METHODS: Ten ligands with reported in vitro and/or in vivo activities against GAPDH were evaluated for their binding interactions through molecular docking studies using AutoDock 4.2 program. The ligand with the best binding energy was then modified to produce 10 derivatives, which were redocked against GAPDH using previous protocols. BIOVIA Discovery Studio Visualizer 2019 was used to explore the ligand-receptor interactions between the derivatives and GAPDH.
RESULTS: Among the 10 ligands, curcumin, koningic acid and folic acid showed the best binding energies. Further analysis on the docking of two folic acid derivatives, F7 (γ-{[tert-butyl-N-(6-aminohexyl)]carbamate}folic acid) and F8 (folic acid N-hydroxysuccinimide ester) showed that the addition of a bulky substituent at the carboxyl group of the glutamic acid subcomponent resulted in improved binding energy.
CONCLUSIONS: Folic acid and the two derivatives F7 and F8 have huge potentials to be developed as targeting agents against the GAPDH receptor. Further study is currently on-going to evaluate the effectiveness of these molecules in vitro.
METHODS: 18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a.
RESULTS AND DISCUSSION: Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅P = 20.73 [12.21, 28.57] mU·L·mmol-1·min-1 and ∅D = 60.42 [26.85, 131.38] mU·L·mmol-1 as compared to the linear least square method, ∅P = 19.67 [11.81, 28.02] mU·L·mmol-1 ·min-1 and ∅D = 46.21 [7.25, 116.71] mU·L·mmol-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10-4 L·mU-1 ·min-1 than the linear least square, SI = 17 × 10-4 L·mU-1 ·min-1.
CONCLUSION: Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.