A Systematic Framework to Optimize and Control Monoclonal Antibody Manufacturing Process

Since the approval of the first therapeutic monoclonal antibody in 1986, monoclonal antibody has become an important class of drugs within the biopharmaceutical industry, with indications and superior efficacy across multiple therapeutic areas, such as oncology and immunology. Although there has been great advance in this field, there are still challenges that hinder or delay the development and approval of new antibodies. For example, we have seen issues in manufacturing, such as quality, process inconsistency and large manufacturing cost, which can be attributed to production failure, delay in approval and drug shortage. Recently, the development of new technologies, such as Process Analytical Tools (PCT), and the use of statistical tools, such as quality by design (QbD), Design of Experiment (DoE) and Statistical Process Control (SPC), has enabled us to identify critical process parameters and attributes, and monitor manufacturing performance. However, these methods might not be reliable or comprehensive enough to accurately describe the relationship between critical process parameters and attributes, or still lack the ability to forecast manufacturing performance. In this work, by utilizing multiple modeling approaches, we have developed a systematic framework to optimize and control monoclonal antibody manufacturing process. In our first study, we leverage DoE-PCA approach to unambiguously identify critical process parameters to improve process yield and cost of goods, followed by the use of Monte Carlo simulation to validate the impact of parameters on these attributes. In our second study, we use a Bayesian approach to predict product quality for future manufacturing batches, and hence mitigation strategies can be put in place if the data suggest a potential deviation. Finally, we use neural network model to accurately characterize the impurity reduction of each purification step, and ultimately use this model to develop acceptance criteria for the feed based on the predetermined process specifications. Overall, the work in this thesis demonstrates that the framework is powerful and more reliable for process optimization, monitoring and control

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

Cost Evaluation and Portfolio Management Optimization for Biopharmaceutical Product Development

The pharmaceutical industry is suffering from declining R&D productivity and yet biopharmaceutical firms have been attracting increasing venture capital investment. Effective R&D portfolio management can deliver above average returns under increasing costs of drug development and the high risk of clinical trial failure. This points to the need for advanced decisional tools that facilitate decision-making in R&D portfolio management by efficiently identifying optimal solutions while accounting for resource constraints such as budgets and uncertainties such as attrition rates. This thesis presents the development of such tools and their application to typical industrial portfolio management scenarios. A drug development lifecycle cost model was designed to simulate the clinical and non-clinical activities in the drug development process from the pre-clinical stage through to market approval. The model was formulated using activity-based object-oriented programming that allows the activity-specific information to be collected and summarized. The model provides the decision-maker with the ability to forecast future cash flows and their distribution across clinical trial, manufacturing, and process development activities. The evaluation model was applied to case studies to analyse the non-clinical budgets needed at each phase of development for process development and manufacturing to ensure a market success each year. These cost benchmarking case studies focused on distinct product categories, namely pharmaceutical, biopharmaceutical, and cell therapy products, under different attrition rates. A stochastic optimization tool was built that extended the drug development lifecycle cost evaluation model and linked it to combinatorial optimization algorithms to support biopharmaceutical portfolio management decision-making. The tool made use of the Monte Carlo simulation technique to capture the impact of uncertainties inherent in the drug development process. Dynamic simulation mechanisms were designed to model the progression of activities and allocation of resources. A bespoke multi-objective evolutionary algorithm was developed to locate optimal portfolio management solutions from a large decision space of possible permutations. The functionality of the tool was demonstrated using case studies with various budget and capacity constraints. Analysis of the optimization results highlighted the cash flow breakdowns across both activity categories and development stages. This work contributed to the effort of providing quantitative support to portfolio management decision-making and illustrated the benefits of combining cost evaluation with portfolio optimization to enhance process understanding and achieve better performance

Economic analysis of Uricase production under uncertainty: Contrast of chromatographic purification and aqueous two-phase extraction (with and without PEG recycle)

