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EBIInformationEventsTACBAC 07

TACBAC 07 Speakers

Speaker Biographies & Abstracts



Simon F. Campbell CBE, FRS - Royal Society of Chemistry, UK
Simon Campbell is an organic chemist who received his BSc and PhD from Birmingham University followed by postdoctoral research in Chile and Stanford, then as Visiting Professor at the Universidade de Sao Paulo in 1970. In 1972, Dr Campbell joined Pfizer UK, and he retired in October 1998 as SVP for WW Discovery and Medicinals R&D Europe. He has co-authored over 110 publications and patents, and was a key member of the research teams that discovered doxazosin amlodipine and sildenafil (ViagraTM). Currently, Simon is a member of various Scientific Advisory Boards across the World. He acts as consultant to Abingworth, Almirall and Jerini and is immediate Past President of the Royal Society of Chemistry.
Abstract - The Future for drug discovery, a voice from the past

Despite a significant increase in investment in pharmaceutical R&D, the output of new chemical entities has not kept pace and the past few years have been particularly disappointing. For example in 2005, the FDA approved only 20 new market introductions, although 13 received priority review. Moreover, increasing drug costs and limited healthcare budgets mean that even these new therapies enjoy only limited uptake outside the US.

There were great expectations in the early 1990s that introduction of new technologies and sequencing of the human genome would lead to a new golden era of drug discovery, but these dreams have not yet been realised. However, extended timelines of 10–15 years from target to market suggest that such hopes may have been optimistic, particularly as many technology advances were introduced during major consolidations in the pharmaceutical sector, with consequent challenges of integrating culture, style and values.

This presentation will provide a personal view on the state of pharmaceutical R&D and prospects for the future.

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Douglas Bassett - Merck & Co., Inc., Seattle, WA, USA
Dr Basset is currently Co-site Head at Merck’s Rosetta site in Seattle, WA USA and provides leadership for the Molecular Profiling organization in support of target and biomarker discovery efforts in Merck’s therapeutic franchises worldwide. Doug assumed this role in 2007 after heading the company’s Informatics organization since 2004. He joined Rosetta in 1997 with responsibility for its Computational Biology team and assumed management of Rosetta Biosoftware when the independent business unit was formed in 2001. Before joining Rosetta, Dr Bassett was an intramural research fellow at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH), where he developed bioinformatics software solutions and conducted research in the field of computational biology. Dr Bassett holds a PhD in human genetics from Johns Hopkins University as well as a Masters of Business Administration and a Bachelor of Science in biology, both from the University of Washington.
Abstract - Molecular profiling, pathway analysis and implications for the future of oncology healthcare

Microarray and miRNA expression profiling, in synergy with siRNA screening and pathway analysis in oncology, are enabling us to better understand the biology underlying diverse metastatic potential and variable response to clinical treatment in tumours. Studies carried out in collaboration with centres such as the Netherlands Cancer Institute (NKI) and the Moffitt Cancer Center bring high quality, well-annotated cancer samples together with rich profiling data and pathway models to aid in the discovery of clinical subtypes and assessment of likelihood of patient outcomes and treatment response. Data integration and analysis challenges and opportunities will be discussed, and specific examples from the NKI/Rosetta breast cancer collaboration will illustrate the potential impact of these studies on the future of oncology healthcare.

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Stephen Frye - GlaxoSmithKline, Research Triangle Park, NC, USA
Stephen obtained a BS in Chemistry at North Carolina State University in 1983 and a PhD in Organic Chemistry at the University of North Carolina in 1987 under the direction of Ernest Eliel. During his PhD studies, Stephen obtained an off-campus fellowship to work in Lausanne, Switzerland in the labs of John McGarrity, applying a novel NMR technique to the study of organometallic reaction mechanisms. On completion of his degree, Stephen started work at the newly initiated US research site for Glaxo Inc. in temporary facilities on the UNC campus and subsequently led the project that resulted in Avodart, GSK’s dual 5-reductase inhibitor for treatment of benign prostatic hyperplasia. Shortly after the merger with Wellcome in 1995, he established a new chemistry department based upon kinase target-class science and oncology, and GSK’s ERBB2/EGFR inhibitor, Tykerb, was discovered within this department. In 1999 Stephen began a secondment at GW’s Stevenage site, leading a research unit in medicinal chemistry. Following the merger to form GSK in the spring of 2000, he was selected to lead GSK’s High Throughput Chemistry group, which has now evolved into Discovery Medicinal Chemistry. Stephen has been married to Susan for 23 years and they have three children, Aaron (17), Jeremy (16) and Rachel (10).
Abstract - Target identification and validation in the context of gene families

Protein target classes provide an organizing scientific framework within which target identification, validation and lead discovery are facilitated. The talk will exemplify this approach in the areas of nuclear receptors, protein kinases and seven transmembrane receptors

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Marc Vidal - Dana Farber Cancer Institute, Boston, MA, USA
Dr Vidal originally trained as a bioengineer at the Faculté Universitaire des Sciences Agronomiques de Gembloux (FUSAGx), Gembloux, Belgium. He received his PhD from the FUSAGx with work performed at Northwestern University (Evanston, IL, USA), where he identified a new molecular process of gene regulation.

During his post-doctoral training at the Massachusetts General Hospital Cancer Center and Harvard Medical School (HMS; Boston, MA, USA), Dr Vidal developed and patented a new technology called the ‘reverse two-hybrid system’. Many laboratories now use this method to characterize protein–protein interactions involved in biological processes. He started his own research group in 1997 with the goal of understanding how global and local properties of macromolecular networks relate to normal biological processes and human disease. In 2000, he moved his laboratory to the Dana-Farber Cancer Institute (DFCI) and HMS (Boston, MA, USA). He is now Associate Professor of Genetics at HMS and Director of the DFCI Center for Cancer Systems Biology (CCSB). He has co-authored more than 100 peer-reviewed articles, which have been cited collectively more than 5,000 times. Dr Vidal is the recipient of an Abbott Bioresearch Award (Boston, MA, USA). He currently holds an International Francqui Chair (Brussels, Belgium).
Abstract -Interactome networks

For over half a century it has been conjectured that macromolecules form complex networks of functionally interacting components, and that the molecular mechanisms underlying most biological processes correspond to particular steady states adopted by such cellular networks. However, until recently, systems-level theoretical conjectures remained largely unappreciated, mainly because of lack of supporting experimental data.

