John Washbrook

Decision-support tool for assessing biomanufacturing strategies under uncertainty: Stainless steel versus disposable equipment for clinical trial material preparation

This paper presents the application of a decision‐support tool, SimBiopharma, for assessing different manufacturing strategies under uncertainty for the production of biopharmaceuticals. SimBiopharma captures both the technical and business aspects of biopharmaceutical manufacture within a single tool that permits manufacturing alternatives to be evaluated in terms of cost, time, yield, project throughput, resource utilization, and risk. Its use for risk analysis is demonstrated through a hypothetical case study that uses the Monte Carlo simulation technique to imitate the randomness inherent in manufacturing subject to technical and market uncertainties. The case study addresses whether start‐up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The effects of fluctuating product demands and titers on the performance of a biopharmaceutical company manufacturing clinical trial material are analyzed. The analysis highlights the impact of different manufacturing options on the range in possible outcomes for the project throughput and cost of goods and the likelihood that these metrics exceed a critical threshold. The simulation studies highlight the benefits of incorporating uncertainties when evaluating manufacturing strategies. Methods of presenting and analyzing information generated by the simulations are suggested. These are used to help determine the ranking of alternatives under different scenarios. The example illustrates the benefits to companies of using such a tool to improve management of their R&D portfolios so as to control the cost of goods.

Modelling biopharmaceutical manufacture: Design and implementation of SimBiopharma

Abstract This paper presents the implementation of a conceptual framework, for modelling a biopharmaceutical manufacturing plant, into a prototype decision-support tool, S im B iopharma . The tools scope covers the ability to evaluate manufacturing alternatives in terms of cost, time, yield, resource utilisation and risk. Incorporating uncertainty means that investment appraisal can be based on both the expected outputs and the likelihood of achieving them. A hierarchical approach to represent the key activities in a manufacturing process is introduced. Emphasis is placed on how a closer integration of bioprocess and business process modelling can be achieved by capturing common information in an object-oriented environment, G2 (Gensym Corporation, Cambridge, MA). The key features of S im B iopharma are highlighted; these include interactive graphics, task-oriented representation and dynamic simulation which create a much more flexible environment for modelling processes. Examples of typical outputs generated by S im B iopharma , when addressing the impact of manufacturing options on strategic operational and financial indicators, are given.

Application of a decision-support tool to assess pooling strategies in perfusion culture processes under uncertainty.

Biopharmaceutical manufacture is subject to numerous risk factors that may affect operational costs and throughput. This paper discusses the need for incorporating such uncertainties in decision‐making tools in order to reflect the inherent variability of process parameters during the operation of a biopharmaceutical plant. The functionalities of a risk‐based prototype tool to model cost summation, perform mass balance calculations, simulate resource handling, and incorporate uncertainties in order to evaluate the potential risk associated with different manufacturing strategies are demonstrated via a case study. The case study is based upon the assessment of pooling strategies in the perfusion culture of mammalian cells to deliver a therapeutic protein for commercial use. Monte Carlo simulations, which generate random sample behaviors for probabilistic factors so as to imitate the uncertainties inherent in any process, have been applied. This provides an indication of the range of possible output values and hence enables trends or anomalies in the expected performance of a process to be determined.

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

First-generation expert systems have significant limitations, often attributed to their not being sufficiently deep . However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

A temporal reasoning framework used in the diagnosis of skeletal dysplasias

When gallium phosphide is etched with hot phosphoric acid from the surface of a crystal having a (1 1 1) plane, the etched surface becomes a flat and smooth plane inclined at an angle of 45 DEG to 55 DEG to the (1 1 1) plane. Accordingly, when an electroluminescent diode is manufactured by forming a p-n junction on a gallium phosphide crystal having a (1 1 1) plane and etching the crystal with a hot concentrated phosphoric acid etching solution to form a mesa structure, the side faces of the resulting crystal becomes inclined to the plane of the junction at an angle of nearly 45 DEG so that the visible rays generated in the p-n junction are totally reflected on the side faces, thus markedly increasing the intensity of emitted rays in the direction of the optical axis perpendicular to the principal plane of the p-n junction.

