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UID:20260813T105702Z - 39127@eupv422
DTSTART;TZID=Europe/Berlin:20260910T170000
DTEND;TZID=Europe/Berlin:20260910T180000
CREATED:20260813T105702Z
DESCRIPTION:<a href="https://www.knauer.net/event/lnp-webinar-250/register"
 >Webinar: "Autonomous Impinging Jets Mixing for LNP Process Development" b
 y BIZON Labs & KNAUER</a>\nRegister now A live webinar hosted by Life Scie
 nce Connect\, presented by KNAUER in partnership with BIZON Labs (a spin-o
 ff of an MIT research project) Lipid nanoparticles are now central to the 
 delivery of genetic medicines\, yet LNP process development remains empiri
 cal and resource-intensive. Identifying process parameters for scalable\, 
 reproducible\, and robust manufacturing is challenging\, as particle quali
 ty attributes are highly sensitive to formulation and processing condition
 s. This webinar explores how impinging jets mixing can be integrated with 
 automation\, inline analytics\, and data-driven optimization to accelerate
  LNP process development. Attendees will see how automated design of exper
 iments\, dynamic process sweeps\, self-optimization\, and predictive model
 ing can map complex process-property relationships and rapidly identify co
 nditions that achieve target quality attributes. We'll show how closed-loo
 p experimentation can transform LNP development from an empirical workflow
  into a faster\, more resource-sparing process\, and discuss the challenge
 s that remain on the path to fully autonomous nanoparticle manufacturing. 
 What you'll learn: How impinging jets mixing parameters influence LNP crit
 ical quality attributes such as size\, structure\, and morphology How auto
 mation enables faster\, data-rich LNP process development How design of ex
 periments\, dynamic sweeps\, self-optimization\, and data-driven modeling 
 support predictive and scalable LNP manufacturing Who should attend: Formu
 lation scientists working on RNA\, mRNA\, siRNA\, or gene therapy delivery
  Process development scientists and engineers in nanoparticle manufacturin
 g CMC\, MSAT\, and manufacturing scientists supporting LNP scale-up R&D le
 aders evaluating automated or data-driven approaches to drug delivery deve
 lopment Speakers: Peter Sagmeister\, PhD\, BIZON Labs (spinning out of MIT
 ) Peter Sagmeister [...]
DTSTAMP:20260813T105702Z
SUMMARY:Webinar: "Autonomous Impinging Jets Mixing for LNP Process Developm
 ent" by BIZON Labs & KNAUER
X-ALT-DESC;FMTTYPE=text/html:<a href="https://www.knauer.net/event/lnp-webi
 nar-250/register">Webinar: "Autonomous Impinging Jets Mixing for LNP Proce
 ss Development" by BIZON Labs & KNAUER</a>\nRegister now A live webinar ho
 sted by Life Science Connect\, presented by KNAUER in partnership with BIZ
 ON Labs (a spin-off of an MIT research project) Lipid nanoparticles are no
 w central to the delivery of genetic medicines\, yet LNP process developme
 nt remains empirical and resource-intensive. Identifying process parameter
 s for scalable\, reproducible\, and robust manufacturing is challenging\, 
 as particle quality attributes are highly sensitive to formulation and pro
 cessing conditions. This webinar explores how impinging jets mixing can be
  integrated with automation\, inline analytics\, and data-driven optimizat
 ion to accelerate LNP process development. Attendees will see how automate
 d design of experiments\, dynamic process sweeps\, self-optimization\, and
  predictive modeling can map complex process-property relationships and ra
 pidly identify conditions that achieve target quality attributes. We'll sh
 ow how closed-loop experimentation can transform LNP development from an e
 mpirical workflow into a faster\, more resource-sparing process\, and disc
 uss the challenges that remain on the path to fully autonomous nanoparticl
 e manufacturing. What you'll learn: How impinging jets mixing parameters i
 nfluence LNP critical quality attributes such as size\, structure\, and mo
 rphology How automation enables faster\, data-rich LNP process development
  How design of experiments\, dynamic sweeps\, self-optimization\, and data
 -driven modeling support predictive and scalable LNP manufacturing Who sho
 uld attend: Formulation scientists working on RNA\, mRNA\, siRNA\, or gene
  therapy delivery Process development scientists and engineers in nanopart
 icle manufacturing CMC\, MSAT\, and manufacturing scientists supporting LN
 P scale-up R&D leaders evaluating automated or data-driven approaches to d
 rug delivery development Speakers: Peter Sagmeister\, PhD\, BIZON Labs (sp
 inning out of MIT) Peter Sagmeister [...]
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