To generate the information necessary to eventually address how complex cellular networks relate to biology, we initiated, at the scale of the whole proteome, an integrated approach for modelling protein–protein interaction or ‘interactome’ networks. Our main questions are: How are interactome networks organized at the scale of the whole cell? How can we uncover local and global features underlying this organization, and how are interactome networks modified in human disease, such as cancer?

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Alexander Tropsha - University of North Carolina at Chapel Hill, USA
Dr Tropsha gained his PhD from Moscow State University, Russia, in 1986, where he remained until 1998 pursuing postodoctoral work in quantitative structure–activity relationships (QSAR) and drug design. He then moved to the University of North Carolina, Chapel Hill, to work with J.S. Kizer, J.P.Bowen and J. Hermans at the Brain and Development Research Center. Since 1991 he has been at the Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, UNC-Chapel Hill, where he is now Director. His research interests include development of new methodologies and software tools for computer-assisted drug design, development of new approaches to protein 3D structure analysis, and prediction based on the principles of statistical geometry.
Abstract - Can primary high-throughput screening data be analysed in a meaningful way?

High-throughput technologies have changed the way we approach and analyse biological assay results. In the past, a typical dataset available for computational molecular modelling would include a few dozen molecules tested in a single assay. Nowadays, even publicly available datasets (made available due to projects such as PubChem) may include thousands of compounds. Modern datasets are characterized by substantial chemical diversity of tested compounds, frequent availability of assay results across multiple targets, interest in the analysis and prediction of compound physical (e.g., solubility) and general biological (e.g. drug likeness, carcinogenicity, mutagenicity) properties, and a strong imbalance between typically small number of ‘active’ vs. large number of ‘inactive’ molecules.

I shall discuss our computational drug discovery workflow (termed ChemBench), which incorporates modules for combinatorial quantitative structure–activity relationships (QSAR) model development (i.e. using all possible binary combinations of available descriptor sets and statistical data modelling techniques), validation, and virtual screening of available chemical databases for hit identification. Particular attention will be given to specific methodologies used for rigorous model validation, approaches used for the analysis of datasets with the unbalanced distribution of active and inactive compounds, and those used to define model applicability domains in the chemistry space. I will present examples of studies demonstrating that in several cases of blind prediction (metabolic stability, genotoxicity, protein binding, anticonvulsant activity), we have achieved high external prediction accuracy validated by experimental studies. I will summarize the presentation by suggesting that the nature of modern chemical genomics research requires a paradigm shift regarding the role of modelling approaches in the analysis of primary biological data. The shift concerns viewing the models not only as exploratory and explanatory (i.e., useful for lead optimization) but rather as reliable decision support tools helping experimental scientists in designing chemical libraries and selecting compounds for focused biological screening (i.e., useful for lead identification).

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Steve Brown - MRC Mammalian Genetics Unit, Harwell, UK
Steve Brown is Director of the Medical Research Council’s Mammalian Genetics Unit at Harwell, Oxfordshire, UK. He did his PhD at Cambridge University and before he joined the MRC, he was Professor of Genetics at Imperial College, London. His research interests cover mouse functional genomics, including the use of mouse mutagenesis and comparative genomic analysis to study the genetic basis of disease. A particular focus has been the use of mouse models to study the molecular basis of genetic deafness. Under his direction, Harwell has been at the forefront of the development and application of new approaches in mutagenesis and genomics for the systematic study of gene function in mammals.
Abstract - Comprehensive functional annotation of the mouse genome – computational challenges in phenotyping and ontologies

With the completion of the mouse genome sequence, a key goal for functional genomics is the creation of a series of mutant alleles for every mammalian gene. Several international projects are underway (EUCOMM, NORCOMM, KOMP) to generate knock-outs for every mouse gene. An even greater challenge will be the determination of phenotypic outcomes for each mutation. A vital element of this endeavour, being undertaken by the EUMORPHIA and EUMODIC programmes, is the development and application of standardized phenotyping platforms that allow for reproducibility of test outcome over both time and place. All phenotype data will be made publicly available through the EuroPhenome database. Representing phenotypic information in a standardized way presents further challenges. Development of phenotype ontological structures that take into account assay protocol, genetic background and environment will be crucial. In addition, the mining of phenotypic characters for correlations indicative of underlying processes will require the availability of databases of raw phenotype data.

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David Searls - GlaxoSmithKline, Philadelphia, PA, USA
David Searls is Senior Vice-President of Bioinformatics at GlaxoSmithKline. Formerly he was Research Associate Professor of Genetics, with a secondary appointment in Computer and Information of Science, at the University of Pennsylvania, where he maintains an adjunct appointment. He has undergraduate degrees in Life Sciences and in Philosophy from the Massachusetts Institute of Technology, a Master’s in Computer and Information Science from the University of Pennsylvania and a PhD in Biology from the Johns Hopkins University. His research interests include linguistic analysis of biological sequences and scientific data integration.
Abstract - Darwin and drug discovery

The process of drug discovery necessarily focuses on targets as they exist in present-day human populations. However, a cognizance of targets in an evolutionary context promises to provide a richer understanding of many aspects of drug targets, ranging from pathophysiology to toxicology to pharmacogenetics. Just as the latter field teaches us that targets cannot be considered to be uniform across populations, we may soon need to regard targets, not as frozen in time, but as being in the midst of evolutionary change.

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John Overington - Inpharmatica, London, UK
Dr Overington has over 16 years’ experience in industry, in both large Pharma and Biotech sectors. As head of Inpharmatica’s Discovery Informatics business, John leads a central London-based team developing and applying knowledge management tools to drug discovery problems.
Abstract - The druggable genome revisited

It is clear that drug discovery productivity is nowhere near the heady heights predicted when investments were made in sequencing the human genome, with overall industry output approaching all-time lows. The reasons for this state of affairs are clearly complex, but it appears, on the basis of empirical data analyses, that only a small fraction of proteins can be successfully modulated with currently available drug technologies (e.g. small molecules and protein therapeutics). The talk addresses the molecular targets of the current pharmacopiae, and presents the development and application of a number of computer-based approaches to ‘scoring’ new targets for their tractability. Examples are presented from a global analysis of the entire human genome, through to specific issues with well-validated but ‘tough’ targets. Finally, recent work aimed at the identification of targets for neglected diseases is also outlined.

