QSAR Studies on the IC50 of a Class of Thiazolidinone/Thiazolide Based Hybrids as Antitrypanosomal Agents

Design and Synthesis of Echitamine-inspired Hybrid Analogues Containing Thiazolidinediones as Potential Pancreatic Lipase Inhibitors

Background:

Obesity is a multifactorial metabolic disease characterised by excessive accumulation of triglycerides. The prevalence and morbidity rates associated with obesity are increasing tremendously, posing a significant risk to society. Pancreatic lipase (PL) is a key enzyme responsible for the digestion of dietary triglycerides; hence its inhibition is considered an attractive target in obesity.

Methods:

In this present work, a new series of echitamine-inspired indole-based thiazolidinedione hybrid analogues were designed, synthesized, and evaluated for their in vitro PL inhibitory potential. The nature of inhibition has been identified by enzyme kinetic analysis, whereas in silico molecular modelling tools (molecular docking and dynamic studies) were used for the identification of the mode of action at the catalytic site of PL (PDB ID: 1LPB). Fluorescence quenching was used for the identification of the interaction between the potent analogues with PL.

Results:

The condensation reaction of substituted indole derivatives with TZD in the presence of aqueous KOH resulted in the formation of the titled analogues. Analogues 7k and 7p displayed a potential PL inhibitory activity (IC50 = 11.36 and 11.87 μM, respectively). A competitive mode of PL inhibition was revealed in the enzyme kinetic analysis. A static quenching mechanism was exhibited by the screened agents on PL. The obtained MolDock scores were aligned with the in vitro PL inhibitory activity (Pearson’s r - 0.7575, p<0.05). Moreover, the PL-ligand complexes were stable in the dynamic conditions.

Conclusion:

Analogue 7k exerted the potential activity, and further studies might result in novel lead molecules.

Prediction of anti-proliferation effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives by random forest and mix-kernel function SVM with PSO

In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relationship (QSAR) studies were performed. Based on five descriptors selected from descriptors pool, four QSAR models were established by heuristic method (HM), random forest (RF), support vector machine with radial basis kernel function (RBF-SVM), and mix-kernel function support vector machine (MIX-SVM) including radial basis kernel and polynomial kernel function. Furthermore, the model built by RF explained the importance of the descriptors selected by HM. Compared with RBF-SVM, the MIX-SVM enhanced the generalization and learning ability of the constructed model simultaneously and the multi parameters optimization problem in this method was also solved by particle swarm optimization (PSO) algorithm with very low complexity and fast convergence. Besides, leave-one-out cross validation (LOO-CV) was adopted to test the robustness of the models and Q² was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R² = 0.927, Q² = 0.916, MSE = 0.027 for the training set and R² = 0.946, Q² = 0.913, MSE = 0.023 for the test set. This study reveals five key descriptors of 1,2,3-TPD and will provide help to screen out efficient and novel drugs in the future.

QSAR Model Study of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of Cystic-brosis-transmembrane Conductance-regulator Gene Potentiators

Background:

Cystic fibrosis (CF) is a genetic disease, which has no effective treatment.

Objective:

The aim of this study is to predict the EC50 value of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole core as a novel chemotype of potentiators to establish a highly predicting quantitative structure-activity relationship model.

Methods:

41 products were optimized, and a linear model was built by a heuristic method in CODESSA program. In this study, 3 descriptors were selected and utilized to build a nonlinear model in gene expression programming.

Results:

The square of the correlation coefficient of the heuristic method is 0.57, and the s2 is 0.30. In gene expression programming, the square of correlation coefficient and the mean square error for the training set are 0.74 and 0.13, respectively. The square of correlation coefficient and the mean square error for the test set are 0.70 and 0.27, respectively.

Conclusion:

The GEP model has stronger predictive ability to help develop the novel structure of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of cystic-brosis-transmembrane conductance-regulator gene potentiators.