Sustained-release And Controlled-release Formulation Optimization Based On Generalized Estimating Equations And Multi-objective Genetic Algorithm

With the ever-changing pharmaceutical technology, pharmaceutical research hasentered a new era and new drug delivery systems, for example sustained-release formulation is more and more used by people. The sustained-release and controlled-release formulations can slow down the drug "peak-valley phenomenon" effectively, to maintain stable long-lasting effective blood concentration, lasting therapeutic effect, reducing the number of convenient medication. However, it involves problems of the design, modeling and optimization in the process of pharmaceutical research. The impact factors are usually more than one, so the designed programs are multi-factors. Often use factorial design, orthogonal design, uniform design, central composite design, mixture design and so on; Currently there are multiple linear regression, quadratic regression model and higher order regression model to be used in the process of building models, but they require the data is independent. The data of sustained-release and controlled-release formulations is the repeated measurement data, and its evaluation index is usually used as the cumulative release rate, measuring the cumulative release of the same prescription at different time points, in order to screen the formulation and process, its data of different time points is highly correlated. Therefore, the traditional modeling method can not reveal its inherent characteristics, and sometimes can get the wrong conclusion; The cumulative release at different point time is a multi-objective optimization problem in the process of optimization of sustained-release and controlled-release formulations prescription. The more commonly used is the weight of each objective, then multi-objective problem can be converted to a single objective, to optimize by the more mature single-objective optimization algorithm. But they have disadvantages obviously, such as converting into single objective optimization and each calculation can only produce a solution, furthermore the weight coefficients are often unable to determine.Genrealized Estimating Equations is a statistical analysis method for the analysis of non-independent data, and it can handle with repeated measurements, cluster sampling data and so on. Currently it is used in clinical medicine, epidemiology research, health services, biomedical research, but it has not been reported in the field of sustained-release and controlled-release formulations. Genetic Algorithm is a search method from the biological evolution discipline. Multi-objective genetic algorithm provides decision-makers with a set of alternative, non-controlled and the best solution set through balancing all the sub-objectives. NSGA-Ⅱ is a novel multi-objective genetic algorithm, which is used in multi-objective optimization problems to apply in a large number of foreign countries to achieve excellent results. But it is limited in the multi-objective optimization of sustained-release and controlled-release formulations.The topic will apply the generalized estimating equations and multi-objective genetic algorithm to the optimization of sustained-release and controlled-release formulations. On the basis of the generalized estimating equation modeling,it can search the optimal prescription by multi-objective genetic algorithm. There are3parts of the subject to describe and discuss:Part1The concepts and principles of generalized estimating equations. Generalized estimating equations is a quasi-likelihood estimation method based on generalized linear models and longitudinal data quasi-likelihood estimation and it can be used in the repeated measurement of non-independent data. In refers to the basic structure and link function of the GLM,the steps of GEE, the type of work correlation Matrix.Part2Multi-objective optimization base on Genetic algorithm. There are the concepts of multi-objective optimization, the basic concepts of Pareto solution,the basic principles of genetic algorithms. Focuses on the basic principles of NSGA-Ⅱ.Part3Instance of the application Sustained-Release Formulation Optimization Based On Generalized Estimating Equations combined with multi-objective genetic algorithm. The topic will model and optimize the data of sustained release preparation for the three different dynamics models.Zero-order dynamics model, the topic using aceclofenac sustained-release formulations can model by generalized estimating equation and generalized linear models of three related structures which are exchangeable correlation, autocorrelation and unstructure correlation.Results show that the MSE and MAD of exchangeable correlation structure are the minimum. They are respectively8.8036and2.3649, and the model is well fitted. So it uses the generalized estimating equation of the exchangeable correlation structure to model. Using NSGA-II to optimize the multi-objective model:Low molecular weight poly (ethylene oxide) is210.2mg; NaCl is28.9mg; polyethylene glycol is4.4g; the weight gain is7.2%; The result is9.96%of the Q2under this condition,44.74%in Q6,92.05%in Q12.The results were closed to the best release.One-order dynamics model, the topic using Gliclazide sustained-release formulations can model by generalized estimating equation and generalized linear models of three related structures which are exchangeable correlation, autocorrelation and unstructure correlation. Results show that the MSE and MAD of exchangeable correlation structure are the minimum. They are respectively6.4354and2.0829, and the model is well fitted. So it uses the generalized estimating equation of the exchangeable correlation structure to model. Using NSGA-Ⅱ to optimize the multi-objective model:HPMC100cps is24.59mg;HPMC4000cps is17.74mg and Sodium alginate is7.34mg; The result is16.5%of the Q2under this condition,56.8%in Q6,85.9%in Q12.The results were closed to the best release.Higuchi dynamics model, the topic using ketoprofen sustained-release formulations can model by generalized estimating equation and generalized linear models of three related structures which are exchangeable correlation, autocorrelation and unstructure correlation. Results show that the MSE and MAD of exchangeable correlation structure are the minimum. They are respectively2.6501and11.6378, and the model is well fitted. So it uses the generalized estimating equation of the exchangeable correlation structure to model. Using NSGA-Ⅱ to optimize the multi-objective model:Hypromellose is28.53g, Lactose is29.57mg, EC is36.88g; The result is27.9%of the Q2under this condition,59.2%in Q6,99.9%in Q12.The results were closed to the best release.In summary, the generalized estimating equations take full account of the correlation characteristics of the sustained-release and controlled-release preparation of data, modeling is feasible; the multi-objective genetic algorithm is better than the single optimization of the integrated score breakthrough Pharmacopoeia, and overcomes the subjective characteristics of the traditional method largely; The results of sustained-release and controlled-release formulations are satisfactory and simplefeasible by generalized estimating equation combining with the multi-objective genetic algorithm for modeling and optimization.