Discovery,Development, and Patent Trends on Molnupiravir: A Prospective Oral Treatment for COVID-19

The COVID-19 pandemic needs no introduction at present. Only a few treatments are available for this disease, including remdesivir and favipiravir. Accordingly, the pharmaceutical industry is striving to develop new treatments for COVID-19. Molnupiravir, an orally active RdRp inhibitor, is in a phase 3 clinical trial against COVID-19. The objective of this review article is to enlighten the researchers working on COVID-19 about the discovery, recent developments, and patents related to molnupiravir. Molnupiravir was originally developed for the treatment of influenza at Emory University, USA. However, this drug has also demonstrated activity against a variety of viruses, including SARS-CoV-2. Now it is being jointly developed by Emory University, Ridgeback Biotherapeutics, and Merck to treat COVID-19. The published clinical data indicate a good safety profile, tolerability, and oral bioavailability of molnupiravir in humans. The patient-compliant oral dosage form of molnupiravir may hit the market in the first or second quarter of 2022. The patent data of molnupiravir revealed its granted compound patent and process-related patent applications. We also anticipate patent filing related to oral dosage forms, inhalers, and a combination of molnupiravir with marketed drugs like remdesivir, favipiravir, and baricitinib. The current pandemic demands a patient compliant, safe, tolerable, and orally effective COVID-19 treatment. The authors believe that molnupiravir meets these requirements and is a breakthrough COVID-19 treatment.     

CoFe-LDH nanowire arrays on graphite felt: A high-performance oxygen evolution electrocatalyst in alkaline media

Developing non-noble-metal oxygen evolution reaction(OER) electrocatalysts with high performance is critical to electrocatalytic water splitting. In this work, we fabricated Co Fe-layered double hydroxide(LDH) nanowire arrays on graphite felt(Co Fe-LDH/GF) via a hydrothermal method. The Co Fe-LDH/GF, as a robust integrated 3 D OER anode, exhibits excellent catalytic activity with the need of low overpotential of 252 and 285 m V to drive current densities of 10 and 100 m A/cm2 in 1.0 mol/L KOH, r...     

Cyclodysidins A–D,cyclic lipopeptides from the marine sponge-derived Streptomyces strain RV15

Four new cyclic lipopeptides, cyclo-(AFA-Ser-Gln-Asn-Tyr-Asn-Ser-Thr), named cyclodysidins A–D, were isolated from the broth culture of Streptomyces strain RV15 associated with the marine sponge Dysidea tupha. The sequences of the amino acid building blocks in the compounds and their structures were determined by 1D- and 2D-NMR techniques and CID-MS/MS experiments. The absolute configurations of all α-amino acids were determined by HPLC analysis after derivatization with Marfey’s reagent and comparison with commercially available reference samples, while those two of the β-amino fatty acids were determined by using racemic and enantiopure reference samples synthetically prepared.     

Development,In-Vitro Characterization and Preclinical Evaluation of Esomeprazole-Encapsulated Proniosomal Formulation for the Enhancement of Anti-Ulcer Activity

Objective: The present study aimed to develop and optimize esomeprazole loaded proniosomes (EZL-PNs) to improve bioavailability and therapeutic efficacy. Method: EZL-PNs formulation was developed by slurry method and optimized by 33 box-Bhekhen statistical design software. Span 60 (surfactant), cholesterol, EZL concentration were taken as independent variables and their effects were evaluated on vesicle size (nm), entrapment efficiency (%, EE) and drug release (%, DR). Furthermore, optimized EZL-PNs (EZL-PNs-opt) formulation was evaluated for ex vivo permeation, pharmacokinetic and ulcer protection activity. Result: The EZL-PNs-opt formulation showed 616 ± 13.21 nm of vesicle size, and 81.21 ± 2.35% of EE. EZL-PNs-opt exhibited negative zeta potential and spherical confirmed scanning electron microscopy. EZL-PNs-opt showed sustained release of EZL (95.07 ± 2.10% in 12 h) than pure EZL dispersion. The ex-vivo gut permeation result exhibited a significantly (p < 0.05) enhanced flux than pure EZL. The in vivo results revealed 4.02-fold enhancement in bioavailability and 61.65% protection in ulcer than pure EZL dispersion (43.82%). Conclusion: Our findings revealed that EZL-PNs formulation could be an alternative delivery system of EZL to enhance oral bioavailability and antiulcer activity.     

