Integrated Filtration and Washing Modeling: Optimization of Impurity Rejection for Filtration and Washing of Active Pharmaceutical Ingredients.

A digital design tool that can transfer material property information between unit operations to predict the product attributes in integrated purification processes has been developed to facilitate end-to-end integrated pharmaceutical manufacturing. This work aims to combine filtration and washing operations frequently using active pharmaceutical ingredient (API) isolation. This is achieved by coupling predicted and experimental data produced during the upstream crystallization process. To reduce impurities in the isolated cake, a mechanistic model-based workflow was used to optimize an integrated filtration and washing process model. The Carman-Kozeny filtration model has been combined with a custom washing model that incorporates diffusion and axial dispersion mechanisms. The developed model and approach were applied to two systems, namely, mefenamic acid and paracetamol, which are representative compounds, and various crystallization and wash solvents and related impurities were used. The custom washing model provides a detailed evolution of species concentration during washing, simulating the washing curve with the three stages of the wash curve: constant rate, intermediate stage, and diffusion stage. A model validation approach was used to estimate cake properties (e.g., specific cake resistance, cake volume, cake composition after washing, and washing curve). A global systems analysis was conducted by using the calibrated model to explore the design space and aid in the setup of the optimization decision variables. Qualitative optimization was performed in order to reduce the concentration of impurities in the final cake after washing. The findings of this work were translated into a final model to simulate the optimal isolation conditions.

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

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.

Rapid Assessment of Crystal Nucleation and Growth Kinetics: Comparison of Seeded and Unseeded Experiments.

In this work, we outlined an experimental workflow enabling the rapid assessment of primary and secondary nucleation and crystal growth kinetics. We used small-scale experiments in agitated vials with in situ imaging for crystal counting and sizing to quantify nucleation and growth kinetics of α-glycine in aqueous solutions as a function of supersaturation at isothermal conditions. Seeded experiments were required to assess crystallization kinetics when primary nucleation is too slow, especially at lower supersaturations often encountered in continuous crystallization processes. At higher supersaturations, we compared results from seeded and unseeded experiments and carefully analyzed interdependencies of primary and secondary nucleation and growth kinetics. This approach allows for the rapid estimation of absolute values of primary and secondary nucleation and growth rates without relying on any specific assumptions about functional forms of corresponding rate expressions used for estimation approaches based on fitting population balance models. Quantitative relationships between nucleation and growth rates at given conditions provide useful insights into crystallization behavior and can be explored to rationally manipulate crystallization conditions for achieving desirable outcomes in batch or continuous crystallization processes.

Development of a Digital Twin for the Prediction and Control of Supersaturation during Batch Cooling Crystallization.

Fine chemicals produced via batch crystallization with properties dependent on the crystal size distribution require precise control of supersaturation, which drives the evolution of crystal size over time. Model predictive control (MPC) of supersaturation using a mechanistic model to represent the behavior of a crystallization process requires less experimental time and resources compared with fully empirical model-based control methods. Experimental characterization of the hexamine-ethanol crystallization system was performed in order to collect the parameters required to build a one-dimensional (1D) population balance model (PBM) in gPROMS FormulatedProducts software (Siemens-PSE Ltd.). Analysis of the metastable zone width (MSZW) and a series of seeded batch cooling crystallizations informed the suitable process conditions selected for supersaturation control experiments. The gPROMS model was integrated with the control software PharmaMV (Perceptive Engineering Ltd.) to create a digital twin of the crystallizer. Simulated batch crystallizations were used to train two statistical MPC blocks, allowing for in silico supersaturation control simulations to develop an effective control strategy. In the supersaturation set-point range of 0.012-0.036, the digital twin displayed excellent performance that would require minimal controller tuning to steady out any instabilities. The MPC strategy was implemented on a physical 500 mL crystallizer, with the simulated solution concentration replaced by in situ measurements from calibrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Physical supersaturation control performance was slightly more unstable than the in silico tests, which is consistent with expected disturbances to the heat transfer, which were not specifically modeled in simulations. Overall, the level of supersaturation control in a real crystallizer was found to be accurate and precise enough to consider future adaptations to the MPC strategy for more advanced control objectives, such as the crystal size.

Machine Learning-Derived Correlations for Scale-Up and Technology Transfer of Primary Nucleation Kinetics.

