Application of Transition-Metal Catalysis, Biocatalysis, and Flow Chemistry as State-of-the-Art Technologies in the Synthesis of LCZ696.

LCZ696 is a novel treatment for patients suffering from heart failure that combines the two active pharmaceutical ingredients sacubitril and valsartan in a single chemical compound. While valsartan is an established drug substance, a new manufacturing process suitable for large-scale commercial production had to be developed for sacubitril. The use of chemocatalysis, biocatalysis, and flow chemistry as state-of-the-art technologies allowed to efficiently build up the structure of sacubitril and achieve the defined performance targets.

Fouling of Flow Reactors in Organolithium Mediated Transformations: Experience on Scale-up and Proposed Solution.

Continuous processing has been demonstrated to be a superior approach when applied to fast and energetic chemical transformations. Indeed, whereas classical batch or semi-batch methods require cryogenic conditions and slow addition rates of reactive species, flow technologies enable rapid mixing of synthetic partners in a highly controlled environment. As a result, low yielding and dangerous processes in batch can be performed at scale in a cost competitive and safer continuous manner. Despite the advantages of higher quality and safety, the perennial problems of solids build-up and pipe fouling threaten the robustness and reliability of flow processes. In this contribution, a new methodology to prevent reactor fouling is reported and discussed. The implementation of this methodology has been decisive in solving fouling issues encountered during the piloting of an organolithium based flow process.

Protein adsorption on ion exchange resins and monoclonal antibody charge variant modulation.

A novel multicomponent adsorption equilibrium model for proteins on ion-exchange resins is developed on a statistical thermodynamic basis including surface coverage effects and protein-resin and protein-protein interactions. The resulting model exhibits a general competitive Langmuirian behavior and was applied to the study and optimization of the separation of monoclonal antibody charge variants on two strong cation exchangers. The model accounts explicitly for the effect of both pH and salt concentration, and its parameters can be determined in diluted conditions, that is, through physically sound assumptions, all model parameters can be obtained using solely experiments in diluted conditions, and be used to make predictions in overloaded conditions. The parameterization of the model and optimization of the separation is based on a two-step approach. First, gradient experiments in diluted conditions are undertaken in order to determine the model parameters. Based on these experiments and on information about the proteins of interest and the stationary phase used, all the model parameters can be estimated. Second, using the parameterized model, an initial Pareto optimization is undertaken where overloaded operating conditions are investigated. Experiments from this Pareto set are then used to refine the estimation of the model parameters. A second Pareto optimization can then be undertaken, this time with the refined parameters. This can be repeated until a satisfactory set of model parameters is found. This iterative approach is shown to be extremely efficient and to provide large amounts of knowledge based on only a few experiments. It is shown that due to the strong physical foundation of the model and the very low number of adjustable parameters, the number of iterations is expected to be at most two or three. Furthermore, the model based tool is improved as more experimental knowledge is provided, allowing for better estimations of the chromatographic processes considered at each iteration. This makes it a very suitable tool for the design and the development of preparative and industrial purification processes, including the determination of both the optimal operating conditions, as well as the allowable process operating space.

A two level hierarchical model of protein retention in ion exchange chromatography.

Predicting protein retention in ion exchange chromatography (IEX) from first principles is a fascinating perspective. In this work a two level hierarchical modeling strategy is proposed in order to calculate protein retention factors. Model predictions are tested against experimental data measured for Lysozyme and Chymotrypsinogen A in IEX columns as a function of ionic strength and pH. At the highest level of accuracy Molecular Dynamics (MD) simulations in explicit water are used to determine the interaction free energy between each of the two proteins and the IEX stationary phase for a reference pH and ionic strength. At a lower level of accuracy a linear response model based on an implicit treatment of solvation and adopting a static protein structure is used to calculate interaction free energies for the full range of pHs and ionic strengths considered. A scaling coefficient, determined comparing MD and implicit solvent simulations, is then introduced in order to correct the linear response model for errors induced by the adoption of a static protein structure. The calculated free energies are then used to compute protein retention factors, which can be directly compared with experimental data. The possibility to introduce a third level of accuracy is explored testing the predictions of a semiempirical model. A quantitative agreement between the predicted and measured protein retention factors is obtained using the coupled MD-linear response models, supporting the reliability of the proposed approach. The model allows quantifying the electrostatic, van der Waals, and conformational contributions to the interaction free energies. A good agreement between experiments and model is obtained also using the semiempirical model that, although requiring parameterization over higher level models or experimental data, proves to be useful in order to rapidly determine protein retention factors across wide pH and ionic strength ranges as it is computationally inexpensive.

Combined Yamamoto approach for simultaneous estimation of adsorption isotherm and kinetic parameters in ion-exchange chromatography.