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Brian Shoichet - University of California, San Francisco, CA, USA
Brian Shoichet received a BSc in Chemistry and a BSc in History in 1985, from Massachusetts Institute of Technology (MIT). MIT appears to have no record of this. He received his PhD for work with Tack Kuntz on molecular docking in 1991, from the University of California, San Francisco (UCSF). Shoichet’s postdoctoral research was largely experimental, focusing on protein structure and stability with Brian Matthews at the Institute of Molecular Biology in Eugene, Oregon, as a Damon Runyon Fellow. Colleagues from Eugene have only sketchy memories of his time there. One recalls, ‘He seemed to travel a lot.’ Matthews himself could not be reached for comment. Shoichet joined the faculty at Northwestern University in the Dept of Molecular Pharmacology & Biological Chemistry as an Assistant Professor in 1996. He promoted to a tenured Associate Professor in 2002, only one year after his younger sister, Molly Shoichet, received tenure at the University of Toronto. Shoichet strongly denies any sensitivity around this issue. Around that time he was recruited back to UCSF, where he is now a Professor in the Department of pharmaceutical Chemistry. ‘We confused him with Kevan Shokat,’ admits a member of the recruiting committee at UCSF. Research in the Shoichet Lab uses computational and experimental techniques to investigate enzyme structure, function, stability and inhibition, and the links among them. It is supported by the NIH.
Abstract - Relating protein pharmacology by ligand chemistry

I will describe a technique to quantitatively group and relate proteins based on the chemical similarity of their ligands. We begin with 246 target/ligand sets, each composed of hundreds of ligands, from the MDDR database. The similarity between each pair of sets is calculated using ligand topology. For robustness, a statistical model of random similarity was developed to rank the significance of the resulting similarity scores, allowing us to calculate expectation values to rank the set similarities. These rankings may be expressed as a minimum spanning tree that maps the sets together. Although this map arises solely from chemical similarity calculations, biologically sensible clusters nevertheless emerge. Links among unexpected targets also emerged, and we have begun to test these experimentally. This has led to the discovery of off-target pharmacology for three well-known drugs, as confirmed by biochemical and cell-based assays. Relating receptors by ligand chemistry organizes biology in new ways to reveal unexpected relationships that may be tested directly by the ligands themselves.

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Aram Adourian - BG Medicine, Inc., Waltham, MA, USA
Dr Aram Adourian leads the Computational Sciences group at BG Medicine in Waltham, Massachusetts. In this position, he and his group are responsible for the statistics, data analysis, data interpretation and bioinformatics functions within the company. Prior to joining BG Medicine, Dr Adourian was a staff member of the Whitehead Institute for Biomedical Research at the Massachusetts Institute of Technology, where he served as Project Manager for Bioinformatics specializing in the development of novel systems and approaches for biomolecular sequencing, analysis and modelling. Dr Adourian earned his PhD at Harvard University in Statistical Physics, where he was a recipient of the Rudenberg Research Prize.

His research interests span many areas of computational chemistry and biology, including computer-assisted drug design, combinatorial chemistry and organic synthesis, molecular diversity, QSAR, artificial intelligence, and software engineering.
Abstract - Systems as drug targets

The pharmaceutical value chain, from disease diagnosis to the marketing of approved medicines, must become more successful and cost effective in order to satisfy the expectations of many stakeholders in future healthcare. Molecular systems analysis is emerging as a promising approach to understanding disease and the responses to drugs (respectively, systems pathology and systems pharmacology). Comprehensive ‘multi-omic’ analysis of biological systems has recently been facilitated by advances in instrumentation and computation but depends critically on the quality of datasets and the techniques employed to integrate and interpret disparate datasets derived from transcriptomic, proteomic and metabolomic bioanalytical platforms. Examples of multi-omic molecular systems pathology/pharmacology and the use of molecular network correlation analysis will be presented to illustrate the types and levels of information that can be applied to the discovery and development of new drug therapies.

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David Reif - Vanderbilt University, Nashville, TN, USA
David M. Reif is a biologist at the National Center for Computational Toxicology within the U.S. Environmental Protection Agency. His research focuses on integrating environmental exposure information with physiological biomarker data to address public health issues. He earned a B.S. (Biology) from The College of William & Mary, where he was a Monroe Scholar. His M.S. (Applied Statistics) from Vanderbilt University concerned the development of software for visual and statistical analysis of high-throughput biological data. His PhD (Human Genetics) from Vanderbilt centred around the integration of multiple data types (genetic, proteomic, and immunologic) for analysing complex health outcomes.
Abstract - Detection and characterization of gene–gene and gene–environment interactions in common human diseases and complex clinical endpoints

Biological organisms are complex systems that dynamically integrate inputs from a multitude of physiological and environmental factors. Therefore, in addressing questions concerning the aetiology of complex health outcomes, it is essential that the systemic nature of biology be taken into account. Information from multiple sources – both extrinsic (e.g. ambient air quality and chemical exposure) and intrinsic (e.g. genetic variation and protein expression) – must be integrated to reliably assess cumulative risk. Novel analysis methods are needed to detect relevant non-additive interactions in the diverse types of high-dimensional data afforded by modern experimental technologies. Analytical approaches that can characterize the interactions both within and among numerous data types provide a more comprehensive portrayal of the mechanisms underlying complex health outcomes. Results from both clinical and exposure study settings will be presented, along with discussion of ongoing experiments that combine information of both types.

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Douglas Kell - University of Manchester, UK
Douglas Kell received his B.A. (Hons) in Biochemistry at St John’s College, Oxford in 1975 and completed his D.Phil. (Oxon) in 1978. He then moved to the Department of Botany & Microbiology, University College of Wales, Aberystwyth as a postdoctoral fellow, progressing to ‘New Blood’ lecturer in Microbial Physiology in 1983 and Reader in Microbiology at the Dept of Biological Sciences, UCW, Aberystwyth in 1988. He gained a Personal Chair at the University of Wales in 1992 before becoming Director of Research, Institute of Biological Sciences, UWA in 1997-2002. He moved to Manchester in 2002 to become EPSRC/RSC Research Professor of Bioanalytical Science at UMIST, and is now Director of the BBSRC Manchester Centre for Integrative Systems Biology(www.mcisb.org/). He has been founding director of two companies - Aber Instruments Ltd, Aberystwyth and Aber Genomic Computing and is the recipient of several awards. He has published over 350 scientific papers, 15 of which have been cited over 100 times.
Abstract - Novel metabolomic biomarkers: what and how?

