1. 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

2. Jun Qiu, Bristol-Myers Squibb
Outline
Motivation
Gaps
Historical perspective
Major automation challenges
How we solved these challenges
Case study
Jun Qiu, Bristol-Myers Squibb

3. Jun Qiu, Bristol-Myers Squibb
Chemical Development
Major operations in a chemical synthesis step:
Charging
Reaction
Quenching
Crystallization
Jun Qiu, Bristol-Myers Squibb

4. Value of Solubility Datasets
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

5. Solubility Dataset and Crystallization Development
1) Heat batch to dissolve
2) Cool to reach seeding point
3) Add seeds to begin crystallization
5) Cool for isolation
4) Add heptane (anti-solvent)
Extensive solubility data set and regression enables modeling solubility as conditions change in crystallization process

6. Late Phase Solubility and DynoChem Modeling
Initial Solubility Screen
Crystallization Selection for Late Phase
ab initio Simulation
Late Phase Solubility
Study Design
Late Phase Solubility
Study Execution
DynoChem Model for Crystallization Development

7. Gaps for Late Phase Solubility
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

8. Historical Automation Perspective
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

9. Thermodynamic Solubility: Shake Flask Method
Add solvents
Add compound
Filtration
Dilution
Incubation
HPLC
APIs, intermediates, reagents, by-products, impurities
Organic and aqueous solutions
Various temperatures
Jun Qiu, Bristol-Myers Squibb

10. 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

11. 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

12. Major Automation Challenges
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

13. 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

14. 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

15. Solving Mass Fraction Challenge
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:
Jun Qiu, Bristol-Myers Squibb

16. 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

17. 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

18. 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

19. Jun Qiu, Bristol-Myers Squibb
Case Study
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

20. 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

21. Modeled Solubility at Seeding Point
Jun Qiu, Bristol-Myers Squibb

22. Modeled Solubility During Heptane Addition
Jun Qiu, Bristol-Myers Squibb

23. Solubility Dataset and Crystallization Development
Yield (%)
Modeling the crystallization process can help identify potential issues (e.g. rapid mass deposition leading to undesirable powder properties, impurity occlusion) and for process optimization (yield, volumes, cycle time)
Theoretical rate of crystallization(% / min)

24. Automated Workflow and Model Accuracy
manual
automated
Manual and automated data agree well
Empirical and model predicted data also agree well

25. Jun Qiu, Bristol-Myers Squibb
Conclusion
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

26. Jun Qiu, Bristol-Myers Squibb
Acknowledgements
Erik Rubin
Jose Tabora
Michelle Mahoney
Amit Joshi
Masano Sugiyama
Keming Zhu
Jun Qiu, Bristol-Myers Squibb