Favipiravir, remdesivir, and lopinavir: metabolites, degradation products and their analytical methods.

Severe acute respiratory syndrome 2 (SARS-CoV-2) caused the emergence of the COVID-19 pandemic all over the world. Several studies have suggested that antiviral drugs such as favipiravir (FAV), remdesivir (RDV), and lopinavir (LPV) may potentially prevent the spread of the virus in the host cells and person-to-person transmission. Simultaneously with the widespread use of these drugs, their stability and action mechanism studies have also attracted the attention of many researchers. This review focuses on the action mechanism, metabolites and degradation products of these antiviral drugs (FAV, RDV and LPV) and demonstrates various methods for their quantification and discrimination in the different biological samples. Herein, the instrumental methods for analysis of the main form of drugs or their metabolite and degradation products are classified into two types: optical and chromatography methods which the last one in combination with various detectors provides a powerful method for routine and stability analyses. Some representative studies are reported in this review and the details of them are carefully explained. It is hoped that this review will be a good guideline study and provide a better understanding of these drugs from the aspects investigated in this study.

Effects of Ge, Si, and B doping on the adsorption and detection properties of C60 fullerene towards methadone in gas and aqua phases: a DFT study.

CONTEXT: Methadone can be abused and caused addictive and has various side effects. Therefore, the development of a fast and reliable diagnosis technique for its monitoring is essential. In this work, applications of C60, GeC59, SiC59, and BC59 fullerenes were investigated utilizing density functional theory (DFT) to find a suitable probe for methadone detection. The C60 fullerene indicated weak adsorption energy for methadone sensing. Therefore, for the construction of the fullerene with good property for methadone adsorption and sensing, the GeC59, SiC59, and BC59 fullerenes have been studied. The adsorption energy of GeC59, SiC59, and BC59 in the most stable complexes were calculated at -2.08, -1.26, and -0.71 eV, respectively. Although GeC59, SiC59, and BC59 all showed strong adsorption, only BC59 present a high sensitivity for detection. Further, the BC59 fullerene showing a proper short recovery time (about 1.11 × 10-6 s for methadone desorption). Water as a solution is used to simulate the behavior of fullerenes in the body fluids, and results indicated that the selected pure and complex nanostructures are stable in water. The UV-vis spectrums indicated that the after adsorption of methadone on the BC59 exhibits shift toward the lower wavelengths (blue shift). Therefore, our investigation indicated that the BC59 fullerene is an excellent candidate for methadone detection. METHODS: The interaction of methadone with pristine and doped C60 fullerenes surfaces was calculated using the density functional theory calculations. The GAMESS program and M06-2X method with a 6-31G(d) basis set were used for computations. Since the M06-2X method overestimates the LUMO-HOMO energy gaps (Eg) of carbon nanostructures, the HOMO and LUMO energies and Eg were investigated at the B3LYP/6-31G(d) level of theory using the optimization calculations. UV-vis spectra of excited species were obtained through the time-dependent density functional theory. To simulate the human biological fluid, the solvent phase was also evaluated in adsorption studies, and water was considered a liquid solvent.

Development of a ß-cyclodextrin-modified gold nanoparticle-assisted electromembrane extraction method followed by capillary electrophoresis for methadone determination in plasma.

In this study, gold nanoparticles (AuNPs) modified with ß-cyclodextrin (ß-CD) were used to assist with electromembrane extraction (EME) and were coupled with capillary electrophoresis (CE) and ultraviolet (UV) detection (CE-UV) for the extraction and measurement of methadone from plasma samples. A ß-CD-modified AuNP-reinforced hollow fiber (HF) was utilized in this work. The ß-CD-modified AuNPs act as an absorbent and provide an extra pathway for the analyte extraction. For obtaining the effect of the presence of ß-CD-modified AuNPs in the HF pores, the extraction efficiency of the EME and ß-CD-modified AuNPs/EME techniques were compared. Different parameters influencing the extraction efficacy of the EME and ß-CD-modified AuNPs/EME methods were optimized. Optimal extractions were performed with 1-octanol as the organic solvent in the supported liquid membrane (SLM), with an applied voltage of 10 V as the driving force across the SLM, and with pH 7.0 in the donor solutions with a stirring speed of 1000 rpm after 20 min and 25 min for the ß-CD-modified AuNPs/EME and EME methods, respectively. Under optimal conditions, compared with the EME method, the ß-CD-modified AuNPs/EME method exhibited increased extraction efficacy in a short time. The ß-CD-modified AuNPs/EME technique demonstrated a lower limit of detection (5.0 ng mL-1), higher extraction recovery (68%), and a more optimal preconcentration factor (135). Furthermore, this method was successfully utilized for measuring methadone in real plasma samples.

