Pfizer, Sandwich, UK
The UK QSAR & Chemoinformatics Meeting returns to Pfizer in Sandwich on Thursday, the 14th of May. The meeting will start with an overview by Tony Wood (Pfizer) on the challenges and opportunities for drug design and then proceed with studies that illustrate how some aspects are being addressed. There will be a poster session during lunch.
Graeme Robb (AstraZeneca), Steve Muskal (Eidogen-Sertanty) and John Liebeschuetz (CCDC) will each discuss approaches to encourage ideas for ligand design. George Papadatos (Sheffield) will extend our understanding of similarity using molecular fingerprints. Steve Enoch (Liverpool JM) will describe reaction chemistry principles as a means to predict toxicity whilst Neil Benson (Pfizer) will show that analysis of biological pathways can help reduce attrition. The meeting will close with Paul Bamborough’s (GlaxoSmithKline) studies on the kinase profiling and selectivity.
Please enrol by Friday, the 1st of May. Details of travel, accomodation and the pre-meeting dinner will follow in the acknowledgement of your registration.
- Paul Bamborough (GlaxoSmithKline)
Design of kinase inhibitors [Slides] [Abstract]
- Neil Benson (Modelling and Simulation, Department of Pharmacokinetics, Dynamics and Metabolism. Pfizer Global Research and Development)
Pathway analysis: Converting data into knowledge using mathematical models [Abstract]
- Steven J. Enoch (School of Pharmacy and Chemistry, Liverpool John Moores University)
Chemical category formation using electrophilic reaction chemistry principles to predict toxicity [Slides] [Abstract]
- John W. Liebeschuetz (Cambridge Crystallographic Data Centre)
Insight into molecular geometry and interactions using small molecule crystallographic data [Slides] [Abstract]
- Steven Muskal (Eidogen-Sertanty)
Using receptor-site and protein structural similarity to generate new matter ideas [Slides] [Abstract]
- G. Papadatos (Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield)
How similar is similar? A study of the similarity principle using molecular fingerprints in the context of lead optimisation [Slides] [Abstract]
- Graeme Robb (AstraZeneca Alderley Park)
Hypothesis-driven drug design using wiki-based collaborative tools [Slides] [Abstract]
- Tony Wood (Pfizer)
Challenges and opportunities for drug design [Slides] [Abstract]
Presentation: Paul Bamborough
Design of kinase inhibitors
Studies on kinase profiling and selectivity.
Presentation: Neil Benson
Pathway analysis: Converting data into knowledge using mathematical models
Modelling and Simulation, Department of Pharmacokinetics, Dynamics and Metabolism. Pfizer Global Research and Development
High rates of attrition from ideas in early research to clinical use are the norm in drug discovery . An analysis of the underlying causes in 2000  concluded that 62% of compounds entering initial efficacy (Phase II) trials failed and that the predominant reasons for this were lack of efficacy and safety. This implies that despite the explosion of information regarding the fundamental biology, including completion of the sequencing of the genome, our understanding of biological systems and our confidence in predicting the outcomes of perturbing such systems remains low. This presentation will focus on the application of well established mathematical methods to build models of signal transduction pathways (such as NFKB pathway [3-5] and the benefits of taking a quantitative approach to the problem of understanding system complexity.
- Mervis, J. (2005). “Productivity counts–but the definition is key.” Science 309(5735): 726.
- Kola, I. and J. Landis (2004). “Can the pharmaceutical industry reduce attrition rates?” Nat Rev Drug Discov 3(8): 711-5.
- Ihekwaba, A. E., D. S. Broomhead, et al. (2004). “Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha.” Syst Biol (Stevenage) 1(1): 93-103.
- Ihekwaba, A. E., D. S. Broomhead, et al. (2005). “Synergistic control of oscillations in the NF-kappaB signalling pathway.” Syst Biol (Stevenage) 152(3): 153-60.
- Nelson, D. E., A. E. Ihekwaba, et al. (2004). “Oscillations in NF-kappaB signaling control the dynamics of gene expression.” Science 306(5696): 704-8.
Presentation: Steven J. Enoch
Chemical category formation using electrophilic reaction chemistry principles to predict toxicity
School of Pharmacy and Chemistry, Liverpool John Moores University [Slides]
A number of important human toxicological endpoints are thought to involve the formation of adducts between proteins or DNA and exogenous chemicals. Importantly, the chemistry of these interactions is well understood for many compounds, especially electrophilic reactions. The toxicity endpoints that can be considered in this manner include, amongst others, skin and respiratory sensitisation, mutagenicity and liver toxicity. The REACH legislation and Cosmetics Directive demand the reduction in animal usage for toxicological testing for chemical risk assessment and prioritisation, thus alternative methods, including in silico approaches, are increasingly being considered. One such in silico method is chemical category formation which attempt to form groups of “similar” chemicals and allow for read-across within those categories. This presentation will outline how electrophilic reaction chemistry principles can be used to form chemical categories – with a particular reference to endpoints that are of known regulatory importance. It will also be demonstrated that within such categories transparent methods such as trend analysis and read across can be used to estimate toxicological potencies. Importantly, such category formation and read across methods are in keeping with the regulatory requirements set out in the OECD Principles for the Validation of (Q)SARs. The funding of the European Union 6th Framework CAESAR Specific Targeted Project (SSPI-022674-CAESAR) and the European Chemicals Agency (EChA) Service Contract No. ECHA/2008/20/ECA.203 is gratefully acknowledged.
