Pharmacokinetic analysis and anticonvulsant activity of glycine and glycinamide derivatives

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques

BACKGROUND

Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones.

INTRODUCTION

Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group.

METHODS

Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs.

RESULTS

We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems.

CONCLUSION

The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

Synthesis, NMR, DFT and antimicrobial studies of Zn(II) complexes with N-benzyloxycarbonyl-S-alanine

In this study, the first complexes of Zn(II) with the N-benzyloxycar- bonyl-S-alaninato ligand (N-Boc-S-ala) were synthesized. The new complexes were characterized by elemental analysis, conductometric measurements, IR, 1H-NMR, 13C-NMR and 2D-NMR spectroscopy. On the basis of the experimental data, tetrahedral geometry of the Zn(II) complexes was proposed. A very good agreement between the NMR and DFT calculated data was obtained. Investigation of antimicrobial activity of the newly synthesized complexes was also performed. It was established that [Zn(N-Boc-S-ala)2] was selective and acts only on Candida albicans.

The structural requirements for the design of antiepileptic-glycine derivatives

Glycine is a major inhibitory neurotransmitter and recent reports have shown that certain lipophilic derivatives of glycine demonstrate anticonvulsant activity in intact animals. In these studies, glycinamide derivatives were found to be more potent than their corresponding glycine analogues. Consequently, the objective of the current study was to investigate the pharmacokinetics and pharmacodynamics (anticonvulsant activity and neurotoxicity) of the following phenyl derivatives of glycinamide: N'-benzyl glycinamide, N-benzyloxycarbonyl glycinamide (Z-glycinamide), Z-glycine, N-Z,N'-benzyl glycinamide and N-phenylacetyl glycinamide. The antiepileptic activity and neurotoxicity was carried out in classical animal models for antiepileptic screening. The pharmacokinetics of the active compounds were studied in dogs, a common animal model for comparative crossover pharmacokinetic studies. Of the compounds investigated in this study, Z-glycinamide, N'-benzyl glycinamide and N-Z,N'-benzyl glycinamide were found to be active. Therefore, the disposition of Z-glycinamide and N-Z,N'-benzyl glycinamide in comparison to Z-glycine was studied in plasma, brain, liver and urine of rats. The disposition of Z-glycinamide and N-Z,N'-benzyl glycinamide into the brain was better than that of Z-glycine. Unlike glycine or glycinamide, Z-glycinamide and N-Z,N'-benzyl glycinamide showed antiepileptic activity in animal models due to their better pharmacodynamic and pharmacokinetic properties. The pharmacokinetics of Z-glycinamide was similar in dogs and rats. Substitution of the Z group with the analogous phenylacetyl moiety led to inactive compounds. In an analogous series of compounds, the loss of the anticonvulsant activity may be due to pharmacodynamic and pharmacokinetic reasons. This study provides certain clues concerning the structural requirements for the design of antiepileptic-active glycine derivatives.