Uricase is the enzyme responsible for the breakdown of uric acid, the key molecule leading to gout in humans, into allantoin, but it is absent in humans. It has been produced as a PEGylated pharmaceutical where the purification is performed through three sequential chromatographic columns. More recently an aqueous two-phase system (ATPS) was reported that could recover Uricase with high yield and purity. Although the use of ATPS can decrease cost and time, it also generates a large amount of waste. The ability, therefore, to recycle key components of ATPS is of interest. Economic modelling is a powerful tool that allows the bioprocess engineer to compare possible outcomes and find areas where further research or optimization might be required without recourse to extensive experiments and time. This research provides an economic analysis using the commercial software BioSolve of the strategies for Uricase production: chromatographic and ATPS, and includes a third bioprocess that utilises material recycling. The key parameters that affect the process the most were located via a sensitivity analysis and evaluated with a Monte Carlo analysis. Results show that ATPS is far less expensive than chromatography, but that there is an area where the cost of production of both bioprocesses overlap. Furthermore, recycling doesn't impact the cost of production. This study serves to provide a framework for the economic analysis of Uricase production using alternative techniques. This article is protected by copyright. All rights reserved

A Novel Proposal To Advance The Discipline And To Quantitatively Safeguard Important Hygienic Bio-Processes

A novel proposal that will significantly advance the discipline of chemical engineering, through an improved understanding of unanticipated process risk, and which will safeguard risk in hygienic bio-processing of foods, water and wastes is presented and illustrated. The proposal builds on established chemical engineering unit operations principles. If adopted by the discipline a major outcome would be to expand the current knowledge base and scientific understanding of process risk. This is because a key insight is to show that an accumulation and combination of a series of indiscernible changes in otherwise well-operated plant parameters can lead unanticipatedly in one-direction and leverage highly significant, and sometimes catastrophic, changes in process or product. Currently bio-process engineers are limited to largely ineffective sensitivity analyses or semi-quantitative assessments such as HAZOP (HAZard and OPerability), HACCP (Hazard Analysis Critical Control Point) or Reliability Engineering (i.e. to "fail well"). Additional outcomes would include new technology and components to simulate the unanticipated risk of failure of hygienic processes in a novel library of risk-modules involving microbial growth and death. These new modules longer term will be able to be coupled with existing commercial design software for e.g. Aspen Plus® or Batch Process Developer® to provide significantly more powerful design and assessment techniques and tools than are currently used. These outcomes could then be used to quantitatively underpin new regulatory requirements for future bio-process plant and systems at the design, and operational stages and add intelligent and sophisticated new simulation capability to the discipline.Kenneth Daveyhttp://www.chemeca2010.com/abstract/495.as

Applications of simulation within the healthcare context

This is a pre-print of an article published in Journal of the Operation Research Society. The definitive publisher-authenticated version Katsaliaki, K., Mustafee, N.,(2010). Applications of simulation within the healthcare context. Journal of the Operation Research Society. 62, 1431-1451 is available online at: http://www.palgrave-journals.com/jors/journal/v62/n8/full/jors201020a.htmlA large number of studies have applied simulation to a multitude of issues related to healthcare. These studies have been published over a number of unrelated publishing outlets, and this may hamper the widespread reference and use of such resources. In this paper we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner. Hence, the aim of this paper is to conduct a review of the literature pertaining to simulation research within healthcare in order to ascertain its current development. A review of approximately 250 high quality journal papers published between 1970 and 2007 on healthcare-related simulation research was conducted. The results present: a classification of the healthcare publications according to the simulation techniques they employ; the impact of published literature in healthcare simulation; a report on demonstration and implementation of the studies’ results; the sources of funding; and the software used. Healthcare planners and researchers will benefit from this study by having ready access to an indicative article collection of simulation techniques applied in healthcare problems that are clustered under meaningful headings. This study facilitates the understanding of the potential of different simulation techniques for solving diverse healthcare problems