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Drivers

(based on organisational needs)

Value proposition

(what difference it will it make)

Contribution

(what you/your company are prepared to do to support the initiative)

  • Have well curated, more diverse (different animal models, different Abs, etc.) and evolving data on reference molecules.
  • Have all clinical PK data in one place and move away from comparing the same parameter across different molecules irrespective of the way this has been measured. (Dave)
  • Collect enough data to derive meaningful PK predictions - risk is there is not enough public data available so companies must be open to sharing non-public data. (Terry)
  • Derive biophysical data from suitably classified mAbs and understand potential correlations with clinical PK. (Yves)
  • Use the dataset to improve the likelihood of developing new predictive models
  • Use the dataset for better standardisation; allowing for robust comparison, cross referencing, identifying outliers and further analyses.
  • Time and effort
  • Generating new biophysical data (Yves)
  • Sharing newly generated biophysical data (Yves)
  • Sharing non-public data

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