Physical Stability of an Amorphous Spray Dried Dispersion

Graeme Horne

SOS 2019 Amsterdam

14 & 15 October 2019

Marketed 30%, Development 60-70%

Marketed 35%, Development 5-10%

Solubilising the Insoluble

14 & 15 October 2019

SOS 2019 Amsterdam

2

Enabling The Drug Development Process

BCS

II

BCS

IV

BCS

III

BCS

I

solubility

pe

rme

ab

ility

BCS I

BCS II

BCS III

BCS IV

Marketed 25%, Development 5-10%

Marketed 10%, Development 10-20%

cost

co

mp

lexity

• Rate and extent of oral absorption governed

by solubility, dissolution rate, permeability

• Enabling technologies can be utilised to

overcome barriers to bioavailability

• Approaches include: crystal modifications,

lipidic delivery, particle size reduction,

amorphization

• Shifting trend to develop drugs that have low solubility and/or low permeability

Pharmaceutics 2017, 9, 50

Amorphous Solid Dispersions (ASD)

3

Stabilising the Unstable

Time

Dru

g C

on

ce

ntr

ati

on

Crystalline Drug

Amorphous Drug

Amorphous

Dispersion

• Most Active Pharmaceutical Ingredients (APIs) are crystalline materials possessing long-range

order and well-defined structures with stability, solubility and bioavailability (BA) related to the

crystalline form

• Amorphous materials do not have long-range order but exhibit short-range order

Crystalline

Ordered

Stable

Low solubility

Amorphous

Disordered

Unstable

High solubility

Amorphous Dispersion

Disordered, Stabilised

High & Sustained

Solubilitypolymer

API

ASDs in Drug Discovery

• Offer greater physical/chemical stability relative to

the amorphous form

• Improve dissolution and extend lumenal

supersaturation

• Enhance extent of oral absorption compared with

crystalline materials

• Can reduce the effects of pH and food on absorption

14 & 15 October 2019

SOS 2019 AmsterdamJ.Pharm.Sci. 2009, 98, 2549–2572

Commercialised Amorphous Dispersions

4

A growing trend

• Growing interest in the technology: ca. 4000 scientific articles since 1974; 2000 since 2014

• Amorphous dispersions have successfully been applied at late stage and in marketed products

to increase solubilisation and oral bioavailability of BCS class II/IV compounds

Routes to commercial manufacture:

• Spray Drying, HME, Microprecipitation, Spray Coating, Solvent Impregnation

Product API Company BCS Polymer

Cesamet Nabilone Valeant 2 / 4 PVP

Incivek Telaprevir Vertex 2 / 4 HPMCAS

Intelence Etravirine Janssen 4 HPMC

Kalydeco Ivacaftor Vertex 2 / 4 HMPCAS

Kaletra Lopinavir & Ritonavir AbbVie 2 & 4 PVP VA

Norvir Ritanovir AbbVie 4 PVP VA

Prograf Tacrolimus Astellas 2 HPMC

Sporanox Itraconazole Janssen 2 HPMC

Onmel Itraconazole Stiefel 2 HPMC

Xtandi Enzalutamide Astellas 2 HPMCAS

14 & 15 October 2019

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Amorphous Solid Dispersions

5

The Physical Stability Challenge

• Physical stability of ASDs is often raised as a significant obstacle to development and

commercialization

• Physical degradation often controlled by molecular mobility in the solid state and is

restricted at temperature and humidity conditions below the Tg

• Crystallization can occur throughout process, supply and shelf-life and can have an

adverse impact on product quality, performance, and safety

Crystalline

API

Crystallization

Homogenous Dispersion Crystallized Dispersion

• Predicting the physical stability of amorphous dispersions is key when assessing

developability and viability for clinical candidates

14 & 15 October 2019

SOS 2019 Amsterdam

Manufacturing Amorphous Spray Dried Dispersions (SDD)Assessing the Physical Stability Risk

API, Polymer

Solvent

Solution

Tank

Atomiser

Drying

Chamber

Product

CollectionSecondary

Drying

Storage /

Supply

Finished

Product

Downstream

Processing

Supersaturation

Process time

and condition

Wet SDD

(%RS)

Drying temperature

and duration

Dosage form &

process train

Packaging, duration, condition

Packaging,

condition,

In-use

• Manufacture of SDDs involves mixing the drug and polymer in a suitable solvent

• Polymer stabilises the amorphous form and can inhibit crystallization

• Process, storage, supply can all impact physical stability

14 & 15 October 2019

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Case Study:Physical Stability Modelling of a Spray Dried Amorphous Solid Dispersion

