Solvent System Selection
Choose solvent and antisolvent systems against solubility leverage, form risk, safety, recovery, and downstream isolation.
- Temperature-dependent solubility
- Solvent and antisolvent ranking
- Yield and EHS trade-offs
AI for Pharmaceutical Solid Form Development
AI for Crystallization Process Development
Design a crystallization that repeatedly delivers the intended form, yield, purity, particle size, and filtration behavior. CD ComputaBio combines phase-equilibrium data, kinetic experiments, mechanistic models, and scale-aware process design to replace recipe hunting with a controlled operating trajectory.
Boundary mappedKnow solubility and metastable limits
Kinetics measuredSeparate nucleation from growth
Scale awareAccount for mixing, heat transfer, and hold times
Service coverage
Crystallization Development Services
Supersaturation Mapping
Define where the target form can grow without uncontrolled nucleation, oiling, or competing-phase formation.
- Metastable-zone assessment
- Oiling and phase-separation risk
- Cooling and addition boundaries
Seeding Strategy
Specify seed form, loading, size, preparation, addition point, and aging conditions to make form control reproducible.
- Seed-quality requirements
- Secondary nucleation considerations
- Seed survival and growth
Yield and Impurity Control
Balance recovery with impurity rejection by understanding how the batch trajectory changes supersaturation and incorporation.
- Mother-liquor and purge strategy
- Impurity impact on kinetics
- Wash and endpoint rationale
Particle Engineering
Tune nucleation, growth, agglomeration, and breakage to deliver filtration, drying, and formulation-ready particles.
- Particle-size distribution
- Morphology and agglomeration
- Milling and wet-processing options
Scale-Up and Control
Convert laboratory knowledge into measurable process parameters, operating ranges, and a monitoring plan.
- Mixing and heat-transfer review
- PAT and endpoint options
- Control-space and deviation scenarios
Critical quality outcomes
Process Risk Assessment
A crystallization is judged by more than isolated yield. We evaluate how thermodynamics, kinetics, mixing, and solids handling affect form purity, chemical purity, particle attributes, and operability. The bars illustrate assessment dimensions, not measured performance.
Yield
Form purity
Nucleation control
Particle quality
Isolation
Lower attentionIllustrative prioritizationHigher attention
Four scale-aware stages
Crystallization Development Workflow
01
Map the boundaries
Measure temperature-dependent solubility, identify phase behavior, and define solvent, antisolvent, and form constraints.
02
Quantify kinetics
Use induction, desupersaturation, growth, and seeding experiments to separate thermodynamic opportunity from kinetic risk.
03
Design the trajectory
Select seed conditions, cooling or addition profile, aging, endpoint, and wash strategy to meet yield, purity, and particle targets.
04
Verify scale-up and control
Challenge mixing, heat transfer, sampling, holds, and disturbances; then define measurable parameters, ranges, and response actions.
Project readiness
Inputs and Deliverables
Recommended inputs
- Target form and available PXRD, thermal, microscopy, and spectroscopy data
- Temperature-dependent solubility and prior batch observations
- Impurity profile, yield target, solvent constraints, and EHS limits
- Equipment geometry, agitation, heat-transfer, filtration, and drying constraints
Quality controls
- Mass balance and phase identity checked across experiments
- Nucleation, growth, agglomeration, and breakage not conflated
- Scale-dependent mixing and thermal effects assessed
- Recommended ranges tied to critical quality attributes
01Solvent and phase map
02Seed specification
03Batch trajectory
04Control strategy
Published dataCASE 01 · MODEL-DRIVEN DEVELOPMENT CASE 02 · PARTICLE ENGINEERING
Published Crystallization Studies
The schematics below are original method-to-decision summaries created for this page; they do not reproduce publication figures or represent CD ComputaBio project results.
A kinetic workflow was integrated into an industrial facility
Generate dataVary supersaturation and temperature to study nucleation, growth, and induction behavior.
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Fit modelsEstimate kinetic parameters and connect experiments to a process representation.
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Support designUse model outputs to guide API crystallization process development.
Industrial workflowKinetic parametersSupersaturationProcess model
Pickles and colleagues described integration of a model-driven workflow into an industrial pharmaceutical facility, including a designed kinetic study across supersaturation and temperature. It demonstrates how structured experiments can support process decisions rather than merely reproduce a recipe.[1]
View publicationUltrasonics and temperature cycling addressed form and particle risks
Identify problemsRecognize polymorphism, oiling out, and undesirable particle-size behavior.
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Apply interventionsUse ultrasonics and temperature cycling within the crystallization process.
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Improve isolationEngineer particles while maintaining a developable API process.
UltrasonicsTemperature cyclingOiling outParticle size
Kim, Wei, and Kiang reported crystallization process development in which ultrasonics and temperature cycling were used to address polymorphism, oiling out, and particle-size challenges. The work shows why form control and particle engineering should be developed together.[2]
View publicationPractical guidance
Crystallization Development FAQs
The development plan should reflect the target solid form, impurity purge, particle requirements, and the realities of the intended equipment.
What data are needed before seeding studies?
At minimum, the target form identity, a credible solubility map, seed-quality information, and the main competing phases should be understood. Otherwise, a seed-loading number can look precise while remaining non-transferable.
How do you prevent oiling out?
We identify liquid–liquid or amorphous phase-separation risk and redesign the trajectory by changing solvent composition, temperature, antisolvent addition, concentration, mixing, or seeding timing.
Can yield and particle size be optimized together?
Yes, but they may conflict. High supersaturation can improve recovery while increasing nucleation, fines, agglomeration, and filtration burden. We design trade-off experiments against both material and process targets.
Why does a lab process fail at scale?
Mixing time, local antisolvent concentration, heat-removal rate, seed dispersion, sampling, and hold times all change with equipment. Scale-up must preserve the relevant supersaturation and solids-history trajectory.
What does the control strategy include?
The deliverable can define material attributes, seed specification, addition and cooling ranges, agitation rationale, PAT or sampling checkpoints, endpoint criteria, hold limits, and actions for common deviations.
Move from recipe to control
Request a Project PlanStart Your Crystallization Project
Share the target form, solubility or batch data, impurity profile, particle requirements, and scale constraints. We will propose the shortest evidence path to a robust, transferable process.
Scientific References
- Pickles, T., Svoboda, V., Marziano, I., Brown, C. J., and Florence, A. J. Integration of a model-driven workflow into an industrial pharmaceutical facility: supporting process development of API crystallisation. CrystEngComm 26, 4678–4689 (2024). https://doi.org/10.1039/D4CE00358F
- Kim, S., Wei, C., and Kiang, S. Crystallization Process Development of an Active Pharmaceutical Ingredient and Particle Engineering via the Use of Ultrasonics and Temperature Cycling. Organic Process Research & Development 7, 997–1001 (2003). https://doi.org/10.1021/op034107t