Simultaneous Estimation of Escitalopram and Clonazepam in Tablet Dosage Forms Using HPLC-DAD Method and Optimization of Chromatographic Conditions by Box-Behnken Design

The study aimed to develop a new reverse-phase high-performance liquid chromatography (RP-HPLC) method with diode array detection (DAD) detection for simultaneous estimation of escitalopram (EST) and clonazepam (CZP) in tablet dosage forms with a quality by design (QbD) approach. The chromatographic conditions were optimized by Box-Behnken design (BBD) and developed method was validated for the linearity, system suitability, accuracy, precision, robustness, sensitivity, and solution stability according to International Council for Harmonization (ICH) guidelines. EST and CZP standard drugs peaks were separated at retention times of 2.668 and 5.046 min by C-18 column with dimension of 4.6 × 100 mm length and particle size packing 2.5 µm. The mobile phase was methanol: 0.1% orthophosphoric acid (OPA) (25:75, v/v), with a flow rate of 0.7 mL/min at temperature of 26 °C. The sample volume injected was 20 µL and peaks were detected at 239 nm. Using the standard calibration curve, the % assay of marketed tablet was founded 98.89 and 98.76 for EST and CZP, respectively. The proposed RP-HPLC method was able to detect EST and CZP in the presence of their degradation products, indicating the stability-indicating property of the developed RP-HPLC method. The validation parameter’s results in terms of linearity, system suitability, accuracy, precision, robustness, sensitivity, and solution stability were in an acceptable range as per the ICH guidelines. The newly developed RP-HPLC method with QbD application is simple, accurate, time-saving, and economic.     

Development and Optimization of Ciprofloxacin HCl-Loaded Chitosan Nanoparticles Using Box–Behnken Experimental Design

Various chitosan (CS)-based nanoparticles (CS-NPs) of ciprofloxacin hydrochloride (CHCl) have been investigated for therapeutic delivery and to enhance antimicrobial efficacy. However, the Box–Behnken design (BBD)-supported statistical optimization of NPs of CHCl has not been performed in the literature. As a result, the goal of this study was to look into the key interactions and quadratic impacts of formulation variables on the performance of CHCl-CS-NPs in a systematic way. To optimize CHCl-loaded CS-NPs generated by the ionic gelation process, the response surface methodology (RSM) was used. The BBD was used with three factors on three levels and three replicas at the central point. Tripolyphosphate, CS concentrations, and ultrasonication energy were chosen as independent variables after preliminary screening. Particle size (PS), polydispersity index (PDI), zeta potential (ZP), encapsulation efficiency (EE), and in vitro release were the dependent factors (responses). Prepared NPs were found in the PS range of 198–304 nm with a ZP of 27–42 mV. EE and drug release were in the range of 23–45% and 36–61%, respectively. All of the responses were optimized at the same time using a desirability function based on Design Expert^® modeling and a desirability factor of 95%. The minimum inhibitory concentration (MIC) of the improved formula against two bacterial strains, Pseudomonas aeruginosa and Staphylococcus aureus, was determined. The MIC of the optimized NPs was found to be decreased 4-fold compared with pure CHCl. The predicted and observed values for the optimized formulation were nearly identical. The BBD aided in a better understanding of the intrinsic relationship between formulation variables and responses, as well as the optimization of CHCl-loaded CS-NPs in a time- and labor-efficient manner.     

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

Using artificial neural networks to predict the rheological behavior of non-Newtonian graphene–ethylene glycol nanofluid

Journal of Thermal Analysis and Calorimetry - The ability of the artificial neural network (ANN) to predict the viscosity of graphene nanosheet/ethylene glycol ( $$\mu_{{\text{Gr/EG}}}$$ ) was...     

Electrical properties and thermal conductivity of AgTlTe2 in the solid and liquid phases

Measurements of the electrical conductivity, thermelectric power and thermal conductivity of an AgTlTe₂ semiconductor in the solid and liquid states were carried out in a wide range of temperatures. In the liquid state the data analyzed in terms of a model developed for the density of states and electrical transport in solid amorphous semiconductors. Positive thermoelectric power suggests a large predominance of holes in electrical transport.     

Periodically Arranged Arrays of Dendritic Pt Nanospheres Using Cage‐Type Mesoporous Silica as a Hard Template

Dendritic Pt nanospheres of 20 nm diameter are synthesized by using a highly concentrated surfactant assembly within the large‐sized cage‐type mesopores of mesoporous silica (LP‐FDU‐12). After diluting the surfactant solution with ethanol, the lower viscosity leads to an improved penetration inside the mesopores. After Pt deposition followed by template removal, the arrangement of the Pt nanospheres is a replication from that of the mesopores in the original LP‐FDU‐12 template. Although it is well known that ordered LLCs can form on flat substrates, the confined space inside the mesopores hinders surfactant self‐organization. Therefore, the Pt nanospheres possess a dendritic porous structure over the entire area. The distortion observed in some nanospheres is attributed to the close proximity existing between neighboring cage‐type mesopores. This new type of nanoporous metal with a hierarchical architecture holds potential to enhance substance diffusivity/accessibility for further improvement of catalytic activity.