(349c) Dynamic Simulation and Optimisation of Ampicillin Crystallisation | AIChE

(349c) Dynamic Simulation and Optimisation of Ampicillin Crystallisation


Diab, S. - Presenter, University of Edinburgh
Gerogiorgis, D., University of Edinburgh
Boudouvis, A. G., National Technical University of Athens (NTUA)
Dafnomilis, A., National Technical University of Athens (NTUA)

Crystallisation is an integral process in the pharmaceutical industry. It is commonly used for the separation of intermediates or as the final process in the production of an active pharmaceutical ingredient (API). The final size, shape and form of the crystals influences not only downstream operations but also the physical and chemical properties of the solid product.1 The Food and Drug Administration (FDA) and other regulatory agencies constantly set new regulations to ensure the safety and stability of pharmaceutical products. The precise control of crystallisation is of the outmost importance to ensure that the final product has the desired attributes while satisfying the international regulations.

Ampicillin is a semi-synthetic penicillin derived β-lactam antibiotic commonly used to treat various bacterial infections such as urinary and respiratory tract infections, it is one of the ten most consumed antibiotics worldwide.2 In a recent report the World Health Organization (WHO) characterized penicillins as critically important and high priority antimicrobials essential for the world healthcare system.3 For this reason, it is paramount to ensure that the global production of ampicillin is sufficient, within international regulations and economically viable for the pharmaceutical companies. Optimal design and operating parameters are necessary to ensure optimal manufacturing of this societally important antibiotic.

Recent works published by our groups and others show the utility and versatility of conducting conceptual studies for pharmaceutical processes in order to ascertain the benefits and drawbacks of different design scenarios as well as to elucidate the optimal operating conditions of the processes.4 Furthermore, work performed for the dynamic optimisation of bioprocesses illustrates the potential of the use of dynamic optimisation for the analysis of batch bioprocesses, which can establish optimal control variable trajectories to meet some design or operational objective.5 Dynamic optimisation for the batch crystallisation of ampicillin has not yet been implemented. A recently published article6 provides a crystallisation kinetic model for ampicillin suited for dynamic crystallisation optimisation. Size independent kinetics for primary, secondary nucleation and crystal growth were fitted from experiments which took place under different scenarios, providing parameters that are able to predict system behavior under a wide range of different conditions. The population balance is solved with the method of moments. The behaviour of ampicillin as a function of pH allows crystallisation by pH-variation.7 The article utilizes extended Pitzer parameters to model ampicillin solubility.

In this work dynamic simulation under a variety of conditions for design space investigation is performed. The crystallisation of ampicillin is simulated for different linearly decreasing pH profiles varying the pH change rate and the initial and final pH values. For each scenario, the mother solute concentration as a function of time and the final mean crystal size are computed. The objective is to minimise the crystallization batch time subject to various constraints, such as the target mean crystal size and final mother liquor solute concentration. The optimisation studies are performed using the orthogonal collocation on finite elements method.8 The IPOPT (Interior Point Optimizer) algorithm is employed to solve the Non-Linear Programming (NLP) problem. Results from each optimisation strategy are obtained and discussed. Finally, the economic potential and the environmental impact of the different designs are investigated.


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(2) WHO Regional Office for Europe. Antimicrobial medicines consumption (AMC) network; 2014.

(3) WHO list of critically important antimicrobials for human medicine (WHO CIA list); 2017.

(4) Diab, S.; Gerogiorgis, D.I. Technoeconomic optimization of continuous crystallization for three active pharmaceutical ingredients: cyclosporine, paracetamol, and aliskiren. Ind. Eng. Chem. Res. 2018, 57 (29), 9489–9499.

(5) Rodman, A.D.; Gerogiorgis, D.I. An investigation of initialisation strategies for dynamic temperature optimisation in beer fermentation. Comput. Chem. Eng. 2019, 124, 43–61.

(6) Encarnación-Gómez, L.G.; Bommarius, A.S.; Rousseau, R.W. Crystallization kinetics of ampicillin using online monitoring tools and robust parameter estimation. Ind. Eng. Chem. Res. 2016, 55 (7), 2153–2162.

(7) Franco, L.F.M.; Mattedi, S.; Filho, P.D.A.P.A new approach for the thermodynamic modeling of the solubility of amino acids and β-lactam compounds as a function of pH. Fluid Phase Equilib. 2013, 354, 38–46.

(8) Čižniar, M.; Salhi, D.; Fikar, M.; Latifi, M.A. Matlab dynamic optimization code DynOpt, user’s guide. 2006, 1–7.