Application of a Backpropagation Artificial Neural Network in Predicting Plasma Concentration and Pharmacokinetic Parameters of Oral Single‐Dose Rosuvastatin in Healthy Subjects

et al. 2022

BMC Pharmacol Toxicol

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Background The effects of age and gender were explored on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population and a plasma concentration prediction model was developed. All the data (demographic characteristics and results of clinical laboratory tests) were collected from healthy Chinese subjects in pharmacokinetics study using 20 mg omeprazole enteric-coated tablets. A noncompartmental method was used to calculate pharmacokinetic parameters, and 47 subjects were divided into two groups based on the calculation of the median age. Pharmacokinetic data from the low-age and high-age groups or male and female groups were compared by Student t-test. After a total of 12 variables were reconstruct and convert into independent or irrelative variables by principal component analysis, particle swarm optimization (PSO) was used to construct a backpropagation artificial neural network (BPANN) model. Result The model was fully validated and used to predict the plasma concentration in Chinese population. It was noticed that the Cmax, AUC0-t, AUC0-∞ and t1/2 values have significant differences when omeprazole was administered by low-age groups or high-age groups while there were slight or no significant differences of pharmacokinetic data were found between male and female subjects. The PSO-BPANN model was fully validated and there was 0.000355 for MSE, 0.000133 for the magnitude of the gradient, 50 for the number of validation checks. The correlation coefficient of training, validation, test groups were 0.949, 0.903 and 0.874. Conclusion It is necessary to pay attention to the age and gender effects on omeprazole and PSO-BPANN model could be used to predict omeprazole concentration in Chinese subjects to minimize the associated morbidity and mortality with peptic ulcer. Trial registration The study was registered in China Drug Clinical Trial Registration and Information Publicity Platform (http://www.chinadrugtrials.org.cn), the registration number was CTR20170876, and the full date of registration was 04/AUG/2017.

Section: Discussionmentioning

confidence: 99%

Development of a particle swarm optimization-backpropagation artificial neural network model and effects of age and gender on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population

et al. 2022

BMC Pharmacol Toxicol

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“…BPANN is widely used during our pharmacokinetic research and plasma concentration prediction. In previous research, we used to predict the plasma concentration and pharmacokinetic parameters of four bioequivalence studies of rosuvastatin calcium tablets [20] , predict pharmacokinetic and the effect of genetic polymorphisms of deferasirox [21] , and predict plasma concentration of febuxostat from different formulations [22] . Although we achieved good prediction results nally, both the stuck at the local minimum of BPANN and the variable selection trouble us a lot during the research process.…”

Section: Discussionmentioning

confidence: 99%

Development of A Particle Swarm Optimization-Backpropagation Artificial Neural Network Model And Effects of Age And Gender On Pharmacokinetics Study of Omeprazole Enteric-Coated Tablets In Chinese Population

et al. 2021

Preprint

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Background: The effects of age and gender were explored on pharmacokinetics study of omeprazole enteric-coated tablets in Chinese population and a plasma concentration prediction model was developed. All the data (demographic characteristics and results of clinical laboratory tests) were collected from healthy Chinese subjects in pharmacokinetics study using 20 mg omeprazole enteric-coated tablets. A noncompartmental method was used to calculate pharmacokinetic parameters, and 47 subjects were divided into two groups based on the calculation of the median age. Pharmacokinetic data from the low-age and high-age groups or male and female groups were compared by Student t-test. After a total of 12 variables were reconstruct and convert into independent or irrelative variables by principal component analysis, particle swarm optimization (PSO) was used to construct a backpropagation artificial neural network (BPANN) model. Result: The model was fully validated and used to predict the plasma concentration in Chinese population. It was noticed that the Cmax, AUC0-t, AUC0-∞ and t1/2 values have significant differences when omeprazole was administered by low-age groups or high-age groups while there were slight or no significant differences of pharmacokinetic data were found between male and female subjects. The PSO-BPANN model was fully validated and there was 0.000355 for MSE, 0.000133 for the magnitude of the gradient, 50 for the number of validation checks. The correlation coefficient of training, validation, test groups were 0.949, 0.903 and 0.874.Conclusion: It is necessary to pay attention to the age and gender effects on omeprazole and PSO-BPANN model could be used to predict omeprazole concentration in Chinese subjects to minimize the associated morbidity and mortality with peptic ulcer.

“…In our previous research, the data from four bioequivalence studies using reference 10‐mg rosuvastatin calcium tablets were collected to construct the BPANN model predicting the plasma concentration and pharmacokinetic parameters 20 . In this study, a model for predicting the plasma concentration of febuxostat and providing theoretical data support for clinical medication was established from a completely new perspective.…”

Section: Discussionmentioning

confidence: 99%

Development of LC‐MS/MS determination method and backpropagation artificial neural networks pharmacokinetic model of febuxostat in healthy subjects

et al. 2020

J Clin Pharm Ther

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What is known and objective Febuxostat is a well‐known drug for treating hyperuricemia and gout. The published methods for determination of febuxostat in human plasma might be unsuitable for high‐throughput determination and widespread application. We need to develop a highly selective, sensitive and rapid liquid chromatography‐tandem mass spectrometry method. Methods The chromatographic separation was achieved on a Hypersil Gold‐C18 (2.1 mm × 100 mm, 1.9 μm) column with mobile phase A (Water containing 0.1% formic acid) and mobile phase B (acetonitrile containing 0.1% formic acid). Multiple reaction monitoring (MRM) mode was used for quantification using target ions at m/z 315.3 → m/z 271.3 for febuxostat and m/z 324.3 → m/z 280.3 for Febuxostat‐d9 (IS). A backpropagation artificial neural network (BPANN) pharmacokinetic model was constructed by the data of bioequivalence study. Results and Discussion After the LC‐MS/MS method validated, it was successfully applied to the bioequivalence study of 30 human volunteers under fed condition. The predicted concentrations generated by BPANN model had a high correlation coefficient with experimental values. What is new and conclusion A sensitive LC‐MS/MS method had been developed and validated for determination of febuxostat in healthy subjects under fed condition, and a BPANN model was developed that can be used to predict the plasma concentration of febuxostat.