Late Phase Solubility: an Advanced Automated
Workflow to Support Crystallization Development
Jun Qiu and Benjamin Cohen
Late Phase Chemical Development
Bristol-Myers Squibb
New Brunswick, NJ
Outline
 Motivation
 Gaps
 Historical
perspective
 Major automation challenges
 How we solved these challenges
 Case study
Jun Qiu, Bristol-Myers Squibb
Chemical Development
Major operations in a chemical synthesis step:
1. Charging
2. Reaction
3. Quenching
4. Crystallization
Jun Qiu, Bristol-Myers Squibb
Value of Solubility Datasets
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Combination of empirical data and solubility model
Enables decision making in crystallization development
Key decisions

Solvent & anti-solvent volumes

Heating temp

Seeding point

Filtration temp
Impact

Yield

Purity

Powder property

Process robustness

Cycle time

Cost of goods
Jun Qiu, Bristol-Myers Squibb
Solubility Dataset and Crystallization
Development
1) Heat batch to
dissolve
crystalliz ation scenario
80.0
64.0
2) Cool to reach
seeding point
Solution.Temperature (C)
heptane vol % (%)
solubility (wt%) (%)
3) Add seeds to
begin crystallization
5) Cool for
isolation
48.0
4) Add heptane
(anti-solvent)
32.0
Extensive solubility data set
and regression enables
modeling solubility as
conditions change in
crystallization process
16.0
0.0
0.0
115.22
230.44
345.66
Time (min)
460.88
576.1
Late Phase Solubility and
DynoChem Modeling
Initial
Solubility
Screen
Crystallization
Selection for
Late Phase
Late Phase Solubility
Study Design
Late Phase Solubility
Study Execution
DynoChem Model for
Crystallization
Development
ab initio
Simulation
Gaps for Late Phase Solubility
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High resolution solubility map
 Extensive solubility data in defined list of solvent
mixtures, at range of temperatures
Engineers typically conduct manual late phase solubility
measurements
Challenges with manual method:
• Sampling at temperatures other than RT
• Variability in techniques
• Time-consuming and labor intensive
Empirical solubility model fitting is not always achievable
with existing tools
• DynoChem tool can only model one or two solvent
systems
• Crystallization systems with three or more solvents are
common in manufacturing
Jun Qiu, Bristol-Myers Squibb
Historical Automation Perspective
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Automated solubility screening workflow has been in
place for 9 years
Hundreds of studies per year
Large number of empirical solubility data collected
Solubility in solvent mixtures
Solubility in the presence of additives including
impurities
Solubility at temperatures other than RT
Accurate and consistent solubility data
Jun Qiu, Bristol-Myers Squibb
Thermodynamic Solubility: Shake
Flask Method
Add
compound
Add solvents
Incubation
Filtration


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Dilution
APIs, intermediates, reagents, by-products, impurities
Organic and aqueous solutions
Various temperatures
Jun Qiu, Bristol-Myers Squibb
HPLC
Automated Workflow – Hardware Examples
Freeslate batch reactor and filter
plate (isothermal)
Freeslate Liquid Handler with:
• Temperature controlled zones
• On-deck stirring
• Heated needle (22 gauge)
Powder Dispenser
Jun Qiu, Bristol-Myers Squibb
Automated Workflow – Software Examples
Freeslate Library Studio enables
comprehensive parallel experiment
design.
Freeslate Automation Studio
executes experiment designs.
Pictures courtesy of Freeslate
Jun Qiu, Bristol-Myers Squibb
Major Automation Challenges
1.
2.
3.
4.
Solubility in mass fraction (wt%) instead of
mg/ml
Filtration of saturated solutions at high
concentration and at high temperature (e.g. 20
wt% and 90 ºC)
High resolution of solubility map (especially
solubility vs. temperature)
Understanding of accuracy and consistency
Jun Qiu, Bristol-Myers Squibb
Solving Mass Fraction Challenge
 Solubility
in mass fraction (wt%) instead of
mg/ml
 Use an internal standard to quantitate final
solution volume



