Thomas
Pickles
a,
Vaclav
Svoboda
b,
Ivan
Marziano
c,
Cameron J.
Brown
a and
Alastair J.
Florence
*a
aCMAC Future Manufacturing Research Hub, Technology and Innovation Centre, The University of Strathclyde, Glasgow, G1 1RD, UK. E-mail: alastair.florence@strath.ac.uk
bChemical Research and Development, Worldwide Research and Development, Pfizer, Groton, Connecticut, USA
cPfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, UK
First published on 29th July 2024
Developing crystallisation processes in the pharmaceutical industry is material and resource intensive due to the large design space, i.e. many different process parameters and combinations thereof. Furthermore, small scale experimental results don't necessarily translate when volume is scaled up due to changes in liquid flow, mixing and heat transfer surface area. However, indications that knowledge-driven resource reduction on a small scale is possible and results may be worth investigating at larger scales. Therefore, this study presents and evaluates a knowledge-driven workflow which achieves its goal of reducing the necessary resources previously required to develop a crystallisation process suitable for commercial manufacture in a pharmaceutical setting. By following this workflow, thermodynamic and isothermal kinetic data for the cooling crystallization of (3S,5R)-3-(aminomethyl)-5-methyl-octanoic acid (PD-299685) within an 8-week timeframe were obtained. Moreover, the workflow was expanded to include isothermal kinetic parameters from a 50-fold scaled-up cooling crystallisation, as well as antisolvent and seeded crystallisation of PD-299685. The systematic and standardised data collection facilitated by this workflow enabled the design and optimisation of the PD-299685 crystallisation process. The proposed scalable industrial crystallisation route for PD-299685 combines cooling and antisolvent techniques, offering a wide metastable zone width to facilitate speck-free filtration and effective seeding. This approach allows for control over product quality, resulting in particles with a desired aspect ratio of 0.766 and a d(v,90) value of 234 μm through wet milling. These parameters align with the proposed API material target specifications for solid oral dosage form quality, specifically oral bioavailability and content uniformity, and efficient drug product manufacture all whilst demonstrating a significant reduction in material usage.
(3S,5R)-3-(Aminomethyl)-5-methyl-octanoic acid (PD-299685) had been clinically evaluated for the reduction of hot flashes associated with menopause as a replacement for hormone replacement therapy.5 PD-299685 as the final product comes from a complex many-step synthesis where previous crystallisation routes included cooling crystallisation in 50:
50 water/isopropanol or 50
:
50 water/ethanol by volume.6 Given that the final crystallization had been the object of previous development efforts, this study set out to assess whether novel, workflow-based approaches to crystallization development would lead either to a more efficient process compared to that previously published, or the faster identification of a comparable process.
Typical workflows for developing crystallisation processes consist of solubility and metastable zone width (MSZW) measurements to find a suitable solvent that primarily gives a high yield and high throughput/low process mass intensity (PMI) within safe operating temperatures and the desired polymorph. Once a solvent system and crystallization type is chosen then various experiments can be performed, generally at 50–100 mL scale, to determine most kinetic parameters and whether seeding or other process controls, such as milling, are necessary to control additional product attributes such as purity or particle size. Process development and optimisation are usually carried out following known domain relationships and chemistry intuition, or by design of experiments (DoE).7 A step-by-step workflow to assist with consistent data collection for the early stages of process understanding has been developed,8 as shown in Fig. 1. Execution of the workflow produces a comprehensive dataset of thermodynamic and kinetic parameters for cooling crystallisation via isothermal experiments. The workflow consists of:
![]() | ||
Fig. 1 Schematic diagram of the workflow for crystallisation data collection, adapted from ref. 8. *Minimum of 3–4 data points must be used for reliable estimates of R2 values. For some solvent systems, this may not be achievable as qualitative solubility can still be used to eliminate potential solvents. |
1. Aim setting.
2. Collating of prior knowledge.
A. Decision A determines whether enough experimental raw material data is already known.
3. Characterising raw material.
4. Setting target parameters.
5. A solubility study.
6. Analysis.
7. Choosing a solvent system.
B. Decision B is whether a van't Hoff solubility line can be plotted for the solvents and solvent mixtures trialled, based upon predictions, with an r2 accuracy exceeding 0.90.
8. A kinetic parameter study is then completed varying the supersaturation and temperature looking for the effects on nucleation rate, growth rate and induction time.