Presentation: John W. Liebeschuetz
Insight into molecular geometry and interactions using small molecule crystallographic data
Cambridge Crystallographic Data Centre [Slides]
An understanding of structural geometry and the nature of intermolecular interactions is often important for the efficient design or selection of drug candidates for synthesis or testing. Therefore easy-to-use tools that provide relevant information should be of interest to modelers and medicinal chemists.
Currently over 450,000 small- molecule crystallographic structures are deposited in the Cambridge Structural Database  (CSD) and new structures continue to be deposited at an ever faster rate. These structures cover a very wide number of chemical types and it is now possible to use this structural data to make accurate statements about the preferred geometries and preferred modes of interaction of a wide variety of molecular substructures.
This information can be used in numerous ways. For instance structure based design projects rely on good X-Ray or NMR derived ligand/protein models. Small molecule structural data can be used to validate the quality of those models where there is doubt.
Similarly, the ‘bound’ geometry of a ligand designed to fit a pharmacophore or a protein active site, will ideally represent a true low energy conformation. Using experimental structural data, to check on conformer quality, is a quick and reliable alternative to carrying out high level theoretical calculations. In an analogous manner the engineered interactions a designed ligand is predicted to make with elements of the active site, can be probed by comparison with similar interactions made in the crystalline state.
This talk will review the ways in which today’s pharmaceutical researchers are using the Cambridge Structural Database and associated tools to improve the efficiency of their molecular design. We will also look forward and imagine how these tools may be developed to be even more useful for the drug discovery researcher.
- F. H. Allen. The Cambridge Structural Database: a quarter of a million crystal structures and rising. Acta Crystallogr., B58, 380-388, 2002.
Presentation: Steven Muskal
Using receptor-site and protein structural similarity to generate new matter ideas
For several years, researchers have leveraged protein sequence and structural similarity in numerous ways, including but not limited to target hypothesis, target prioritization, ligand design, lead optimization, etc. The growing body of over 55K publicly available apo- and co-complex structures provides a strong basis and solid foundation to proliferate reliable models to expound on structural understanding within and across many species. With this expanded view on the structurally-resolved proteome, automated design of novel matter by ligand hybridization or LigandCross is well positioned for success. We present validated examples of new-matter generation through LigandCross and target clustering methods.
Presentation: G. Papadatos , V. Gillet, P. Willett, C. Luscombe, I. McLay, T. Cooper, G. Bravi, S. Pickett
How similar is similar? A study of the similarity principle using molecular fingerprints in the context of lead optimisation
Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield[Slides]
According to the similarity principle , small structural changes defined by a molecular descriptor are likely to lead to small property changes (a concept specifically known as neighbourhood behaviour). This study examines two well-known methods for the quantification of neighbourhood behaviour: the optimal diagonal method of Patterson et al.  and the optimality criterion method of Horvath and Jeandenans . The methods are evaluated using twelve different types of fingerprint (both 2D and 3D) with screening data derived from several lead optimisation projects at GlaxoSmithKline. Evidence suggests that the optimality criterion method provides a better quantitative description of neighbourhood behaviour than the optimal diagonal method. Further insights are given regarding the relative performance of different fingerprints, their optimal (dis)similarity thresholds, as well as the nature of the datasets’ underlying SAR landscapes.
- Johnson, M. A.; Maggiora, G. M., Concepts and application of molecular similarity. Wiley and sons: New York, 1990.
- Patterson, D. E.; Cramer, R. D.; Ferguson, A. M.; Clark, R. D.; Weinberger, L. E., Neighborhood behavior: a useful concept for validation of “molecular diversity” descriptors. Journal of Medicinal Chemistry 1996, 39, (16), 3049-59.
- Horvath, D.; Jeandenans, C., Neighborhood Behavior of in silico structural spaces with respect to in vitro activity spaces – A novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles. Journal of Chemical Information and Computer Sciences 2003, 43, (2), 680-690.
Presentation: Graeme Robb
Hypothesis-driven drug design using wiki-based collaborative tools
AstraZeneca Alderley Park [Slides]
The quality of a compound and its potential as a drug are fixed at the moment of conception. Molecular design is therefore a key part of the drug-discovery process. Rather than attempting to enhance design by improving the odds through ever-increasing numbers of compounds, we can ask relevant questions of the chemistry and design compounds in order to answer those questions. This hypothesis-driven approach allows us to prosecute design systematically, potentially with the minimum number of compounds and the minimum number of design cycles.
Wiki-based collaborative tools have been created to track design hypotheses from multiple designers and so encourage a culture of close multidisciplinary collaboration. All hypotheses, suggested compounds and outcomes are stored in a database, facilitating peer-review of ideas, the use of predictive computational models on virtual compounds (aiding in the selection for synthesis), test-scheduling and rapid analysis.
Presentation: Tony Wood
Challenges and opportunities for drug design
An overview on the challenges and opportunities for drug design.