Integrating high-throughput experimentation with advanced decision-support tools for chromatography process development

The development and commercialisation of a new therapeutic drug is a lengthy and expensive process hindered with uncertainties and high attrition rates. Monoclonal antibodies are a major contributor to the continuous growth of the global biopharmaceutical industry. Chromatography remains the workhorse in antibody purification despite its complex process development and the high operating cost. The research here presents the establishment of an integrated and data-driven decision-support framework in early-stage protein chromatography process development. The key focus of the research is the development of a systematic and rational methodology to automate and accelerate data analysis and decision-making. A novel workflow was developed that combined high-throughput experimentation (HTE) at micro-scale with design of experiments (DoE), multi-variate data analysis, multi-attribute decision-making and a robustness analysis technique to screen and optimise chromatography resins. DoE was linked with an advanced chromatogram analysis method to cope with the large datasets resulting from HTE by automating raw data manipulation. Additionally, the approach offers the ability to correlate the trade-offs between purity and yield with process parameters through a regression analysis. High-throughput purification data were further leveraged using a decision-support tool for the chromatographic purification train linked with a bioprocess economics spreadsheet model. The bioprocess economics model was also used to provide insights regarding the cost-effectiveness of pre-packed chromatography columns as an alternative to conventional self-packed columns for clinical and commercial manufacture. The implementation of the framework demonstrated the synergy of different decision-support tools and allowed for the rapid evaluation of multiple chromatographic purification trains in order to determine the most cost-effective resin sequence and column type considering the whole manufacturing process. Additionally, it is demonstrated that chromatography process development activities could be accelerated by defining platform purification processes and identifying manufacturing bottlenecks fast and with limited feedstock material

Towards continuous biomanufacturing a computational approach for the intensification of monoclonal antibody production

Current industrial trends encourage the development of sustainable, environmentally friendly processes with reduced energy and raw material consumption. Meanwhile, the increasing market demand as well as the tight regulations in product quality, necessitate efficient operating procedures that guarantee products of high purity. In this direction, process intensification via continuous operation paves the way for the development of novel, eco-friendly processes, characterized by higher productivity compared to batch (Nicoud, 2014). The shift towards continuous operation could advance the market of high value biologics, such as monoclonal antibodies (mAbs), as it would lead to shorter production times, decreased costs, as well as significantly less energy consumption (Konstantinov and Cooney, 2015, Xenopoulos, 2015). In particular, mAb production comprises two main steps: the culturing of the cells (upstream) and the purification of the targeted product (downstream). Both processes are highly complex and their performance depends on various parameters. In particular, the efficiency of the upstream depends highly on cell growth and the longevity of the culture, while product quality can be jeopardized in case the culture is not terminated timely. Similarly, downstream processing, whose main step is the chromatographic separation, relies highly on the setup configuration, as well as on the composition of the upstream mixture. Therefore, it is necessary to understand and optimize both processes prior to their integration. In this direction, the design of intelligent computational tools becomes eminent. Such tools can form a solid basis for the: (i) execution of cost-free comparisons of various operating strategies, (ii) design of optimal operation profiles and (iii) development of advanced, intelligent control systems that can maintain the process under optimal operation, rejecting disturbances. In this context, this work focuses on the development of advanced computational tools for the improvement of the performance of: (a) chromatographic separation processes and (b) cell culture systems, following the systematic PAROC framework and software platform (Pistikopoulos et al., 2015). In particular we develop model-based controllers for single- and multi-column chromatographic setups based on the operating principles of an industrially relevant separation process. The presented strategies are immunized against variations in the feed stream and can successfully compensate for time delays caused due to the column residence time. Issues regarding the points of integration in multi-column systems are also discussed. Moreover, we design and test in silico model-based control strategies for a cell culture system, aiming to increase the culture productivity and drive the system towards continuous operation. Challenges and potential solutions for the seamless integration of the examined bioprocess are also investigated at the end of this thesis.Open Acces

Evaluation of the financial and technical impacts of changing commercial-scale pharmaceutical manufacturing processes.

Growing pressures in the pharmaceutical industry are driving the need to optimise processes used for the manufacture of drugs at commercial-scale, in order to improve cost of goods, product throughput and production times. Evaluating the impacts of process optimisation upon these metrics presents a challenge due to complexities and trade-offs that are often encountered when developing a typical bioprocess. Such factors have resulted in a range of novel simulation- and experimental- based techniques being developed which enable rapid, accurate and cost effective assessment of manufacturing options for commercial-scale production. This thesis proposes a combination of modelling and experimental methods for evaluating the business- and process-related impacts of implementing changes to pre-existing commercial-scale pharmaceutical manufacturing processes. The approaches are illustrated through an industrial case study, focusing upon a process operated by Protherics U.K. Limited for the manufacture of the FDA-approved rattlesnake anti-venom CroFab (Crotalidae Polyvalent Immune Fab (Ovine)). The novel methods developed and illustrated in this thesis include: Investigating the effects of process changes upon calculated yields and processing times within the production framework for a pre-existing FDA-approved bio-manufacturing process Evaluating the impacts of both developing and implementing process changes, combining output metrics into a single value to simplify the assessment Developing a multi-layered simulation methodology for the rapid and efficient evaluation of bio- manufacturing process options Applying advanced sensitivity analysis techniques to identify the most critical factors that influence product yield and throughput Evaluating a novel synthetic Protein A matrix for the recovery and purification of polyclonal antibodies from hyperimmunised ovine serum Developing decision-support software to aid the design of chromatography steps for antibody purification at industrial scale Demonstrating the utility of such models by application to data and constraints derived from a full-scale industrial facility

