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With the application of high-throughput
approaches from genomics, combinatorial chemistry, and screening, the
pharmaceutical industry is now faced with an overabundance of potential leads
rather than a shortage of initial hits. Technologies are emerging to help better
identify the most promising leads, but it is critical that these technologies be
able to attain validation and acceptance by regulatory agencies as well as have
the ability to run in parallel with other techniques in order to accelerate lead
selection decisions. This conference will provide a forum for problem solving in
this arena.
CHAIRPERSONS
Dr. Richard D. Beger, U.S. Food and Drug Administration
Dr. Stephen K. Durham, Bristol-Myers Squibb Pharmaceutical Research Institute
Dr. Stephen R. Johnson, SmithKline Beecham Pharmaceuticals
Dr. Peter Jurs, Pennsylvania State University
ADDITIONAL SPEAKERS
Dr. Michael H. Abraham, University College London (UK)
Dr. Kathe Andrews-Cramer, Phase-1 Molecular Toxicology, Inc.
Dr. Darwin Asa, Tecan, Inc.
Dr. Susan Bassett, Bioreason, Inc.
Dr. Richard D. Beger, U.S. Food and Drug Administration
Dr. Timothy Carlson, Camitro Corporation
Mr. Lawrence H. Cohen, Pfizer, Inc.
Dr. Stephen K. Durham, Bristol-Myers Squibb Pharmaceutical Research Institute
Dr. William J. Egan, Pharmacopeia, Inc.
Dr. Kathe Andrews-Cramer, Phase-1 Molecular Toxicology, Inc.
Dr. Steven L. Gallion, ArQule, Inc.
Dr. Peter Gund, Pharmacopeia, Inc.
Dr. Thorsten Hartmann, Nimbus Biotechnology (Germany)
Dr. Stephen R. Johnson, GlaxoSmithKline R&D
Dr. Peter Jurs, Pennsylvania State University
Dr. Edward H. Kerns, Wyeth-Ayerst Research
Dr. Gilles Klopman, Case Western Reserve University
Dr. Pil H. Lee, Pharmacia & Upjohn, Inc.
Dr. Glenn M. Monastersky, Proxima Genetics, Inc.
Dr. Joseph A. Rininger, CuraGen Corporation
Dr. Patrick Sinko, Trega Biosciences, Inc.
Dr. Timothy G. Terrell, VistaGen, Inc.
Dr. Hongwu Wang, Schering-Plough Corporation
PREDICTIVE TOXICOLOGY AND EFFICACY
Paradigm Shift to Virtual Determination of Drug Safety Liabilities
Preclinical Toxicological Screening of Drug Candidates
Predictive Toxicology and Efficacy: Stem Cell-Based Drug Profiling
Pharmacogenomic Selection of Candidates based on Gene Expression
Knowledge-Based Techniques to Automate the Discovery of the Structural Basis for
Activity
Differential mRNA Expression Technology for Drug-Induced Efficacy and Toxicity
Profiles
PROFILING
An Integrated Approach to Lead Identification and Optimization
Parallel Integrated Lead Optimization Technologies (PILOT™) for Accelerated
Drug Discovery
Developing SDAR and QSDAR Models
Pharmaceutical Profiling for Lead Selection and Optimization
Vivid™ Technology for Compound Profiling
Cell-Based High-Throughput ADME-Tox Screening
BIOAVAILABILITY
Aqueous Solubility Prediction from Molecular Structure
High-Throughput Physicochemical Screening
Computational Modeling for Drug Solubility
Computational Tools for Predicting ADME Characteristics of Potential Drug
Candidates
New Approach to the Evaluation of Bioavailability of Drugs
PREDICTIVE ADME
In Silico ADME Profiling for Smarter Lead Optimization
Computational ADME Modeling
Decision Support Systems for Lead Discovery, Follow-up, and Optimization
Modeling of Cellular Permeability for Nonpeptide CCK-A Agonists
High-Throughput Prediction of Physiochemical and Biochemical Processes from
Structure
Computational Approaches to Lead Optimization
SUNDAY, MARCH 18
6:00-7:30pm Early Registration and Poster
and Exhibit Set-up
MONDAY, MARCH 19
7:30am Registration, Poster and Exhibit
