Late Phase Solubility: an Advanced Automated Workflow to Support Crystallization Development

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

3. 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 compound • APIs, intermediates, reagents, by-products, impurities • Organic and aqueous solutions • Various temperatures Add solvents Incubation Filtration Dilution HPLC 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. 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. 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. Acknowledgements • Erik Rubin • Jose Tabora • Michelle Mahoney • Amit Joshi • Masano Sugiyama • Keming Zhu Jun Qiu, Bristol-Myers Squibb