Application of model-based design is appealing to support the development of protein chromatography in the biopharmaceutical industry. However, the required efforts for parameter estimation are frequently perceived as time-consuming and expensive. In order to speed-up this work, a new parameter estimation approach for modelling ion-exchange chromatography in linear conditions was developed. It aims at reducing the time and protein demand for the model calibration. The method combines the estimation of kinetic and thermodynamic parameters based on the simultaneous variation of the gradient slope and the residence time in a set of five linear gradient elutions. The parameters are estimated from a Yamamoto plot and a gradient-adjusted Van Deemter plot. The combined approach increases the information extracted per experiment compared to the individual methods. As a proof of concept, the combined approach was successfully applied for a monoclonal antibody on a cation-exchanger and for a Fc-fusion protein on an anion-exchange resin. The individual parameter estimations for the mAb confirmed that the new approach maintained the accuracy of the usual Yamamoto and Van Deemter plots. In the second case, offline size-exclusion chromatography was performed in order to estimate the thermodynamic parameters of an impurity (high molecular weight species) simultaneously with the main product. Finally, the parameters obtained from the combined approach were used in a lumped kinetic model to simulate the chromatography runs. The simulated chromatograms obtained for a wide range of gradient lengths and residence times showed only small deviations compared to the experimental data.

Model-based prediction of monoclonal antibody retention in ion-exchange chromatography.

In order to support a model-based process design in ion-exchange chromatography, an adsorption equilibrium model was adapted to predict the protein retention behavior from the amino acid sequence and from structural information on the resin. It is based on the computation of protein-resin interactions with a colloidal model and accounts for the contribution of each ionizable amino acid to the protein charge. As a verification of the protein charge model, the experimental titration curve of a monoclonal antibody was compared to its predicted net charge. Using this protein charge model in the computation of the protein-resin interactions, it is possible to predict the adsorption equilibrium constant (i.e. retention factor or Henry constant) with an explicit pH and salt dependence. The application of the model-based predictions for an in silico screening of the protein retention on various stationary phases or, alternatively, for the comparison of various monoclonal antibodies on a given cation-exchanger was demonstrated. Furthermore, considering the structural differences between charge variants of a monoclonal antibody, it was possible to predict their individual retention times. The selectivity between the side variants and the main isoform of the monoclonal antibody were computed. The comparison with the experimental data showed that the model was reliable with respect to the identification of the operating conditions maximizing the selectivity, i.e. the most promising conditions for a monoclonal antibody variant separation. Such predictions can be useful in reducing the experimental effort to identify the parameter space.

Simulation model for overloaded monoclonal antibody variants separations in ion-exchange chromatography.

A model was developed for the design of a monoclonal antibody charge variants separation process based on ion-exchange chromatography. In order to account for a broad range of operating conditions in the simulations, an explicit pH and salt concentration dependence has been included in the Langmuir adsorption isotherm. The reliability of this model was tested using experimental chromatographic retention times as well as information about the structural characteristics of the different charge variants, e.g. C-terminal lysine groups and deamidated groups. Next, overloaded isocratic elutions at various pH and salt concentrations have been performed to determine the saturation capacity of the ion-exchanger. Furthermore, the column simulation model was applied for the prediction of monoclonal antibody variants separations with both pH and salt gradient elutions. A good prediction of the elution times and peak shapes was observed, even though none of the model parameters was adjusted to fit the experimental data. The trends in the separation performance obtained through the simulations were generally sufficient to identify the most promising operating conditions. The predictive column simulation model thus developed in this work, including a set of parameters determined through specific independent experiments, was experimentally validated and offers a useful basis for a rational optimization of monoclonal antibody variants separation processes on ion-exchange chromatography.

Electrostatic model for protein adsorption in ion-exchange chromatography and application to monoclonal antibodies, lysozyme and chymotrypsinogen A.

A model for the adsorption equilibrium of proteins in ion-exchange chromatography explicitly accounting for the effect of pH and salt concentration in the limit of highly diluted systems was developed. It is based on the use of DLVO theory to estimate the electrostatic interactions between the charged surface of the ion-exchanger and the proteins. The corresponding charge distributions were evaluated as a function of pH and salt concentration using a molecular approach. The model was verified for the adsorption equilibrium of lysozyme, chymotrypsinogen A and four industrial monoclonal antibodies on two strong cation-exchangers. The adsorption equilibrium constants of these proteins were determined experimentally at various pH values and salt concentrations and the model was fitted with a good agreement using three adjustable parameters for each protein in the whole range of experimental conditions. Despite the simplifications of the model regarding the geometry of the protein-ion-exchanger system, the physical meaning of the parameters was retained.