High-throughput polymorph screening of active pharmaceutical ingredients
Applications | 2019 | Unchained LabsInstrumentation
Polymorphism in active pharmaceutical ingredients (APIs) influences key properties such as solubility, stability and bioavailability. Early and comprehensive screening ensures identification of all crystalline forms, minimises late-stage surprises and supports intellectual property coverage. High-throughput methods accelerate this process while conserving material and time.
This study demonstrates the application of an automated high-throughput platform (Big Kahuna) configured for preformulation polymorph screening. The goal was to explore up to 384 crystallization conditions in parallel, using approximately one gram of API in under five days. The workflow integrates solvent library design, crystallization experiments and multi-modal analysis to discover new crystalline forms.
The workflow comprised four parallel crystallization methods: slurry, evaporation, cooling and anti-solvent precipitation. Key steps and instruments included:
Screening revealed several wells with unique PXRD patterns and Raman spectra. Key findings:
The Big Kahuna system enables single-operator execution of hundreds of crystallization experiments with minimal material. Automated data capture and integration into the LEA database streamline analysis, accelerating early-stage API development and intellectual property strategies.
Advances in machine-learning algorithms for pattern recognition and predictive solubility modelling will enhance solvent library design. Integration with in-situ monitoring techniques and robotics can further reduce screening timelines and improve polymorph space coverage.
The automated high-throughput platform achieved rapid exploration of 384 crystallization conditions, identified multiple new API forms and demonstrated scalability. This approach offers a robust solution for comprehensive polymorph screening in pharmaceutical development.
No external references provided in the source.
Sample Preparation, Particle characterization
IndustriesPharma & Biopharma
ManufacturerUnchained Labs
Summary
Importance of Topic
Polymorphism in active pharmaceutical ingredients (APIs) influences key properties such as solubility, stability and bioavailability. Early and comprehensive screening ensures identification of all crystalline forms, minimises late-stage surprises and supports intellectual property coverage. High-throughput methods accelerate this process while conserving material and time.
Objectives and Overview of the Study
This study demonstrates the application of an automated high-throughput platform (Big Kahuna) configured for preformulation polymorph screening. The goal was to explore up to 384 crystallization conditions in parallel, using approximately one gram of API in under five days. The workflow integrates solvent library design, crystallization experiments and multi-modal analysis to discover new crystalline forms.
Methodology and Instrumentation
The workflow comprised four parallel crystallization methods: slurry, evaporation, cooling and anti-solvent precipitation. Key steps and instruments included:
- Solvent library design with Library Studio (LEA software)
- Automated solid dispensing (Powdernium) and liquid handling (single-tip dispenser, positive-displacement tip)
- Crystallization assemblies on the Big Kahuna platform (384 conditions)
- Birefringence imaging (Zeiss AxioVert 200M microscope)
- Powder X-ray diffraction (Bruker D8 Discover with GADDS)
- Raman spectroscopy (Horiba instrument)
- HPLC quantification for solubility and stability assessment
Main Results and Discussion
Screening revealed several wells with unique PXRD patterns and Raman spectra. Key findings:
- Potential new polymorphs identified in slurry (wells A12, B12, D7, F9), evaporation (A6, D1, D7) and cooling (B6) experiments
- API solubility ranged widely (0.00–11.42 mg/mL) across solvent mixtures
- Scale-up to 50 mg confirmed new solvates and one metastable polymorph via DSC, TGA and DVS
Benefits and Practical Applications
The Big Kahuna system enables single-operator execution of hundreds of crystallization experiments with minimal material. Automated data capture and integration into the LEA database streamline analysis, accelerating early-stage API development and intellectual property strategies.
Future Trends and Applications
Advances in machine-learning algorithms for pattern recognition and predictive solubility modelling will enhance solvent library design. Integration with in-situ monitoring techniques and robotics can further reduce screening timelines and improve polymorph space coverage.
Conclusion
The automated high-throughput platform achieved rapid exploration of 384 crystallization conditions, identified multiple new API forms and demonstrated scalability. This approach offers a robust solution for comprehensive polymorph screening in pharmaceutical development.
References
No external references provided in the source.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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