21^st Annual European Pharma Congress May 20-22, 2019 Mövenpick Hotel Zürich Regensdorf, Switzerland

Supavadee Boontha is a Lecturer at School of Pharmaceutical Sciences at University of Phayao. She studied B. Pharm at the Naresuan University, and also received her Ph.D. in Pharmaceutical technology from Faculty of Pharmaceutical Sciences at the Naresuan University, working on vaccine delivery and delivery system of herbal medicine for cancer treatment.\r\n

This study were aimed to determine cytotoxic and anti-migration effects of Morinda citrifolia L. fruit extract on human breast cancer cells and to prepare microemulsions for the extract. The ethanolic Morinda citrifolia L. fruit extract showed cytotoxicity with IC50 values of 220.0 ±15.0 µg/ml and anti-migration effect on human MCF-7 cancer cells with significant effect at 100 µg/ml. Microemulsion (ME) systems were developed by titration method and further selected to incorporate the extract based on the suitable physicochemical characteristics of the ME prepared. The ME systems consisted of olive oil or isopropyl myristate as an oil phase, PEG40 hydrogenated castor oil or Tween 80 as a surfactant and Span 80 as a co-surfactant. The developed ME containing the extract were evaluated regarding their physical appearance, viscosity and pH before and after stability test. The stability study was carried out at room temperature and 45 oC for 60 days. After stability test at different temperatures, the ME containing the extract consisting of 56% w/w olive oil, 20% w/w tween 80, 20% w/w span 80, 0.7% w/w the extract, and 4% w/w water phase was shown to be the most attractive ME for treatment breast cancer via topical route.

Benjaporn Buranrat is a Asst. Prof. at the Faculty of Medicine at the Mahasarakham University. She studied Public Health Science at the Khon Kaen University, and received her PhD in Pharmacology from Faculty of Medicine at the Khon Kaen University, working on mevalonate pathway inhibitors and cancer. She did her postdoctoral work at Penn State University, USA, working on ferritin effects and MCF-7 cancer cells. She has authored or co-authored over 15 manuscripts. Her laboratory has worked in cancer cells death, cell apoptosis, mevalonate pathway.

In this study we examined how Oroxylum indicum fruit extracts can be affected the viability and migration of the human breast cancer cells and the mechanism of actions as well. The results, O. indicum extract strongly induced MCF-7 cell death in a dose- and time-dependent manner, with IC50 values of 131.3 + 19.2 ug/mL at 48 h. Moreover, O. indicum extracts also caused a dose-dependent decrease in colony forming ability with IC50 values of 51.46 + 6.43 ug/mL. Further, O. indicum extract caused reduction of cell viability and induction of MCF-7 cells apoptosis along with induced ROS formation and increased caspase 3 activity. Moreover, the extracts caused the inhibition migration accompanied with reduced MMP 9 protein expression and decreased MMP 9 and ICAMP1 gene expression. Furthermore, O. indicum extracts strongly decreased expression of the cell cycle regulatory protein Rac1. In conclusion, O. indicum extract inhibits breast cancer cells viability and reduces cell migration. It could also be valuable for augmenting the activity of chemotherapeutic drugs used to treat breast cancer.

Whitney Shatz received her M.S. in Biochemistry and Molecular Biology from the University of California in Santa Barbara, characterizing bacterial enzymes involved in the epigenetic process of DNA methylation. Since 2007, she has worked within the research organization at Genentech, supporting production and characterization of large molecule biologics. During her 11-year tenure, she has made significant contributions to the investigation of structure activity/relationship in antibody-dependent cell cytotoxicity (ADCC), as well as to the advancement of novel bispecific antibodies in a variety of disease areas. More recently, her focus has shifted to the development and characterization of protein-polymer bioconjugates for long-acting drug delivery. In addition, since 2016 she has been concurrently pursuing a doctorate in Pharmaceutical Sciences at the University of Geneva.

