Practical use of primary drying models in an industrial environment with limited availability of equipment sensors.

In the pharmaceutical industry, lyophilization is typically adopted to extend long-time stability of valuable thermolabile medicines and vaccines. Primary drying is the most time-consuming and energy-intensive step of the entire process; thus, accelerating and optimizing the primary drying recipe is a key process development goal. To that purpose, mathematical models have been proposed and successfully validated. However, models typically require invasive experiments and/or sensors (e.g. product temperatures) for parameter estimation, which are rarely available in good manufacturing practice (GMP) environment. This represents a severe limitation when leveraging the model to transfer operation recipes across different facilities and for scale-up. In this study, we assess the possibility to exploit limited industrial data for model parameter estimation, namely pressure measurements and gravimetric tests, by defining a calibration protocol that is tested on two different pieces of equipment. Results are verified on a recently proposed model, and show that statistically meaningful estimates can be obtained without the need of product temperature measurements. Model predictions and optimal inputs trajectories are comparable to those obtained from the model calibrated using the full set of temperature and pressure data.

Clinically proven specification setting for a meningococcal serogroup a conjugate vaccine.

GSK is currently working to improve the commercial presentation of the licensed quadrivalent conjugate vaccine (Menveo) for use against meningococcal serogroup A, C, W, Y (MenACWY) infections. Menveo consists of a primary, lyophilized vial, containing the serogroup A antigen that is reconstituted with the content of a second, liquid, vial that contains the serogroup C, W, Y antigens, to give the final liquid MenACWY product. Since the MenA structure is prone to hydrolytic degradation in liquid formulations, we used mathematical models to rationally design a clinical Phase 2 development plan and provide end of shelf-life (EoSL) and release specification setting for the MenACWY liquid product. By using development and clinical stability data, statistical models were built and used to predict both the MenA free saccharide (FS) and O-Acetyl (OAc) content during long-term storage conditions at 5 °C and stressed (accelerated) stability studies at 15 °C, 22.5 °C, 25 °C, 37 °C and 50 °C. This approach allowed us to define an aging plan for the clinical material to reach at least the required levels of MenA FS and OAc levels at product EoSL. The clinical material was then exposed to a temperature of 22.5 ± 2.5 °C for 59 days to generate FS OAc content of about 35% and 40%, respectively, which was then delivered to the patients in the clinical trial. To the best of our knowledge, this work represents the first example in the field of vaccine research where statistical models have been used to rationally design tailored lots, with the goal of setting EoSL and release specification limits based on data collected on artificially aged clinical material, in which the FS and OAc levels tested were intended to support a product shelf-life of at least 24 months.

Towards in silico Process Modeling for Vaccines.

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy.

Prediction of protein solubility in Escherichia coli using logistic regression.

In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli. The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previously. We tested several different implementations of logistic regression with varied results. The best implementation, which is the one we report, exhibits excellent overall prediction accuracies: 94% for the model and 87% by cross-validation. For comparison, we also tested discriminant analysis using the same parameters, and we obtained a less accurate prediction (69% cross-validation accuracy for the stepwise forward plus interactions model).

Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: a review.

The introduction of the Quality-by-Design (QbD) initiative and of the Process Analytical Technology (PAT) framework by the Food and Drug Administration has opened the route to the use of systematic and science-based approaches to support pharmaceutical development and manufacturing activities. In this review we discuss the role that latent variable models (LVMs) can play in the practical implementation of QbD paradigms in the pharmaceutical industry, and the potential they may have in assisting the development and manufacturing of new products. The ultimate scope is to provide practitioners with a perspective on the effectiveness of the use of LVMs in any phase of the development of a pharmaceutical product, from its design up to its commercial production. After an overview of the main regulatory paradigms the QbD initiative is founded on, we show how LVMs can be feasibly used to support pharmaceutical development and manufacturing activities while matching the regulatory Agencies' requirements. Three main areas are identified, wherein the use of LVMs can provide significant benefits: (i) process understanding, (ii) product and process design, and (iii) process monitoring and control. For each of them, the main contributions recently appeared in the literature are reviewed. Issues open for further research are also identified.

General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study.

Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.