Viewing, and Light Continental Breakfast
PREDICTIVE TOXICOLOGY AND
EFFICACY
8:30 Welcome and Opening Comments by Session
Chairperson
Dr. Stephen K. Durham, Lead Safety Assessment, Bristol-Myers Squibb
Pharmaceutical Research Institute
8:40 Paradigm Shift from Traditional to
Virtual for the Determination of Drug Safety Liabilities
Dr. Stephen K. Durham
The rapidly escalating costs of drug development necessitate the early
identification of drug candidate safety liabilities which could result in
failure during clinical trials. Favorable characteristics from a
biopharmaceutical, ADME, and preclinical drug safety perspective must be
incorporated into the desired drug candidate whenever possible. The aggressive
implementation of in silico analysis as a sentinel filter for drug candidate
safety liabilities, followed by rapid predictive in vitro assays, is a
progressive strategy for improving the selection process.
9:10 Preclinical Toxicological Screening of
Drug Candidates
Dr. Kathe Andrews-Cramer, Director, Pre-Clinical
Business Development, Phase-1 Molecular Toxicology, Inc.
Summary unavailable at time of printing.
9:40 Predictive Toxicology and Efficacy:
Stem Cell-Based Drug Profiling
Dr. Timothy G. Terrell, Vice President, Preclinical
Development,VistaGen, Inc.
VGEN's stem cell platform provides the bridge between laboratory and
clinical studies useful throughout the pharmaceutical industry, dramatically
improving conventional cell-based assays with complex interacting multitissue
(e.g., blood, vessels, nerves, bone, muscle, etc.) systems that mimic human
biological systems—all within a single culture assay—for more clinically
predictive screening systems.
10:10 Poster and Exhibit Viewing,
Refreshment Break
10:45 Pharmacogenomic Selection of New
Molecular Entities Using a Swine Tissue-Specific Expression Platform
Dr. Glenn M. Montastersky, Co-founder, Proxima Genetics, Inc.
Proxima Genetics, Inc. uses proprietary animal gene expression databases,
most notably from swine, to provide the prognostic pharmacogenomic and
toxicogenomic evaluation of early-stage and medium-stage drug candidates for
human and animal marketplaces. This genomics-based platform may be applied 1)
to the selection of candidates for drug discovery projects and 2) to the
relatively rapid in vivo screening of drug candidates prior to the costly
Safety and Clinical Trial phases of drug Development. The swine database is
based on restriction enzyme-digested cDNA from expressed genes in multiple
tissues and contains over 35,000 contigs that have been organized into over
140 protein structural classes.
11:15 Knowledge-Based Techniques to Automate
the Discovery of the Structural Basis for Activity
Dr. Susan Bassett, Vice President, Technology, Bioreason, Inc.
Knowledge-based techniques that automate the discovery of the structural
basis for activity can be applied to a wide variety of problems in lead
optimization. These methods can assist project leaders not only in choosing
promising lead families for follow-up screening based upon potency-related
structure activity relationship, but can also provide early detection of ADME
properties such as protein binding and potential toxicity issues by
identification of toxicophores. The earlier such information is available, the
better the decision process is, particularly when specific structural
characteristics can be used to form the basis of the decision. Augmenting
captured human expertise with the data-driven discoveries that can be made
with this technology is a powerful step forward in building automated systems
for lead optimization analysis.
