Developing a model-driven workflow for the digital design of small-scale batch cooling crystallisation with the antiviral lamivudine

Pickles, Thomas; Mustoe, Chantal; Boyle, Christopher; Cardona, Javier; Brown, Cameron J.; Florence, Alastair J.

doi:10.1039/d3ce00897e

Cited by 2 publications

(5 citation statements)

References 44 publications

(82 reference statements)

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“…Lamivudine recrystallizing from ethanol typically has relatively slow kinetics [i.e., large MSZW and induction times in the hours (SS ∼ 2)], 22 whereas aspirin in ethyl acetate typically has fast kinetics [i.e., narrow MSZW and induction times in the minutes (SS ∼ 2)]. 27 Comparing the performance of the DoE and BO optimization methods for APIs with different inherent kinetic profiles can provide evidence for the generalizability of application of these methods.…”

Section: Resultsmentioning

confidence: 99%

“…Solubility profiles were generated for each API in a solvent selected from previous work. 22,27 3.2. Optimization: Input Parameter Bounds, Target Parameter Objectives, and Approaches.…”

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

“…Images were captured every 5 s, and an in-house convolutional neural network (CNN) image analysis algorithm , was used to extract kinetic parameters. X-ray powder diffraction (XRPD) patterns were collected using a Bruker D8 Discover, and the data were visualized using DIFFRAC.EVA and Matplotlib (Python).…”

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

“…X-ray powder diffraction (XRPD) patterns were collected using a Bruker D8 Discover, and the data were visualized using DIFFRAC.EVA and Matplotlib (Python). Solubility profiles were generated for each API in a solvent selected from previous work. , …”

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

“…The input parameter bounds were varied for each API to accommodate the different sizes of their metastable zone width (MSZW). , Lamivudine showed a broad MSZW (>30 °C) and therefore was deemed unlikely to observe any nucleation at low supersaturations within the time constraints of the experiment. Aspirin displayed a narrow MSZW (mean of 16 °C) and thus nucleation was likely to be feasible at low supersaturations (generally below 1.2).…”

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

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Comparative Study on Adaptive Bayesian Optimization for Batch Cooling Crystallization for Slow and Fast Kinetic Regimes

Pickles,

Mustoe,

Brown

et al. 2024

Crystal Growth & Design

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Crystallization kinetic parameter estimation is important for the classification, design, and scale-up of pharmaceutical manufacturing processes. This study investigates the impact of supersaturation and temperature on the induction time, nucleation rate, and growth rate for the compounds lamivudine (slow kinetics) and aspirin (fast kinetics). Adaptive Bayesian optimization (AdBO) has been used to predict experimental conditions that achieve target crystallization kinetic values for each of these parameters of interest. The use of AdBO to guide the choice of the experimental conditions reduced material usage up to 5-fold when compared to a more traditional statistical design of experiments (DoE) approach. The reduction in material usage demonstrates the potential of AdBO to accelerate process development as well as contribute to Net-Zero and green chemistry strategies. Implementation of AdBO can lead to reduced experimental effort and increase efficiency in pharmaceutical crystallization process development. The integration of AdBO into the experimental development workflows for crystallization development and kinetic experiments offers a promising avenue for advancing the field of autonomous data collection exploiting digital technologies and the development of sustainable chemical processes.

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Section: Resultsmentioning

confidence: 99%

“…Solubility profiles were generated for each API in a solvent selected from previous work. 22,27 3.2. Optimization: Input Parameter Bounds, Target Parameter Objectives, and Approaches.…”

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

Section: Experimental Methods and Optimizationmentioning

confidence: 99%

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