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Model based process design for bioprocess optimisation: case studies on precipitation with its applications in antibody purification

Developing a bioprocess model can not only reduce cost and time in process development, but now also assist the routine manufacturing and guarantee the quality of the final products through Quality by Design (QbD) and Process Analytical Technology (PAT). However, these activities require a model based process design to efficiently direct, identify and execute optimal experiments for the best bioprocess understanding and optimisation. Thus an integrated model based process design methodology is desirable to significantly accelerate bioprocess development. This will help meet current urgent clinical demands and also lower the cost and time required. This thesis examines the feasibility of a model based process design for bioprocess optimisation. A new process design approach has been proposed to achieve such optimal design solutions quickly, and provide an accurate process model to speed up process understanding. The model based process design approach includes bioprocess modelling, model based experimental design and high throughput microwell experimentation. The bioprocess design is based on experimental data and a computational framework with optimisation algorithm. Innovative model based experimental design is a core part in this approach. Directed by the design objectives, the method uses D-optimal design to identify the most information rich experiments. It also employs Random design and Simplex to identify extra experiments to increase the accuracy, and will iteratively improve the process design solutions. The modelling and implementation method by high throughput experimentation was first achieved and applied to an antibody fragment (Fab’) precipitation case study. A new precipitation model based on phase equilibrium has been developed using the data from microwell experimentation, which was further validated by statistical tests to provide high confidence. The precipitation model based on good data accurately describes not only the Fab' solubility but also the solubility of impurities treated as a pseudo-single protein, whilst changing two critical process conditions: salt concentration and pH. The comparison study has shown the model was superior to other published models. The new precipitation model and the Fab' microwell data provided the basis to test the efficiency and robustness of the algorithms in model based process design approach. The optimal design solution with the maximum objective value was found by only 5 iterations (24 designed experimental points). Two parameterised models were obtained in the end of the optimisation, which gave a quantitative understanding of the processes involved. The benefit of this approach was well demonstrated by comparing it with the traditional design of experiments (DoE). The whole model based process design methodology was then applied to the second case study: a monoclonal antibody (mAb) precipitation process. The precipitation model was modified according to experimental results following modelling procedures. The optimal precipitation conditions were successfully found through only 4 iterations, which led to an alternative process design to protein A chromatography in the general mAb purification platform. The optimal precipitation conditions were then investigated at lab scale by incorporating a depth filtration process. The final precipitation based separation process achieved 93.6% (w/w) mAb yield and 98.2 % (w/w) purity, which was comparable to protein A chromatography. Polishing steps after precipitation were investigated in microwell chromatographic experimentation to rapidly select the following chromatography steps and facilitate the whole mAb purification process design. The data generated were also used to evaluate the process cost through process simulations. Both precipitation based and protein A chromatography based processes were analysed by the process model in the commercial software BioSolve under several relevant titre and scale assumptions. The results showed the designed precipitation based processes was superior in terms of process time and cost when facing future process challenges

Evaluating the Potential of Continuous Processes for Monoclonal Antibodies: Economic, Environmental and Operational Feasibility

The next generation of monoclonal antibody (mAb) therapies are under increasing pressure from healthcare providers to offer cost effective treatments in the face of intensified competition from rival manufacturers and the looming loss of patent exclusivity for a number of blockbusters. To remain completive in such a challenging environment companies are looking to reduce R&D and manufacturing costs by improving their manufacturing platform processes whilst maintaining flexibility and product quality. As a result companies are now exploring whether they should choose conventional batch technologies or invest in novel continuous technologies, which may lead to lower production costs. This thesis explores the creation of a dynamic tool as part of a decision-support framework that is capable of simulating and optimising continuous monoclonal antibody manufacturing strategies to assist decision-making in this challenging environment. The decision-support framework is able to tackle the complex problem domain found in biopharmaceutical manufacturing, through holistic technology evaluations employing deterministic discrete-event simulation, Monte Carlo simulation and multi-attribute decision-making techniques. The hierarchal nature of the framework (including a unique sixth hierarchal layer; sub-batches) made it possible to simulate multiple continuous manufacturing scenarios on a number of levels of detail, ranging from high-level process performance metrics to low-level ancillary task estimates. The framework is therefore capable of capturing the impact of future titres, multiple scales of operation and key decisional drivers on manufacturing strategies linking multiple continuous unit operations (perfusion cell culture & semi-continuous chromatography). The work in this thesis demonstrates that the framework is a powerful test bed for assessing the potential of novel continuous technologies and manufacturing strategies, via integrated techno-economic evaluations that take proof-of-concept experimental evaluations to complete life-cycle performance evaluations