11:45 GeneCalling®: A Comprehensive
Differential mRNA Expression Technology Effective in Generating Drug-Induced
Efficacy and Toxicity Gene Expression Profiles
Dr. Joseph A. Rininger, Senior Research Scientist, Pharmacogenomics,
CuraGen Corporation
Whether based upon markers of toxicity or efficacy, the ability to reduce
costs of preclinical and clinical drug development hinges upon the capability
to efficiently triage lead compounds. GeneCalling®, an mRNA transcript
profiling technology, has enabled us to create gene expression profiles of
drug-induced transcriptional responses in various target tissues. This
presentation will describe the GeneCalling® technology as well as highlight a
subset of our data that illustrates how this technology can be utilized to
characterize the molecular pharmacology and risk among lead drug candidates.
12:15 Panel Discussion with All Morning
Speakers
12:30 Lunch (on your own)
PROFILING
1:45 Comments by Session Chairperson
Dr. Richard D. Beger, Division of Chemistry, National Center for
Toxicological Research, U.S. Food and Drug Administration
1:50 The Application of Parallel Integrated
Lead Optimization (PILOT™) for Accelerated Drug Discovery
Dr. Steven L. Gallion, Research Fellow, ArQule, Inc.
The need to deliver higher quality GLP toxicology candidates in a more
efficient manner is paramount to increasing the number of IND candidates and
success through clinical development. We are implementing a process that
leverages our integrated technology platform, PILOT™, to rapidly explore SAR
and eADMET profiles. The experimental data are used to generate and refine
computational design and predictive models against classes of targets or
therapeutic areas. Using a variety of computational, experimental, and
predictive tools, we have defined subset libraries from our entire repository
(>700,000 compounds) suitable for lead generation efforts or
target-directed assays. Furthermore, the Compass Array library, a diverse
subset (approximately 60,000 compounds) of the compound repository, has been
profiled for cellular viability and inhibition of four CYP450 isozymes. This
information can be used to select the most qualified leads to advance to a
lead optimization program. During the lead optimization process, eADMET data
are generated in parallel with SAR data. Initial experimental data
illustrating the utility of computational design, eADMET profiling, and
predictive models during lead generation and optimization will be presented.
2:20 Developing Spectrometric Data-Activity
Relationships (SDAR) Models and Quantitative Spectrometric Data-Activity
Relationships (QSDAR) Models
Dr. Richard D. Beger
Methods are disclosed for establishing a relationship between
spectrometric data and binding of a compound to a receptor. Spectral data,
including data from nuclear magnetic resonance (NMR), infrared (IR), and mass
spectrometric (MS), are used along with endpoint data to train a pattern
recognition program. The methods disclosed give significant, practical
advantages of speed and simplicity to the methods involved in Quantitative
Structure Activity Relationships (QSAR).
2:50 Pharmaceutical Profiling Program for
Lead Selection and Optimization
Dr. Edward H. Kerns, Chemical Sciences, Wyeth-Ayerst Research
Discovery teams have traditionally focused on biological activity
optimization. Unfortunately, unsuitable physicochemical or pharmacokinetic
properties result in attrition or delay during development. Investment in
leads for which these properties have not been measured runs the risk of
blocking compounds with better overall pharmaceutical profiles. Proactive
early measurement of property information provides the opportunity to (1)
select advantageous HTS hits and leads, (2) understand the specific
liabilities of pharmacophores, and (3) build favorable pharmaceutical
properties into lead series structures in parallel with activity optimization.
Property measurements also add greater detail and certainty to preliminary
computational approaches. Our integrated program uses high-throughput profiles
to measure the predictive ADME properties: solubility, permeability,
lipophilicity, stability (chemical, physical, physiological), and metabolism.
It provides targeted information for specific processes in drug absorption,
metabolism, and pharmaceutics. For example, PAMPA permeability profiling
provides insights on passive diffusion through physiological membranes,
without complication by efflux, active transport, or metabolic processes. The
development of "pharmaceutical profiles" and their implementation
and application will be discussed.
