Tristan Free
speaker
289 appearances
6 recordings
1 series
first heard Jun 2026
last heard 23 Jul
Tristan Free’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 6 in all, peaking in Jun 2026 with 4.
Appearances
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Thank you for listening and goodbye.
Today we're talking about antibody viscosity and the improvement of subcutaneous delivery for antibody therapeutics.
This is the Talking Techniques Podcast and you are listening to our mini series documenting key papers and findings from the ongoing article collection in the journal MAPS on artificial intelligence and machine learning in antibody development.
I'm your host, Bartonique Senior Editor Tristan Free, and I'm joined by Peter Tessier, the Albert M.
Delving further into the antibody and formulation properties responsible for this undesirable characteristic.
Peter, it's great to have you on the podcast.
Um so I suppose we should start with um subcutaneous delivery of antibody therapeutics.
Um what are the challenges associated with this delivery method?
Okay, fantastic.
And and so you mentioned that um so obviously it has these challenges, but uh you mentioned it was beneficial for the patients.
Um could you take us through like why it's it is one of the most common delivery methods for antibody therapeutics and what what its benefits are?
Fantastic.
And and so what is it that makes uh an antibody that you're gonna use as a therapeutic well suited to subcutane subcutaneous delivery?
And so your first paper published back in in twenty twenty four now details the um development of a machine learning model to predict the viscosity of antibody candidates.
Um can you describe that model?
How is it trained, how does it operate, and w what kind of information can it give you?
Fantastic.
And and is that vod model is that available for others to use?
Um and and if it is, have you got any tips for someone who might want to work with that model?
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