Slide 1

Modeling Phase Separation Risk During Spray Drying from Mixed Solvents

Date

Jonathan Cape Ph.D.

Slide 2

Legal disclaimer

This presentation (“Presentation”) is the property of Lonza AG and its affiliates (“Lonza”) and any unauthorized use or interception of this Presentation is illegal.

The information contained herein is believed to be correct. However, no warranty is made, either expressed or implied, regarding its accuracy or the results to be obtained from the use of such information. Lonza disclaims any liability for the use of this Presentation and the use of the information contained herein is at your own risk.All trademarks belong to Lonza or to their respective third-party owners and are used here only for informational purposes.

All copyrighted material has been reproduced with permission from their respective owners, all other materials ©2020 Lonza.

Slide 3

Session Description and Objectives

Solvent Selection is a critical decision point in process development for spray dried amorphous

dispersions. Low organic solubility compounds often require the use of mixed solvents to increase

solubility, though their use can create phase separation risks during drying. A model is presented

that aids solvent selection by assessing the thermodynamic landscale for phase separation and

identifying low risk solvent compositions for processing.

Optimize process spray drying process throughput by choosing optimized

solvent compositions

Minimize phase separation risks by identifying high risk solvent compositions

Apply modeling tools to a representative quaternary spray solution system

Learning

Objectives:

Slide 4

Biography and Contact Information

Jonathan Cape

is a Principal Scientist

at Lonza in Bend,

OR, USA

email:

jon.cape@lonza.com

Ph.D. in Biochemistry and Biophysics (2006) from Washington State

University

Research Interests:

• NMR spectroscopic approaches to understand phase state and diffusivity

• Kinetic modeling of drug release mechanisms from MR dosage forms

• Analytical approaches to aid process understanding

Post-doctoral studies at WSU and Los Alamos National Laboratory.

Slide 5

Bioavailability Enhancement with Amorphous Solid Dispersions

Amorphous Solid Dispersions (ASDs) are a highly successful approach to improving the bioavailability of low aqueous solubility compounds

Development of successful ASD intermediates can be challenging

Amorphous dispersions

increase solubility…

…which increases the

absorption rate and

bioavailability

Absorption

↑ [Csat- API]

Slide 6

Bioavailability Enhancement with Amorphous Solid Dispersions

Stability• Physical

• Chemical

Manufacture• Throughput

• Scale-up

• Cost

Performance• Dissolution

• Permeation

• Sustainment

Spray Drying is one of the more prevalent process approaches used to produce ASDs

10-6 sec

~1 sec

10-2 sec

ATOMIZATION & DRYINGTHE

PROCESS

30 microns

Nozzle

DRYING

GAS

DR

YIN

G C

HA

MB

ER

FEED

SOLUTION

Slide 7

Solvent Selection – a Key Decision Point in Spray Drying Process Development

Solvent Selection has large

impacts to process

throughput

Process throughput

becomes particularly

sensitive to solvent

properties as API solubility

decreases

Slide 8

Solubility Slump Solubility Slump

Mixed Solvents Systems Can Improve Process Throughput

0.0001

0.001

0.01

0.1

1

10

100

Mebendazole Ritonavir

So

lub

ilit

y,

mg

/mL

acetone solubility, mg/mL 95/5 or 90/10 acetone/water solub, mg/mL water solubility mg/mL

API Solubility in Acetone/Water Mixed Sovlents

Mixed solvents systems can improve starting

spray solution solubility and therefore improve

process throughput

Solubility behavior is highly API dependent

Mixed solvent systems can also become poor

solvents as lower volatility components are

enriched during drying (e.g. water)

Optimization Problem

• Best process throughput

(highest solubility, lowest ΔHv, highest P)

• Least risk to physical state

Slide 9

Drying Model with Phase State Calculation as a Risk Assessment Tool for Solvent

Selection

A “minimal” drying model for

a four-component system has

been constructed in order to

track the composition of the

droplet during the drying

process. The drying

simulation is coupled to a

Flory Huggins calculation for

approximation of

thermodynamic phase state.

Slide 10

Composition of Droplet During Drying

(80% Methanol, 20% Water Solvent System)

Composition of Droplet During Drying

(85% Methanol / 15% H2O Solvent System)

Application of the Drying Model to Solvent Selection for the Ritonavir / PVP-VA /

Methanol / Water System

0

10

20

30

40

50

60

70

80

90

0 0.2 0.4 0.6 0.8 1 1.2

% C

om

po

nen

t

Drying Time (s)

% Methanol % Water

0

10

20

30

40

50

60

70

80

90

0 0.2 0.4 0.6 0.8 1 1.2

% C

om

po

nen

t

Drying Time (s)

% Methanol % Water

5% increase in

solvent

water content

To what extent

does this

increase Φ

separation risk?

In order to test the utility of the model, we have applied it to a model system (RTV / PVPVA / MeOH / H2O), which

undergoes amorphous phase separation in certain MeOH/H2O solvent systems

Slide 11

Application of the Drying Model to Solvent Selection for the Ritonavir / PVP-VA /

Methanol / Water System

In the case of the (RTV /

PVPVA / MeOH / H2O)

system we find that a

solubility boundary is

encountered at about

11.5% starting content of

water in the spray solvent

Slide 12

Application of the Drying Model to Solvent Selection for the Ritonavir / PVP-VA /

Methanol / Water System

Experimentally, the onset

of phase separation may

occur somewhere around

this predicted solvent

content, with a broadening

of the Tg and a second

enthalpy peak occurs

starting around the 90/10

MeOH/H2O starting

solvent content.

Slide 13

Drying Model with Phase State Calculation as a Risk Assessment Tool for Solvent

Selection

Recap of

Learning

Objectives:

Spray drying process throughput can be optimized by choosing a solvent that

exhibits optimal process characteristics (low ΔHvap, high P, low viscosity) and

solubility for the API / polymer system

Mixed solvent systems can improve solubility for low organic solubility APIs, but

can also lead to phase separation risks due to differential drying rates

Kinetic modeling of the drying process allows compositional trajectories to be

assessed, which can then be plotted against a quaternary phase diagram to

identify high risk solvent systems to avoid during process development

Slide 14

Questions