Pradip K. Bhatnagar, Ph.D.
Former President & Head, Daiichi Sankyo Life Science Research Centre, India
Strategies for Diseases/Target Selection for Drug Discovery and a Multi-Targeted Approach to Metabolic Disorder
Drug discovery and development is a high risk and expensive undertaking. Although, technologies, such as, bioinformatics, genomics, high throughput screening and computer-aided design have helped identify targets, biomarkers, lead candidates and reduced the time required for advancing an idea from bench to clinic, but it still takes 10-12 years and costs approximately one billion dollars to bring a drug to market globally. Therefore, it is imperative that the strategies to reduce the risk and increase efficiency are carefully selected. In this presentation I would discuss strategies for selecting potential diseases, targets and provide an example of multi-targeted approach to metabolic disorder.
Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy
Realistic modeling: new insight into the functions of the cerebellar network
Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work. In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.
This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.
Correspondence should be addressed to
Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it
Acknowledgments
This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).