9. Further analysis.
C. Decision C allows for the user to change the solvent system if any unexpected fouling occurred.
D. Decision D forms part of an optimisation loop until the target parameters set earlier are achieved.
E. Decision E, the main extension from previous reported work,8,9 allows for a call-out to seeded, antisolvent, validation and a crystallisation process design.
10. Optimisation and feedback loops.
11. Additional experiments into other modes of crystallisation.
Advancements in data-driven algorithms such as solubility predictive models, enhancements in machine learning image analysis10 and availability of scale-up predictive models for unit operations such as filtration and drying11 have been deployed to enhance the workflow. With the extension into seeded and antisolvent crystallisation and validation from 2 mL to 50–100 mL scale, a new crystallisation of PD-299685 was designed and optimised.
With the major advancements in small-scale crystallisation hardware and automation, the overarching aim of this paper was to successfully develop a batch crystallisation process that supersedes past methods, using much less material and time.
The pure solvents (≧99%) were chosen to ensure functional diversity while following solvents guidelines by the International Conference on Harmonization (ICH)12i.e., avoiding class 1.
X-ray powder diffraction (XRPD) patterns were collected using a D8 Endeavor (Bruker)16 and the data was visualised in DIFFRAC.EVA software.17 Off-line dry powder size and shape measurements were collected using Morphologi G3 (Malvern Panalytical).18 Differential scanning calorimetry (DSC) was performed on the DSC3+ (Mettler Toledo).19 Microscopy images were collected using an Eclipse Ci POL (Nikon)20 equipped with a Micropix camera.21
1. Heat at a rate of 0.5 °C min−1 to 10 °C below the solvent boiling point or 90 °C (whichever is lowest) and hold at this temperature for 10 minutes.
2. Cool at a rate of 0.5 °C min−1 to 5 °C and hold at this temperature for 10 minutes.
3. The above temperature cycle was repeated two more times.
The stir rate was fixed at 900 RPM for all the above steps.
Image analysis was carried out to extract the point of dissolution and the primary nucleation threshold for each vial, and subsequently, the metastable zone width (MSZW) was calculated. Solid state analysis, via XRPD, was performed on samples where deviations were observed from the expected crystal morphology. The data obtained from the experiments were analysed to understand the effect that different solvent systems have both on the crystal and the process.
Initial concentration values and additional solvent systems to trial were predicted using COSMO-RS22 and a group contribution UNIFAC model, implemented in Dynochem (Scale-up systems).23 The model has the underlying theory that molecules of solute and solvent are broken down into their constituent functional groups and interactions can then be parameterised. Predicted and measured solubility values were done in an iterative process. Measured data on single solvent systems allowed for refinement of the model for single solvent systems and also predictions in binary and ternary solvent systems.
1. The vials were heated at a rate of 1 °C min−1 to 60 °C and held at this temperature for 10 minutes.
2. The vials were rapidly cooled at a rate of 10 °C min−1 to the isothermal temperature of interest, with no stirring.
3. The vials were held at this temperature for 3 hours.
4. The above temperature cycle was repeated four more times.
The agitator geometry was varied across this study to assess magnetic flea, hook stirrer, 3-blade pitched impeller and double 3-blade pitched impeller as different stirring methods. The stir rate was fixed at 900 RPM.
Image analysis of the Crystalline image data was done using a CNN algorithm,8,10 to extract the induction time, the growth rate, the aspect ratio (defined as ferret max/ferret min) and an arbitrary secondary nucleation rate from the isothermal hold. Samples were filtered, washed with acetone, and dried using a standard vacuum filtration setup. The data obtained from the experiments were analysed to understand a good spread of the design space between process conditions and crystallisation kinetic parameters.
For the validation experiments, an EasyMax (Mettler Toledo) was equipped with a 50 mL glass one-piece vessel. A known mass of PD-299685 was added to the reactor vessel and then 45 mL of 55:
45 (v/v) water/1-propanol was added, with agitation using an overhead PTFE half-moon stirrer. The experiment used the same temperature program as detailed in section 2.3.1.2.
1. Heated to 75 °C at a rate of 1 °C min−1 and held for 30 minutes.
2. Cooled to 60 °C at a rate of 0.2 °C min−1.
3. Seed material was added and then held for 30 minutes.
4. Antisolvent was added over 45 minutes and held for 30 minutes.
5. Cooled to 5 °C at a rate of 0.2 °C min−1.
6. Additional specific method section as detailed in section 2.3.2.1 and 2.3.2.2.
This base profile was determined as a product of the workflow based upon a covariance analysis of the kinetic parameters which allowed prediction of process parameters to get the desired product and performance. The predicted process, that complies with operational contracts and objectives set, was developed in Crystalline and then validated in the EasyMax equipped with a Blaze probe.