3:20 Poster and Exhibit Viewing, Refreshment
Break
4:00 Vivid™ Technology for Compound
Profiling
Mr. Lawrence H. Cohen, Assistant Scientist, Candidate Enhancement,
Pfizer, Inc.
With the advancement of high-throughput synthesis and early discovery ADME
support, high-throughput screening methods have become critical to drug
metabolism. Therefore, high-throughput assays have been evaluated for
drug-drug interaction studies in early discovery at Pfizer. In this
presentation, a description of a high-throughput screening system, which uses
fluorescent probes, minimal reagents, and automated data handling, will be
discussed. An overview of the enzyme kinetics, speed, and efficiency of the
assay, comparison of conventional probes versus fluorescent probes, and data
handling will be offered.
4:30 LabCD Microfluidics Technology for
Cell-Based High-Throughput ADME-Tox Screening
Dr. Darwin Asa, Chief Scientific Officer, Drug Discovery, Tecan, Inc.
Summary unavailable at time of printing.
5:00 Panel Discussion with All Afternoon
Speakers
5:30 Networking Reception (sponsored by
Cambridge Healthtech Institute)
TUESDAY, MARCH 20
8:00am Poster and Exhibit Viewing and Light
Continental Breakfast
BIOAVAILABILITY
8:30 Comments by Session Chairperson
Dr. Peter Jurs, Professor of Chemistry, Pennsylvania State University
8:35 Aqueous Solubility Prediction of Two
Classes of Organic Compounds from Molecular Structure
Dr. Peter Jurs
QSARs can be developed using calculated molecular structure descriptors,
genetic algorithm feature selection, and quantitative predictive models
constructed with statistical or computational neural network methods. The
method is inductive—the models are based on generalizations taken from a
training set of compounds, and the models have predictive ability for new
compounds. Several specific, recent QSAR studies will be presented as examples
of the approach.
9:05 High-Throughput Physicochemical
Screening
Dr. Thorsten Hartmann, Sales and Scientific Support, Nimbus Biotechnology
For prediction of the ADME parameters of lead compounds, it is important
to determine the physicochemical parameters for solubility, lipophilicity, and
protein binding. Up to now the standard parameter for lipophilicity was the
octanol-water partitioning coefficient. But this method is not well suited for
the prediction of membrane permeation and absorption, since it cannot mimic a
natural cell membrane. Nimbus Biotechnology has developed a high-throughput
method for measuring lipophilicity/membrane affinity with solid supported
lipid bilayers, which mimic natural cell membranes. This new technology is
compared to more traditional methods and to results obtained from CaCo-2
cells.
9:35 Development of Computational Model for
Drug Solubility and Its Use in Drug Discovery
Dr. Pil H. Lee, Computer-Aided Drug Discovery, Pharmacia & Upjohn,
Inc.
Knowledge of a drug's aqueous solubility is useful for predicting its
bioavailability. We will describe our efforts to develop computational models
for drug solubility and their use in the drug discovery. (Co-authors: Hua Gao,
Philip S. Burton, and Gerry M. Maggiora)
10:10 Poster and Exhibit Viewing,
Refreshment Break
10:45 Applications of Computational Tools
for Predicting ADME Characteristics of Potential Drug Candidates
Dr. Patrick Sinko, Chief Scientist, Discovery
Technologies, Trega Biosciences, Inc.
In today's fast-paced drug discovery environment, early access to a
compound's absorption, distribution, metabolism, and elimination (ADME) has
become a critical requirement for enhancing decision making in drug research.
Organizations can save time, money, and experimental resources by anticipating
which compounds are likely to have the best ADME characteristics rather then
relying solely on animal testing or in vitro assays for this information. The
Absorption Module of the iDEA (in Vitro Determination for the Estimation of
ADME) Predictive ADME Simulation System is a computational system developed to
predict the rate and extent of human oral drug absorption. IDEA can be used
during lead generation and lead optimization to facilitate the decision to
promote compounds through the drug discovery process. Applications of using
iDEA to predict the ADME characteristics of compounds and how computational
simulation systems can accelerate drug discovery will be discussed.