Deep and shallow models in medical expert systems

Abstract In the context of medical expert systems a deep system is often used synonymously with a system that models some kind of causal process or function. We argue that although causality might be necessary for a deep system it is not sufficient on its own. A deep system must manifest the expectations of its user regarding its flexibility as a problem solver and its human-computer interaction (dialogue structure and explanation structure). These manifestations are essential for the acceptability of medical expert systems by their users. We illustrate our argument by evaluating a representative sample of medical expert systems. The systems are evaluated from the perspective of how explicitly they incorporate their particular models of expertise and how understandably they progress towards solutions. The dialogue and explanation structures of these systems are also evaluated. The results of our analysis show that there is no strong correlation between causality and acceptability. On the basis of this we propose that a deep system is one that properly explicates its underlying model of human expertise.

Combining Multiple Quantitative and Qualitative Goals When Assessing Biomanufacturing Strategies under Uncertainty

This paper reports how financial and operational results from bioprocess simulations can be combined with other criteria pertinent to decision‐making predictions to provide a more holistic approach to the evaluation of biomanufacturing alternatives. The classical additive weighting method, which is a multiattribute decision‐making technique that can account for both the quantitative and qualitative parameters that ultimately need to be considered, is used. Its application is demonstrated through a case study that addresses whether start‐up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The technique is extended to allow for uncertainty in parameters. An illustration of its use to compare alternatives based on cumulative frequency curves of the aggregate scores is provided. For cases where it is difficult to discriminate between the options, plots of risk versus reward are shown to be useful for identifying the best alternative based on the risk preference of the companyapos;s management.

A tool for modeling strategic decisions in cell culture manufacturing.

The development of a prototype tool for modeling manufacturing in a biopharmaceutical plant is discussed. A hierarchical approach to modeling a manufacturing process has been adopted to confer maximum user flexibility. The use of this framework for assessing the impact of manufacturing decisions on strategic technical and business indicators is demonstrated via a case study. In the case study, which takes the example of a mammalian cell culture process delivering a therapeutic for clinical trials, the dynamic modeling tool indicates how manufacturing options affect the demands on resources and the associated manufacturing costs. The example illustrates how the decision‐support software can be used by biopharmaceutical companies to investigate the effects of working toward different strategic goals on the cost‐effectiveness of the process, prior to committing to a particular option.

A decisional-support tool to model the impact of regulatory compliance activities in the biomanufacturing industry

This paper discusses the need for decisional-support tools in the biotech industry and presents the configuration of a prototype tool for modelling bioprocesses and regulatory compliance activities such as quality control (QC), quality assurance (QA) and batch documentation in a biopharmaceutical plant. The impact of employing a range of manufacturing options on financial and technical performance was used to evaluate the functionalities of the tool. In a case study investigating pooling strategies in mammalian perfusion culture, the modelling tool provides indications as to the feasibility of different manufacturing options and ultimately is an aid in the decision-making process. The tool enables the effect of regulatory compliance activities on operational costs and demands on resources to be evaluated. The study demonstrates the use of such a software tool for the facilitation of early planning of process development and the appropriate allocation of resources.

A Software Tool to Assist Business‐Process Decision‐Making in the Biopharmaceutical Industry

Conventionally, software tools for the design of bioprocesses have provided only limited business‐related information for decision‐making. There is an industrial need to investigate manufacturing options and to gauge the impact of various decisions from economic as well as process perspectives. This paper describes the development and use of a tool to provide an assessment of whole flowsheets by capturing both process and business aspects. The tool is demonstrated by considering the issues concerned when making decisions between two potential flowsheets for a common product. A case study approach is used to compare the process and business benefits of a conventional process route employing packed chromatography beds and an alternative that uses expanded bed adsorption (EBA). The tool allows direct evaluation of the benefits of capital cost reduction and increased yield offered by EBA against penalties of using potentially more expensive EBA matrix with lower lifetimes. Furthermore, the tool provides the ability to gauge the process robustness of each flowsheet option.