The optimization of scientific instrumentation involves many settings or parameters, and makes this a combinatorial optimization problem of high dimensionality. Heuristic methods such as those based on genetic search are appropriate for tackling such problems, while closed loop methods make these still-large search spaces tractable. Because there are several things to optimize (such as number of peaks, run time and signal : noise), multi-objective methods are appropriate. We have combined these ideas and developed closed-loop optimization methods for metabolomic instrumentation. Such strategies have served to increase tenfold the number of metabolite peaks we can detect, leading (when coupled to appropriate numerical techniques) to considerable benefits for biomarker detection in disease diagnosis and for other metabolomic purposes.

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Manfred Kansy - F. Hoffmann-La Roche Ltd, Basel, Switzerland
Dr Kansy is Head of the Molecular Properties & Structure Property Correlations Section at Roche’s Basel Research site. With his team, Dr Kansy is responsible for compound profiling of research compounds and the development of predictions tools for molecular properties and linked ADME/TOX effects. Dr Kansy has published more than 40 research papers and patents in the field of compound characterization and in silico tool development. In 2005 Dr Kansy received the New Safe Medicines Faster Award (NSMF) from the European Federation of Pharmaceutical Sciences (EUFEPS) for the development of the PAMPA method together with Prof. Per Artursson, Uppsala University, Sweden who was awarded for the application of the Caco-2 system in drug profiling (the award was sponsored by Sanofi-Aventis). Dr Kansy studied pharmacy at the University of Kiel and obtained his PhD in pharmaceutical chemistry in 1986. He did postdoctoral research for 2 years at the Borstel Research Institute (with Prof. J. Seydel) combined with a short research stay at the Central Drug Research Institute (Lucknow, India).
Abstract - Examining the impact of in silico techniques on the R&D process: current status and future perspectives

The Pharma industry is challenged by an increasing pressure to improve its declining productivity, caused by increasing R&D expenditures at relatively stable numbers of new market introductions. Among others, biomarkers and in silico techniques are considered to be major strategic components that will positively influence Pharma R&D productivity, especially if applied with success in the modelling of complex biological systems (systems biology, disease modelling). Today, modelling and simulation (M & S) tools are of increasing value in the extrapolation from late-stage pre-clinical development to clinical phases. Mechanistic pharmacokinetic and pharmacodynamic (PK/PD) modelling, population modelling and trial simulations are gaining increasing attention at clinical phases. Disease modelling approaches, currently at an early development stage, will support target selection. Major progress has been achieved at the early phases of drug discovery and lead optimization, owing to the increasing amount of information generated in the past decade. This is now influencing the development of new,high-quality M & S tools for ADME/TOX, allowing the necessary link to molecular structure and its properties. These in silico tools, combined with a strategy shift in screening [‘all screen all to selected assays and molecules) are a precondition for the reduction of wet lab experiments. Further necessary improvements in the quality of M & S, its wider application and acceptance is dependent on fast access to data and information generated along the R&D value chain. Only the full exploitation of data, information, knowledge and its integration into the R&D process via appropriate techniques will guarantee a continuous increase in the value of in silico technologies.

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Gilles Klopman - MULTICASE Inc., Beachwood, OH, USA
A native of Belgium, Dr Gilles Klopman earned both his undergraduate and graduate degrees from the University of Brussels, working under the direction of Professors Prigogine and Martin. He received his Doctorate with highest honours in 1960 for his thesis on the Synthesis and Study of the Physical Chemical Properties of Non Alternant Aromatic Hydrocarbons. After a year in the Military, where he worked on the determination of trace elements in blood and tissues, he joined the Organic Group at the Cyanamid European Research Institute in Geneva Switzerland. In Geneva, his interest in mechanistic and applied theoretical chemistry grew from his collaboration with Profs R.F. Hudson and C.K. Jorgensen. It is in the early 60s that his pioneering work on the development of semi-empirical methods of calculating the properties of nonconjugated molecules attracted attention to his work. This prompted an invitation to spend a sabbatical year with Professor M.J.S. Dewar at the University of Texas in Austin, where he developed, in 1965, what was to become the MINDO method. In 1967, He joined Case Western Reserve University as an associate professor and moved through the ranks until his retirement from the University in 2003 as the Charles F. Mabery Professor of Research. Dr Klopman has authored or coauthored three books and more than 330 research papers. His research interests range from experimental determination of reactivity indices and substituent constants to the development of reactivity theories. His descriptions of perturbation theory and of the concept of charge and orbital controlled reactions are now widely used to explain the ambient selectivity of nucleophiles and links the linear free energy type correlations (Hammett, HSAB, etc.) to more fundamental chemical concepts. Dr Klopman’s interests have gradually moved towards problems of artificial intelligence and its general use to predict the potential harmful effects of new chemicals. He currently serves as the President and CEO of MULTICASE Inc., a company he co-founded in 1995 and whose business licenses his predictive software and toxicological databases.
Abstract - Machine intelligence in the design of safer chemicals

Structure–activity studies are important in many areas of mechanistic and exploratory chemistry. They are based on the premise that a relationship may exist between the chemical or biological properties of molecules and their chemical structure. The major advantages of successful structure–activity studies is that they are much cheaper and permit predictions to be made for chemicals that have not yet been synthesized. This improves productivity. In this lecture, applications of the MCASE methodology to the discovery of toxic structural alerts and the concomitant evaluation of adverse effects of organic molecules will be described. Prominent among these potential toxic effects is their ability to induce mutations. One of the problems encountered in creating such models is the need to cover as much of the chemical universe as possible or, in other words, increase the applicability domain of the model. In this presentation, we will report on the results we obtained by creating such models expanded by including proprietary data obtained from the US Food and Drug Administration as well as from pharmaceutical companies under a Shared Mutagenicity Data project sponsored by the US National Institutes of Health.

