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(528b) Computer-Aided Design of Solvent Blends for the Crystallisation of Ibuprofen – Regulating Crystal Morphology By the Spiral Growth Model

Watson, O. L. - Presenter, Imperial College London
Jackson, G. - Presenter, Imperial College London
Jonuzaj, S., Imperial College London
Galindo, A., Imperial College London
Adjiman, C. S., Imperial College London
Solid dose pharmaceuticals continue to be the most common means of drug delivery worldwide; because of this, pharmaceutical production is dependent on effective crystallisation systems. The majority of industrial crystallisers are solvent based – hence, decisions surrounding solvent choice can drastically affect the outcome of the crystallisation process. Although attaining a high crystal yield of the active pharmaceutical ingredient (API) is typically the dominant objective during the solvent selection process, solvent and crystal properties can also influence health, safety, environmental, and operational performance. Indeed, the shape of the crystalline product can greatly impact downstream processes1; filtration can be hampered by needle- or plate-like particles that form dense cakes, high aspect ratio crystalline material may not readily flow during transportation, and tabletting via direct compression can fail if the solid product fractures during compaction. Moreover, crystal morphology can affect the in-vivo efficacy of the final pharmaceutical product.

Despite the many material, time and cost constraints present during initial design stages, solvent selection for the crystallisation of novel API molecules is currently performed via time-consuming and expensive experiments, heavily reliant on past experience and rules-of-thumb2. Over the last decades, computer-aided molecular design (CAMD) methods have been developed to overcome such difficulties3, with the aim of guiding lab-based experiments towards optimal candidate molecules for crystallisation4,5. Whilst it is generally agreed that crystal shape has a significant role when designing optimal solvents, the integration of shape considerations within CAMD methods has principally focused on empirical approaches4 combining an understanding of the underlying chemistry with prior experimentation. Direct, quantitative methods, where mechanistic interpretations of crystal growth are considered6, have not yet received significant attention in formulating the solvent design problem. This is likely due to the absence of applicable group contribution approaches to these models, as well as the additional complexity of formulating and resolving a mixed-integer optimisation problem to represent these design choices.

We extend the recent work of Watson et al.7, in which the SAFT-γ Mie equation of state8 was employed in a CAMD framework to identify optimal solvent blends and process conditions for integrated cooling and antisolvent crystallisation, to include the target morphology of the pharmaceutical product. By assuming a low supersaturation growth environment – a standard condition for industrial crystallisation – it is possible to calculate the relative growth rates of corresponding crystal faces6, thus providing a quantitative measure of aspect ratio of the crystalline API. The design formulation is implemented in gPROMS and is applied to the design of solvent mixtures for the crystallisation of ibuprofen, whereby the impact of crystal shape on the optimal solvent molecules and process conditions is investigated.

  1. Dandekar, P., Kuvadia, Z.B., Doherty, M.F., 2013. Annual Review of Materials Research 43, 359-386.
  2. Brown, C.J., McGlone, T., Yerdelen, S., et al., 2018. Molecular Systems Design & Engineering 3(3), 518-549.
  3. Gani, R., 2004. Computers & Chemical Engineering 28(12), 2441-2457.
  4. Karunanithi, A.T., Achenie, L.E., Gani, R., 2006. Chemical Engineering Science 61(4), 1247-1260.
  5. Jonuzaj, S., Gupta, A., Adjiman, C.S., 2018. Computers and Chemical Engineering 116, 401-421.
  6. Li, J., Tilbury, C.J., Kim, S.H., Doherty, M.F., 2016. Progress in Materials Science 82, 1-38.
  7. Watson, O.L., Galindo, A., Jackson, G., Adjiman, C.S., 2019. Computer Aided Chemical Engineering 46, 949-954.
  8. Papaioannou, V., Lafitte, T., Avendano, C., et al., 2014. The Journal of chemical physics 140(5), 054107.