Clinical and Pharmaceutical Background

Indication

• Variation in GI motility and malabsorption

• Fed patients equivalent to fasted NHVs

8

Drug Profile

Property Result

BCS II

MW < 250

LogP > 4

MP < 175 °C

Solubility

(Biorelevant)Practically insoluble

Polymorphism 4 + amorphous

Tg < 30 °C

• Paediatric chronic condition

• Aqueous suspension of crystalline SMT-API progressed into Phase 1 studies (NHV & patient)

• Low MW, low Tg result in high mobility and propensity to crystallize

• Reformulation necessary to maximise exposure and enable full exploration of

therapeutic effect

14 & 15 October 2019

SOS 2019 Amsterdam

Reformulation

• 25% w/w SMT-API SDD progressed into

Phase 1 studies in NHVs and patients to

assess safety and PK:

• Safe and well tolerated

• > Six-fold increase in patient plasma

levels of SMT-API relative to

suspension formulation @ 40% of

dose

• Higher exposure with the SDD

allowed for further exploration of

therapeutic effect

• SDD entered Phase 2 study in parallel

to optimising the formulation ahead of

potential commercial use

9

Maximising the Exposure Profile Through Amorphization

0

50

100

150

200

250

300

350

400

Ave

rag

e C

max

(ng

/mL

)Crystalline Amorphous

Patient Exposure

14 & 15 October 2019

SOS 2019 Amsterdam

Optimising the SDD

• Processing challenges to scaled manufacture necessitated refinement of the 25%

SDD formulation to one that is scalable yet meets the needs of the Target Product

Profile (TPP)

• Impact of process, polymer and drug loading on SDD stability (physical/chemical)

and performance assessed

• Output would identify the SDD with the profile aligned with the TPP

• 15% loaded HPMCAS SDD identified as preferred dispersion

• low miscibility of SMT-API in polymer & low MW, low Tg of SMT-API necessitate low

loading

• Manufacturing and processing viability

• Equivalent in vitro and in vivo profile to the clinical 25% SDD

10

Addressing Manufacturability and Stability

Can we model the physical stability of the nominated SDD to de-risk the program?

14 & 15 October 2019

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Accelerated Stability Programs

• Science and risk-based prediction with many stability applications to clinical development:

• API salt / polymorphic forms, ranking prototype formulations, process development (API /

DP), packaging and excursion evaluation, retest / shelf-life prediction, etc…

• Multiple storage conditions with varying temperature and humidity (open dish typically

used)

• Conditions tailored to the drug based on physical and chemical stability

• Model ideally verified using real time data

However:

• Primary use: modelling and prediction of small molecule chemical degradation

• Limited reports on application to physical degradation (e.g. amorphous to crystalline)

Two approaches considered:

• Model rate of crystallisation as a function of Tg/T (accounts for humidity)

• Model rate of crystallisation using humidity modified Arrhenius equation (accounts for

moisture uptake with packaging)

11

General Principles and Applications

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Selecting the Stability Conditions

• %RH needs to be taken into consideration when selecting accelerated conditions for

amorphous products

• Moisture can depress Tg: increases molecular mobility and crystallization potential

12

Tg vs. %RH

Conditions Timepoints (hr)

40°C/75%RH 5, 24, 72, 168, 208, 504

50°C/75%RH 3,5, 20, 44, 72

60°C/29%RH 5, 24, 72, 168, 208, 504

70°C/9%RH 5, 24, 72, 168, 208, 504

70°C/0%RH 8, 24, 75, 168, 336

80°C/0%RH 1, 3, 8, 24, 75

90°C/0%RH 1, 3, 8

14 & 15 October 2019

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Modelling the Physical Stability of the SDD

SDD: SDDs manufactured at 15% w/w loading of SMT-API with HPMCAS-M as the

polymer carrier. Manufacture was undertaken at development scale using a PSD-2 spray

dryer with acetone as process solvent.

Crystallinity Challenge: Dried SDDs were exposed to varying temperature and humidity

conditions with rate of crystallisation, up to a limit of 10%, determined. In addition, dried

SDD (2g) was packaged into HDPE containers with HIS and placed on a 12-month

stability study (25°C/60%RH, 30°C/65%RH, 40°C/75%RH)

Crystallinity Determination: All samples were analysed for crystallinity using DSC via

relative heat of fusion to nominal 100% crystalline SMT-API (scan rate of 10°C/min and

no modulation). For each SDD sample at each condition heat of fusion was plotted

against time and linear fits up to 10J/g API were used to calculate the initial rate of

crystallisation (J/g API.min). Rates of crystallisation were modelled as a function of T,

%RH and/or Tg using modified Arrhenius equations with predicted stability compared to

real time data.

