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Symposium on Streamlining Drug Discovery
上传时间:2018-03-13 13:51:47
主办单位: Optibrium, Lhasa Limited,BioSolveIT
时  间: 2018/05/31
地  点: 上海市北京西路968号英国领事馆
收费标准: 免费
规模人数: 50
主 讲 人: Edmund Champness,Scott McDonald,Marcus Gastreich

                    

Overview - Shanghai                                                      
 
Join us for this free to attend one-day symposium highlighting new approaches and technologies being applied to the search for future therapeutics.
 
The day is aimed at scientists from biotechnology, pharmaceutical organizations, not-for-profits and academia who wish to get a broad appreciation of the latest advances in drug discovery delivered by key scientists and thought leaders from leading organizations.
 
We will be announcing a number of high-profile speakers who have already confirmed that they will present at this event.
 
Speakers
 
We are delighted to announce that the following speakers will present at this meeting, more will be annonced very soon:
 
Edmund Champness
Optibrium Ltd
 
    After graduating with a degree in Mathematics in 1995 Ed joinedGlaxoWellcome working as part of a pioneering team building predictive pharmaceutical tools. He developed the first graphical user-interfaces for working with predictive models which were adopted globally withinGlaxo Wellcome. He was a core member of the team which established the UK operation of Camitro in 2001 and remained with that company (now operating within BioFocus DPI following merger and acquisition) until 2008.During this time he designed and built the StarDrop software and, in 2009,co-founded Optibrium.
 
Scott McDonald 
Lhasa Limited
 
     After graduating with a degree in Chemistry in 1999 Scott joined Croda International as a Research Chemist developing novel slip additives for plastics before moving into the promotion of their speciality chemicals within the cosmetic and pharmaceutical markets. In 2003 he joined the not-for-profit organisation, Lhasa Limited, and as the Director of Member Services leads the promotion of their innovative in silico prediction and database systems which support the pharmaceutical industry in bringing safe, life enhancing medicines to market.
 
Marcus Gastreich
BioSolveIT GmbH
 
    Marcus Gastreich is BioSolveIT's Director Application Science acting as a strategic interface between pharma clients and IT development. BioSolveIT is a globally acting provider of drug modeling software and services. He holds a diploma degree in chemistry from the University of Bonn, Germany and did his doctorate in Theoretical Chemistry undersupervision of Prof.Christel M. Marian on ab initio NMR simulation of solids and force field development for amorphous materials, with a minor in Bioinformatics.In the late 90’s, he went to London for a research stay with Julian Gale at Imperial College. In 1999, shortly before BioSolveIT had been founded as a spin-off from Fraunhofer Gesellschaft (FhG) in 2001, he joined Prof.Thomas Lengauer's chem- and bioinformatics group in St. Augustin,Germany, where BioSolveIT's popular FlexX molecular docking programhad initially been developed.
 
Abstracts
 
 
Edmund Champness 
Optibrium Ltd
 
      Compound design requires a combination of knowledge and expertise from different perspectives: understanding of structure-activity relationships   (SAR), based on data from previously studied compounds; expertise from diverse fields to define the multi-parameter optimisation (MPO) objectives of a project; and knowledge of synthetic strategies that may be applicable to create the next rounds of compounds for investigation. All of these forms of knowledge can be captured and applied computationally: Machine learning methods can generate quantitative structure-activity relationship (QSAR) models to predict the properties of novel, virtual compounds; MPO methods capture the desired property criteria for a successful compound for a specific project and rigorously prioritise ideas for consideration; and, optimisation strategies can be captured as structural transformations that reflect steps made in previous chemistry projects.
 
     Inthis presentation, we will describe these methods and illustrate how they can be seamlessly combined to rigorously explore new, relevant compound ideas and prioritise those most likely to achieve a project objective. This approach can help to stimulate the search for new optimisation strategies and explore a much broader range of compounds than could be achieved based on a single chemist’s or even a project team’s experience. Example applications include the optimisation of compounds with a desired polypharmacology or selectivity profile and exploration of  lead hopping strategies to overcome pharmacokinetic issues, while maintaining target potency.
 
Scott McDonald 
Lhasa Limited
 
    Expert assessment is a fundamental part of the assessment of the mutagenic potential of impurities under the ICH M7 guideline and it makes a specific provision for the application of expert knowledge to support or overturn an in silico prediction. Whilst expert assessment has been successfully applied in this context, there remains some uncertainty as to what constitutes expert analysis.
 
   This presentation will outline a framework to more clearly understand the process of expert assessment under ICH M7 and the role that both in silico prediction methodologies and supporting data play. The use of such a framework can assist in dealing with relatively straightforward situations where the two required in silico methodologies agree to more complicated cases of model disagreement or “indeterminate” or “out-of-domain” predictions.
 
 
Programme 
 
This free to attend symposium will run from approximately 09:00 to 17:00 on Thursday, 31 May 2018. Presentations will last approximately 30 minutes each.
 
Lunch will be provided along with refreshments during the breaks. An informal drinks reception will also be held at the end of the presentations to provide an opportunity for you to network
 
 
Location
 
This free to attend symposium will be held at The British Centre, Brtish
 
Consulate General, Shanghai on Thursday, 31 May 2018.