Strategic Biopharmaceutical Production Planning for Batch and Perfusion Processes

Capacity planning for multiple biopharmaceutical therapeutics across a large network of manufacturing facilities, including contract manufacturers, is a complex task. Production planning is further complicated by portfolios of products requiring different modes of manufacture: batch and continuous. Capacity planning decisions each have their own costs and risks which must be carefully considered when determining manufacturing schedules. Hence, this work describes a framework which can assimilate various input data and provide intelligent capacity planning solutions. First of all, a mathematical model was created with the objective of minimising total cost. Various challenges surrounding the biomanufacturing of both perfusion and fed-batch products were solved. Sequence-dependent changeover times and full decoupling between upstream and downstream production suites were incorporated into the mixed integer linear program, which was used on an industrial case study to determine optimal manufacturing schedules and capital expenditure requirements. The effect of varying demands and fermentation titres was investigated via scenario analysis. To improve computational performance of the model, a rolling time horizon was introduced, and was shown to not only improve performance but also solution quality. The performance of the model was then improved via appropriate reformulations which consider the state task network (STN) topology of the problem domain. Two industrial case studies were used to demonstrate the merits of using the new formulation, and results showed that the STN improved performance in all test cases, and even performed better than the rolling time horizon approach from the previous model in one test case. Various strategic options regarding capacity expansion were analysed, in addition to an illustration of how the framework could be used to de-bottleneck existing capacity issues. Finally, a multi-objective component is added to the model, enabling the consideration of strategic multi-criteria decision making. The ε-constraint method was shown to be the superior multi-objective technique, and was used to demonstrate how uncertain input parameters could affect the different objectives and capacity plans in question

Optimisation Methodologies for the Design and Planning of Water Systems

This thesis addresses current topics of design and planning of water systems from water treatment units to a country-wide resources management schemes. The methodologies proposed are presented as models and solution approaches using mathematical programming, and mixed integer linear (MILP) and non-linear (MINLP) programming techniques. In Part I of the thesis, a synthesis problem for water treatment processes using superstructure optimisation is studied. An MINLP model is developed for the minimisation of water production cost considering physicochemical properties of water and operating conditions of candidate technologies. Next, new alternative path options are introduced to the superstructure. The resulting MINLP model is then partially linearised (plMINLP) and also presented as a mixed integer linear fractional programming (MILFP) model in order to improve the convergence of the optimisation model. Various linearisation and approximation techniques are developed. As a solution procedure to the fractional model, a variation of the Dinkelbach's algorithm is proposed. The models are tested on theoretical examples with industrial data. In Part II, an optimisation approach formulated as a spatially-explicit multi-period MILP model is proposed for the design of planning of water resources at regional and national scales. The optimisation framework encompasses decisions such as installation of new purification plants, capacity expansion, trading schemes among regions and pricing, and water availability under climate change. The objective is to meet water demand while minimising the total cost associated with developing and operating the water supply chain. Additionally, a fair trade-o between the total cost and reliability of the supply chain is incorporated in the model. The solution method is applied based on game theory using the concept of Nash equilibrium. The methodology is implemented on a case study based on Australian water management systems

Developing methodologies for determining operating strategies for bioprocesses.

This thesis examines techniques for analysing bioprocess flowsheet simulations so as to determine operating strategies. Currently approaches cited in the literature for analysing bioprocesses employ visualisation of two-dimensional subsets of the feasible region. However this approach is restricted to two control variables and relies heavily on the engineer's judgement to estimate the potential impact of uncertainties in both the model and the process operation. The objective of this research was to generate methods capable of locating robust operating points for multivariate bioprocesses. Increasingly the biopharmaceutical firms are under economic pressure to speed up process development. This had lead to an increased interested in computer simulation as a tool to develop robust bioprocess. Whilst simulation has been applied extensively in the process industries it has not often been applied to bioprocesses as these tend to be more complex to model and frequently only a partial understanding of behaviour exists. Recent work has led to a capacity to simulate complete bioprocess sequences using models that capture the interactions between the unit operations. However, a major limitation is the interpretation of results from such simulations. In conventional process engineering studies optimisation routines have been used to identify the best operating conditions for a given set of objectives. Such techniques have not been applied effectively to bioprocesses due to limitations in the reliability of the models. These limitations mean that results obtained via such an approach are unlikely be useful as, in practice, the optimal points found are unlikely to be robust. The work in this thesis also looks at defining methodologies that are able to analyse multivariable bioprocesses. It looks at the application of techniques developed in the chemical process industry that can be used to account for the variability in the control variables and process parameters and at the application of statistical techniques for analysing bioprocess robustness. Overall work highlights the nature of the bioprocess insights that can be obtained through simulation and explores the utility of the application of the developed methods of analysis