Liquid antisolvent crystallization of pharmaceutical compounds: current status and future perspectives

The present work reviews the liquid antisolvent crystallization (LASC) to prepare the nanoparticle of pharmaceutical compounds to enhance their solubility, dissolution rate, and bioavailability. The application of ultrasound and additives is discussed to prepare the particles with narrow size distribution. The use of ionic liquid as an alternative to conventional organic solvent is presented. Herbal compounds, also known for low aqueous solubility and limited clinical application, have been crystalized by LASC and discussed here. The particle characteristics such as particle size and particle size distribution are interpreted in terms of supersaturation, nucleation, and growth phenomena. To overcome the disadvantage of batch crystallization, the scientific literature on continuous flow reactors is also reviewed. LASC in a microfluidic device is emerging as a promising technique. The different design of the microfluidic device and their application in LASC are discussed. The combination of the LASC technique with traditional techniques such as high-pressure homogenization and spray drying is presented. A comparison of product characteristics prepared by LASC and the supercritical CO₂ antisolvent method is discussed to show that LASC is an attractive and inexpensive alternative for nanoparticle preparation. One of the major strengths of this paper is a discussion on less-explored applications of LASC in pharmaceutical research to attract the attention of future researchers.

Systems level roadmap for solvent recovery and reuse in industries

Recovering waste solvent for reuse presents an excellent alternative to improving the greenness of industrial processes. Implementing solvent recovery practices in the chemical industry is necessary, given the increasing focus on sustainability to promote a circular economy. However, the systematic design of recovery processes is a daunting task due to the complexities associated with waste stream composition, techno-economic analysis, and environmental assessment. Furthermore, the challenges to satisfy the desired product specifications, particularly in pharmaceuticals and specialty chemical industries, may also deter solvent recovery and reuse practices. To this end, this review presents a systems-level approach including various methodologies that can be implemented to design and evaluate efficient solvent recovery pathways.

Computer Aided Design of Solvent Blends for Hybrid Cooling and Antisolvent Crystallization of Active Pharmaceutical Ingredients

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

An automated modular assembly line for drugs in a miniaturized plant

We report here a fully automated, end-to-end, integrated continuous manufacturing process for a small-molecule generic medication with built-in quality assurance. The entire process fits into a box of 30.7 m2 modular footprint and a total residence time of less than 30 h, with a throughput up to 40.3 × 106 tablets per year.

A Hybrid Framework for Simultaneous Process and Solvent Optimization of Continuous Anti-Solvent Crystallization with Distillation for Solvent Recycling

Anti-solvent crystallization is frequently applied in pharmaceutical processes for the separation and purification of intermediate compounds and active ingredients. The selection of optimal solvent types is important to improve the economic performance and sustainability of the process, but is challenged by the discrete nature and large number of possible solvent combinations and the inherent relations between solvent selection and optimal process design. A computational framework is presented for the simultaneous solvent selection and optimization for a continuous process involving crystallization and distillation for recycling of the anti-solvent. The method is based on the perturbed-chain statistical associated fluid theory (PC-SAFT) equation of state to predict relevant thermodynamic properties of mixtures within the process. Alternative process configurations were represented by a superstructure. Due to the high nonlinearity of the thermodynamic models and rigorous models for distillation, the resulting mixed-integer nonlinear programming (MINLP) problem is difficult to solve by state-of-the-art solvers. Therefore, a continuous mapping method was adopted to relax the integer variables related to solvent selection, which makes the scale of the problem formulation independent of the number of solvents under consideration. Furthermore, a genetic algorithm was used to optimize the integer variables related to the superstructure. The hybrid stochastic and deterministic optimization framework converts the original MINLP problem into a nonlinear programming (NLP) problem, which is computationally more tractable. The successful application of the proposed method was demonstrated by a case study on the continuous anti-solvent crystallization of paracetamol.

Systematic Selection of Green Solvents and Process Optimization for the Hydroformylation of Long-Chain Olefines

Including ecologic and environmental aspects in chemical engineering requires new methods for process design and optimization. In this work, a hydroformylation process of long-chain olefines is investigated. A thermomorphic multiphase system is employed that is homogeneous at reaction conditions and biphasic at lower temperatures for catalyst recycling. In an attempt to replace the toxic polar solvent N,N-dimethylformamide (DMF), ecologically benign alternatives are selected using a screening approach. Economic process optimization is conducted for DMF and two candidate solvents. It is found that one of the green candidates performs similarly well as the standard benchmark solvent DMF, without being toxic. Therefore, the candidate has the potential to replace it.