Scaling up and technology transfer of crystallization processes have been and continue to be a challenge. This is often due to the stochastic nature of primary nucleation, various scale dependencies of nucleation mechanisms, and the multitude of scale-up approaches. To better understand these dependencies, a series of isothermal induction time studies were performed across a range of vessel volumes, impeller types, and impeller speeds. From these measurements, the nucleation rate and growth time were estimated as parameters of an induction time distribution model. Then using machine learning techniques, correlations between the vessel hydrodynamic features, calculated from computational flow dynamic simulations, and nucleation kinetic parameters were analyzed. Of the 18 machine learning models trained, two models for the nucleation rate were found to have the best performance (in terms of % of predictions within experimental variance): a nonlinear random Forest model and a nonlinear gradient boosting model. For growth time, a nonlinear gradient boosting model was found to outperform the other models tested. These models were then ensembled to directly predict the probability of nucleation, at a given time, solely from hydrodynamic features with an overall root mean square error of 0.16. This work shows how machine learning approaches can be used to analyze limited datasets of induction times to provide insights into what hydrodynamic parameters should be considered in the scale-up of an unseeded crystallization process.

Digital Design of Filtration and Washing of Active Pharmaceutical Ingredients via Mechanistic Modeling.

To facilitate integrated end-to-end pharmaceutical manufacturing using digital design, a model capable of transferring material property information between operations to predict product attributes in integrated purification processes has been developed. The focus of the work reported here combines filtration and washing operations used in active pharmaceutical ingredient (API) purification and isolation to predict isolation performance without the need of extensive experimental work. A fixed Carman-Kozeny filtration model is integrated with several washing mechanisms (displacement, dilution, and axial dispersion). Two limiting cases are considered: case 1 where there is no change in the solid phase during isolation (no particle dissolution and/or growth), and case 2 where the liquid and solid phases are equilibrated over the course of isolation. In reality, all actual manufacturing conditions would be bracketed by these two limiting cases, so consideration of these two scenarios provides rigorous theoretical bounds for assessing isolation performance. This modeling approach aims to facilitate the selection of most appropriate models suitable for different isolation scenarios, without the requirement to use overly complex models for straightforward isolation processes. Mefenamic acid and paracetamol were selected as representative model compounds to assess a range of isolation scenarios. In each case, the objective of the models was to identify the purity of the product reached with a fixed wash ratio and minimize the changes to the crystalline particle attributes that occur during the isolation process. This was undertaken with the aim of identifying suitable criteria for the selection of appropriate filtration and washing models corresponding to relevant processing conditions, and ultimately developing guidelines for the digital design of filtration and washing processes.

Temperature Correction of Spectra to Improve Solute Concentration Monitoring by In Situ Ultraviolet and Mid-Infrared Spectrometries toward Isothermal Local Model Performance.

Changes in temperature can significantly affect spectroscopic-based methods for in situ monitoring of processes. As varying temperature is inherent to many processes, associated temperature effects on spectra are unavoidable, which can hinder solute concentration determination. Ultraviolet (UV) and mid-infrared (IR) data were acquired for l-ascorbic acid (LAA) in MeCN/H2O (80:20 w/w) at different concentrations and temperatures. For both techniques, global partial least squares (PLS) models for prediction of LAA concentration constructed without preprocessing of the spectra required a high number of latent variables to account for the effects of temperature on the spectra (root mean square error of cross validation (RMSECV) of 0.18 and 0.16 g/100 g solvent, for UV and IR datasets, respectively). The PLS models constructed on the first derivative spectra required fewer latent variables, yielding variable results in accuracy (RMSECV of 0.23 and 0.06 g/100 g solvent, respectively). Corresponding isothermal local models constructed indicated improved model performance that required fewer latent variables in the absence of temperature effects (RMSECV of 0.01 and 0.04 g/100 g solvent, respectively). Temperature correction of the spectral data via loading space standardization (LSS) enabled the construction of global models using the same number of latent variables as the corresponding local model, which exhibited comparable model performance (RMSECV of 0.06 and 0.04 g/100 g solvent, respectively). The additional chemometric effort required for LSS is justified if prediction of solute concentration is required for in situ monitoring and control of cooling crystallization with an accuracy and precision approaching that attainable using an isothermal local model. However, the model performance with minimal preprocessing may be sufficient, for example, in the early phase development of a cooling crystallization process, where high accuracy is not always required. UV and IR spectrometries were used to determine solubility diagrams for LAA in MeCN/H2O (80:20 w/w), which were found to be accurate compared to those obtained using the traditional techniques of transmittance and gravimetric measurement. For both UV and IR spectrometries, solubility values obtained from models with LSS temperature correction were in better agreement with those determined gravimetrically. In this first example of the application of LSS to UV spectra, significant improvement in the predicted solute concentration is achieved with the additional chemometric effort. There is no extra experimental burden associated with the use of LSS if a structured approach is employed to acquire calibration data that account for both temperature and concentration.

Direct Image Feature Extraction and Multivariate Analysis for Crystallization Process Characterization.

