Seeding in crystallisation

Crystal seeding is the process of adding homogeneous or heterogeneous crystals to a crystallising solution to nucleate and/or grow more crystals. Seeding has emerged as one of the most critical steps in optimising the crystallisation process (O’Sullivan B, Smith B, Baramidze G, Recent advances for seeding a crystallization process. Mettler Toledo Auto-Chem, Columbia, 2012). An aptly designed seeding technique would ensure product reproducibility between batches or over time. This is achieved primarily by controlling the crystal size distribution and polymorphism of the crystals that are formed. In this chapter, aspects of crystal nucleation, the importance of seeding and crystallisation methods employed will be discussed

Template-induced nucleation for controlling crystal polymorphism: from molecular mechanisms to applications in pharmaceutical processing

Over the last two decades, the use of template surfaces to induce heterogeneous crystal nucleation has been explored primarily to address two different goals: first, as an alternative to the conventional seeding technique used for polymorph control and, second, as a technique to promote the nucleation rate in novel crystallisation processes and formulations. The former need conceivably arises due to the risk of crystallising a new polymorph despite pre-seeding the solution with the desired crystal form. In this context, we review ongoing efforts in the research area of template-induced crystallisation, covering both experimental and simulation studies directed towards deeper understanding of the underpinning mechanisms. In addition, we report on the use of template-induced crystal nucleation as a process intensification technology for formulating drug substances and as a technique for enabling nucleation and polymorphic control during continuous manufacturing of active pharmaceutical ingredients

Influence of solvent polarity and supersaturation on template-induced nucleation of carbamazepine crystal polymorphs

Studies on the use of template surfaces to induce heterogeneous crystal nucleation have gained momentum in recent years-with potential applications in selective crystallisation of polymorphs and in the generation of seed crystals in a continuous crystallisation process. In developing a template-assisted solution crystallisation process, the kinetics of homogeneous versus heterogeneous crystal nucleation could be influenced by solute-solvent, solute-template, and solvent-template interactions. In this study, we report the effect of solvents of varying polarity on the nucleation of carbamazepine (CBZ) crystal polymorphs, a model active pharmaceutical ingredient. The experimental results demonstrate that functionalised template surfaces are effective in promoting crystallisation of either the metastable (form II) or stable (form III) polymorphs of CBZ only in moderately (methanol, ethanol, isopropanol) and low polar (toluene) solvents. A solvent with high polarity (acetonitrile) is thought to mask the template effect on heterogeneous nucleation due to strong solute-solvent and solvent-template interactions. The current study highlights that a quality-by-design (QbD) approach-considering the synergistic effects of solute concentration, solvent type, solution temperature, and template surface chemistry on crystal nucleation-is critical to the development of a template-induced crystallisation process

The effect of polymorphism on surface energetics of D-mannitol polymorphs

The aim of this work was to assess the effect of different crystalline polymorphism on surface energetics of D-mannitol using finite dilution inverse gas chromatography (FD-IGC). Pure α, β and δ polymorphs were prepared via solution crystallisation and characterised by powder X-ray diffraction (P-XRD). The dispersive surface energies were found to range from 43 to 34 mJ/m(2), 50 to 41 mJ/m(2), and 48 to 38 mJ/m(2) , for α, β, and δ, respectively, for surface coverage ranging from 0.006 to 0.095. A deconvolution modelling approach was employed to establish their energy sites. The primary sites corresponded to maxima in the dispersive surface energy of 37.1 and 33.5; 43.3 and 39.5; and 38.6, 38.4 and 33.0; for α, β, and δ, respectively. This methodology was also extended to an α-β polymorph mixture to estimate the amount of the constituent α and β components present in the sample. The dispersive surface energies of the α-β mixture were found to be in the range of 48 to 37 mJ/m(2) with 40.0, 42.4, 38.4 and 33.1 mJ/m(2) sites. The deconvolution modelling method extracted the energy contribution of each of the polymorphs from data for the polymorphic mixture. The mixture was found to have a β-polymorph surface content of ∼19%. This work shows the influence of polymorphism on surface energetics and demonstrates that FD-IGC coupled with a simple modelling approach to be a powerful tool for assessing the specific nature of this energetic distribution including the quantification of polymorphic content on the surface