Physicochemical Property Analysis
（物理化学性质分析学会）
改善物理性质最适化创药的制程
4月18-19日

由于对生物内的吸收和分布造成各种影响等，使化合物物理性质(物性)最适化已成为创药制程的基本要件。
调查化合物的物性，能获得与生物内化合物输送过程有关的信息，掌握物性，选出能因应所定课题的最适合化合物。
此外，在进入开发阶段前的设计阶段，藉由适当与正确分析化合物，亦能节省经费和时间。在创药制程，考虑化合物疎水性对已开发医药品溶解度造成的影响，化合物的投入量以及与利用输送机制的吸收之互相作用问题就变得很重要。
此外，利用预测模式也很重要，若未能好好考虑新的化合物实际具备之物理化学性质，则可能导致利用到与实际状态不同的数据进行分析。
本学会针对开发精选出效果高的化合物，早期分析该化合物物理化学的性质，正确理解生物学数据所需的要素来展开讨论。

现在募集演讲者

募集想出迄今没有过的方法者、拥有可公布的新数据者、想将个案研究和经验传达给其他研究人员者等。想在学会宣布最新的研究成果和专业知识的药学领域研究人员与专业技术人员，请踊跃应徵。应徵时，请告知联系方式。

由于能参加演讲人数有限，故以属于制药公司和生物科技关联企业、监督机关、研究中心等优先。此外，依据CHI的方针，虽然提供生物医药品研究人员的产品和服务之销售者及谘询公司有限，然亦基于各种企业赞助商计画准备了发表计画。

第1天 | 第2天

4月18日, 星期三

12:30 pm Registration

1:30 Chairperson’s Opening Remarks

THE PATH AHEAD

1:40 Getting Physical in Drug Discovery; Where Next for Property Profiling and Predictive Methods?

Robert J. Young, Ph.D., GlaxoSmithKline

A growing body of evidence indicates that much of the attrition in drug discovery can be attributed to the sub-optimal physical properties of experimental molecules, especially in pre-clinical activities. Molecules that are overly lipophilic and/or highly aromatic have been shown to posses greatly increased risk in studies to assess developability and promiscuity profiles; with impact over and above the inherent correlation between logP and #Ar rings. This led to the so-called property forecast indices, (PFI = log P or log DpH7.4 + #Ar), simplistic yet powerful predictors of risk; these are enhanced by the utilisation of improved lipophilicity measures and estimates based on chromatographic methods. Data illustrating these principles will be discussed, posing questions for future predictive methods. Successful molecules can be further discriminated when PFI is taken together with ligand efficiency measures, pointing to a useful indicator of likely success in drug discovery.

2:55 Getting Physical in Drug Discovery: A Suite of Physicochemical Methods to Enable Successful Drug Discovery

Alan P. Hill, Ph.D., Team Leder, PhysChem, Department of Analytical Chemistry, GlaxoSmithKline, Stevenage UK

Physicochemical measurements are key enablers for successful medicinal chemistry. However to maximise the likelihood of a success, it is essential that assays offered provide data that is appropriate to the stage of the project and additionally have sufficient capacity to meet demands. The presentation will provide an overview of a cohesive approach to physicochemical measurement (as applied within GSK) that effectively guides medicinal chemistry projects to produce robust candidate molecules with optimal physicochemical properties.

3:10 Modulating Physicochemical Properties to Improve ADME Outcomes

Jan Wahlstrom, Ph.D., Principal Scientist, Amgen

Alterations of physicochemical properties such as molecular weight, log d, polar surface area or pKa may influence the absorption, distribution, metabolism or excretion (ADME) of a chemical series in a chemotype-dependent manner. Application of mechanistic approaches to solving ADME issues provides insight as to why a change in properties causes a desired outcome in some cases, while leading to poorer outcomes in others. This seminar will focus on case studies outlining common ADME issues faced during the drug discovery process and the approaches used to resolve them.

3:40 Networking Refreshment Break in Exhibit Hall with Poster Viewing

4:20 Practical Concepts in Fragment-Based Drug Ddiscovery

Marcel Verdonk, Ph.D., Director, Computational Chemistry and Informatics, Astex Therapeutics, Inc.

INTERPRETING DATA

4:50 The Intricacies of Interpreting Pharmaceutical Data

Terry Stouch, Ph.D., President, R&D, Science for Solutions, LLC

Pharmaceutical research data that is extremely valuable within context could be worthless outside of that context or when lumped with other data. We will discuss these issues supported by many practical examples from several pharmaceutical companies, commercial and open source databases, and the literature. Diverse types of pharmaceutical data will be discussed including physicochemical properties such as solubility, logP, logD, Caco-2, and pKa. The vital meta data, its importance in the proper use of the data, the mistakes and problems that arise from ignoring it, and the reasons it is often ignored, will be outlined. Recommendations that will ameliorate these problems and enhance data use and value will be offered for better data capture and data basing and for holistic data presentation that will aid interpretation.

5:20 Interactive Breakout Discussions

In this interactive session, several topics will be offered for discussions and delegates are invited to choose a breakout topic of interest and join the moderated discussion at hand. In this informal setting, participants are encouraged to share examples from their work, vet ideas with peers and be part of a group problem-solving endeavor. We emphasize that this discussion is an informal exchange amongst scientists and is not meant to be, in any way, a product discussion.

Topic 1: Challenges of Thermodynamic Analysis

Moderator: Ernesto Freire, Ph.D., Henry Walters Professor, Johns Hopkins University

Topic 2: Hydrophobicity in Drug Discovery: Measurement or Calculation: Which Moves Drug Discovery Forward?

Moderator: Alan P. Hill, Ph.D., Team Leder, PhysChem, Department of Analytical Chemistry, GlaxoSmithKline, Stevenage UK

• When is hydrophobicity important?
• Is calculation good enough?
• logP or LogD?

6:30 End of Day