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Control of Eating Behavior Using a Novel Feedback System
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Personalizing Polymyxin B Dosing Using an Adaptive Feedback Control Algorithm.

Elizabeth A Lakota1,2, Cornelia B Landersdorfer3,4, Roger L Nation3

  • 1University at Buffalo, Buffalo, New York, USA.

Antimicrobial Agents and Chemotherapy
|May 16, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a personalized dosing algorithm for polymyxin B, an antibiotic for resistant infections. The adaptive feedback control system aims to optimize drug exposure, reducing nephrotoxicity risk and improving treatment effectiveness.

Keywords:
adaptive feedback controlmultidrug-resistant organismsnephrotoxicitypolymyxins

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Area of Science:

  • Pharmacology
  • Infectious Diseases
  • Computational Biology

Background:

  • Polymyxin B is a critical last-resort antibiotic for multidrug-resistant Gram-negative infections.
  • Nephrotoxicity is a significant adverse effect associated with polymyxin B therapy.
  • Current dosing strategies may not adequately balance efficacy and toxicity.

Purpose of the Study:

  • To establish a polymyxin B therapeutic window based on target area under the concentration-time curve (AUC) values.
  • To develop and evaluate an adaptive feedback control algorithm for personalized polymyxin B dosing.
  • To improve the probability of achieving therapeutic polymyxin B exposure while minimizing nephrotoxicity risk.

Main Methods:

  • Pharmacometric meta-analysis of polymyxin B nephrotoxicity data to define the upper bound of the therapeutic window.
  • Murine thigh infection pharmacokinetic/pharmacodynamic studies to determine the lower bound.
  • Utilized a population pharmacokinetic model and Monte Carlo simulations to assess algorithm performance with sparse sampling.
  • Developed an adaptive feedback control algorithm for personalized dosing.

Main Results:

  • A target AUC0-24 window of 50 to 100 mg·h/liter was established.
  • Standard polymyxin B dosing achieved target AUCs in only 71% of simulated subjects.
  • The adaptive algorithm, using a single 24-hour pharmacokinetic sample, enabled >95% target attainment.
  • Increased sampling improved target attainment further.

Conclusions:

  • The developed algorithm enables precise, personalized polymyxin B dosing.
  • The adaptive feedback control system significantly increases the probability of achieving therapeutic drug exposure.
  • This approach is valuable for optimizing polymyxin B treatment in vulnerable patient populations, potentially reducing adverse events.