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Mark Cronin - Liverpool John Moore’s University, UK
Mark Cronin is Professor of Predictive Toxicology in the School of Pharmacy and Chemistry at Liverpool John Moore’s University. He has over 15 years’ experience in applying computational techniques to estimate environmental and human health effects of compounds. Current research interests include development of integrated strategies and computational tools to reduce animal testing in toxicology. This research activity has resulted in over 150 publications.
Abstract - Computational prediction of toxicity and metabolic transformation

A wide variety of computational techniques may be applied to model, and hence provide predictions for, toxicity and metabolism. These approaches range from the searching of existing data held within databases, to read-across or analogue methods and on to an incredible variety of quantitative structure–activity relationship (QSAR) techniques. These methods can be used to screen compounds in early product development, assist in the optimal design of lead compounds, and also for regulatory risk assessment. These approaches are valuable in isolation. However, their real power comes when they are combined or developed into what are increasingly being termed integrated testing strategies. Approaches to develop rational strategies will be described, along with incorporating data from high-throughput, in vitro and chemical reactivity (in chemico) screens. Recent advances in techniques to evaluate existing industry chemicals will be highlighted, with an emphasis on the tools being made available for the forthcoming REACH legislation.

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Michael Bolger - Simulations Plus, Inc., Lancaster, CA, USA
Dr Bolger is Chief Scientist at Simulations Plus, Inc., Lancaster, California. He obtained a B.A. in Biology and Chemistry from UC San Diego in 1973 and his PhD in Pharmaceutical Chemistry from UC San Francisco in 1978. From 1978 to 1980 he was an NIH Postdoctoral Fellow in pharmacology at the UC San Diego School of Medicine. For the next 23 years, he was a professor of Pharmaceutical Sciences at USC School of Pharmacy, Los Angeles, California and he retired from USC in the spring of 2004. During this period he developed an interest in the computational aspects of drug design and drug development. As a faculty member at USC School of Pharmacy, his research was supported by several NIH basic science research grants and he published over 50 peer-reviewed publications. From 1989 to 1991 he served as a US Food and Drug Administration National Science Advisor. He is Chair of the AAPS Data Mining Focus Group, a member of the editorial board of Pharmaceutical Research and AAPS PharmSci, and has served as a reviewer for numerous scientific publications. He is currently Adjunct Associate Professor of Pharmaceutical Sciences at USC. From 1987 to 1993, simultaneous with his academic duties, he was a founder and Director of Medicinal Chemistry at CoCensys Inc. There, he studied the chemistry and use of novel neuroactive steroids for treatment of anxiety, epilepsy, and sleep disorders. Drug candidates emanating from seven of Dr Bolger’s patents have been tested in Phase I and II clinical trials for petite mal epilepsy, sleep disorders and migraine. Currently, he oversees a team of scientist/programmers at Simulations Plus, Incin the development of software programs (ADMET Predictor, GastroPlus, ADMET Modeler, DDDPlus, and MembranePlus) for estimation of biopharmaceutical properties, simulations of absorption and bioavailability, automated generation QSP/AR model building, in vitro dissolution, and cell culture permeability simulation. He was elected to the rank of Fellow of the American Association for the Advancement of Science in 1996.
Abstract - In silico prediction of fraction absorbed

My talk will compare in silico methods of predicting fraction absorbed (Fa). Three in silico methods have been applied to predicting Fa. First, computational alerts and statistical modelling have been applied estimation of Fa. Second, Fa has been calculated directly from in silico, or in vitro values of permeability. Third, the advanced compartmental absorption and transit (ACAT) model has applied to a mechanistic simulation of drug dissolution, gastrointestinal transit, and membrane permeability to get an integrated estimate of Fa. QSPR methods, computational alerts, and direct calculation of Fa fail to accurately estimate non-linear dose dependence, complex mechanisms, and formulation specific changes in Fa, whereas the ACAT model provides accurate results due to the integration of physico-chemical and biopharmaceutical properties. Fa is a complex property that is based on the integration of partition coefficient, pKa, solubility, permeability, dissolution, and formulation factors.

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Neil Parrott - F. Hoffmann-La Roche Ltd, Basel, Switzerland
Neil Parrott graduated in physics from the University of Bristol, UK, then specialized in information technology and medical imaging at the University of Aberdeen, UK. In 1988 he started work with Sandoz Pharma as a developer of chemistry and biology databases and later, in the combinatorial chemistry group, developed software and automation supporting high-throughput solid-phase synthesis. In 1998 he joined the Informatics group in Roche Pharma Research developing pharmacokinetic data analysis tools and leading a global project to integrate drug metabolism and pharmacokinetics (DMPK) data into the Research data warehouse. While working in the DMPK area he became interested in physiologically based pharmacokinetics and transferred to Research Modeling and Simulation where he is now involved in pre-clinical projects.
Abstract - Application of physiologically based modelling in pre-clinical to clinical pharmacokinetic and pharmacodynamic prediction

This talk describes how physiologically based models of pharmacokinetics may be applied as an integrated part of the research and pre-clinical development of novel drugs. The modelling and simulation tools and techniques used are briefly reviewed and the strategy for application in drug research is described. Examples will illustrate how such models may be applied at different stages, ranging from early application before in vivo studies through clinical candidate selection to the estimation of human kinetics and dose selection before clinical studies. Although there are obvious restrictions related to limited input data at the earlier stages, the examples illustrate some of the advantages of the approach compared with other more empirical methods. These advantages are increasingly exploited with more widespread use of physiological models as powerful and user-friendly software make them accessible to non-specialists.

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Robert Sheridan - Merck & Co., Inc., Rahway, NJ, USA
Dr Sheridan earned his PhD in Biochemistry at Princeton University in 1979. Between 1979 and 1983 he did two postdocs, one at the Fox Chase Cancer Center in Philadelphia (working on NMR spectroscopy – his only professional experience as an experimental scientist), and one at Rutgers University (molecular dynamics). He joined Lederle Laboratories (now Wyeth) as a molecular modeller in 1983. He moved to Merck in 1991. Dr Sheridan has spent the past 23 years in the pharmaceutical industry developing and validating molecular modelling and chemoinformatics methods.
Abstract - Empirical and semiempirical models for regioselectivity in human CYPs: ignoring mechanisms can be a good thing

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Regioselectivity models attempt to predict which parts of a molecule will be oxidized by CYPs in the hope that chemists will be able to more quickly design more stable compounds. I will discuss quantitative structure–activity relationship (QSAR)-based regioselectivity models that are derived from data in the literature. One type of model is purely empirical, using substructure descriptors that encode the local chemical environments of each candidate atom. Another type, more consistent with previous efforts in the literature, simulates a specific mechanism such as hydrogen radical abstraction with AM1 calculations. It is clear that purely mechanistic models are not sufficient to explain the literature data and that it is necessary to include empirical parameters. The cross-validated predictions of the models are compared to predictions from other methods.