Mass of API (g) | Mass of seed (mg) | Additional specific method details | HSWM details |
---|---|---|---|
4.992 | 99.67 | Isothermally held at 5 °C for 10 hours after the final cool | IKA T25 Ultra Turrax equipped with a S25KD-25F dispersing tool for 2 minutes at 5000 rpm |
5.013 | 101.8 | Milled after the final cool and the slurry underwent a thermocycling of heating to 20 °C at 1 °C min−1, held for 30 minutes, cooled to 5 °C at 0.2 °C min−1 three times. Isothermally held for 8 hours | IKA T25 Ultra Turrax equipped with a S25KD-25F dispersing tool for 2 minutes at 5000 rpm |
5.055 | 102.8 | Isothermally held at 5 °C for 14 hours after final cool, milled, and isothermally held for 1 hour | IKA T25 Ultra Turrax equipped with a S25KD-25F dispersing tool for 2 minutes at 3000 rpm |
5.061 | 99.7 | Isothermally held at 5 °C for 12 hours after final cool, milled, and isothermally held for 1 hour | IKA MagicLab equipped with fine teeth for a single pass at 14![]() |
The aim of the solubility and solvent effects study was to collect quantitative and qualitative data on the thermodynamic crystallisation behaviour and to evaluate the UNIFAC solubility prediction24 regression model. The aim of the kinetic parameter study focused on collecting quantitative kinetic data using a range of agitation methods, volumes, and crystallisation techniques. Additional experimentation was aimed at expanding and validating the workflow by using various stirrer types (to evaluate mixing sensitivity) and particle size and shape analysis methods (to simplify data analysis). The investigation of the seeded, antisolvent, and validation experiments aimed to explore the design space and enhance the capabilities of the workflow. This research built upon prior work8,9 developing efficient process development workflows, offering an extension and enhancement of its application.
The collected data, both quantitative and qualitative, were utilised to inform a novel crystallisation process for the API. The process was optimised to align with drug product attributes, highlighting the industrial advantages of the workflow's approach. The study aimed to show that the use of workflows allows for efficient and effective process development direction for any given API.
For the kinetic parameter study, the primary objective target was to collect data over the design space to determine induction time, growth rate, and nucleation rate under different process conditions, such as supersaturation and temperature.
The target parameters for the end crystallisation process design and optimization were yield, particle shape, and particle size. The study aimed to maximize yield while maintaining an aspect ratio greater than 0.5 (ref. 4) and a particle size (d90) of 100–350 μm. Improvements to the manufacturability of the process such as increasing the MSZW for speck free filtration and avoiding mechanical milling were desirable.
The data from this iteration was used to fit a Dynochem UNIFAC model, and binary solubility prediction was run at 5 °C, 25 °C, 50 °C and 70 °C for the covered solvents. Highly synergetic effects were observed for water/alcohol and water/acetonitrile mixtures (Fig. S1 in the ESI†). The higher predicted solubility of PD-299685 at high temperatures, compared to pure and 50:
50 (v/v) solvents, allows for more economic solvent and vessel usage, whereas minimal change at low temperatures enhances a predicted cooling crystallisation yield to more than 98% for all systems.
The best binary solvent compositions, due to highest synergistic solubility, for further study from this iteration were 40:
60 (w/w) ethanol/water, 40
:
60 (w/w) IPA/water and 40
:
60 (w/w) 1-propanol/water which equates to volume fractions of 45
:
55. Acetonitrile/water has good synergy (where the solubility exceeds that of the single solvents) at 30
:
70 (w/w), equating to 33
:
66 (v/v), and was also further explored. Acetone/water mixtures were eliminated from the study due to low solubility at the upper operating temperature of acetone due to its low boiling point. Ethanol/glycerol had the highest predicted synergy for an organic/organic solvent mixture but with still very low solubility so was also eliminated from the study.
The UNIFAC regression model fitted with the new experimental data returned recommended solvent systems the same as the previous iteration, therefore termination criteria for the solubility and solvent effects study had been met. Section 3.1.7. discusses the classification, ranking and selection of a solvent system.
• 1-Butanol, 1-propanol, 2-methyl THF, acetone, acetonitrile, ethyl acetate, heptane, isopropanol and isopropyl acetate are antisolvents.
• Ethanol and DMSO are practically unusable pure solvent systems due to low solubility.
• Water/organic mixtures gave good synergistic solubility.
