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UID:pretalx-juliacon-2026-MGNSFV@pretalx.com
DTSTART;TZID=CET:20260812T120000
DTEND;TZID=CET:20260812T121500
DESCRIPTION:Virtual Populations (VPops) are widely used in Quantitative Sys
 tems Pharmacology (QSP) to represent variability in patient responses to t
 herapy using parameterized dynamical models. Unlike traditional modeling a
 pproaches that focus on average treatment effects\, VPop methods aim to re
 produce the full distribution of clinical outcomes observed in trials.\nWe
  introduce VPopMIP\, a Julia package implementing a Mixed-Integer Programm
 ing (MIP) formulation for generating VPops that match clinical endpoints. 
 In contrast to many existing approaches that require individual-level pati
 ent data\, VPopMIP enables calibration to published clinical summary stati
 stics (e.g.\, response rates\, medians\, and confidence intervals)\, which
  are more commonly available in practice.\nThe method formulates virtual p
 atient selection as a constrained optimization problem that enforces agree
 ment with multiple outcome measures across therapies.\nWe demonstrate the 
 methodology using a solid tumor model with multiple efficacy endpoints acr
 oss treatment regimens. The results illustrate how MIP-based selection pro
 vides an efficient way to construct clinically consistent virtual populati
 ons.
DTSTAMP:20260502T102650Z
LOCATION:Room 4
SUMMARY:VPopMIP: A Mixed-Integer Programming Approach to Virtual Population
  Generation - Ivan Borisov\, Evgeny Metelkin
URL:https://pretalx.com/juliacon-2026/talk/MGNSFV/
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