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Modelling exposure to pharmaceutical agents  J W Cherrie  1 , A T Gillies  2 , A Sleeuwenhoek  1 ,  M van Tongeren  1 , P McDonnell  3 , M Coggins  3 , S R Bailey  4   1. Institute of Occupational Medicine, UK. 2. Gillies Associates Ltd, UK. 3. National University of Ireland, Galway, Ireland. 4. GlaxoSmithKline, UK.
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Background… ,[object Object],[object Object],[object Object],[object Object],[object Object]
The model… ,[object Object],[object Object],[object Object],[object Object],[object Object]
Typical validation data…
The model… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The model… ,[object Object],[object Object],[object Object],[object Object]
The model… ,[object Object],[object Object],[object Object],[object Object],[object Object]
Adaptions for the pharma industry ,[object Object]
Validation… ,[object Object],[object Object],[object Object],[object Object]
Results…
Discussion… ,[object Object],[object Object],[object Object],[object Object]

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Inhaled Particles presentation on exposure modelling

  • 1. Modelling exposure to pharmaceutical agents J W Cherrie 1 , A T Gillies 2 , A Sleeuwenhoek 1 , M van Tongeren 1 , P McDonnell 3 , M Coggins 3 , S R Bailey 4 1. Institute of Occupational Medicine, UK. 2. Gillies Associates Ltd, UK. 3. National University of Ireland, Galway, Ireland. 4. GlaxoSmithKline, UK.
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Editor's Notes

  1. Thank you. This paper describes work that is being carried out collaboratively with the National University of Galway, Gillies Associates and GSK. I’d like to acknowledge the contributions of my coauthors.
  2. I’ll set out the background to the project, describe the model that we have been using for a number of years to reconstruct inhalation exposures to a wide range of substances and the adaption's we have made to apply it in the pharma industry to assess exposure to poowders, and the results of a validation exercise. We believe the methodology could be very useful for the industry in helping focus efforts on control and in maximizing the impact of any monitoring carried out. I’ll discuss these and other uses that the model has.
  3. Correlation on the log scale is 0.91 Bias Outliers
  4. Allow some variation to account for systems that are either better or worse than the indicated level Handling covered 4-orders of magnitude form careful weighing to sweeping with a broom Intrinsic emission not different
  5. Correlation between 0.88 and 0.97 (excluding the data <100 micro-g/m3 reduced the correlation slightly) Note the scatter is partly due to error in the assessment and partly the error in the measurements, which are mostly based on small numbers. Two assessors with a slight positive bias and one with a negative bias. <7% of the assessments are <10% of the mean measurement
  6. ART Bayesian updating helps eliminate the bias