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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 it will 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)
  • Have a repository of well documented and high quality large molecule PK data in one place and help drive a better targeted data generation and interpretation across compound developers (Vincent)
  • 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

  • Initial focus is on mAbs

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  • Include information on ADCs (this needs to be contextualised in the database)

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


Approach:>Collate PK on

<Link to Meeting Minutes>

<Link to Google drive folder with additional publications of interest>

<Link to draft publication “Guiding principles for antibody PK data quality and classification”>

  • 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.

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

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  • Once we identify T3 data - can we use internal data to move this to T1 or T2?
  • Once we have identified molecules in each Tier can we characterise these from a biophysical perspective and see whether there are correlations between PK and early data


Further needs:-

  • Discussion on sources of data e.g. Jain et al., (2017) publication, FDA drug approval packages

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  • Discussion on technicalities of database build, setup, etc. bearing in mind future inclusion of data from other biologic drug modalities beyond standard mAbs