13

Methodology

14 & 15 October 2019

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Modelling the Physical Stability of the SDD - 1

Crystallinity was measured for each sample

using a fast DSC method that allowed for

the detection of Tm and corresponding heat

of fusion (representative data for

40°C/75%RH hold condition)

14

Quantifying API Crystallinity within the SDD

Temperature (°C)

He

at F

low

(W

/g)

50 100 150

y = 0.0144x - 0.0417R² = 0.9979

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600

Heat of fusion was plotted versus time for

each condition and linear fits of the data up

to 10J/gAPI were plotted (representative

data for 40°C/75%RH hold condition)

14 & 15 October 2019

SOS 2019 Amsterdam

% C

rys

tall

init

y

Temperature (°C)

Signal of

interest

Modelling the Physical Stability of the SDD - 2

• Assessed relationship between the rate of

crystallisation and Tg/T

• Initial crystallisation rates modelled as a

function of Tg/T assuming open conditions

• Equivalent rate of crystallisation was

observed at three different temperatures

with different %RH suggesting a strong

correlation with Tg (40°C/75%RH,

60°C/29%RH, 70°C/9%RH)

15

Results: Ln(k) versus Tg/T

Below the Tg

• Although linear fit of the data shows poor correlation (R2=0.6366) model predicts 0.09%

crystallinity at 21 days at 25°C/60%RH (0.11% observed)

• Despite poor correlation this model accounts for the impact of humidity on the Tg and

demonstrates SDD recrystallisation rate is controlled by moisture-induced plasticization

of the polymer and consequent increased amorphous SMT-API molecular mobility

14 & 15 October 2019

SOS 2019 Amsterdam

Modelling the Physical Stability of the SDD - 3

• ASAPprime® uses modified Arrhenius equation to predict rates of degradation

• Humidity modified Arrhenius equation accounts for moisture uptake within packaging

Lnk = −Ea/RT + lnA + B(%RH)

• Primary application of ASAPprime® is prediction chemical degradation rates

• Limited reports on the application of ASAPprime ® to model physical changes during

stability studies (e.g. amorphous to crystalline)

• In general physical properties show non-Arrhenius behaviour

• However, there is no reason this approach can’t be applied to physical instability

16

ASAPprime®

14 & 15 October 2019

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Modelling the Physical Stability of the SDD - 4

17

Results: ASAPprime®

Including 0% RH Excluding 0% RH

• Initial crystallisation rates were modelled using the humidity modified Arrhenius equation

Including 0% RH Excluding 0% RH

ln(A) 61.1 ± 1.4 38.8 ± 0.2

Ea (kcal/mol) 44.5 ± 1.3 29.8 ± 2.3

B 0.078 ± 0.003 0.064 ± 0.004

R2 0.904 1.0

Lnk = −Ea/RT + lnA + B(%RH)]

14 & 15 October 2019

SOS 2019 Amsterdam

Modelling the Physical Stability of the SDD

18

Accuracy of the Fit

Including 0% RH Excluding 0% RH

• Accuracy of fit was determined through comparison of ASAPprime® output with real time

data at three conditions

• 25°C/60%RH, 30°C/65%RH, 40°C/75%RH

14 & 15 October 2019

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Modelling the Physical Stability of the SDD

• Modelled data was used to predict shelf life (10% crystalline API) for open and/or

closed conditions and compared to real-time data

19

Shelf Life Prediction

Time to 10% crystallinity (years)

Model Conditions Used Open Packaged

ASAPprime® 7 (0% + > 0% RH) 15.3 15.9

ASAPprime® 4 (> 0% RH) 2.4 3.6

• ASAPprime® accurately models the rate of crystallisation for the SDD up to 12 months

and predicts a shelf life of 3.6 years in clinically relevant packaging

Hypotheses for why the 0% RH samples have different rates:

• Samples contain residual moisture and/or unaccounted water. May be

thermodynamically distinct from “bulk” adsorbed water

• This water may be decreasing the rate of crystallisation due to API-water interaction,

different miscibility in the polymer

14 & 15 October 2019

SOS 2019 Amsterdam

Physical Stability Modelling of Amorphous SDD

• Predictive physical stability modelling de-risked the late-stage development

of a chemically stable BCS class II SDD

• ASAPprime® successfully predicted rate of crystallisation over 12 months in

clinically relevant packaging

• Predictive stability modelling can be applied to understanding the physical

stability profile of chemically stable SDDs that have similar mechanisms of

recrystallisation

• Prone to initial phase separation, low miscibility, low MW, low Tg

• Application of predictive stability approaches to physical stability broadens

the opportunity to progress NCE’s with otherwise unfavourable

pharmaceutical profiles

20

Conclusions

14 & 15 October 2019

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Acknowledgements

Summit Therapeutics

Preclinical

Clinical

Peter Timmins

Bend Research

Corey Bloom

Tyler Clikeman

Tim Elwell

Clinical Studies

Patients, Carers/Parents

Investigators

Sites

21

14 & 15 October 2019

SOS 2019 Amsterdam