Abstract
BackgroundCombinatorial 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 combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.ResultsIn this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.ConclusionThis article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.
Highlights
Combinatorial drug therapy for complex diseases, such as herpes simplex virus (HSV) infection and cancers, has a more significant efficacy than single-drug treatment
Performance comparison The response functions of the cells, MCF-7 and BXPC-3, under the combinatorial action of two drugs, paclitaxel (PTX) and doxorubicin hydrochloride (DOX), were constructed based on the biological responses to compare the performance of the algorithm we proposed and the Gur game (GG) and modified Gur game (MGG) algorithms
In this study, a novel Markov chain-based approach was proposed to solve the problem of the concentration optimization of combinatorial drugs
Summary
Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. In the clinical treatment of complex diseases, such as parasitic nematode infections or herpes simplex virus (HSV), a variety of drugs have been used in combination for treatment improvement [7]. Traditional treatments of HSV-I, one of the most common sexually transmitted infections, often include virus-specific drugs, which are effective at the beginning but exhibit limited long-term efficacy as drug-resistant strains develop. A combination of six drugs (IFN-α, acyclovir, IFN-γ, ribavirin, IFN-β and TNF-α) was demonstrated to be the most promising therapy for the reason that the drugs in the combinatorial treatment can act simultaneously on the multiple pathways and cellular protein complexes, and, regulate all relevant pathways, potentially blocking HSV-I replication [11]. In the treatment of non-Hodgkin’s lymphoma, the drugs, pirarubicin, velet, cytarabine and prednisone, are usually used in combination, which the chemotherapy effect is remarkablely enhanced [12]
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