Ming-Chang Wu has completed his PhD at the age of 39years from National Taiwan University, Taiwan. He is the professor at the Department of Food Science, Dean at the College of Agriculture, National Pingtung University of Science and Technology. He run lots of technic assistance on food industry for several countries especial on Central American, Southeastern Asia, Africa. He has published more than 100 papers in reputed journals and has been serving as an editorial board member of repute.
Diabetes mellitus is currently the fastest growing chronic disease in the world, its serious complications have caused substantial amount of financial burden in the healthcare sector. Polygonum cuspidatum has been commonly used for medical treatment among the Asian population. The stem and root has been known to exhibit anti-cancer property and the ability to attenuate diabetes related complications. Nonetheless, the active compounds of P. cuspidatumare still yet to be identified; thus, the objective of this study is to apply the principles of network pharmacology to promptly identify the most promising candidates from P. cuspidatum as well as understanding their functions respectively. Network pharmacology is an approach to determine the process of disease development through understanding the system biology and bionetwork of the disease. Furthermore, by understanding the signal transduction pathways and how the compounds modulate the system, this will help to restore the balance in the affected biological processes, improve the efficacy of the compound and reduce its side effects. Using the Traditional Chinese Medicine Integrative Database, 46 compounds were identified in P. cuspidatum and bibliometrics was applied to measure the correlation between the compounds and diabetes. Among the 46 compounds, there were six compounds that showed clear correlation with diabetes, namely resveratrol, gallic acid, catechin, quercetin, rhein, and apigenin. These compounds will be evaluated in the later stage to fully understand the active compounds from P. cuspidatum that could potentially improve the complications of diabetes, and ultimately be used clinically on diabetic patients.
Dr. Sean Moore’s research is focused on the development of non-destructive testing methods in medical device and pharmaceuticals. A method using Infrared technology was developed as part of the EI funding with NUIG to non-destructively quantify laser bonded polymers and a method to quantify API and excipient components of drug eluting stent coating, using Raman spectroscopy was funded and developed in-house in Abbott Vascular by the PI in collaboration with an Analytical Chemist. Papers from this research have been published or are currently under peer review. Sean has over twenty seven years’ experience in Medical device, Aerospace, and Electronics industries and has held various roles in continuous improvement, operations, engineering, manufacturing, R&D, metallurgical evaluation, chemical & heat treat processing and included interaction with the regulated bodies.
Statement of the Problem: The manufacturing steps of pharmaceutical tablets can broadly be divided into (1) granulation, (2) drying, (3) blending, (4) compression and (5) coating. Currently, monitoring of the drying process, in order to determine its end-point, is inefficient and time-consuming. The implementation of near-infrared (NIR) spectroscopy allows real-time control of the process, leading to an improved production capacity and reduced waste generation and costs. The purpose of this study was to develop and validate a method using NIR for measuring isopropyl alcohol (IPA) in lactose with subsequent potential for in/on-line measurement of this property during fluidized bed drying. Methodology: Chemometric techniques were used to predict the amount of IPA in lactose from the NIR spectra. A partial least squares (PLS) model was constructed with the JMP Pro software using the normalized spectra of 48 calibration mixtures in the range of 1-12 wt.% of IPA in lactose over a spectral range of 1100 – 2500 nm. The model was validated using 11 unknown samples. The loss of IPA during drying of lactose at 30 °C was monitored off-line by NIR and gas chromatography as a reference method. Findings: The prediction capability of the PLS model was high with a correlation coefficient (R2) of 99.6 % and a root mean square error (RMSE) of 0.2 %. The predicted values obtained during drying showed R2 = 98.2 % and RMSE = 0.5 %. The volatility of IPA poses challenges when handling and analyzing samples. Conclusions and recommendations: The use of NIR for monitoring desired properties, such as solvents, during different process steps offers an improvement of the overall manufacturing efficiency. Application of NIR spectroscopy for in/on-line monitoring at a commercial scale is required to further improve this technique and involve other manufacturing steps and product properties.