Drug metabolism and personalized therapy · 2025
Neural network modelling of experimental diabetes to study new antidiabetic drugs.
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Nurgaliyeva Zhansulu, Dauletkaliyeva Ayazhan, Dauletkaliyev Serzhan, Tolegen Dana, Romanova Rauza
Аннотация
OBJECTIVES: Diabetes mellitus is a complex metabolic disease characterised by chronic hyperglycaemia, which triggers a cascade of pathological changes in the body. Contemporary methods for developing antidiabetic drugs are often lengthy, expensive, and not always effective. The use of neural networks for modelling diabetes opens up new possibilities for accelerating research and increasing the accuracy of predicting the effectiveness of novel therapeutic strategies. The aim of this study was to develop and validate a neural network for studying experimental diabetes and assessing the efficacy of new antidiabetic drugs, as well as to explore the potential of combined therapeutic strategies.
METHODS: The empirical investigation was conducted using laboratory models of diabetes induced in rats with streptozotocin. Three groups were formed: a control group, a diabetic group without treatment, and a diabetic group treated with experimental drugs.
RESULTS: A neural network, based on multilayer perceptrons and recurrent architectures, was trained to predict changes in glucose levels, oxidative stress markers, and the condition of pancreatic tissues. The developed model demonstrated high predictive accuracy of metabolic changes, with an average accuracy of 92.3 %. As a result of treatment with experimental drugs, blood glucose levels in rats decreased by 25-30 % over 28 days, accompanied by a 26 % reduction in oxidative stress markers and partial restoration of pancreatic β-cell function in 30 % of cases. Histological analysis confirmed reduced fibrosis and improved tissue condition in the treatment group. The model also identified that combined therapeutic strategies - for example, the combination of antioxidants with gluconeogenesis inhibitors - had a synergistic effect, lowering glucose levels by up to 40 %.
CONCLUSIONS: The study confirmed the effectiveness of the developed neural network for analysing therapeutic strategies and predicting metabolic changes in diabetes models. The proposed neural network represents a promising tool for investigating new antidiabetic drugs, including efficacy assessment within personalised medicine. Its application may accelerate preclinical research, optimise therapeutic approaches, and contribute to reducing drug development costs.
METHODS: The empirical investigation was conducted using laboratory models of diabetes induced in rats with streptozotocin. Three groups were formed: a control group, a diabetic group without treatment, and a diabetic group treated with experimental drugs.
RESULTS: A neural network, based on multilayer perceptrons and recurrent architectures, was trained to predict changes in glucose levels, oxidative stress markers, and the condition of pancreatic tissues. The developed model demonstrated high predictive accuracy of metabolic changes, with an average accuracy of 92.3 %. As a result of treatment with experimental drugs, blood glucose levels in rats decreased by 25-30 % over 28 days, accompanied by a 26 % reduction in oxidative stress markers and partial restoration of pancreatic β-cell function in 30 % of cases. Histological analysis confirmed reduced fibrosis and improved tissue condition in the treatment group. The model also identified that combined therapeutic strategies - for example, the combination of antioxidants with gluconeogenesis inhibitors - had a synergistic effect, lowering glucose levels by up to 40 %.
CONCLUSIONS: The study confirmed the effectiveness of the developed neural network for analysing therapeutic strategies and predicting metabolic changes in diabetes models. The proposed neural network represents a promising tool for investigating new antidiabetic drugs, including efficacy assessment within personalised medicine. Its application may accelerate preclinical research, optimise therapeutic approaches, and contribute to reducing drug development costs.