Validated HPLC method for simultaneous quantification of mutant IDH1/2 inhibitors (enasidenib,ivosidenib and vorasidenib) in mouse plasma: Application to a pharmacokinetic study

Isocitrate dehydrogenase (IDH) inhibitors comprise a novel class of anticancer drugs, which are approved to treat acute myeloid leukemia patients having mutations on IDH1/2. We report the development and validation of a high‐performance liquid chromatography (HPLC) method for the simultaneous quantitation of IDH inhibitors, namely enasidenib (EDB), ivosidenib (IDB) and vorasidenib (VDB), in mouse plasma as per the US Food and Drug Administration regulatory guidelines. The method involves extraction of EDB, IDB and VDB along with internal standard (IS; phenacetin) from mouse plasma (100 μl) using a simple protein precipitation process. The chromatographic analysis was performed on an HPLC system using a gradient mobile phase (comprising 10 mm ammonium acetate and acetonitrile in a flow‐gradient) and an X‐Terra Phenyl column. The UV detection wave length was set at λ_max 265 nm. EDB, IDB, VDB and the IS eluted at 7.36, 8.60, 9.50 and 5.12 min, respectively, with a total run time of 10 min. The calibration curve was linear over a concentration range of 0.20–12.5 μg/ml for EDB and 0.50–12.5 μg/ml for IDB and VDB (r² = ≥0.998 for all of the analytes). Validation results met the acceptance criteria. The validated HPLC method was successfully applied to a pharmacokinetic study in mice.     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

QbD approached comparison of reaction mechanism in microwave synthesized gold nanoparticles and their superior catalytic role against hazardous nirto‐dye

Microwave irradiation (MI) process characteristically enables extremely rapid “in‐core” heating of dipoles and ions, in comparison to conventional thermal (conductance) process of heat transfer. During the process of nanoparticles synthesis, MI both modulates functionality behaviors as well as dynamic of reaction in favorable direction. So, MI providing a facile, favorable and alternative approach during nanoparticles synthesis nanoparticles with enhanced catalytic performances. Although, conventionally used reducing and capping reagents of synthetic origin, are usually environmentally hazardous and toxic for living organism. But, in absence of suitable capping agent; stability, shelf life and catalytic activity of metallic nanoparticles adversely affected. However, polymeric templates which emerged as suitable choice of agent for both reducing and capping purposes; bearing additional advantages in terms of catalyst free one step green synthesis process with high degree of biosafety and efficiency. Another aspect of current works was to understand role of process variables in growth mechanism and catalytic performances of microwave processed metallic nanoparticles, as well as comparison of these parameters with conventional heating method. However, due to poor prediction ability with previously published architect OFAT (One factor at a time) design with these nanoparticles as well as random selection of process variables with their different levels, such comparison couldn't be possible. Hence, using gum Ghatti (Anogeissus latifolia) as a model bio‐template and under simulated reaction conditions; architect of QbD design systems were integrated in microwave processed nanoparticles to establish mechanistic role these variables. Furthermore, in comparison to conventional heating; we reported well validated mathematical modeling of process variables on characteristic of nanoparticles as well as synthesized gold nanoparticles of desired and identical dimensions, in both thermal and microwave‐based processes. Interestingly, despite of identical dimension, MI processed gold nanoparticles bearing higher efficiency (kinetic rate) against remediation of hazardous nitro dye (4‐nitrophenol), into safer amino (4‐aminophenol) analogues.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Investigating Chain Dynamics in Highly Crosslinked Polymers using Solid‐State 1H NMR Spectroscopy

Solid state ¹H NMR line‐shape analysis and (double quantum) DQ ¹H NMR experiments have been used to investigate the segmental and polymer chain dynamics as a function of temperature for a series of thermosetting epoxy resins produced using different diamine curing agents. In these thermosets, chemical crosslinks introduce topological constraints leading to residual stresses during curing. Materials containing a unique ferrocene‐based diamine (FcDA) curing agent were evaluated to address the role of the ferrocene fluxional process on the atomic‐level polymer dynamics. At temperatures above the glass transition temperature (T_g), the DQ ¹H NMR experiments provided a measure of the relative effective crosslink and entanglement densities for these materials and revealed significant polymer chain dynamic heterogeneity in the FcDA‐cured thermosets. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1143–1156     

Synthesis of fused indolines by interrupted Fischer indolization in a microfluidic reactor

This study describes our development of a microfluidic reaction scheme for the synthesis of fused indoline ring systems found in several bioactive compounds. We have utilized a continuous-flow microfluidic reactor for the reaction of hydrazines with latent aldehydes through the interrupted Fischer indolization reaction to form fused indoline and azaindoline products. We have identified optimal conditions and evaluated the scope of this microfluidic reaction using various hydrazine and latent aldehyde surrogates. This green chemistry approach can be of general utility to rapidly produce indoline scaffolds and intermediates in a continuous manner.     

Medicinal Potential of Isoflavonoids: Polyphenols That May Cure Diabetes

In recent years, there is emerging evidence that isoflavonoids, either dietary or obtained from traditional medicinal plants, could play an important role as a supplementary drug in the management of type 2 diabetes mellitus (T2DM) due to their reported pronounced biological effects in relation to multiple metabolic factors associated with diabetes. Hence, in this regard, we have comprehensively reviewed the potential biological effects of isoflavonoids, particularly biochanin A, genistein, daidzein, glycitein, and formononetin on metabolic disorders and long-term complications induced by T2DM in order to understand whether they can be future candidates as a safe antidiabetic agent. Based on in-depth in vitro and in vivo studies evaluations, isoflavonoids have been found to activate gene expression through the stimulation of peroxisome proliferator-activated receptors (PPARs) (α, γ), modulate carbohydrate metabolism, regulate hyperglycemia, induce dyslipidemia, lessen insulin resistance, and modify adipocyte differentiation and tissue metabolism. Moreover, these natural compounds have also been found to attenuate oxidative stress through the oxidative signaling process and inflammatory mechanism. Hence, isoflavonoids have been envisioned to be able to prevent and slow down the progression of long-term diabetes complications including cardiovascular disease, nephropathy, neuropathy, and retinopathy. Further thoroughgoing investigations in human clinical studies are strongly recommended to obtain the optimum and specific dose and regimen required for supplementation with isoflavonoids and derivatives in diabetic patients.