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Peter Goodfellow
Peter Goodfellow worked for many years as a research scientist specializing in human genetics. His first positions were at the Imperial Cancer Research Fund (now Cancer Research UK) in London and Cambridge University, where he studied human gene mapping and the genetics of sex determination. For the past decade, Peter worked in the pharmaceutical industry. Currently, he is investigating how climate change is affecting horticulture methods in the Isle of Wight.
Abstract - Integrating time and information

Drug discovery and development is more difficult than any other human activity. The cycle time for new drug production is 15–20 years and a drug may be on the market for 20 years or longer. Today’s scientific experiments and new discoveries might benefit your children but are unlikely to help you. The social, economic and scientific structure for the optimal support of drug discovery does not exist. This is particularly true for data capture, sharing and integration.

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Louise Johnson - University of Oxford, UK
Louise Johnson read Physics at University College London and completed her PhD in Molecular Biophysics at the Royal Institution London (1965) under the supervision of Sir Lawrence Bragg and David Phillips, where she worked on the structure of lysozyme, the first enzyme structure to be solved by X-ray diffraction. She joined the Laboratory of Molecular Biophysics at the University of Oxford in 1967. Her more recent work has been with protein kinases and the regulatory molecules of the cell cycle, and has included a structure-based drug design programme. She was appointed to the David Phillips Chair of Molecular Biophysics in Oxford in 1990 and was elected Fellow of the Royal Society, also in 1990. In 2003 she was appointed Director of Life Sciences at the Diamond Light Source, the UK’s new synchrotron source and shares this position 50% with her appointment at Oxford. In 2003 she was awarded the title of DBE in the New Years Honors list.
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Christopher Lipinski - Melior Discovery, Waterford, CT, USA
PDr Christopher Lipinski was Adjunct Senior Research Fellow at the Pfizer Global R&D Groton CT Laboratories following his retirement in June 2002 and is now a Scientific Advisor to Melior Discovery, a drug repurposing startup. He is a member of the American Chemical Society (ACS), AAPS, Society of Biomolecular Sciences (SBS) and EUFEPS. A consultant on drug-like properties, he serves on numerous scientific advisory and journal editorial boards. He is the author of the ‘rule of five’ – a widely used filter to select for acceptable drug oral absorption. In 2006 he received an honorary law degree from the University of Dundee and is also the 2006 Society for Biomolecular Sciences Achievement Award winner. In 2005 he was the American Chemical Society winner of the E. B. Hershberg Award for Important Discoveries in Medicinally Active Substances and in 2004 the winner of the Division of Medicinal Chemistry Award of the ACS Division of Medicinal Chemistry. Since 1984, he has been an adjunct faculty member at Connecticut College in New London CT, and has over 210 publications and invited presentations and 17 issued US patents.
Abstract - The complexity of screening libraries: balancing competing and contradictory criteria

Designing and acquiring screening libraries is far more complex than most people realize and is certainly not just an issue of chemical diversity. What is the screening goal? Is it to find a tool or probe to inform on a biological pathway or is it to find a starting point for a drug discovery programme? Different chemistry principles apply. What is the budget? 10 mg of a tool or probe might cost $16, the same quantity as a starting point for drug discovery might cost $320. Is the screening in the antibiotics area? In this case a normal medicinal chemistry library is near hopeless, leaving a natural products approach, with all its associated difficulties, as the only option. The current mantra is to obtain ‘diverse’ screening compounds but all the recent indicators are that historical medicinal chemistry compounds are incredibly tightly clustered in chemistry space. How does one deal with this? Diversity oriented synthesis (DOS) produces topologically complex compounds that look like small-to medium-size natural products. Where does DOS fit in? Should we filter chemistry structures before screening or after screening? Academics and industrial researchers hold opposite, entrenched and almost intractable positions. Who decides on the content of a screening library? Is it the chemists who have to optimize activity by changing structure or the biologists who are terribly frustrated by past screening failures against biologically very attractive targets?

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Martin Noble - University of Oxford, UK
Martin Noble completed his PhD at the European Molecular Biology Laboratory in Heidelberg in 1992, working on structure-based drug design against trypanosomal targets. After graduating at the Rijksuniversiteit Groningen, he moved to the Department of Biochemistry in Oxford, initially as a postdoc in the group of Professor Louise Johnson, and subsequently as a Royal Society URF, a University Lecturer, and now Professor of Structural Biology. His work is focused on visualizing and understanding the recognition events that are responsible for regulating cellular behaviour, particularly using interdisciplinary approaches and where the interaction might be a target for rational drug design.
Abstract - Structural bioinformatics in kinase inhibitor design

Structural bioinformatics, the extraction of useful information from biological structures, underpins modern drug discovery. As well as informing the process of molecular design, it can serve to identify and highlight target proteins and functional surfaces upon them. It can also offer an insight into the character of likely motions that these targets can undergo. Examples will be offered for all of these contributions in the context of the design of agents that target protein kinases.

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Philip Hajduk - Abbott Laboratories, Abbott Park, IL, USA
Philip Hajduk received a B.S. in Chemistry from the University of Illinois, Urbana, in 1989 and a PhD in Chemistry from the University of Wisconsin, Madison, in 1993. As a post-doctoral fellow at Abbott Laboratories under Dr Stephen Fesik, Phil spearheaded the early development and application of SAR by NMR. In his current role as Project Leader of the Protein NMR, Molecular Modeling, and Cheminformatic groups at Abbott, Phil continues to develop and apply approaches that enable and expedite the drug discovery process. Phil has authored or co-authored more than 65 scientific publications and is a co-inventor on 7 U.S. patents.
Abstract - Protein–ligand interactions: lessons from a decade of NMR-based screening

Understanding the process of molecular recognition lies at the heart of successful drug discovery. In this presentation, insights into protein–ligand interactions will be described that have been learned from over a decade of NMR-based screening and fragment-based drug design. Concepts such as protein druggability, successful lead optimization, and capturing therapeutically relevant binding will be presented.

