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, often leading the drug substance to fall short of size specifications. These technical hurdles were associated with a high business risk, also because delays were unaffordable. The transfer had to be successful on the first attempt.

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The Solution

The process team needed support with the crystallization transfer. Our labs have created an automated computation framework for modeling stirred reactors used in pharmaceutical applications. This framework effectively handles urgent situations, using a specialized method ideal for comparative crystallization studies. We suggested the ideal batch size, reactor specifics, and stirring settings within two weeks, with minimal input from the customer. The particle size was met with the first batch and could be successfully reproduced for all subsequent batches.

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The Company

One of the world's largest pharmaceutical companies, headquartered in Basel, Switzerland, focuses on discovering, developing, and delivering innovative treatments through its scientific research and development. Many of its treatments are based on active ingredients obtained from crystallization processes in stirred reactors. To respond flexibly to fluctuating demand, outsourcing to CDMOs is a common strategy, 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 manufacturing company's proposed reactors. The particle size distribution (PSD), affected by fluid dynamics, was the key specification.

Several reactors were suggested by the manufacturer. 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 the batch size and which reactor to use to meet the particle size requirements. Each available reactor 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.

The quality of the drug substance can be influenced by different fluid dynamics quantities depending on the crystallization process. Even within the same process, the critical fluid dynamic quantity can change based on the process's current state. In this scenario, one fluid dynamic quantity was particularly important at the beginning of antisolvent addition to set the final crystal size. Another quantity was essential later in the process to ensure similar crystal growth, important for the crystal's structure.

Using this information and our workflow, we suggested the right reactor for the required 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. Then we had a virtual project kickoff. 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 batch size and the best reactor to use to meet particle size requirements. We created CFD models for the reference reactor and the ones suggested by the manufacturing company. These simulations were run in parallel in a high-performance computing environment to get results within the project timeframe. We kept the team updated with progress reports and discussed potential changes.

We had to consider how many runs the available drug substance would allow. The question was, which proposed reactor would best handle the desired number of runs? We looked at different batch sizes because more batches meant more chances for trial and error, though fewer, larger batches would be more cost-effective. Based on our findings, we suggested a cost-effective strategy with larger batches. During the process, our experts were available for questions and the customer kept us updated on the particle size results between batches. This allowed us to propose adjusted operating conditions based on CFD if needed.

All batches were successfully processed and the particle sizes met the specifications. At the end of the project, we handed over all data and a presentation of the results to the customer.


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 doing 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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