Physicochemical Property Analysis
（物理化學性質分析學會）
改善物理性質最適化創藥的製程
4月18-19日

由於對生物內的吸收和分佈造成各種影響等，使化合物物理性質(物性)最適化已成為創藥製程的基本要件。
調查化合物的物性，能獲得與生物內化合物輸送過程有關的資訊，掌握物性，選出能因應所定課題的最適合化合物。
此外，在進入開發階段前的設計階段，藉由適當與正確分析化合物，亦能節省經費和時間。在創藥製程，考慮化合物疎水性對已開發醫藥品溶解度造成的影響，化合物的投入量以及與利用輸送機制的吸收之互相作用問題就變得很重要。
此外，利用預測模式也很重要，若未能好好考慮新的化合物實際具備之物理化學性質，則可能導致利用到與實際狀態不同的數據進行分析。
本學會針對開發精選出效果高的化合物，早期分析該化合物物理化學的性質，正確理解生物學數據所需的要素來展開討論。

現在募集演講者

募集想出迄今沒有過的方法者、擁有可公佈的新數據者、想將個案研究和經驗傳達給其他研究人員者等。想在學會宣佈最新的研究成果和專業知識的藥學領域研究人員與專業技術人員，請踴躍應徵。應徵時，請告知聯繫方式。

由於能參加演講人數有限，故以屬於製藥公司和生物科技關聯企業、監督機關、研究中心等優先。此外，依據CHI的方針，雖然提供生物醫藥品研究人員的產品和服務之銷售者及諮詢公司有限，然亦基於各種企業贊助商計畫準備了發表計畫。

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