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Gisbert Schneider - University of Frankfurt, Germany
Gisbert Schneider studied biochemistry at the Free University of Berlin, Germany. He prepared his doctoral thesis on machine learning systems for peptide de novo design. Post-doctoral research focused on the design of artificial antigens, mitochondrial targeting sequences, and sequence-based prediction of membrane proteins. In 1997 he joined the pharmaceuticals division of F. Hoffmann-La Roche in Basel, Switzerland, where he became Head of Cheminformatics. Since 2002 he has been a full professor of Chem- and Bioinformatics (Beilstein Endowed Chair for Cheminformatics) at Goethe-University in Frankfurt, Germany, where he concentrates on the development and application of software methods for virtual screening and molecular design.
Abstract - Computational de novo design of drug-like molecules

Molecular de novo design produces novel molecular structures with desired pharmacological properties from scratch. We have focused on fragment-based virtual synthesis strategies for ligand-based molecule assembly. These methods rely on adaptive evolutionary optimization of a compound library using pharmacophore-based fitness functions. The concepts of this approach, its advantages and limitations will be presented together with recent application examples.

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Alexander Kiselyov - ChemDiv, Inc., San Diego, CA, USA
Dr Kiselyov’s main interest is in the area of lead discovery, with a focus on medicinal chemistry, chemoinformatics, and high-throughput screening. His main interests include development of specific and dual small molecule modulators for kinase targets, chemokine receptors and Hh/Wnt signaling cascades. Most recently, Dr Kiselyov was Assistant Vice President of Chemistry for ImClone Systems, Inc. Before mving to ImClone, Dr Kiselyov directed the oncology team and combinatorial chemistry efforts at Amgen, Inc. Dr Kiselyov did his doctoral work at Georgia State University and postdoctoral study at Columbia University and the University of Chicago. Dr Kiselyov has over 13 years of industrial experience in medicinal and organic chemistry and has authored more than 100 publications in the areas of synthetic and medicinal chemistry, including oncology. His work has resulted in 26 patents and patent applications. Dr Kiselyov is now Executive Vice President of R&D at ChemDiv, Inc., San Diego, USA.
Abstract - Focused diversity: application to identification of specific inhibitors for the Hh signalling pathway

Aberrant activation of the Hedgehog (Hh) pathway has been associated with numerous malignancies including basal cell carcinoma, medulloblastoma, and pancreatic cancer. Several reports also suggest that positive regulators of Hh pathway could be used in the treatment of neurodegenerative diseases. Chemical Diversity identified and evaluated seven distinct series of potent, drug-like, trackable chemical series that antagonize Hh signalling. These classes have been further expanded using a structure–activity relationship-based approach to yield compounds with ~5–150 nM functional activity in both C3H10T1/2 and Shh-LIGHT2 assays. These series did not have non-specific cytotoxicity. Epistatic studies including competition (Schild) analysis performed with Shh and reported synthetic agonists and antagonists of the Hh pathway (Curis) linked our compounds to three distinct signalling points in the cascade. These included disruption of Hh signalling at the Hh/Ptch, Smo and Smo-downstream levels. Three Smo-antagonist series displayed different behavior in the competition assays, suggesting discrete binding sites on this seven transmembrane-region receptor. Detailed pharmacokinetic and toxicology studies allowed us to further prioritize molecules for the in vivo efficacy evaluation.

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Christopher Murray - Astex Therapeutics, Cambridge, UK
Chris Murray studied for his undergraduate degree and doctorate at the University of Cambridge. After a post-doc at Indiana University, he joined the Biotech company Protherics, where he helped to develop docking methods and applied structure-based drug design to serine proteases such as Factor Xa. Since 2000 he has been Director of Computational Chemistry and Informatics at Astex Technology (now Astex Therapeutics), where he has worked on establishing fragment-based approaches to drug discovery. He has acted as project leader on a number of drug discovery projects at Astex including the -secretase, hsp90 and FGFR3 programmes.
Abstract - Fragment-based drug design

This presentation will focus on a structure-based approach to the design of potent inhibitors starting from weakly binding fragments. There will be a comparison of fragment-linking and fragment-growing strategies, and a consideration of the theoretical advantages of the technique. The approach will be illustrated on two applications: (1) the aspartic protease -secretase, where novel fragments were identified and used to design potent lead molecules; and (2) the discovery of a pre-clinical candidate for the oncology target hsp90.

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Richard Begent - University College London, UK and National Cancer Research Institute, UK
Richard Begent is Ronald Raven Professor of Clinical Oncology and Head of the Department of Oncology at the Royal Free and University College Medical School of University College London. He is also Chairman of the National Cancer Research Institute (NCRI) Informatics Taskforce, which aims to create an internationally compatible informatics platform in the UK that facilitates access to, and integrated analysis of, data generated from research. A medical oncologist with a practice in gastrointestinal oncology, he directs an academic department with 120 staff engaged in teaching and research, with a focus on cancer pathophysiology and therapeutics. His own research group, working on antibody targeting and vascular therapy of cancer with a major informatics component, is funded by Cancer Research UK, the Department of Health, the Medical Research Council and the European Union.
Abstract - Coordinating cancer biomedical informatics

Whilst cancer is a disease caused by genetic or epigenetic variation, the translation from these origins to the effects on patients and populations is highly complex. Simple associations between a pattern of genetic variation and clinical syndromes or responses to treatment are valuable but give an incomplete picture because of progressing genetic instability in cancers and diversity in the systems biology and medicine of cancer, which influence the effects of genes on outcomes. This complexity can be addressed if there is commonality of standards across the different types of data and if data are shared and integrated between researchers. The National Cancer Research Institute (NCRI) has developed policies for sharing data and a resource for guiding researchers in the types of standards that are required and the resources currently in the public domain (www.cancerinformatics.org.uk/ planning_matrix.htm). Developing metadata standards for single parameter research data such as gene expression microarray output is being addressed by groups of experts in these fields. For multi-parameter research data – for instance that in physiology, pathology or therapeutics – the problem of metadata definition is more complex but is probably best addressed by groups of experts in these fields working with experts in informatics. One approach to this, based on definition of common data elements, will be shown for antibody therapeutics. This approach is designed to facilitate the integration of data between research domains from the molecular to the clinical. The aim is to address complex problems with a view to improving understanding of cancer biology and the outlook for patients.

