Growth of Indomethacin (High S>3)
Drug crystal Bulk medium
Adsorption
layer
Diffusion layer
Drug molecule Excipient
Case 1: Viscosity effect
Higher viscosity-Lower
diffusivity
HPMC & PVP
Case 2: Complexation in diffusion layer
Diffusivity differences of free +
complexed species
Cyclodextrins
Case 3: Surface
adsorption
Inhibit surface
integration
HPMC & PVP
gsbGb ccAk
dt
dc
Empirical crystal growth model
Patel et al. Journal of Pharmaceutical Sciences, July 2011
Patel et al, Molecular Pharmaceutics 2014
HP-β-CD’s Effect on Bulk Diffusion Controlled Crystal Growth of Indomethacin
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0 0.005 0.01 0.015 0.02
Cry
stal
Gro
wth
In
hib
itio
n F
act
or
(R)
HP-β-CD Concentration (M)
HP-β-CD (Predicted)
HP-β-CD (Experimental)
Model predictions in good agreement with experimental values at lower HP-β-CD concentrations
Deviation at higher concentrations: HP-β-CD adsorption to the growing surface
Drug
crystal
Bulk medium
Adsorption
layer
Diffusion layer
Drug
molecule
Cyclodextrin Drug-
cyclodextrin
complex
Patel and Anderson, Molecular Pharmaceutics 2014
Reactive diffusion layer theory (assumes concentration
gradient due to complexation in the diffusion layer)
29
sbCDHAsbHAg CDHACDHADHAHAD
hJ
dt
dm
AR ][][][][
11
Indomethacin: Effect of PI on Crystal Growth Inhibition
0.91
0.02 0.04
0.78
0.01 0.02
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
HPCD PVP HPMC
Ind
om
eth
aci
n C
ryst
al
Gro
wth
Inh
ibit
ion
Fact
or
(R)
0.05% w/w
0.2% w/w
Drug
crystal Bulk medium
Adsorption
layer
Diffusion layer
Drug molecule Polymer
Hypothesis for PVP and HPMC effect at high S:
Change in rate limiting step:
From bulk diffusion to surface integration
PVP & HPMC could adsorb onto growing surface-
significantly decrease surface integration rate
Surface adsorption barrier for surface integration Lower crystal growth rate
Patel and Anderson, Molecular Pharmaceutics 2014
30
HPMC and PVP: No viscosity effect
Indomethacin Crystal Growth Inhibition: Effect of PVP Molecular Weight
No PPI N-vinylpyrrolidone PVP K12 PVP K16-18 PVP K29-32
Ind
om
eth
acin
Cry
sta
l Gro
wth
Ra
te C
oe
ffic
ient
(k
G, cm
/se
c)
0.000
0.001
0.002
0.003
0.004
0.005
0.006
Ind
om
eth
acin
Cry
sta
l Gro
wth
Inhib
itio
n F
acto
r (R
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
kG
R
Patel and Anderson, Journal of Pharmaceutical Sciences 2015
PVP is more effective PPI than its monomer, N-vinylpyrrolidone
Higher MW PVP is better PI than lower MW PVP
31
Adsorption of PVP onto Indomethacin Crystals: Effect of Molecular Weight
Patel and Anderson, Journal of Pharmaceutical Sciences 2015
PVP K12 and K16-18: train conformation
PVP K29-32: loops and tails conformation
Thickness of adsorption layer: 2-fold higher for
PVP k29-32 than PVP K16-18
Theoretical value: ~1 mg/m2
32
Indomethacin Crystal Growth Inhibition by PVP
0.0 0.1 0.2 0.3
0
1e-4
2e-4
3e-4
4e-4
5e-4
6e-4
0.5
1.0
1.5
2.0
kG
PVP adsorbed
Ind
om
eth
acin
Cry
sta
l Gro
wth
Ra
te C
oe
ffic
ient
(kG, cm
/se
c)
PVP K29-32 Concentration (% w/w)
Am
ount o
f P
VP
Ad
so
rbe
d (
mg
/m2)
Fractional Indomethacin Surface Coverage by PVP K29-32
0.0 0.5 1.0 1.5 2.0 2.5Degre
e o
f In
dom
eth
acin
Cry
sta
l G
row
th I
nhib
itio
n (
1/R
)
0
100
200
300
400
500
600
Greater inhibitory effects of PVP at
higher surface coverage: thicker
adsorbed layer
3-fold greater inhibition when
adsorbed layer thickness increased
by 2-foldPatel and Anderson, Journal of Pharmaceutical Sciences 2015 33
Conclusions
Supersaturating drug delivery systems (SDDS) could be successfully utilized to mitigate biopharmaceutical risks of new molecular entities (NMEs).
In-Vitro tools help in understanding supersaturation generation and the effect of PIs on its maintenance.
PIs could influence amorphous phase separation, nucleation and crystal growth during precipitation inhibition.
Adsorption and complexation of PIs with NMEs play a significant role in precipitation inhibition.
In-vitro supersaturation assessment could be leveraged to optimize in-silico predictions (GastroPlus).
Comprehensive understanding of in-vitro and in-silico supersaturation maintenance and drug precipitation inhibition is critical to control and predict in-vivo performance of SDDS.
34