Customized and Optimized Treatment of Extended-Spectrum-Beta-Lactamase Bacteria for Individual
Overview
Antibiotic resistance renders antimicrobial drugs - which had hitherto rescued innumerable lives from fatal bacterial infections - futile. It is imperative to prevent further evolution of multi-drug tolerant bacteria in the most economical way possible. This paper discusses optimizing and customizing intravenous-drip therapies for patients infected with antibiotic-resistant bacteria, namely the Extended-Spectrum-Beta-Lactamase bacteria. Utilizing ordinary differential equations to model the system’s dynamic, efficiencies of four types of IV-drip pulse function treatments and the time it takes for each treatment to suppress the pathogen population were evaluated. Results revealed that a trapezoid pulse function intravenous delivery of antibiotics is most favorable. Subsequently, its parameters were randomized to identify optimal ranges of antibiotic regimen. Thereupon, efficiency of each randomized treatment was evaluated using a score matrix assessing the dosing length, dosing interval, maximum rate of antibiotic infusion, net consumption of antibiotics, and total number of treatments. Through radar charts and box plots, it was discovered that for more severely infected patients with higher initial population and growth rate, it is important to decrease the time interval and increase the time of maximum infusion rate - vice versa for mild infections. More specifically, for severe infections with high Beta-Lactamase secretion rate, the time interval and period for different rates of infusion are also key values to manipulate. Under all conditions, there is a limit value to the maximum infusion rate of high-scoring treatments. Additionally, compared to initial bacteria population density, growth rate and Bla production rate play more important roles in impacting efficacy.
Competition history
- JSHS 2022
Resources
Related projects
ISEF · 2016
Reversing Antibiotic-Resistance: Discovery, Evaluation, and Optimization of Extended-Spectrum Beta-Lactamase Inhibitors
CSEF · 2019
Using Machine Learning and Surface Enhanced Raman Spectroscopy to Analyze the Effects of Antibiotics on P. aeruginosa
ISEF · 2024
Predictive Model for the Development and Spread of Bacterial Resistance in Relation to Antibiotic Consumption in Hospital Settings
CSEF · 2015
Toward a Strategy for Extending Antibiotic Effectiveness Indefinitely: Introducing Antibacterial Bio-Restriction
ISEF · 2021
A Versatile Population Dynamics Model of Bacterial Resistance, Tolerance, and Persistence
CSEF · 2026
Machine Learning Based Diagnostic Utility for Predicting Ertapenem Resistance in K.pneumoniae Using MALDI-TOF MS Spectra
CSEF · 2009
The Race Against Resistance: How Fast Do Bacteria Become Antibiotic Resistant?
ISEF · 2021
A Multi-Factor Study Deriving a Comprehensive Evaluation of Natural Antibiotic Hybrids Bound to Novel Nanoreactors in an Inorganic Model of an Enterococcus faecalis Infected Jejunum Cross-Analyzed through Static Biofilm Production and Quorum Sensing (Year 5)
Closest projects by meaning, across every fair and year in the corpus.