Application Of Global Sensitivity Analysis Research Articles

Global sensitivity analysis for the determination of parameter importance in the chromatographic purification of polyclonal antibodies

AbstractBACKGROUND: The financial and technical challenges currently being faced by the pharmaceutical industry have prompted the development of computer models that rapidly evaluate manufacturing strategies for commercial‐scale drug production. The utility of a model is enhanced by the application of sensitivity analysis techniques (SA), which seek to quantify the relative importance of different input variables. Among several SA methods available, global sensitivity analysis (GSA) provides a powerful means of ranking inputs based on the contribution that each makes towards the variance of a model. To demonstrate the value of the approach, this paper describes the application of GSA to a mathematical model of the synthetic adsorbent‐based chromatographic capture of antibodies from ovine serum, a strategy being considered by Protherics UK Limited for production of an FDA‐approved rattlesnake anti‐venom.RESULTS: Results indicated that the binding capacity of the adsorbent for the antibody molecules was the most significant factor influencing their recovery, followed closely by the flowrate at which the ovine serum was applied to the column and then finally the antibody concentration in the feedstock. Together, these accounted for almost 94% of the model variance and hence suggest that these would be the key variables to focus upon during process development.CONCLUSION: Such data illustrate how GSA enables development studies to be concentrated upon those factors which exert the greatest influence on manufacturing performance. Ultimately, this reduces the size of the design space, making it easier to optimize a process and identify the most suitable operating conditions for large‐scale drug production. Copyright © 2007 Society of Chemical Industry

A role for sensitivity analysis in presenting the results from MCDA studies to decision makers

The aim of sensitivity analysis (SA) is to ascertain how much the uncertainty in the output of a model is influenced by the uncertainty in its input factors. An SA can be performed using different methods, which are classified according to various criteria. One possible classification is that in which global and local approaches are identified. This paper strengthens the role of global SA methods and suggests their use in the context of MCDA. Useful applications of global SA already exist in a variety of fields where numerical models are considered, e.g. in economics, engineering and chemistry. Global SA is quite different in its formulation and application from local SA, which is seen more frequently in the literature. The global sensitivity indices adopted are derived from a variance decomposition scheme: they can be estimated by alternative computational strategies, such as the extended FAST or the method proposed by Sobol’. A truly global SA is capable of apportioning the output uncertainty according to any subgroup of input factors. Hence, the output uncertainty of an MCDA can, for instance, be decomposed into a part due to uncertain model inputs and a part due to poorly defined (or variable) weights attached to the criteria. This information could be useful to the decision maker (DM) since it explains synthetically how much the assessment of an MCDA study is biased by the assessor judgements. Alternative regrouping of the uncertain input elements might shed light on other features of the problem addressed by the DM. Copyright © 1999 John Wiley & Sons, Ltd.