Brønsted acid-catalyzed simple and efficient synthesis of 1,2,4-triazoles and 1,2,4-oxadiazoles using 2,2,2-trichloroethyl imidates in PEG

A facile and highly efficient synthesis of 3,4,5-trisubstituted 1,2,4-triazoles and 3,5-disubstituted 1,2,4-oxadiazoles from 2,2,2-trichloroethyl imidates using PEG as a solvent and employing PTSA as the catalyst under mild conditions is described.     

Development and validation of a sensitive ELISA for quantitation of Grastim (rhG-CSF) in rat plasma: Application to a pharmacokinetic study

Grastim is bacterially produced recombinant counterpart of human granulocyte colony stimulating factor (G-CSF). It has biological activity similar to that of endogenous G-CSF. In the present work a sensitive, accurate, precise and enzyme-linked immunosorbent assay (ELISA) for the quantitation of G-CSF in rat plasma was developed and validated. The ELISA method employed a technique in which anti-human-G-CSF was adsorbed onto 96-well maxisorp plates and used to capture the G-CSF in rat plasma samples. The captured G-CSF was then detected using streptavidin-HRP amplification system. Absolute recovery was >90% from rat plasma. The validation includes assessments of method accuracy and precision, range of reliable response, lower limit of quantitation (LLOQ), storage stability (30 days) in rat plasma and assay specificity. The standard curve for G-CSF was linear (R2 > 0.996) in the concentration range 4.88-625 pg/mL. The LLOQ was established at 4.88 pg/mL. The inter- and intra-day precisions in the measurement of quality control (QC) samples, 15, 250 and 500 pg/mL, were in the range 3.00-8.66% relative standard deviation (RSD) and 1.03-4.69% RSD, respectively. Accuracy in the measurement of QC samples was in the range 87.28-110.79% of the nominal values. The assay shows dilutional linearity and specificity. Stability of G-CSF was established for 30 days at -80 degrees C and through three freeze-thaw cycles. The validated assay was successfully employed for the assessment of pharmacokinetic disposition of G-CSF in rats.     

Review of DBS methods as a quantitative tool for anticancer drugs

An overview of published dried blood spot (DBS) methods for the quantitation of various classes of anticancer drugs from clinical and preclinical studies is presented. The increased reporting of DBS methods in the literature for quantitation of various classes of drugs is a testimony to their utility in bioanalytical applications. While DBS offers several advantages as compared with conventional wet sampling techniques, there remain a number of nuances that may impede the assay adaptability of DBS method in routine quantitative bioanalysis. This review covers several case studies of DBS application in the quantitation of anticancer drugs. Some perspectives are provided on the optimization of the DBS method with respect to the selection of DBS card, spot volume, hematocrit effect and other regular validation parameters, which are essential in quantitative bioanalysis. Some thoughts are provided on the existing gaps in the DBS method and possible remedial measure(s) to address such gaps. Although DBS methods have great potential, there is the need for a global consensus including regulatory support on the type of validation experiments to be performed to support quantitative data.     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Mechanical performance of Buckminster fullerene-reinforced composite with interface defects using finite element method through homogenization techniques

Several times, preferred properties are not accomplished when the fiber-reinforced composites are tested after they are prepared and the similar observations are also observed in the case of nanoparticle-reinforced composites. While the composites are manufactured under controlled conditions with stringent quality measures, it is complex to circumvent fiber-matrix interfacial debonding. The amount of debonding decides the final mechanical properties of composite material. In this study, an effort is made to quantify the debonding effect of nanoparticles on mechanical properties of nanoparticle-filled composites using finite element models with homogenization approach. Buckminster fullerene and conventional fiber T300 are selected as reinforcement medium. The predictions revealed that the longitudinal Young’s modulus is not affected by debonding.