Fluorescence Spectrometric Determination of Drugs Containing α-Methylene Sulfone/Sulfonamide Functional Groups Using N-Methylnicotinamide Chloride as a Fluorogenic Agent

RNPredATC: A Deep Residual Learning-Based Model With Applications to the Prediction of Drug-ATC Code Association

The Anatomical Therapeutic Chemical (ATC) classification system, designated by the World Health Organization Collaborating Center (WHOCC), has been widely used in drug screening, repositioning, and similarity research. The ATC classification system assigns different codes to drugs according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Correctly identifying the potential ATC codes for drugs can accelerate drug development and reduce the cost of experiments. Several classifiers have been proposed in this regard. However, they lack of ability to learn basic features from sparsely known drug-ATC code associations. Therefore, there is an urgent need for novel computational methods to precisely predict potential drug-ATC code associations in multiple levels of the ATC classification system based on known associations between drugs and ATC codes. In this paper, we provide a novel end-to-end model, so-called RNPredATC, to predict potential drug-ATC code associations in five ATC classification levels. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from the degradation problem by a novel deep residual learning. We extensively compare our method with some state-of-the-art methods, including NetPredATC, SPACE, and some multi-label-based methods. Our experimental results show that RNPredATC achieves better performances in five-fold and ten-fold cross validations. Furthermore, the visualization analysis of hidden layers and case studies of predicted associations at the fifth ATC classification level confirm that RNPredATC can effectively identify the potential ATC codes of drugs.

An optoelectronic flow-through detectors for active ingredients determination in the pharmaceutical formulations

An optoelectronic flow-through detector for active ingredients determination in pharmaceutical formulations is explained. Two consecutive compact photodetector's devices operating according to light-emitting diodes-solar cells concept where the LEDs acting as a light source and solar cells for measuring the attenuated light of the incident light at 180˚ have been developed. The turbidimetric detector, fabricated of ten light-emitting diodes and five solar cells only, integrated with a glass flow cell has been easily adapted in flow injection analysis manifold system. For active ingredients determination, the developed detector was successfully utilized for the development and validation of an analytical method for warfarin determination in pure and pharmaceutical preparations. The developed method is based on the forming of a white, turbid product as a result of a reaction between the warfarin and semicarbazide which was used as an oxidizing agent. The developed flow-through detector system is semi mechanized, economic in materials consumption, easy to operate and characterized by excellent analytical results. Both developed analytical devices used in two channels flow injection system allow for turbidimetric measurements of warfarin in 0.9-154 μg ml^-1 and 123-1600 μg ml^-1 ranges of concentration, with limits of detections 0.73 μg ml^-1 and 24.66 μg ml^-1 for photodetectors 1& 2 respectively. The turbidity measurement procedure for the current flow system offers to conduct 60 tests per hour of the warfarin which is the most needs of quality control analysis in industrial applications. To ensure the analytical usefulness of the flow system, the warfarin has been analyzed in the real samples with a fully acceptable agreement and a correlation between the results offered by the developed flow system and the official method.