Effect of Drug Load and Surface Area on Degradation Rate ...– Drug product exhibited chemical...

Effect of Drug Load and Surface Area on Degradation Rate in Solids

Garry ScrivensScience of Stability Conference

October 2019

Presentation Overview

◼ Part 3 of a series:

– SOS 2015 (Steve Baertschi)

– SOS 2017

– SOS 2019

◼ Brief recap of investigations into API : Excipient binary mixtures

◼ Applying this work to accelerate drug development:

– How data from binary mixtures might be used to predict the stability of multicomponent mixtures

– The effect of changing API and excipient particle size (surface area)

– Designing excipient compatibility studies

– Accelerating formulation development by pre-screening studies and ‘Digital Design’

Background

Long-Term Aim: Build a comprehensive understanding of the causes of instability…

Ability to predict stability performance from first principles

Approach: Build a multiscale understanding of degradation processes

Chemical Degradation

in Solid State Drug

Product

Environmental Conditions

API Intrinsic Molecular Properties

API Solid Form Individual API batch Characteristics

Excipient Properties

Individual Drug Product batch Characteristics

Packaging

Surface Area

Particle SizePolymorph

Salt Form

Degradation Mechanisms

Energetics of degradation

Bond Dissociation Energies

Crystal Defects

Lattice Energies Degree of Crystallinity

Amorphous Content

Surface Disorder

Temperature

Humidity

Light

Oxygen Level

Drug Product Manufacturing / Processing conditions

API Manufacturing / Processing conditions

Impurity Levels

Drug Product Design

Individual Batch Excipient Properties

Surface pHProperties of Deg. Prods

Surface Area

Particle Size

Impurity Levels

Likely Impurities

Compression Force

Blending

Milling

Excipient : API Interactions

MVTROTR

Opacity

Solid Fraction

Drug Load

Surface Area

Hardness

Water Activity

Surface pH

Crystal Surface force fields

Surface Area

ASAP and Packaging Simulations

Zeneth / QM Calculations

Recap

◼ Lower drug loads usually have higher degradation rates

◼ The effect of drug load was studied in detail in recent years, focussing on binary mixtures for simplicity

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Drug Load, L (%)

Journal of Pharmaceutical Sciences, 2019, 108, 1746-1755.

DOI: https://doi.org/10.1016/j.xphs.2018.12.003

https://doi.org/10.1016/j.xphs.2018.12.003

Experimental Set Up

◼ Binary blend samples were prepared by mixing API and excipient over a range of different weight ratios and tumbling using a Turbula mixer

◼ The samples were stored under accelerated stability conditions and samples withdrawn and analysed at regular intervals

◼ Samples were analysed using HPLC to:

1. Determine the amount of degradation product (%w/w relative to main band)

2. Provide an accurate (experimentally determined) assay value (drug load) of the sub-sample

Three Case Studies

◼ Product 1: Dicalcium Phosphate (DCP)

◼ Degradation at 70°C/75%RH

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Drug Load, L (%)

◼ Product 2 : Avicel (MCC)


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Drug Load, L (%w/w)

◼ Product 3 : Avicel (MCC)


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Modelling the Degradation – Drug Load Relationship

◼ Observation: increasing proportion of excipient increases degradation rate

◼ The degradation rate sometimes correlates quite well with simple Excipient : API ratio:

deg rate Excipient : API ratio = (1 – L)

◼ However, this simple model begins to break down at high excipient ratios:

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Excipient : API Ratio (1-L)/L

Excipient Load

API LoadL

Modelling the Degradation – Drug Load Relationship

◼ Instead, it was found that the degradation was proportional to the fraction of API in contact with excipient (fcontact):

Surface area of the excipient in a sample

Total surface area of the samplefcontact Deg Rate

(1 – L)SE

(LSAPI) + (1 – L)SEfcontact

1 - L

RASA.L + 1 - Lfcontact

SE = ‘Available’

surface area of

excipient (m2/g)

SAPI = ‘Available’

surface area of

API (m2/g)Divide top and bottom of this equation by SE to give:

