Optimal Antibody Purification Strategies Using Data-Driven Models

This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models

Meta-heuristic & hyper-heuristic scheduling tools for biopharmaceutical production

The manufacturing of biopharmaceuticals requires substantial investments and necessitates long-term planning. Complicating the task of determining optimal production plans are large portfolios of products and facilities which limit the tractability of exact solution methods, and uncertainties & stochastic events which often render plans obsolete when reality deviates from the expectation. This thesis therefore describes decisional tools that are able to cope with these complexities. First, a capacity planning problem for a network of facilities and multiple products was tackled. Inspired by meta-heuristic approaches to job shop scheduling, a tailored construction heuristic that builds a production plan based on a sequence — optimised by a genetic algorithm—of product demands was proposed. Comparisons to a mathematical programming model demonstrated its competitiveness on certain scenarios and its applicability to a multi-objective problem. Next, a custom object-oriented model was introduced for a manufacturing scheduling system that utilised a failure-prone perfusion-based bioprocess. With this, process design decisions such as cell culture run time and process configuration, and single-product facility scheduling strategies were evaluated whilst incorporating simulations of stochastic failure events and uncertain demand. This model was then incorporated into a larger hyper-heuristic to determine optimal scheduling policies for a multi-product problem. Various policy representations are tested and a few policies are adapted from the literature to fit this specific problem. In addition, a novel policy utilising a look-ahead heuristic is proposed. The benefit of parameter tuning using evolutionary algorithms is demonstrated and shows that tuned policies perform much better than a policy that estimates parameters based on service level considerations. In addition, the disadvantages of relying on a fixed or rigid production sequence policy in the face of uncertainty is highlighted

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

A dynamic simulation framework for biopharmaceutical capacity management

In biopharmaceutical manufacturing there have been significant increases in drug complexity, risk of clinical failure, regulatory pressures and demand. Compounded with the rise in competition and pressures of maintaining high profit margins this means that manufacturers have to produce more efficient and lower capital intensive processes. More are opting to use simulation tools to perform such revisions and to experiment with various process alternatives, activities which would be time consuming and expensive to carry out within the real system. A review of existing models created for different biopharmaceutical activities using the Extend® (ImagineThat!, CA) platform led to the development of a standard framework to guide the design and construct of a more efficient model. The premise of the framework was that any ‘good’ model should meet five requirement specifications: 1) Intuitive to the user, 2) Short Run-Time, 3) Short Development Time, 4) Relevant and has Ease of Data Input/Output, and 5) Maximised Reusability and Sustainability. Three different case studies were used to test the framework, two biotechnology manufacturing and one fill/finish, with each adding a new layer of understanding and depth to the standard due to the challenges faced. These Included procedures and constraints related to complex resource allocation, multi-product scheduling and complex ‘lookahead’ logic for scheduling activities such as buffer makeup and difficulties surrounding data availability. Subsequently, in order to review the relevance of the models, various analyses were carried out including schedule optimisation, debottlenecking and Monte Carlo simulations, using various data representation tools to deterministically and stochastically answer the different questions within each case study scope. The work in this thesis demonstrated the benefits of using the developed standard as an aid to building decision-making tools for biopharmaceutical manufacturing capacity management, so as to increase the quality and efficiency of decision making to produce less capital intensive processes

Decisional Tools for Supply Chain Economics of Cell and Gene Therapy Products

Gene therapy products have tremendous therapeutic potential for indications such as cancer and even curative potential for some genetic diseases. Most of today’s gene therapy products are viral vector-based, typically relying on plasmid DNA supply for their production, and many are autologous ex vivo applications (e.g. chimeric antigen receptor T-cell therapy – CAR T), hence the supply chain of these products is highly complex. Given the relative infancy of the sector, there is a strong drive towards adopting technologies that minimise costs and supply chain complexity. This thesis aims to explore these avenues by developing and applying advanced decisional tools that analyse the gene therapy supply chain systematically whilst capturing multiple stakeholder perspectives. The decisional tools employed in this thesis included bioprocess economics models tailored to autologous CAR T-cell therapy and viral vector products. From the cost perspective, models were built to compute manufacturing costs, namely cost of goods (COG) and fixed capital investment (FCI), and coupled with brute force optimisation to identify optimal manufacturing strategies. In addition, a cost of drug development model and a cash flow model were built to evaluate the impact of process changes at different stages in the drug development pathway and evaluate the profitability of different manufacturing strategies. The case studies presented in this thesis explored the autologous supply chains and automation, a range of viral vector manufacturing flowsheets and viral vector process changes. In particular, the autologous supply chain case study provides a feasibility analysis of the optimal number of sites for the decentralised enterprise models and gives new insights into the feasibility of bedside models and impact of quality control (QC) automation. For example, for autologous CAR T cell therapy commercial manufacture, the tool predicted that bedside models such as "GMP-in-a-box" can be more profitable than the regional model for low demand scenarios and identified the critical demand where the regional model starts to outperform bedside manufacture. The viral vector manufacture case study offers the first thorough analysis of the COG associated with a range of flowsheets employing different cell culture technologies for multiple gene therapy product type and process performance scenarios. For lentiviral vector manufacture, it was found that suspension culture or adherent cell culture using fixed bed technology can offer cost savings in the order of 95% when compared to traditional manufacturing approaches in multi-layer vessels. Moreover, suspension cell culture was found to be more suitable for supplying large indications due to its high scalability potential. The process change case study offers a detailed evaluation of the switch from transient transfection to a stable producer cell line for viral vector manufacture by capturing the impact on key financial outputs for both drug development and commercial manufacture, in the case of four topical gene therapy product types. The analysis highlighted that the optimal time to switch was most sensitive to the pDNA requirement and unit cost, the expected delay to market and the titre differences. For example, for products associated with a low pDNA requirement (e.g. CAR T and AAV), switching to stable cell lines post-approval was found to be more attractive than switching early if delays to market were incurred. This thesis provides an account of how the advanced decisional tools employed can help decision-makers create optimal manufacturing strategies so as to maximise patient accessibility and provides a methodology for building decisional tools for emerging products

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