Mathematical Modeling for Antimicrobial Dosing Regimens
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for developing antimicrobial agents are inefficient due to the lack of comprehensive systems for predicting microbial response to dosing regimens, leading to suboptimal dosing strategies that facilitate resistance development, and existing pharmacodynamic modeling is overly simplistic, limiting its predictive ability.
Innovation Solution
A computer-implemented method using mathematical modeling to simulate microbial population behavior over time, estimating parameter values for dosing regimens that optimize antimicrobial agent effectiveness and predict resistance acquisition, allowing for the design of pharmacologically effective dosing strategies to suppress resistance emergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive laboratory evaluation of all dosing regimen scenarios is performed, then the predictive ability of dosing strategies is improved, but the time and resource consumption becomes prohibitive
Solution Approach 1:
The patent applies preliminary action by performing in-silico screening and mathematical modeling before laboratory evaluation. The system uses computational models to predict microbial responses to various dosing regimens, allowing researchers to identify promising candidates in silico before conducting time-consuming wet lab experiments. This preliminary computational assessment filters out ineffective regimens, reducing the number of scenarios that require comprehensive laboratory evaluation.
Solution Approach 2:
The patent employs copying by creating virtual replicas of biological systems through mathematical models. Instead of physically testing every dosing regimen in the laboratory, the system uses computational models that replicate microbial population dynamics and drug-ph microbe interactions. These virtual models allow rapid evaluation of numerous dosing scenarios without the time and resource constraints of physical experiments.
2Device complexity
If conventional pharmacodynamic modeling using surrogate indices is used, then the device complexity is reduced, but the predictive ability is limited
Solution Approach 1:
The patent applies parameter changes by transitioning from simplified surrogate indices (like AUC/MIC or %T>MIC) to a more comprehensive set of pharmacodynamic parameters. The system incorporates multiple parameters including bacterial burden dynamics, drug concentration-time profiles, and resistance emergence rates. This parameter expansion enables the model to capture the complex interplay between dosing regimens and microbial responses, significantly improving predictive ability while maintaining manageable complexity through efficient computational algorithms.
3Productivity
If new antimicrobial agents are developed rapidly, then the ability to combat resistance is improved, but the thoroughness of agent evaluation may be compromised
Solution Approach 1:
The patent applies preliminary action by conducting comprehensive in-silico evaluation of new antimicrobial agents before advancing them to preclinical or clinical development. The mathematical models assess multiple critical aspects including efficacy against wild-type pathogens, propensity to select resistant mutants, and optimal dosing regimens. This preliminary computational thoroughness ensures that agents progressing to later development stages have already been rigorously evaluated, maintaining evaluation quality while accelerating the overall development pipeline.
Solution Approach 2:
The patent applies segmentation by dividing the drug development evaluation process into distinct computational modules. Each module assesses specific aspects: one evaluates killing activity against susceptible pathogens, another predicts resistance emergence, and a third optimizes dosing regimens. This segmented approach allows parallel processing of different evaluation criteria, maintaining comprehensive assessment thoroughness while reducing the time required compared to sequential evaluation methods.
Data Source
AI summary
Provided herein are methods and computer-implemented systems for using computer simulations to predict likelihood of a cell population associated with a pathophysiological condition acquiring resistance to a therapeutic agent, to screen for therapeutic agents effective to suppress acquisition of resistance within a cell population and to treat the pathophysiological conditions associated therewith. The computer simulation comprises at least an input/out system and a mathematical model, including operably linked equations, parameter values and constant values, of growth response over a period of time of a cell population in contact with an therapeutic agent. Also provide is a method for determining a best-fit mathematical model of adaptation of a microbial population to a therapeutic agent over time and using the model to simulate microbial population behavior to a therapeutic agent.