There is a growing trend in the biotherapeutics field to develop molecules with a high degree of multivalency. This can be useful for receptor clustering, T-cell recruiting, agonist activation, and half-life extension. However, many of the currently available “molecular scaffolds” are polymer-based and raise obvious concerns with respect to biocompatibility and the accumulation of by-products. In contrast, protein-based scaffolds offer an attractive, “natural” alternative for modifying therapeutic agent properties and functionality. Ferritin is a ubiquitous protein found in most cell types of humans in addition to invertebrates, higher plants, fungi and bacteria; its primary function is to store iron (1). In mammals, ferritins are composed of 24 subunits that form an icosahedron with an external diameter of ~12 nm and an overall MW of ~474 kDa (2). Ferritin and its derivatives have already demonstrated their utility as “molecular cages” for applications in drug delivery (3,4). In addition, ferritin can be easily coupled covalently to biomolecules through the presence of multiple surface-exposed lysine residues (5). Site directed mutagenesis and the introduction of new amino acid residues create even more opportunities to introduce new functionality (6). Here, we present preliminary results describing the development of antibody fragment (Fab)-ferritin conjugates. In this first step, a strategy was developed for the covalent attachment of multiple Fab units to the ferritin cage, yielding a conjugate with 24 Fabs per ferritin cage (i.e. one Fab per subunit). Following optimization of the conjugation strategy, an in-depth characterization of the conjugates was performed using multiple techniques – including DLS, SEC-MALS, LC/MS, viscosity measurements, and target activity. The results confirmed that Fab-ferritin conjugation was successfully achieved. In addition to the possible modification of Fab elimination kinetics in vivo and the potential for more prolonged therapeutic effect, the conjugates may offer other attributes well-suited for drug delivery applications that require multivalency.

Gerald C Hsu has received his PhD in Mathematics and majored in Engineering at MIT. He has attended different universities over 17 years and studied seven academic disciplines. He has spent a huge time research in T2D research. His approach is “Math-Physics and Quantitative Medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning and AI. His research focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.

Introduction: This paper describes the math-physical medicine approach (MPM) of medical research utilizing mathematics, physics, engineering models, and computer science, instead of the current biochemical medicine approach (BCM) that mainly utilizes biology and chemistry. \r\nMethodology of MPM on Diabetes Research: Initially, the author spent four years of self-studying six chronic diseases and food nutrition to gain in-depth medical domain knowledge. During 2014, he defined metabolism as a nonlinear, dynamic, and organic mathematical system having 10 categories with ~500 elements. He then applied topology concept with partial differential equation and nonlinear algebra to construct a metabolism equation. He further defined and calculated two variables, metabolism index and general health status unit. During the past 8.5 years, he has collected and processed 1.5 million data. Since 2015, he developed prediction models, i.e. equations, for both postprandial plasma glucose (PPG) and fasting plasma glucose (FPG). He identified 19 influential factors for PPG and five both wave and energy theories, he extended his research into the risk probability of heart attack or stroke. In this risk assessment, he applied structural mechanics concepts, including elasticity, dynamic plastic, and fracture mechanics, to simulate artery rupture and applied fluid dynamics concepts to simulate artery blockage. He further decomposed 1,200 glucose waveforms with 21,000 data and then re-integrated them into 3 distinctive PPG waveform types which revealed different personality traits and psychological behaviors of type 2 diabetes patients between two variables, he used spatial analysis. Furthermore, he also applied Fourier Transform to conduct frequency domain analyses to discover some hidden characteristics of glucose waves. He then developed an AI Glucometer tool for patients to predict their weight, FPG, PPG, and A1C. It uses various computer science tools, including big data analytics, machine learning (self-learning, correction, and simplification), and artificial intelligence to achieve very high accuracy (95% to 99%) mg/dL and A1C is 6.5%. Since his health condition is stable, he no longer suffers from repetitive cardiovascular episodes.