Creative reuse of common unit operations
More streamlined compared to alternative
approaches
No new technology/instrument required
Jun Qiu, Bristol-Myers Squibb
Solving Mass Fraction Challenge
 Starts
with a familiarization experiment
 Confirms the use of internal standard has no
impact on solubility
Jun Qiu, Bristol-Myers Squibb
Solving Mass Fraction Challenge
1.
2.
3.
4.
Prepare major component solvents with the same concentration of
internal standard (IS).
X IS = mg IS / mg solvent
Carry out the shake flask method.
Get compound and IS concentrations in mg/mL by HPLC (may require
two injections of different dilutions to quantitate both concentrations).
Calculate wt% solubility (i.e. mg/mg) substrate:
Csubstrate[mg Sub/mg Solv ] 
Csubstrate[mg Sub/mL Soln ]  X IS [mg IS/mg Solv ]
C IS [mg IS/mL Soln ]
Csusbtrate[mg Sub/mg Soln] 
Csubstrate[mgSub / mgSolv ]
1  C substrate[mgSub / mgSolv]
Jun Qiu, Bristol-Myers Squibb
Solving Filtration Challenges

Filtration of saturated solutions at high concentration
and at high temperature (e.g. 20 wt% and 90 ºC)
 Optimize critical unit operations
 Pushing instruments to the limit
 Leveraging Freeslate hardware and software’s
flexibility
Jun Qiu, Bristol-Myers Squibb
Solving High Resolution Challenges

High resolution of solubility map (especially
solubility vs. temperature)
 Increase experiment scale
 Taking multiple samples from the same vial
 Leveraging Freeslate hardware and software’s
flexibility
Jun Qiu, Bristol-Myers Squibb
Solving Accuracy Challenges
 Understanding of
accuracy and consistency
 Collect large amount of data (unit operations
and whole workflow)
 Compare manual and automated measurements
 Compare empirical data and model predictions
 Results will be discussed in Case Study
Jun Qiu, Bristol-Myers Squibb
Case Study
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Detailed solubility map of API with the following parameters:
 MeOH, EtOH, heptane ratios
 Amount of DBU
 Amount of methyl benzoate
 Temperature
Key decisions
 Solvent & anti-solvent volumes
 Heating temp
 Seeding point
 Filtration temp
Jun Qiu, Bristol-Myers Squibb
Empirical Data Collected from the
Automated Workflow
• Data collection was contextualized to the process
• T data was obtained without (or low) n-Heptane
• n-Heptane impact on solubility was obtained at
the addition temperature
Jun Qiu, Bristol-Myers Squibb
Modeled Solubility at Seeding Point
Jun Qiu, Bristol-Myers Squibb
Modeled Solubility During
Heptane Addition
Jun Qiu, Bristol-Myers Squibb
Solubility Dataset and Crystallization
Development
crystalliz ation scenario
100.0
Solution.Temperature (C)
heptane_vol_perc (%)
C_wt_perc (%)
80.0
60.0
40.0
20.0
0.0
0.0
115.22
230.44
crystallization scenario
345.66
460.88
576.1
Time (min)
100.0
yield (%)
80.0
Yield (%)
60.0
40.0
20.0
0.0
crystalliz
0.0 ation scenario
115.22
230.44
0.9
0.72
345.66
460.88
576.1
Time (min)
Theoretical rate of crystallization(% / min)
0.54
0.36
0.18
0.0
0.0
115.22
230.44
345.66
Time (min)
460.88
576.1
Modeling the crystallization
process can help identify
potential issues (e.g. rapid
mass deposition leading to
undesirable powder properties,
mass_dep_rate_perc
(%/min)
impurity occlusion)
and for
process optimization (yield,
volumes, cycle time)
Automated Workflow and Model Accuracy
manual
automated
Manual and automated data agree well
Empirical and model predicted data also agree well
Conclusion
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Automated Late Phase Solubility workflow capable of
delivering large datasets
 Wt%
 Multiple temperatures
 High Accuracy and consistency
Enhanced DynoChem tool capable of modeling complex
systems
 Crystallization systems with more than two solvents
Datasets provided by the combined forces of automation and
modeling lead to comprehensive understanding of the
crystallization process and enable rapid and optimal decision
making
Jun Qiu, Bristol-Myers Squibb
Acknowledgements
 Erik
Rubin
 Jose Tabora
 Michelle Mahoney
 Amit Joshi
 Masano Sugiyama
 Keming Zhu
Jun Qiu, Bristol-Myers Squibb