ArQule UK Ltd.
Cambridge, UK
Presentations
- Alan Beresford (ArQule, UK)
In Silico Lead Optimisation: Will It Ever Fly? [Slides] - Roger Crossley (BioFocus, UK)
GPCR receptors : Variations on a theme [Slides] [Abstract] - Kei Enomoto (University College London, UK)
Application of Linear Solvation Energy Relationships to the Prediction of Physico-Chemical Properties of Agrochemicals [Slides] - Anne Hersey (GlaxoSmithKline, UK)
ADME in-silico models – Tools for Drug Discovery [Slides] [Abstract] - Ken Korzekwa (ArQule, UK)
Modeling Cytochrome P450 Oxidations: Affinity, Regioselectivity and Rates - Peter Murray-Rust (, UK)
Molecular properties and the Semantic Web - Brian Sweatman (GlaxoSmithKline, UK)
Biofluid NMR investigations utilising pattern recognition techniques - Mike Tarbit (Managing Director ArQule UK Ltd, UK)
Opening remarks - Han van de Waterbeemd (Pfizer Global Research & Development, UK)
ADME/tox tools: Wishlist for the next generation [Slides] [Abstract]
Abstracts
Presentation: Alan Beresford
In Silico Lead Optimisation: Will It Ever Fly?
ArQule, UK [Slides]
HASH(0x35d8bb0)
Presentation: Roger Crossley
GPCR receptors : Variations on a theme
BioFocus, UK [Slides]
G-protein coupled receptors (GPCRs) represent the single most important class of receptors for drug discovery with an estimated 40-50% of the drug market targeting them. This situation seems likely to continue for some time and there are almost as many GPCRs with unknown ligands (orphan receptors) as there are with established ligands.
Although we know a great deal about GPCRs in terms of their sequences and a vast amount of information is available on their SAR, knowledge about their structure and function is surprisingly still rudimentary. For example, only one crystal structure of a GPCR , that of rhodopsin at 2.8A, is currently available and, although rhodopsin gives its name to Family A GPCRs, it is far from being a typical member. We sought to develop a method that would avoid the use of rhodopsin as a drug discovery tool and which would enable us to accelerate the hit to lead and lead optimisation processes and which we could use in focused library design. We concentrated on linking the SAR of GPCRs with their sequence information and have produced the technique of Thematic Analysis.
Thematic Analysis directly links the SAR of GPCRs with their sequence. Its use has been validated both in lead optimisation mode and in the synthesis of libraries focused on subsets of GPCRs. It has been used with receptors across the different subclasses of Family A and has been used with both agonists and antagonists. As it does not require knowledge of the natural ligand it is applicable to both agonists and antagonists. Most recently it has been used to reclassify GPCRs according to their ability to bind to small drug-like molecules in a way that is intuitively correct.
Presentation: Kei Enomoto
Application of Linear Solvation Energy Relationships to the Prediction of Physico-Chemical Properties of Agrochemicals
University College London, UK [Slides]
HASH(0x35d921c)
Presentation: Anne Hersey
ADME in-silico models – Tools for Drug Discovery
GlaxoSmithKline, UK [Slides]
An ideal medicine is a balance of pharmacokinetics and safety as well as potency and selectivity. It is therefore important to understand the relationship between ADME properties and molecular structure. In-silico models are useful tools for assessing large numbers of compounds for these “drug-like” properties.
These models can do more than “screen” compounds and identify molecules with “poor” properties. By using molecular descriptors that can be easily interpreted in terms of chemical structure, it is possible to indicate changes in molecular features that will improve the property profiles of compounds. Hence these models can aid the drug discovery process in areas such as library design and selecting compounds for in-vitro and in-vivo screening. This will be exemplified with examples from discovery projects.
Presentation: Ken Korzekwa
Modeling Cytochrome P450 Oxidations: Affinity, Regioselectivity and Rates
ArQule, UK
HASH(0x35d8628)
Presentation: Peter Murray-Rust
Molecular properties and the Semantic Web
, UK
HASH(0x35dbb44)
Presentation: Brian Sweatman
Biofluid NMR investigations utilising pattern recognition techniques
GlaxoSmithKline, UK
HASH(0x35db598)
Presentation: Mike Tarbit
Opening remarks
Managing Director ArQule UK Ltd, UK
HASH(0x35d855c)
Presentation: Han van de Waterbeemd
ADME/tox tools: Wishlist for the next generation
Pfizer Global Research & Development, UK [Slides]
The new drug discovery paradigm based on high-throughput screening of combinatorial libraries produces more hits and leads to evaluate for their ADME/tox properties. Thanks to automation, robotics and rapid progress in analytics, traditional in vivo approaches to ADMET studies now evolved to medium and high-throughput in vitro assays. The in vitro ADMET data have been used to develop a first generation of ADMET predictive in silico tools, which can be used in early stages of the discovery process including library design and file enrichment. Such models tend to be probabilistic in nature. More robust and mechanistic models based on larger experimental data sets are expected in the near future. Furthermore models for new areas of drug metabolism science, such as the role of transporters, will become available. A neat integration in the drug discovery process will be a further challenge.
References
David E. Clark and Peter D.J. Grootenhuis, Progress in computational methods for the prediction of ADMET properties, Curr.Opin.Drug Disc.Dev. 5 (2002) 382-390.
F. Darvas and G. Dorman (Eds.), High-throughput ADMETox Estimation: In Vitro & In Silico Approaches, BioTechniques Press, Westborough, MA (2002)
Han van de Waterbeemd and Eric Gifford, ADME/tox in silico modelling: towards prediction paradise? Nature Reviews Drug Discovery, in press.
Han van de Waterbeemd, High-throughput in silico techniques in drug metabolism and pharmacokinetics, Curr.Opin.Drug Disc.Dev. 5 (2002) 33-43.