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  • Buser, Andreas M., et al. (författare)
  • Good modeling practice guidelines for applying multimedia models in chemical assessments
  • 2012
  • Ingår i: Integrated Environmental Assessment and Management. - : Wiley. - 1551-3777 .- 1551-3793. ; 8:4, s. 703-708
  • Tidskriftsartikel (refereegranskat)abstract
    • Multimedia mass balance models of chemical fate in the environment have been used for over 3 decades in a regulatory context to assist decision making. As these models become more comprehensive, reliable, and accepted, there is a need to recognize and adopt principles of Good Modeling Practice (GMP) to ensure that multimedia models are applied with transparency and adherence to accepted scientific principles. We propose and discuss 6 principles of GMP for applying existing multimedia models in a decision-making context, namely 1) specification of the goals of the model assessment, 2) specification of the model used, 3) specification of the input data, 4) specification of the output data, 5) conduct of a sensitivity and possibly also uncertainty analysis, and finally 6) specification of the limitations and limits of applicability of the analysis. These principles are justified and discussed with a view to enhancing the transparency and quality of model-based assessments.
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  • Eberle, L., et al. (författare)
  • Data-driven tiered procedure for enhancing yield in drug product manufacturing
  • 2016
  • Ingår i: Computers and Chemical Engineering. - : Elsevier BV. - 0098-1354. ; 87, s. 82-94
  • Tidskriftsartikel (refereegranskat)abstract
    • Enhancing efficiency of pharmaceutical batch production processes is an important challenge in times of increasing public pressure on healthcare costs and decreasing research productivity. This study presents a data-based procedure for systematic yield enhancements in drug product manufacturing, based on four steps. On the first step, production is reviewed to select relevant loss causes, which are assessed on the second step deductively with the goal of assigning measurable parameters. Descriptive Statistical Modelling of loss causes is then performed on the third step, enabling model-based enhancements of processes on the fourth step or, if necessary, a loop-back review of a given loss cause. An industrial case study was performed on production data of 88 batches and demonstrated the applicability of the procedure by prioritizing relevant loss causes, reducing required sample quantities by up to 8% and a cosmetic defect by about 70% by a process change.
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