RASA = ratio of ‘available’

surface areas (SAPI/SE)

‘Available’ surface

area of API in sample‘Available’ surface area

of excipient in sample

Bulk, 80.00%

Air, 10.00%

Drug Substance, 8.00%

Excipient 1, 2.00%

Surface in contact with...

fcontact is 0.2

(i.e. 20%)

Does not change if drug

load is the only factor varied

Assumed to remain

approximately constant

if drug load is the only

factor varied

Drug substance in contact with

excipient appears to degrade

much more rapidly than other

environments

Pie Chart Representation of Model

The Effect of Increasing Drug Load

Next to other API

Next to excipient

On Surface

40%

Not on

Surface

60%

Next to Air (10% of surface)

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f A

PI b

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Drug Load

API not on surface (60%, constant, not

affected by drug load, f1 = 0.4)

API on surface next to other API

API on surface next to Excipient (fcontact)

API on surface next to air, assumed to

be approximately constant

1 - L

RASA.L + 1 - Lfcontact

Relating Degradation Rate to Drug Load, L

Observed Overall Degradation Rate = 80% * rate of degradation in bulk (zero?)

+ 10% * rate of degradation on surface next to air

+ 8% * rate of degradation on surface next to DS

+ 2% * rate of degradation on surface next to Excipient

Bulk, 80.00%

Air, 10.00%

Drug Substance, 8.00%

Excipient 1, 2.00%

Surface in contact with...


Observed Overall Degradation Rate =

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0.35%

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Observ

ed D

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DL

Degradation on

surface next to air

Degradation on surface next to

Excipient

(Degradation in

bulk = zero)

Degradation on

surface next to DS


1 - L

RASA.L + 1 - LDegradation (%) = Da + Dc

X

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Degra

dation (

%)

Drug Load

Da

Dc

RASA = 5

RASA = 10

RASA = 20

To simplify, the relationship between degradation and drug

load can be expressed as:

Fitting the Model to Experimental DataProduct 1 + DCP

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Observed Data(Case Study 1)

Model

Da = 0

Dc = 11.8%

RASA = 80

RASA.L + 1 - L

1 - LDegradation (%) = Da + Dc·

Fitting the Model to Experimental DataProduct 2 + Avicel

Da = 0.03

Dc = 0.64%

K = 10

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Drug Load (%w/w)

Observed

Model A

RASA.L + 1 - L


RASA = 10

Fitting the Model to Experimental DataProduct 3 + Avicel

Da = 0.76

Dc = 4.70%

K = 74

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Drug Load (% w/w)

Model

Observed Data

RSSA

RSSA.L + 1 - L


Multicomponent Mixtures

◼ Use data from binary blend (API + single excipient) degradation studies to model the degradation of multicomponent systems

◼ Can the shelf life of multicomponent drug products be predicted from API:excipient binary mixtures?

– Ternary interactions (mechanisms requiring API + 2 excipients)

– Degree of mixing

– Different affinities between components

– Fragmentation during blending

– Effect of compression


fcontact_Excipient 1 =Surface Area of Excipient 1 in Sample

Total Surface Area of Sample

fcontact_Excipient 𝟐 =Surface Area of Excipient 𝟐 in Sample


fcontact_Excipient 𝟑 =Surface Area of Excipient 𝟑 in Sample


Etc.


fcontact_Excipient 1 =LE1 × 𝐒E1

LAPI × 𝐒API + LE1 × 𝐒E1 + LE2 × 𝐒E2 + etc.

fcontact_Excipient 1 =Surface Area of Excipient 1 in Sample


fcontact_Excipient 𝟐 =LE𝟐 × 𝐒E𝟐


Binary Blend Data to Predict Stability of Multicomponent Mixtures

◼ Obtaining degradation rates from individual excipients at 2 (or more) different drug loads provides “DLim” and RASA for each excipient

◼ DAPI is the degradation rate of pure API

E1 E2 E3DLim DLim DLim

RASA

RASA RASA

DAPI

fcontact_Excipient 1 =LE1 × 𝐒E1


fcontact_Excipient 1 =LE1

LAPI × R𝐀SA_E1 + LE1 + LE2 ×R𝐀SA_E1

R𝐀SA_E2+ etc.

fcontact_Excipient 2 =LE2

LAPI × R𝐀SA_E2 + LE1 ×R𝐀SA_E2

R𝐀SA_E1+ LE2 + etc.

