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<p>Aminoglycoside dosing in suspected neonatal sepsis remains difficult due to highly variable pharmacokinetics driven by marked physiological diversity, from extremely preterm to term neonates, and further complicated by acute kidney injury, perinatal asphyxia, and concomitant interventions. We developed multiscale medical digital twins combining a physiologically-based pharmacokinetic model with an eco-evolutionary pharmacodynamic module capturing drug-modulated bacterial growth and resistance. Glomerular filtration rate is continuously updated using a long short-term memory neural network trained on real-world data. Calibrated on 1634 neonates, the framework enables in silico optimization of full-course antibiotic therapy through real and virtual cohorts, balancing efficacy and safety while accounting for resistance-driven changes in the minimum inhibitory concentration (MIC). Nonlinear optimal control achieved bacteriostatic exposure across all digital-twin neonates, with safety preserved in most cases at higher MICs. Model predictive control further reduced bacterial rebound during late therapy. This framework supports evolution-aware precision dosing of renally cleared antibiotics in vulnerable neonatal populations.</p>

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Evolutionary digital twin framework for optimal aminoglycoside dosing in neonates with suspected sepsis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月21日

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  • Michela Prunella,
  • Chiara Romano,
  • Alessandro Borri,
  • Nicola Altini,
  • Maria Domenica Di Benedetto,
  • Pieter Annaert,
  • Karel Allegaert,
  • Anne Smits,
  • Vitoantonio Bevilacqua

摘要

Aminoglycoside dosing in suspected neonatal sepsis remains difficult due to highly variable pharmacokinetics driven by marked physiological diversity, from extremely preterm to term neonates, and further complicated by acute kidney injury, perinatal asphyxia, and concomitant interventions. We developed multiscale medical digital twins combining a physiologically-based pharmacokinetic model with an eco-evolutionary pharmacodynamic module capturing drug-modulated bacterial growth and resistance. Glomerular filtration rate is continuously updated using a long short-term memory neural network trained on real-world data. Calibrated on 1634 neonates, the framework enables in silico optimization of full-course antibiotic therapy through real and virtual cohorts, balancing efficacy and safety while accounting for resistance-driven changes in the minimum inhibitory concentration (MIC). Nonlinear optimal control achieved bacteriostatic exposure across all digital-twin neonates, with safety preserved in most cases at higher MICs. Model predictive control further reduced bacterial rebound during late therapy. This framework supports evolution-aware precision dosing of renally cleared antibiotics in vulnerable neonatal populations.