Abstract
BackgroundMulti-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing.ResultsIn this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms.ConclusionsSimulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications.
Highlights
Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes
We first evaluated the overall performance of the adaptive reference update (ARU) stochastic search algorithm based on four twodimensional drug response functions. (i) HIV inhibitor response f2a(x1, x2), (ii) second De Jong function (Rosenbrock’s saddle) f2b(x1, x2), (iii) normalized lung cancer inhibition response f2c(x1, x2), and (iv) bacterial (S. aureus) inhibition response f2d(x1, x2)
In this paper, we proposed a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, which can be effectively used for optimizing the composition of combinatory drugs
Summary
Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. Considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. It is generally difficult to change their long-term dynamics by blocking a specific gene or intervening in a specific pathway. This is one of the reasons why monotherapy is often not very effective in treating complex diseases, such as cancer and diabetes. Considering the huge number of possible ways to mix multiple drugs, it is practically impossible to find the optimal “drug cocktail” by trial and error or by exhaustive testing.
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