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Case study

How Pharmaceutical Companies and CDMOs reliably scale and transfer Crystallizations using CFD, ensuring timely Drug Supply for their Patients.

Crystallization Scale-up Support by means of Computational Fluid Dynamics (CFD)
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The Challenge

Due to rising market demand, the team needed to move an antisolvent crystallization to a new manufacturer. One of the challenges was maintaining drug substance quality with the new reactor. Past changes to different reactors disrupted the crystallization process, leading the drug substance to fall short of size specifications. This carried the risk of a delayed market supply. The transfer had to be successful on the first attempt.

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The Solution

We used our proprietary computational fluid dynamics (CFD) framework for modeling stirred reactors typically deployed for API manufacturing. With this framework we can run automated comparative CFD studies and effectively handle urgent situations like in the present case study. We suggested the ideal batch size and reactor specifics within just two weeks. The particle size was met with the first batch and successfully reproduced for all subsequent batches.

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The Company

One of the world's largest pharmaceutical companies, headquartered in Basel, Switzerland. Many of its treatments are based on active ingredients obtained from crystallization processes in stirred reactors. To respond flexibly to fluctuating demand, outsourcing to CMOs is a common strategy for such companies, but one that presents business risks, as compliance with FDA-approved specifications is a prerequisite for market supply.

The Challenge

Meeting the PSD specification was crucial for achieving business goals.

The process transfer was overseen by a team consisting of process operators, particle engineers, project managers, and technicians. On the manufacturing side, process operators, engineers, and a project manager contributed. They faced the typical challenge of ensuring transfer success with no knowledge of the target reactor's fluid dynamic properties. Success was defined as meeting drug substance specifications in a specific time frame using one of the CMO’s proposed reactors. The particle size distribution (PSD), affected by fluid dynamics, was the key specification.

Several reactors were suggested by the CMO. Novalabs assisted in choosing a reactor with fluid dynamics that would satisfy the PSD requirement. We utilized our specialized computational framework, designed for comparative reactor studies in pharma crystallizations, to accomplish this.

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"Novalabs supported us in various projects for the development of pharmaceutical processes, scale-up and transfers to CDMOs. They were always super-responsive and supportive from the very beginning. We are very happy with the collaboration and recommend Novalabs to anyone, for achieving business goals.”

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The Approach

The project team needed advice on which reactor to use to meet the particle size requirements. Each available reactor at the CMO had a different setup with varying impeller types and counts, adjustable impeller positions, and customizable baffle arrangements. The crystallization process had been successfully ongoing at a different location for many years. Information about the reactor's dimensions was available from 2D drawings, and particle size data was tracked for numerous batches. This setup was used as a benchmark as it had consistently produced the desired quality.

Using this information and our computational workflow, we suggested the right reactor including an optimized batch size. We also proposed the impeller and baffle setup that would meet the process-specific needs, ensuring similar crystal size and structure.

Project Workflow

First, we assessed the simulation demand and prepared the project execution by gathering reactor and particle size data from the customer. During the virtual project kick off we checked data completeness and discussed the next steps and timeline. We derived the simulation strategy and modeling methods based on the specific requirements of the crystallization process.

The team needed advice on the most suitable reactor to use to meet particle size requirements. We created CFD models for the reference reactor and the ones suggested by the CMO. The reference reactor and the CMO reactors were simulated and compared to each other. We kept the team updated with progress reports and discussed potential changes.

The available amount of drug substance (DS) was initially expected to be distributed across a predefined number of batches. We investigated the reactor setups proposed by the CMO with our CFD framework, to select the most promising setup that would meet this requirement. During the CFD study we also investigated how different batch sizes could affect the fluid dynamics and potentially impair the quality of the DS. Based on our findings, we were able to suggest a more cost-effective strategy with larger batches compared to the initially required batch sizes, resulting in fewer runs and a significantly lower manufacturing overhead.

The process transfer was successful right away. All batches were manufactured on time while meeting the DS specifications for each batch. At the end of the project, we handed over all data and a presentation of the results to the customer.

Summary

Using computational fluid dynamics (CFD) was crucial for completing the transfer successfully on time. Transferring a crystallization to a different reactor always carries some risks and often needs adjustments in the process parameters. At large scale, these adjustments can be costly and time-consuming, potentially threatening the whole business. As a result, it's strongly suggested to use a combined approach where the effects of changes can be checked in the simulations before running the crystallization at the manufacturing site.

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"Novalabs supported us with CFD simulations during the process transfer to a CDMO. They just required technical drawings of the reactors and fluid properties. Novalabs coordinated everything between us and the CDMO, which kept our extra work very low. The crystallization was successful with the first batch. This saved us a lot of time and cost, and we were able to supply our patients on time. We will use this package again and recommend it to anyone who wants to ensure project success."

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