Concentration optimization of combinatorial drugs using Markov chain-based models
| Autor: | Wenxue Wang, Shuang Ma, Lianqing Liu, Yuechao Wang, Dan Dang |
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| Rok vydání: | 2021 |
| Předmět: |
Drug
Mathematical optimization State variable Combinatorial therapy Discretization QH301-705.5 Computer science media_common.quotation_subject Computer applications to medicine. Medical informatics Markov chain R858-859.7 Combinatorial drug optimization Biochemistry Exponential growth Structural Biology Stationary balance distribution Computer Simulation Biology (General) Molecular Biology Probability media_common Stationary distribution Research Applied Mathematics Markov Chains Computer Science Applications Drug Combinations Transition probability Benchmark (computing) Stochastic optimization Algorithms |
| Zdroj: | BMC Bioinformatics BMC Bioinformatics, Vol 22, Iss 1, Pp 1-19 (2021) |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-021-04364-5 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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