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