This can be re-expressed as:

And similarly:


(DAPI × fcontact_API) + (D𝐋im_E1 × fcontact_E1) +(D𝐋im_E2 × fcontact_E2) + (D𝐋im_E3 × fcontact_E3) etc.


Overall Degradation Rate =

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Drug Load

Experimental Data

Predicted OverallDegradation

Contribution from API inabsence of excipient

Contribution fromAPI incontact with excipient

DLim

DAPIRASA is obtained from the

degree of curvature in this

plot (RASA → fcontact)

Predicting Degradation Rates in Multicomponent Mixtures from Binary Blends

◼ Proposed Protocol for excipient compatibility studies:

1. Measure the rate of degradation of pure API

2. Measure the rate of degradation of API:excipient binary blends…at 2 or more different drug loads

◼ Binary blend data can be obtained from (automated) pre-screening work and databased for future reference

– RASA and DLim is obtained from binary blend data

◼ If RASA and DLim data is available for all the excipients in a multicomponent blend, then the stability of the multicomponent blend can be predicted.

◼ A ‘dial – up’ approach to accelerate drug product development:

– Any composition, any drug load

– Any storage condition, any packaging (next slide)

◼ Limitations:

– RASA determined from the binary mixture degradation experiments can be very different from RSSA

obtained by surface area measurements such as BET

– If no degradation is observed in the binary mixtures then no RASA information can be obtained

Combining ASAP and Binary Blend Data

◼ Accelerated Stability Assessment Protocols (ASAP) provide information on the effect of temperature and humidity on the rate of degradation

◼ Using ASAP conditions might allow the stability of final drug product in packaging to predicted from binary blend screening studies

ASAPScreening

Studies

Case Study – Product AImproving Stability by Reducing Excipient Surface Area

◼ Product A

– Drug product exhibited chemical degradation during long-term stability studies

– Data from binary mixtures shows that degradation only occurs in presence of dibasic calcium phosphate (DCP)

– Changing the surface area of DCP from 19 m2/g to 1.4 m2/g significantly reduced the chemical degradation consistent with the model

LOQ

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Lower Surface AreaExcipient

Case Study – Product DProduct D is a multi-composite Formulation

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Labscale, 15% DL, 0.878 SSA




SDM, 15% DL, 0.6258 SSA

SDM, 15% DL, 0.1473 SSA

SDM, 40% DL, 0.6258 SSA

SDM, 40% DL, 0.1473 SSA

8 batches:

• 2 different drug loads

(15% and 40%)

• 4 different drug

substance surface areas

(0.878, 0.626, 0.223 and

0.147 m2/g)

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Labscale Milled, SSA=0.878 m²/g




Experimental data from 8

batches at 2 different drug

loads (0.15 and 0.4) and from

4 different drug substance

surface areas in good

agreement with model

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Observed





SDM, 15% DL, 0.6258 SSA

SDM, 15% DL, 0.1473 SSA

SDM, 40% DL, 0.6258 SSA

SDM, 40% DL, 0.1473 SSA

Deg SDS1 − L

RASAL + 1 − LCase Study – Product D

SDS affects both

DLim and RASA

Summary

◼ Can the shelf life of multicomponent drug products be predicted from API:excipient binary mixtures?

– Ternary interactions (mechanisms requiring API + 2 excipients)

– Degree of mixing

– Different affinities between components

– Fragmentation during blending

– Effect of compression

◼ Data from real-world stability studies are encouraging…consistent with contact surface area model

◼ Studies designed to more rigorously test the model are underway

– Binary blends & multicomposite samples are being tested

◼ Part 4 of Series…

– Results from dedicated investigations

– “Surface Exchange Model” to allow for different affinities between components

– Effect of compression…does simple model based on solid fraction work…?

Thanks for listening

Questions?

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