• Acetone would be an ideal wash solvent for filtration and drying due to low API solubility and low boiling point.
It is worth noting that with the rise of green solvents such as ionic liquids, deep eutectic solvents and supercritical fluids, as part of carbon footprint reduction campaigns then the ranking of solvents in this workflow could also incorporate green metrics weighted alongside quality product attributes.
Additional solubility measurements were carried out to allow for a better fit to the data over a range of temperatures (Fig. 6). A thermal stability trial was also conducted where a highly concentrated sample was held for 24 hours at 75, 80, 82.5 and 85 °C using the Crystal-16 (Technobis)13 and aliquots were sampled using HPLC. Negligible chemical degradation was observed for all temperatures after 2 hours, however a new impurity with molecular weight of 170.2 g mol−1 was observed after 5 hours for vials held at 80 °C and above. Therefore, a maximum temperature of 75 °C will be used for PD-299685 in 55:
45 (v/v) water/1-propanol to allow an extended hold for dissolution if scaled up to plant.
![]() | ||
Fig. 6 Solubility-temperature profile for PD-299685 in 55![]() ![]() |
![]() | ||
Fig. 7 Solubility-temperature profile for PD-299685 in 70![]() ![]() |
An additional series of measurements was conducted for PD-299685 in 64:
36 (v/v) water/1-propanol to fit solubility for the halfway point of antisolvent addition. No fouling was observed through image analysis and observational analysis.
The XRPD pattern (Fig. S2 of the ESI†) confirms that the increase in water content in the solvent composition did not cause the formation of the monohydrate polymorph. The stable form A, was produced. In addition, this solvent composition was stress-tested by holding a slurry of PD-299685 in 70:
30 water/1-propanol at 5 °C for more than 5 days using the Crystal-16 (Technobis)13 where no solid form transformation in the product was observed.
Primary nucleation was not observed for any of the vials ran at a temperature of 16 °C and a supersaturation ratio of 3.2 likely due to the low concentration of the vial. This qualitatively showed that high supersaturation or seeding are required at low temperatures. A comparison of different stirrers on the effect of crystallisation was conducted on vial 7 of each experiment (temperature of 28 °C, supersaturation of 2.91) where the magnetic stirrer showed attrition, the hook-shaped stirrer showed reduced mixing and the 3-blade pitched stirrer showed good mixing. The differences in quality of crystal generation were negligible between the single and the double 3-blade pitched impellers therefore the single was chosen for further study in section 3.2. as it is more commonly used in plant-scale reactors.
The qualitative observations (Fig. 8) between the different stirring methods were validated using microscopy and Morphologi data (Fig. 9) which showed that the particles crystallised in the vial with a magnetic flea had a d(v,90) approximately 3.5 times smaller compared to overhead stirring methods.
![]() | ||
Fig. 8 Crystalline (Technobis) images from the end of the final hold cycle for vial 7 agitated by magnetic flea (a), 3-blade pitched (b), 3-blade double pitched (c) and hook (d) stirrers. |
![]() | ||
Fig. 9 Volume-based particle size distribution (PSD) from Morphologi for all Crystalline stirring methods. NOTE; the difference in smoothing is due to varying number of total particles. |
The kinetic parameters extracted, using CNN image analysis, were visualised using a covariance matrix due to the multiple dimensionalities of the design and measured variable space.
The agitation methods were assigned a discrete eigenvalue to allow for numerical comparisons between continuous and categorical variables across the design space. It can be seen from the co-variance matrix (Fig. S3(a) of the ESI†) that changing between the magnetic flea and the 3-blade pitched impellor, had a large impact on the measured kinetic parameters. It can qualitatively be concluded that the use of the magnetic flea in this study ground the particles and created clouds of fines which created more particle surface area for faster desaturation.
The data presented in Fig. S3(b) (of the ESI†) indicates that there was no statistically significant difference in terms of impact for type of overhead agitation (hook, single 3-blade pitch, double 3-blade pitch) on nucleation, although – as mentioned in an earlier section – the hook impeller gave rise to a poor mixing environment.
PD-299685 can be classified as a slow nucleating and fast growing API across varied process conditions and when using an overhead stirrer, the most representative of a plant scale process, yields large crystals. The d90 of crystals from the isothermal study were larger than 500 μm which exceeded the required particle size required for API formulation in this instance. The general crystal habit was blocks, which were acceptable for downstream processing. Other morphologies, such as needles, are less desirable from a process perspective given that particles exhibiting this shape generally have poor bulk properties, including for instance flow, the information gathered in this stage was foundational for designing an end crystallisation process mainly in terms of the impact of agitation and mixing and knowing the impact of temperature and supersaturation on nucleation and growth.
