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Optimizing Combinatory Drugs using Markov Chain-Based Models
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Abstract
Background: Combinatory drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatory drugs because the number of drug combinations increases exponentially with the types of drugs. Results: In this study, a searching method based on Markov chain is presented to optimize the combinatory drug concentrations. Its performance is compared with four stochastic optimization algorithms as benchmark methods by simulation and biological experiements. Both simulation results and experimental data demonstrate that the Markov Chain-based approach is more reliable and efficient than the benchmark algorithms.Conclusion: This article provides a versatile method for combinatory drug screening, which is of great significance for clinical drug combination therapy.
Title: Optimizing Combinatory Drugs using Markov Chain-Based Models
Description:
Abstract
Background: Combinatory drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment.
However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatory drugs because the number of drug combinations increases exponentially with the types of drugs.
Results: In this study, a searching method based on Markov chain is presented to optimize the combinatory drug concentrations.
Its performance is compared with four stochastic optimization algorithms as benchmark methods by simulation and biological experiements.
Both simulation results and experimental data demonstrate that the Markov Chain-based approach is more reliable and efficient than the benchmark algorithms.
Conclusion: This article provides a versatile method for combinatory drug screening, which is of great significance for clinical drug combination therapy.
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