We present a cloud computing application aimed at the unattended, high-throughput prediction of thermodynamic stability of amorphous pharmaceutical delivery systems. To that end, we discuss the system-agnostic solubility prediction of Vitamin E TPGS and Tween 80 surfactants in Copovidone. Underlying to the computing scheme was a highly parallelized architecture for molecular dynamics and free energy perturbation from which stability critical points were extracted from free energy profiles. Differential scanning calorimetry of physical samples formulated by hot melt extrusion indicated a tight agreement between the computed stability limits of 9.0 and 10.0 wt% vs. the experimental 7 and 9 wt% for Vitamin E TPGS and Tween 80, respectively. Results suggest that stability screening via resource-optimized cloud computing is a physically meaningful and operationally sensible precursor stage to formulation and stress-testing of amorphous pharmaceutical delivery systems.

1. mml
System-agnostic prediction of pharmaceutical placebo stability
via cloud computing and experimental validation
PASC23, Davos 28.06.2023
Georgios S.E. Antipas1
Samuel Kyeremateng2
Regina Reul2
Kristin Voges²
Nikolaos A. Ntallis1
Konstantinos T. Karalis1
Lukasz Miroslaw3
1Molecular Modelling Laboratory, CH-6340 Baar, Switzerland
2AbbVie Deutschland GmbH & Co. KG, 67061 Ludwigshafen, Germany
3Microsoft Corporation, CH-8058 Walliselen, Switzerland

2. mml
mml
- Innovation-driven Computational Materials
Science (CMS) / Chemistry (CC) R&D
- Focus on Pharmaceutics + Chemicals industries
- Provide solutions of industrial relevance = large-
scale (~ 20k cores/study, util. ~ 90-95%/core )
- Optimize cloud-HPC = MS Azure Quantum
Develop/deploy
Advise/facilitate
Anticipate
e.g. CI
Multi scale modelling
- Atomic
- Mesoscale
- Coarse graining
- Continuum/process modelling
- CFD, FEM, bespoke (EAF,
Atomization+Break up)
System classes
- Organic
- Small organics (drugs)
- Biomolecular (Proteins)
- Polymers/excipients
- Inorganic
Azure CMS/CC solutions
1. Solid solution stability (solubility) e.g.
amorphous solid solutions
2. Macromolecule parametrization
3. Chemical reactivity pathways/AI training
sets, e.g., API degradation
4. ReaxFF generation
5. Adsorption

3. mml System-agnostic prediction of pharmaceutical placebo stability via
cloud computing and experimental validation
Drug solubility
DOI 10.1124/pr.112.005660
>
1000
mg/ml
100-1000
mg/ml
33-100
mg/ml
10-33
mg/ml
1-10
mg/ml
0.1-1
mg/ml
>
0.1
mg/ml
(100
ppm)
Approx. 40% of the top 200 oral drug products
are practically insoluble…
DOI 10.1124/pr.112.005660
Practically
insoluble
DOI 10.1021/acs.jcim.1c01540
Flux = Solubility concentration * Permeability coef.

4. mml Solubility enhancement: amorphous solid solutions
DOI 10.1124/pr.112.005660
The lattice energy
DOI 10.1124/pr.112.005660
amorphous solid solutions
What is the main cause
of low solute solubility?
Hydrophobic solute +
hydrophilic polymer matrix
~ 180 ˚C
DOI 10.1016/j.jddst.2021.102452

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DOI 10.1523/JNEUROSCI.1656-20.2020
Binodal
Spinodal
dX
dX2
Solute molar fraction, X →
Amorphous solid solution instability:
phase separation, solute recrystallization & implementation basis
Non-activated
phase separation
Solute molecule
Polymer matrix
Kinetic stabilization
Activated
solute crystallization
via a critical nucleus
ΔG = Xs*μs + (1-Χs)*μp
• Calculate μ via Free Energy
Perturbation (FEP) …
• which mandates use of
Molecular Dynamics (MD)…
• and a solid solution model =
supercell of explicit solute +
polymer molecules
• FEP is compute-intensive = need
to have high CPU availability +
high CPU density per node +
cost efficiency…
• Deploy on Azure
Implementation basis