Small-scale crystallization experiments (1-8 mL) are widely used during early-stage crystallization process development to obtain initial information on solubility, metastable zone width, as well as attainable nucleation and/or growth kinetics in a material-efficient manner. Digital imaging is used to monitor these experiments either providing qualitative information or for object detection coupled with size and shape characterization. In this study, a novel approach for the routine characterization of image data from such crystallization experiments is presented employing methodologies for direct image feature extraction. A total of 80 image features were extracted based on simple image statistics, histogram parametrization, and a series of targeted image transformations to assess local grayscale characteristics. These features were utilized for applications of clear/cloud point detection and crystal suspension density prediction. Compared to commonly used transmission-based methods (mean absolute error 8.99 mg/mL), the image-based detection method is significantly more accurate for clear and cloud point detection with a mean absolute error of 0.42 mg/mL against a manually assessed ground truth. Extracted image features were further used as part of a partial least-squares regression (PLSR) model to successfully predict crystal suspension densities up to 40 mg/mL (R 2 > 0.81, Q 2 > 0.83). These quantitative measurements reliably provide crucial information on composition and kinetics for early parameter estimation and process modeling. The image analysis methodologies have a great potential to be translated to other imaging techniques for process monitoring of key physical parameters to accelerate the development and control of particle/crystallization processes.

A Novel Integrated Workflow for Isolation Solvent Selection Using Prediction and Modeling.

A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying.

Engineering of acetaminophen particle attributes using a wet milling crystallisation platform.

Wet milling coupled with crystallisation has considerable potential to deliver enhanced control over particle attributes. The effect of process conditions and wet mill configuration on particle size, shape and surface energy has been investigated on acetaminophen using a seeded cooling crystallisation coupled with a wet mill unit generating size controlled acetaminophen crystals through an interchangeable rotor-tooth configuration. The integrated wet milling crystallisation platform incorporates inline focused beam reflectance measurement (FBRM) and particle vision measurement (PVM) for in-depth understanding of particle behaviour under high-shear conditions. We used a recently developed computational tool for converting chord length distribution (CLD) from FBRM to particle size distribution (PSD) to obtain quantitative insight into the effect of the competing mechanisms of size reduction and growth in a wet milling seeded crystallisation process for acetaminophen. The novelty of our wet milling crystallisation approach is in delivery of consistent surface energies across a range of particle sizes. This highlights the potential to engineer desirable particle attributes through a carefully designed, highly intensified crystallisation process.

Peripherally administered calcitonin gene-related peptide induces spontaneous pain in mice: implications for migraine.

Migraine is the third most common disease in the world (behind dental caries and tension-type headache) with an estimated global prevalence of 15%, yet its etiology remains poorly understood. Recent clinical trials have heralded the potential of therapeutic antibodies that block the actions of the neuropeptide calcitonin gene-related peptide (CGRP) or its receptor to prevent migraine. Calcitonin gene-related peptide is believed to contribute to trigeminal nerve hypersensitivity and photosensitivity in migraine, but a direct role in pain associated with migraine has not been established. In this study, we report that peripherally administered CGRP can act in a light-independent manner to produce spontaneous pain in mice that is manifested as a facial grimace. As an objective validation of the orbital tightening action unit of the grimace response, we developed a squint assay using a video-based measurement of the eyelid fissure, which confirmed a significant squint response after CGRP injection, both in complete darkness and very bright light. These indicators of discomfort were completely blocked by preadministration of a monoclonal anti-CGRP-blocking antibody. However, the nonsteroidal anti-inflammatory drug meloxicam failed to block the effect of CGRP. Interestingly, an apparent sex-specific response to treatment was observed with the antimigraine drug sumatriptan partially blocking the CGRP response in male, but not female mice. These results demonstrate that CGRP can induce spontaneous pain, even in the absence of light, and that the squint response provides an objective biomarker for CGRP-induced pain that is translatable to humans.

Spray Drying as a Reliable Route to Produce Metastable Carbamazepine Form IV.

Carbamazepine (CBZ) is an active pharmaceutical ingredient used in the treatment of epilepsy that can form at least 5 polymorphic forms. Metastable form IV was originally discovered from crystallization with polymer additives; however, it has not been observed from subsequent solvent-only crystallization efforts. This work reports the reproducible formation of phase pure crystalline form IV by spray drying of methanolic CBZ solution. Characterization of the material was carried out using diffraction, scanning electron microscopy, and differential scanning calorimetry. In situ Raman spectroscopy was used to monitor the spray-dried product during the spray drying process. This work demonstrates that spray drying provides a robust method for the production of form IV CBZ, and the combination of high supersaturation and rapid solid isolation from solution overcomes the apparent limitation of more traditional solution crystallization approaches to produce metastable crystalline forms.