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The mathematics of random mutation and natural selection for multiple simultaneous selection pressures and the
1Coarsegold, CA, 93614, U.S.A.
Abstract:
The random mutation and natural selection phenomenon act in a mathematically predictable behavior, which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures when treating infections and cancers. The underlying principle to impair the random mutation and natural selection phenomenon is to use combination therapy, which forces the population to evolve to multiple selection pressures simultaneously that invoke the multiplication rule of probabilities simultaneously as well. Recently, it has been seen that combination therapy for the treatment of malaria has failed to prevent the emergence of drug-resistant variants. Using this empirical example and the principles of probability theory, the derivation of the equations describing this treatment failure is carried out. These equations give guidance as to how to use combination therapy for the treatment of cancers and infectious diseases and prevent the emergence of drug resistance. Copyright © 2016 John Wiley & Sons, Ltd.
Insights
Combination therapy can prevent drug resistance in infections and cancers by exploiting mathematical principles of random mutation and natural selection. Understanding these principles guides effective treatment strategies to combat evolving pathogens and diseases.
Area of Science:
- Mathematical Biology
- Evolutionary Medicine
- Pharmacology
Background:
- Random mutation and natural selection drive treatment failure in infections and cancers.
- Combination therapy aims to overcome resistance by applying multiple selection pressures.
- Recent failures in malaria treatment highlight the need for improved combination therapy strategies.
Purpose of the Study:
- To mathematically model the failure of combination therapy in preventing drug resistance.
- To derive equations based on probability theory to explain treatment failures.
- To provide guidance on optimizing combination therapy for cancer and infectious disease treatment.
Main Methods:
- Application of probability theory to model evolutionary dynamics.
- Derivation of mathematical equations describing treatment failure.
- Analysis of empirical data from malaria treatment failures.
Main Results:
- Mathematical predictability of mutation and selection processes identified.
- Equations derived to describe the emergence of drug resistance under combination therapy.
- Insights gained into why combination therapies fail to prevent resistant variants.
Conclusions:
- Understanding the mathematical behavior of mutation and selection is crucial for effective treatment.
- Optimized combination therapy strategies can prevent the emergence of drug resistance.
- The derived equations offer a framework for designing more robust anti-infective and anti-cancer treatments.
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