6. mml
dX
dX2
Solute molar fraction, X →
Implementation workflow: ActiveRank
1. Input molecular structures
o Solute (drug, surfactant, etc)
o Polymer
2. Generate FF parameters
o CHARMM-compatible FF
o macromolecule module
3. Create composition
windows/supercells
4. Production: simulations
o MD + FEP → μ
5. Post-production: analysis
ΔG = Xs*μs + (1-Χs)*μp
4. Production: simulations
o MD + FEP → μ
Spot
MPI
Worker 1 MPI
MPI
MPI
Worker 2 MPI
MPI
checkpoint
checkpoint
checkpoint
VM Capacity-restricted only

7. mml
The infrastructure setup comprises an isolated sandbox with restricted user access based
on NSG rules. Users connect to jump hosts via the Bastion service without directly
exposing the head and compute nodes to public internet. Both the jump hosts and head
node have whitelisted outbound connections for data transfer. The sandbox is connected
to a storage account for backup.
ActiveRank: MS Azure infrastructure high-level diagram

8. mml
Placebo system selection (Abbvie): molecular components
Study aims
o Computational blind test at
180 ˚C (melt-extrusion)
o Simulated surfactant
concentration windows per
system: 0 to 100 step 1 wt%
o Predict to within 15% of
experimental phase-
separation limit (spinodal)
o Conduct melt-extrusion
experiments to produce
physical samples
o Experimentally stress-test
samples to compare against
computational results
Uses: solubilizer, absorption and permeation
enhancer, emulsifier and surface stabilizer
Use: stabilizer in aqueous formulations
Use: excipient

9. mml
Computed chemical potential at 180 ˚C
-400
-350
-300
-250
-200
-150
-100
-50
0
0 10 20 30 40 50 60 70 80 90 100
μ
(kJ/mol)
Surfactant load (wt%)
VitaminE Copovidone
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
5
0 10 20 30 40 50 60 70 80 90 100
μ
(kJ/mol)
Surfactant load (wt%)
Tween80 Copovidone

10. mml
Computed Gibbs free energy at 180 ˚C
b1=3.6
s1=17.7
m1=9.0
-0.2
-0.1
0.0
0.1
0.2
0 2 4 6 8 10 12 14 16 18 20
Δg
(kJ/mol)
Surfactant load (wt%)
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0 10 20 30 40 50 60 70 80 90 100
Δg
(kJ/mol)
Surfactant load (wt%)
VitaminE Tween80
b1=4.1
s1=10.0
m1=17.7
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0 5 10 15 20 25 30
Δg
(kJ/mol)
Surfactant load (wt%)
ΔG = Xs*μs + (1-Χs)*μp
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0 10 20 30 40 50 60 70 80 90 100
Δg
(kJ/mol)
Surfactant load (wt%)

11. mml
• All samples were extruded at 180°C and 200 RPM.
• Extrudates with surfactant loads of 3, 5, 7 and 9 wt % Tween 80 and TPGS in Copovidone matrices
were stressed by cycling DSC.
Experimental: hot melt extrusion & detection of phase
separation by Differential Scanning Calorimetry (DSC)

12. mml Detection of phase separation by visual
appearance of extrudate samples
Clear Clear Clear Clear Turbid
Clear Clear Clear Intermediate Turbid

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Detection of phase separation by DSC
Phase separation occurs at:
• 7% surfactant load for TPGS
• 9 % surfactant load for Tween 80

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Comparison of phase separation results
Vitamin E TPGS Tween80
Computed 9 10
Sample visual appearance
(turbidity)
9 9
Stress testing (DSC) 7 9
Average experimental 8 9
Computed limit error 13% 11%
Phase separation limit
(surfactant wt%)

15. mml
System-agnostic prediction of pharmaceutical placebo stability
via cloud computing and experimental validation
PASC23, Davos 28.06.2023
Georgios S.E. Antipas1
Samuel Kyeremateng2
Regina Reul2
Kristin Voges²
Nikolaos A. Ntallis1
Konstantinos T. Karalis1
Lukasz Miroslaw3
Thank you !