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Philip Quirke - Leeds University, UK
Biosketch and abstract unavailable at time of going to press
Abstract - TBA

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Kenneth Buetow - National Cancer Institute, Bethesda, MD, USA
Kenneth H. Buetow, PhD is the National Cancer Institute (NCI) Associate Director for Bioinformatics and Information Technology and the Director of the NCI Center for Bioinformatics (NCICB). As Director of the NCICB, Dr Buetow oversees coordination and deployment of informatics in support of NCI research initiatives and the Center’s participation in the evaluation and prioritization of the NCI’s biomedical informatics research portfolio. Dr Buetow initiated the cancer Biomedical Informatics GridTM (caBIGTM) pilot project and oversees its activities. Dr Buetow is also the Chief of the NCI Laboratory of Population Genetics (LPG). The LPG conducts human genetic and genomics research, both at the bench and using informatics tools. He has spearheaded efforts of the Genetic Annotation Initiative (GAI), an attempt to identify variant forms of the cancer genes identified through the NCI Cancer Genome Anatomy Project (CGAP). Prior to his position at NCI, Dr Buetow was at the Fox Chase Cancer Center in Philadelphia, PA. Dr Buetow received his doctorate of philosophy degree in human genetics from the University of Pittsburgh in 1985.
Abstract unavailable at time of going to press

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Peter van der Spek - Erasmus Medical Center, Rotterdam, The Netherlands
Peter J. van der Spek has been appointed as professor and head of the department of bioinformatics at the Erasmus Medical Center. He obtained his doctoral degree in 1995 in the field of molecular carcinogenesis by cloning cancer predisposition genes.

Van der Spek has 6 years of pharmaceutical experience from Akzo-Nobel and Johnson & Johnson and holds several international academic appointments in Japan, Australia and USA. The bioinformatics group at the Erasmus Medical Center focuses on the support of data mining and analysis. This expertise is used for fundamental research, molecular diagnostics, molecular imaging, (forensic) molecular biology and clinical trials. Erasmus Medical Center is one of the largest medical centers of the Netherlands. Van der Spek runs a neuroscience research programme that provides the biological and technological basis for the bioinformatics group. It concentrates on the way the genome as a whole contributes to the evolution, development, structure and function of the brain.
Abstract unavailable at time of going to press

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Paul Workman - Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey, UK
Professor Paul Workman is Director of the Cancer Research UK Centre for Cancer Therapeutics and Harrap Professor of Pharmacology and Therapeutics at The Institute of Cancer Research, Sutton, UK. He is also Visiting Professor at Leeds and Manchester Universities. He was previously (1993–1997) Cancer Research Bioscience Section Head at AstraZeneca Pharmaceuticals, where he led the biology team on the gefitinib (Iressa) discovery project. Before that he was Professor and Director of Laboratory Research in the Department of Medical Oncology, Beatson Laboratories, University of Glasgow (1990–1993), UICC Visiting Fellow at Stanford University (1989) and scientific staff member of the MRC Clinical Oncology Unit, MRC Centre, Cambridge University (1976–1990). Honours include European School of Oncology Award for Excellence in Oncology Research (1985), Cancer Research Campaign (now Cancer Research UK) Life Fellow (1991) and Fellow of the Academy of Medical Sciences (2002). He is a Scientific Founder of Chroma Therapeutics and PIramed Limited.
Abstract - Combinatorial drugging of multiple cancer genome targets

Anticancer drugs in common use are mainly cytotoxic agents that affect proliferation cells in normal as well as malignant cells. Hence they exhibit, at best, a very modest therapeutic selectivity. Drug resistance is also a major problem. The progressive elucidation of the molecular and genomic abnormalities that initiate malignant transformation and drive malignant progression has allowed us to develop an exciting range of mechanism-based molecular cancer therapeutics targeted to the precise molecular abnormalities in the genomes and hijacked signaling pathways of cancer cells. Successful examples include drugs like Herceptin, Gleevec, Iressa and Tarceva. There are, however, many technical challenges and it is still the case that only one in 20 cancer drugs entering the clinic will be sufficiently effective to gain regulatory approval. I will summarize the current challenges and approaches overcome these, focusing on high-throughput strategies to gather and exploit the huge amount of data that is being gathered on the genomic abnormalities in cancer cells.

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James Wells - University of California, San Francisco, CA, USA
Dr Wells received a B.A. degree in biochemistry from the University of California, Berkeley, and a PhD degree in biochemistry from Washington State University with Dr Ralph Yount. His postdoctoral studies were done at Stanford University Medical School, Department of Biochemistry with Dr George Stark. Dr Wells was the founding member of the Protein Engineering Department at Genentech, Inc. where he worked for 16 years. His research focused on designing new functional properties into enzymes and hormones and developing new technologies for engineering proteins. In 1998, Dr Wells founded Sunesis Pharmaceuticals where he served as President and Chief Scientific Officer and developed a novel fragment discovery technology known as disulfide trapping or Tethering. In 2005, Dr Wells joined UCSF as the Harry W. and Diana Hind Distinguished Professor in Pharmaceutical Sciences. He is a joint Professor in the Departments of Cellular & Molecular Pharmacology, and Pharmaceutical Chemistry. His work is focused on site-directed chemistry and biology for understanding protein allostery and protease signalling pathways for drug discovery.
Abstract - Challenges for early-stage drug discovery

The efficiency of the drug discovery process in terms of drugs approved per R&D dollar spent has steadily declined over the past decade. Some have argued that this decline in productivity reflects the dearth of tractable targets left from a biology and chemistry perspective. An alternative view is that getting to the next level of targets will require significant breakthroughs both in biology and in chemistry. I will discuss four clear challenges for early-stage discovery. First, target validation remains a major challenge as drug efficacy has consistently risen as the major reason for drug failure in the clinic. Second, many of the most highly validated but chemically challenging drug targets are protein–protein interactions. Up until a few years ago, no small molecules were found to bind with dissociation constants even below 1 M. Now there are a handful of structurally well-characterized examples. Are we closer to chemically attacking these highly validated targets? Third, traditional inhibitors of enzyme active sites often prove challenging for selectivity (e.g. kinases) as well as generating good drug-like compounds when the substrates are highly charged (e.g. phosphatases, proteases). Are there allosteric sites that we can approach, and how do we find them? Lastly, and perhaps most challenging, is the growing investor impatience with drug discovery. In the last decade most pharmas have relied heavily on biotech for early-stage target discovery and technology. However, investors have significantly devalued technology and early-stage discovery in big and little pharma. The result has been that most of the early-stage biotechs have either switched to later-stage activities, been acquired, or GOOBed (Gone Out Of Business). Where will innovation come from long-term and who will pay for it? Academic institutions remain a tremendous source of innovation. Can Pharma/biotech begin to work in systematic ways with academic institutions to solve some of the challenges for early-stage discovery?

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Contact Information

If you have any enquiries or problems regarding the symposium please send an e-mail to tacb@ebi.ac.uk.






























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