Attention eLearning Users

We are upgrading our learning platform! As a result, if you have a course in progress, you'll need to complete it by December 24, 2021. Otherwise, you will need to restart the course beginning January 5, 2022 on our new platform. Repurchasing will not be necessary. Contact customer service with any questions.

(625a) Solvent Mixture Design for the Integrated Purification in Pharmaceutical Manufacturing.

Authors: 
Jonuzaj, S. - Presenter, Imperial College London
Watson, O. L., Imperial College London
Ottoboni, S., Univeristy of Strathclyde (CMAC)
Price, C., University of Strathclyde
Sefcik, J., University of Strathclyde
Galindo, A., Imperial College London
Jackson, G., Imperial College London
Adjiman, C. S., Imperial College London
Solvents play a dominant role in the manufacturing of pharmaceutical products, as they are extensively used to facilitate synthetic reactions, enable separation/purification and take part in the final drug formulation. More than 80% of waste and byproducts generated in a typical active pharmaceutical ingredient (API) manufacturing process (i.e., 25-100 kg of byproducts/kg API [1]) is related to solvents used in purification processes [1,2]. Hence, selecting suitable solvents in different purification stages can affect the overall process efficiency and final product quality, as well as cost, environmental, health and safety metrics [3,4]. However, choosing optimal solvents (or solvent mixtures) and the best operating conditions for a given process can be challenging due to the discrete nature of the problem (that can lead to combinatorial explosion) and the trade-offs between competing objectives (e.g. enhance product quality attributes can lead to increased cost and/or environmental impact). In current practice, most pharmaceutical companies employ solvent selection guides to (i) consider the physicochemical properties and/or environmental profile of solvents, and (ii) focus on reducing solvent consumption in order to minimise cost and environmental footprint [4,5]. Despite these advances, most of the methods used are based on heuristic approaches or time-consuming experimental investigations. Such trial-and-error practices can often be expensive and are liable to lead to sub-optimal designs that fail to consider the integrated nature of all process decisions. Thus, the development of efficient methodologies and more sophisticated tools that explore all possible options and consider integrated process decisions would be an indispensable tool in the design of new pharmaceutical processes.

Several solvent mixtures design approaches [6-9] are based on the computer-aided mixture/blend design (CAMbD) framework [10] and are applied to the crystallisation of active pharmaceutical ingredients. To date, most of the proposed methods consider optimising a single purification process unit (e.g., crystallisation) with fixed operating conditions (e.g., fixed temperature). Recently, this has been broadened to the consideration of temperature as a variable, to investigate hybrid anti-solvent and cooling crystallisation [9]. In this work, the scope of design is extended further and a comprehensive solvent mixture design methodology for the integrated crystallisation and isolation processes of pharmaceutical compounds is presented. The proposed integrated approach considers a combined cooling and antisolvent crystallisation process, where optimal solvent and antisolvent mixtures, their proportions in solution, and optimal process temperatures are determined simultaneously, while maximising crystal yield and reducing solvent consumption [9]. In addition, critical interlinked design decisions, such as API solubility across both crystallisation and isolation stages, as well as the miscibility of crystallisation and wash solvents, are taken into account. Furthermore, environmental, health and safety performance measures are considered in the comprehensive model, so that only safe and environmentally friendly solvents are identified.

The design method is applied to identifying high-performance solvent blends for the crystallisation and isolation of mefenamic acid, while removing an impurity, chlorobenzoic acid, from the system. This work demonstrates that optimising simultaneously several design decisions and different performance criteria can lead to significant improvements in overall process efficiency over conventional designs.

  1. W. Cue, J. Zhang, 2009. Green Chemistry Letters and Reviews 2, 193-211.
  2. D. Curzons, C. Jiménez-González, A.L. Duncan, D.J.C. Constable, et al., 2007. The International Journal of Life Cycle Assessment 12, 272-280.
  3. J. Brown, T. McGlone, S. Yerdele, V. Srirambhatla, et al., 2018. Molecular Systems Design & Engineering 3, 518-549.
  4. Jiménez-González, P. Poechlauer, Q.B. Broxterman, B-S. Yang, et al., 2011. Organic Process Research & Development 15, 900-911.
  5. Prat, J. Hayler, A. Wells, 2014. Green Chemistry 16, 4546-4551.
  6. T. Karunanithi, L.E.K. Achenie, R. Gani, 2006. Chemical Engineering Science 61, 1247-60.
  7. Jonuzaj, P.T. Akula, P. Kleniati, C.S. Adjiman, 2016. AIChE Journal 62, 1616-33.
  8. Jonuzaj, A. Gupta, C.S. Adjiman, 2018. Computers & Chemical Engineering 116, 401-421.
  9. L. Watson, A. Galindo, G. Jackson, C.S. Adjiman, 2019. Computer Aided Chemical Engineering 46, 949-954.
  10. Gani, R., 2004. Computers & Chemical Engineering 28, 2441-2457.