Electromembrane extraction of tramadol from exhaled breath condensate and its liquid chromatographic analysis.

Tramadol has extracted from the exhaled breath condensate (EBC) samples through the supported liquid membrane consisting of 2-nitrophenyl octyl ether impregnated in the hollow fiber wall, and the lumen of the hollow fiber was filled with 20 µL of an acceptor phase. Under the optimum conditions of the electromembrane extraction, i.e. the stirring speed of 750 rpm, extraction time of 20 min, acceptor pH at 1.0, donor phase pH at 6.0, and an applied voltage of 170 V across the supported liquid membrane, a preconcentration factor of 128-fold with a extraction recovery of 64% was achieved. Acceptable linearity was obtained in the tramadol concentration range of 5-1000 ng mL-1 (R2 = 0.9999) with a limit of detection of 1.5 ng mL-1 and a limit of quantitation of 5 ng mL-1. The relative standard deviations for the intra-day and inter-day replications were obtained between 0.4% and 2.5%. The validated technique was successfully used to determine tramadol in real EBC samples.

An overview on nanostructure-modified supported liquid membranes for the electromembrane extraction method.

Electromembrane extraction (EME) is an extraction method on the micro scale, in which charged compounds are extracted from a donor phase (sample solution) into an acceptor phase via a supported liquid membrane (SLM) containing a water-immiscible organic solvent. To enhance the extraction efficiency and selectivity in this method, some studies have focused on the modification of the SLM, and thus many strategies have been reported for this purpose. One of these techniques is the introduction of nanomaterials in the SLM structure, which can enhance the extraction efficiency. In the current study, the different nanostructures used for SLM modification in the EME method are reviewed. Furthermore, the related analytical parameters of the developed techniques are classified and tabulated. It is hoped that this review will motivate further research in this field using other nanostructures.

Investigations of adsorption behavior and anti-inflammatory activity of glycine functionalized Al12N12 and Al12ON11 fullerene-like cages.

The adsorption behavior of the amino acid, glycine (Gly), via the carboxyl, hydroxyl, and amino groups onto the surfaces of Al12N12 and Al16N16 fullerene-like cages were computationally evaluated by the combination of density functional theory (DFT) and molecular docking studies. It was found that Gly can chemically bond with the Al12N12 and Al16N16 fullerene-like cages as its amino group being more favorable to interact with the aluminum atoms of the adsorbents compared to carboxyl and hydroxyl groups. Oxygen and carbon doping were reported to reduce steric hindrance for Glycine interaction at Al site of Al12ON11/Gly and Al12CN11/Gly complexes. Interaction was further enhanced by oxygen doping due to its greater electron withdrawing effect. Herein, the Al12ON11/Gly complex where two carbonyl groups of Gly are bonded to the aluminum atoms of the Al12N12 fullerene-like cage is the most stable interaction configuration showing ∆adsH and ∆adsG values of -81.74 kcal/mol and -66.21 kcal/mol, respectively. Computational studies also revealed the frequency shifts that occurred due to the interaction process. Molecular docking analysis revealed that the Al12N12/Gly (-11.7 kcal/mol) and the Al12ON11/Gly (-9.2 kcal/mol) complexes have a good binding affinity with protein tumor necrosis factor alpha (TNF-α). TNF-α was implicated as a key cytokine in various diseases, and it has been a validated therapeutic target for the treatment of rheumatoid arthritis. These results suggest that the Al12N12/Gly complex in comparison with the Al16N16/Gly, Al12ON11/Gly, and the Al12CN11/Gly complexes could be efficient inhibitors of TNF-α.

Prediction of Activities of BRAF (V600E) Inhibitors by SW-MLR and GA-MLR Methods.

BACKGROUND: Quantitative structure-activity relationship (QSAR) models could provide both statistical significance and useful chemical insights for drug design. The QSAR method has found applications for predicting diverse properties of organic compounds, including antiviral activities, toxicities and biological activities. In this work, a quantitative structure-activity relationship was utilized for the prediction of allosteric BRAF (V600E) inhibitory activities. METHODS: A data set which contains 54 molecules was classified into training and test sets. Stepwise (SW) and genetic algorithm (GA) methods were employed for feature selection. The models were validated using the cross-validation and external test set. RESULTS: Results showed that the GA approach is a more powerful technique than SW for the selection of suitable descriptors. The squared cross-validated correlation coefficient for leave-one-out of 0.702 and squared correlation coefficient of 0.793 was obtained for the training set compounds by GA-MLR model. CONCLUSION: The obtained GA-MLR model could be applied as a worthwhile model for designing similar groups of the mentioned inhibitors.