Quantification of endocannabinoids in human cerebrospinal fluid using a novel micro-flowExploration of the Endocannabinoid System Using Metabolomics • February 3rd, 2023
Bingshu He*, Xinyu Di*, Faisa Guled, Aster V.E. Harder, Arn M.J.M. van den Maagdenberg, Gisela M. Terwindt, Elke H.J. Krekels, Isabelle Kohler, Amy Harms, Rawi Ramautar, Thomas Hankemeier
Licence agreement concerning inclusion of doctoralExploration of the Endocannabinoid System Using Metabolomics • February 3rd, 2023
Drug discovery and the successful development of drugs towards the market have become increasingly challenging. During the clinical phase, the overall likelihood of approval for new drugs has dropped to only 7.9% between 2011 and 2020, compared with 9.6% during 2006-20151. The main attrition occurs in the phase II transition (during which a proof-of- concept is established) with a phase success rate of only 28.9%1. Compared with traditional small molecular new molecular entities (NMEs)1,2, biologics-based NMEs currently show higher success rates. The reason behind this low success rate is multidimensional. Notably, a better-established relationship between drug mechanism and disease dynamics in the discovery and early development phase increases the probability of clinical proof-of- concept1,3,4. Therefore, improving the mechanistic understanding of disease dynamics and drug action is necessary to enhance the success rate during drug discovery, for instance using systems biology-based
A platform for the analysis of metabolites in endocannabinoids - related pathways.Exploration of the Endocannabinoid System Using Metabolomics • February 3rd, 2023
Xinyu Di, Wouter P.F. Driever, Corné van der Plas, Zhengzheng Zhang, Timo Wendel, Tom van der Wel, Amy Harms, Elke H.J. Krekels, Mario van der Stelt, Isabelle Kohler, Thomas Hankemeier
Licence agreement concerning inclusion of doctoralExploration of the Endocannabinoid System Using Metabolomics • February 3rd, 2023
This section presents conclusions and suggestions for future improvements related to the content presented in each chapter. Furthermore, a more general perspective for optimizing and applying metabolomics-based workflows in drug development and clinical studies is discussed.
