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Vincent Dubois (MedImmune), Dave Fairman (GSK), Anna Gaulton (EMBL-EBI), Theresa Goletz (EMD Serono), Boris Grinshpun (EMD Serono), Anne Hersey (EMBL-EBI), Tushar Jain (Adimab), Andrew Leach (EMBL-EBI), Chris Lloyd (MedImmune), Yves Fomekong Nanfack (EMD Serono), Friedrich Rippmann (Merck)

Value Drivers, value proposition and use:>Need well contribution(s):

Drivers

(based on organisational needs)

Value proposition

(what difference 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

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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

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Use the dataset for better standardisation; allowing for robust comparison, cross referencing, identifying outliers and further analyses.

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Time and effort
Generating new biophysical data (Yves)
Sharing newly generated biophysical data (Yves)
Sharing non-public data

Scope:

>Initial focus is on mAbs

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>Other IgG-like molecules to be included based on absence of technical limitations

Approach:

>Collate PK on approx. 10 priority marketed Ab drugs which are know to have high quality published data and check whether this meets the above 'gold standard' definition.

>Propose 'data quality' tiers into which data from published molecules can be placed.

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Versions Compared

Old Version 6

New Version 7

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