Quantification and Standardization of Andrographolide in Andrographis Paniculata Samples by Validated RP-HPLC and HPTLC Methods.

Andrographis paniculata (family Acanthaceae) is known as Kalmegh, one of the traditionally used important medicinal plant contains several biologically active phytochemical including andrographolide. A. paniculata is broadly used by healthcare practitioners in India and also used in different traditional medicinal system. In this study, the leaves of A. paniculata were collected from West Medinipur, East Medinipur, South 24 Parganas, Purulia and Hooghly district of West Bengal, India. This study aiming towards validation and development of a simple, precise and reproducible reverse-phase high-performance liquid chromatography (RP-HPLC) and high-performance thin layer chromatography (HPTLC) methods for quantification of andrographolide in A. paniculata extracts. The validated RP-HPLC and HPTLC study confirmed that different concentrations of andrographolide content present in the plant samples, which are collected from above different districts of West Bengal, India. The amounts of andrographolide were found to be 2.71% (w/w), 3.19% (w/w), 1.83% (w/w), 1.73% (w/w) and 2.94% (w/w) in RP-HPLC study and 2.13% (w/w), 2.51% (w/w), 1.01% (w/w), 1.25% (w/w) and 2.15% (w/w) in HPTLC study. This precise, reproducible, accurate and specific method can be used for the quantification of andrographolide in kalmegh, as per the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines recommendations.

Rapid estimation of piperine in black pepper: Exploration of Raman spectroscopy.

INTRODUCTION: The major chemical marker of black pepper (Piper nigrum L) is piperine and its estimation is extremely important for quality assessment of black pepper. The methods for piperine quantification, to date, are laboratory based and use high end instruments like chromatographs, which require tedious sample processing and cause sample destruction. OBJECTIVES: In this article, we present a simple, rapid and green analytical method based on Raman spectroscopy for the quantitative assessment of piperine. MATERIAL AND METHODS: To assess the potential of the technique, we report the complete vibrational characterisation of the piperine with density functional theory (DFT) calculations. RESULTS: The theoretical peaks were obtained at 1097 cm-1 , 1388 cm-1 , 1528 cm-1 , 1578 cm-1 , and at 1627 cm-1 , and this result was verified in a Raman spectrometer followed by a preliminary experiment. Twenty black pepper samples were analysed using high-performance liquid chromatography (HPLC) and used as reference data for Raman analysis. The Raman shift spectra were analysed using partial least squares (PLS) and good prediction accuracy with correlation coefficient of prediction (Rp2 ) = 0.93, root mean square error of prediction (RMSEP) = 0.13 and residual prediction deviation (RPD) = 3.9 obtained. CONCLUSIONS: The results demonstrate the efficacy of the Raman technique for the estimation of piperine in the dry fruit of Piper nigrum.

Enhanced permeability and photoprotective potential of optimized p-coumaric acid-phospholipid complex loaded gel against UVA mediated oxidative stress.

Photo-oxidative skin damage is mainly caused by the UV-A radiation of the sun. Synthetic sunscreens used to counter this acts mostly on the superficial skin layer and possess serious side effects. P-coumaric acid (PCA) is a UV-A protective plant phenolic having quick diffusion and distribution in superficial skin layers limiting its application as herbal sunscreen. The present study was designed to formulate an optimized phospholipid complex of PCA (PCAPC) through response surface methodology to enhance its skin permeation to deeper skin layers providing protection against photo-oxidative stress. PCAPC was characterized by FT-IR, DTA, PXRD, TEM, zeta potential etc. PCAPC was then incorporated into a gel formulation (PCAPC-GE) to facilitate its transdermal delivery. Physicochemical properties of the gel were assessed by pH, homogeneity, rheology, spreadability etc. In-vitro SPF and UVA-PF of the gel was evaluated and compared with conventional gel (PCA-GE). Ex-vivo skin permeation flux, permeability coefficient, skin deposition and dermatokinetic analysis were carried out to measure the rate and level of skin permeation. This was accompanied by in-vivo evaluation of PCAPC-GE and PCA-GE in the experimental rat model by measuring the various oxidative stress markers such as superoxide dismutase, catalase etc. PCAPC-GE provided high SPF and UVA-PF value compared to PCA-GE. The physicochemical parameters were suitable for transdermal application. PCAPC-GE enhanced the permeation rate of PCA by almost 6 fold compared to PCA-GE. Besides, a significant reduction of UV-A induced oxidative stress biomarkers were observed for PCAPC-GE. Thus, the PCAPC-GE may be an effective alternative of synthetic sunscreens due to its enhanced permeation and protection against UVA-induced oxidative stress.

Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine.

Andrographis paniculata (Burm. F) Nees, has been widely used for upper respiratory tract and several other diseases and general immunity for a historically long time in countries like India, China, Thailand, Japan, and Malaysia. The vegetative productivity and quality with respect to pharmaceutical properties of Andrographis paniculata varies considerably across production, ecologies, and genotypes. Thus, a field deployable instrument, which can quickly assess the quality of the plant material with minimal processing, would be of great use to the medicinal plant industry by reducing waste, and quality grading and assurance. In this paper, the potential of near infrared reflectance spectroscopy (NIR) was to estimate the major group active molecules, the andrographolides in Andrographis paniculata, from dried leaf samples and leaf methanol extracts and grade the plant samples from different sources. The calibration model was developed first on the NIR spectra obtained from the methanol extracts of the samples as a proof of concept and then the raw ground samples were estimated for gradation. To grade the samples into three classes: good, medium and poor, a model based on a machine learning algorithm - support vector machine (SVM) on NIR spectra was built. The tenfold classification results of the model had an accuracy of 83% using standard normal variate (SNV) preprocessing.