The covariance matrix (Fig. S4(a) of the ESI†) shows that an increase in seed mass caused both growth and nucleation rates to increase where the growth rate dominated over the nucleation rate. Nucleation however dominated over growth at higher temperatures and higher supersaturations, as expected. Microscopy of samples taken of vial 3 (supersaturation of 1.60, temperature of 35 °C) showed many small crystals with some large agglomerates compared to vial 7 (supersaturation of 1.55, temperature of 28 °C) which showed dispersed square crystals over 500 μm.
It can be seen from the covariance matrix (Fig. S4(b) of the ESI†) that the addition of antisolvent had a positive impact on nucleation and a negative impact on growth. This was ideal for limiting the d90 size of the final crystals. There was also a largely positive relationship between the addition of antisolvent at higher temperatures on nucleation with minimal impact on growth. Therefore, nucleation would largely dominate over growth at high temperatures and higher antisolvent composition.
It can be seen from the covariance matrix (Fig. S4(c) of the ESI†) that there are major variances between the Crystalline and EasyMax nucleation and growth data which can be accounted for by differences in PAT and reactor geometry. The Blaze probe used for the EasyMax image collection had a much smaller field of view (FOV) compared to the Crystalline cameras, and therefore it was challenging to track nucleation and growth for such large particles due to particle sample size per image. Despite the large variances, microscopy images (Fig. 10) showed good qualitative similarities in final crystal size between EasyMax and Crystalline when using the 3-blade pitched double impellor.
The PSDs as shown in Fig. 11 also showed good similarity, where the d(v,90) was 384 μm and 328 μm for the Crystalline 3-blade double pitched and EasyMax PTFE stirrer respectively. Therefore, it can be concluded that for the best estimations of scaling up from Crystalline to EasyMax, the 3-blade double pitched should be used at the millilitre scale. However alternative PAT should be used for online measurements of kinetic parameters for a fast growing compound as the one used here such as imaging with a wider field of view (FOV). However, this is compound specific and the set-up discussed in this paper would most times suffice.
As shown in section 3.1.5., the solubility and solvent effects study, the solvent that gave the highest solubility and temperature dependence was 55:
45 water/1-propanol with a predicted yield of over 90% for a cooling crystallisation only. The addition of water for a final solvent composition of ∼70
:
30 reduced the solubility at low temperatures further pushing the predicted yield to 95%. Therefore, a cooling and antisolvent hybrid crystallisation was proposed as the ideal mode of crystallisation. The local kinetic parameter study (section 3.1.8.) showed that the use of an overhead blade impeller would yield crystals to align with shape and size target parameters set out and would be most like the actual manufacturing vessel. Analysis of covariance between isothermal experiments in Crystalline and EasyMax showed good comparisons between the different scales. This enabled process understanding of kinetic parameters at millilitre scale before process optimisation. Due to past issues of large particle size, the proposed crystallisation route should allow for nucleation to dominate over growth. Analysis of covariance in the seeded and antisolvent experiments in section 3.1.11. showed that this could be achieved where seeding was done at higher temperatures and using lower seed mass. Furthermore, a fast antisolvent addition at higher temperatures should also allow for more nucleation. A cooling rate of 0.2 °C min−1 was determined due to heat transfer constraints associated with plant-sized equipment.
Particle size data in Table 2 shows that a wide range of particle sizes can be accessed by adjusting the wet mill parameters which suggests that ‘tuning’ particle size in later development would be possible upon scale.
Run | d(v,90) (μm) | Mean aspect ratio | Yield (%) | Comment(s) |
---|---|---|---|---|
a Refers to Fig. S5 (of the ESI†). | ||||
1 | 476 | 0.688 | 91 | HSWM reduced d(v,90) from 664 μm, slow filtration |
2 | 600 | 0.743 | 92 | Hold and thermocycling after HSWM allowed for excessive growth, narrow PSDa (less fines), slow filtration |
3 | 523 | 0.725 | 92 | Hold after HSWM allowed recovery of yield but also growth, slow filtration |
4 | 234 | 0.766 | 92 | Narrow PSD,a fast filtration |
Footnote |
† Electronic supplementary information (ESI) available: Integration of a model-driven workflow into an industrial pharmaceutical facility: supporting process development of API crystallisation. See DOI: https://doi.org/10.1039/d4ce00358f |
This journal is © The Royal Society of Chemistry 2024 |