Integrated filtration and washing modelling : 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

Digital process design to define and deliver pharmaceutical particle attributes

A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experimental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120µm) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation, and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection criteria. A wet milling model was parameterised and used to successfully produce a 90g product batch with a particle size D90 of 49.3µm, which was then used as the seeds for cooling crystallisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, and successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120µm in a seeded cooling crystallisation using a 5-stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2g of agglomerated product with a primary particle D90 of 128µm. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration terms for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45minutes 104.0mg of the D90 of 49.3µm material had dissolved, compared with 90.5mg of the agglomerated material with D90 of 128µm. Overall, 1513g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digital-first approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size

Impurity removal during filtration and washing – a mechanistic modelling approach

The focus of the work reported here combines filtration and washing operations commonly used in active pharmaceutical ingredient (API) purification and isolation by combining predicted and experimental data generated during upstream crystallization process. In detail, this work focuses on the development of a mechanistic model-based workflow for the optimization of an integrated filtration and washing model, with a view to minimize impurities in the isolated cake. A Carman-Kozeny filtration model is integrated with a custom diffusion with an axial dispersion washing modelling approach. The custom washing model describes a washing process where the feed wet packed bed obtained by filtering a suspension to dryland is washed by diffusion-dispersion mechanisms. To effectively track impurities in the cake, the diffusion-dispersion wash model considers dissolution of the solid phase. The model was designed as a series of 10 continuous stirred-tank reactors (CSTR) where the approach used to mimic the dispersion washing mechanism modelled with the plug flow (PF) approach. The integrated modelling tool uses information on the product crystal suspension characteristics predicted using gPROMS FormulatedProducts to predict filtration time, filtrate flow rate, and the composition of the filter cake and filtrate generated during filtration. The washing of the wet filtered cake is then simulated to predict: washing efficiency and to generate washing curves, cake and filtrate composition, and residual cake moisture content and composition. Mefenamic acid and paracetamol were selected as representative test compounds. Three different crystallization solvents were used for mefenamic acid and for paracetamol case, with relative structurally-related impurities deriving from synthesis. As first stage of the optimization workflow, a model validation approach has been used to estimate cake properties (e.g. specific cake resistance, cake volume, cake composition after washing, washing curve). The data used for validation was generated via smallscale batch pressure filter experiments. Following on, the validated model was used to explore the design space and aid in the set-up of the optimization entity decisions. The optimization problem was then configured to reduce the impurity concentration in the final cake after washing. The findings from this were translated to a final model to simulate the optimal operating point

Integrated filtration and washing modelling : optimization of impurity rejection for the filtration and washing of active pharmaceutical ingredients

Context: Transitioning pharmaceutical manufacturing from batch to continuous provides opportunity to improve sustainability. Aim: Develop an integrated mechanistic filtration and washing model and accompanying optimization workflow to minimize impurities in the isolated filter cake

Digital process design to define and deliver pharmaceutical particle attributes

A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experimental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120 µm) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation, and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection criteria. A wet milling model was parameterised and used to successfully produce a 90 g product batch with a particle size D90 of 49.3 µm, which was then used as the seeds for cooling crystallisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120 µm in a seeded cooling crystallisation using a 5-stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2 g of agglomerated product with a primary particle D90 of 128 µm. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration terms for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45 minutes 104.0 mg of the D90 of 49.3 µm material had dissolved, compared with 90.5 mg of the agglomerated material with D90 of 128 µm. Overall, 1513 g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digitalfirst approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size. </p