11:15 New Approach to the Evaluation of
Bioavailability of Drugs
Dr. Gilles Klopman, Charles F. Mabery Professor of Research, Department
of Chemistry, Case Western Reserve University
Increasing the bioavailability of drugs and developing new drugs to be
administered by oral route are important objectives in the drug discovery
industry. It is thus valuable to determine, early during the drug development
program, the bioavailability values of the potential drugs, and hence it is
imperative to have good predictive models. For this reason, a drug oral
bioavailability model was constructed on the basis of inverted oral
bioavailability data for 362 drugs. The Multiple Computer-Automated Structure
Evaluation Program, M-CASE, was used for the construction of the model, which
separates the total data set into groups of drugs with common structural
features. For each of these groups a multiparameter Quantitative
Structure-Activity Relationship (QSAR) was obtained. The model was shown to be
able to predict correctly the percentage of oral drug bioavailability for -80%
of compounds with an average error of only -8%.
11:45 Panel Discussion with All Morning
Speakers
12:15 Luncheon (sponsored by Cambridge
Healthtech Institute)
PREDICTIVE ADME
1:15 Comments by Session Chairperson
Dr. Stephen R. Johnson, Chemoinformatics Department, GlaxoSmithKline
R&D
1:20 In Silico ADME Profiling for Smarter
Lead Optimization
Dr. Stephen R. Johnson
From lead selection through library design through iterative medicinal
chemistry, greater emphasis is being placed on the developability of drug
candidates. By striking a better balance between potency and developability,
the difficulty of developing compounds with poor pharmacokinetics can be
lessened. This will reduce the time used in development, saving valuable
patent-covered marketing time and bringing much-needed medicines to the
patient population faster. We will present a collection of computational ADME
models used to profile compounds in every phase of the discovery process.
Including permeability, solubility, P-450 inhibition, and serum albumin
binding, this presentation will discuss our efforts in developing these models
and their use throughout the lead optimization process.
1:50 Computational ADME Modeling
Dr. William J. Egan, Research Scientist, Center for Informatics and
Drug Discovery, Pharmacopeia, Inc.
The development of predictive models for ADME properties at Pharmacopeia
will be presented. The utility of the models for lead optimization and library
design will be discussed.
2:20 Decision Support Systems for Lead
Discovery, Follow-up, and Optimization: Use of Predictive Affinity, ADME, and
Toxicity Models
Dr. Peter Gund, Senior Director, Molecular Modeling, Pharmacopeia, Inc.
Project leaders are continually making decisions on project directions
based on incomplete knowledge and equivocal data. There is anecdotal evidence
that decisions to continue or to terminate research projects are often poorly
made. Thus, on the one hand, only one in ten candidates given IND approval
will ultimately be approved as a drug; on the other hand, killed projects have
been known to resurface and ultimately succeed. Modern computer systems for
data mining, data visualization and analysis, and property prediction can
result in better candidate development judgments. This talk will detail the
successful application of such systems in Pharmacopeia lead discovery and
optimization projects and will recount our plans to further develop these
capabilities.
2:50 Poster and Exhibit Viewing, Refreshment
Break
3:15 Modeling of Cellular Permeability for
Nonpeptide CCK-A Agonists
Dr. Hongwu Wang, Senior Scientist, Schering-Plough Corporation
A quantitative structure-permeability relationship model was developed
based on a large data set of 290 nonpeptide CCK-A agonists. Substructure
counts, calculated molecular properties, and indicator variables defined by
recursive analyses were used as descriptors. The relationships between
permeability and some of the physicochemical properties believed to correlate
with cellular permeation were investigated. The model can be effectively used
for structural modifications to improve the permeability characteristics of
CCK-A agonists, and the methods can be used in modeling of PK/ADME properties.
3:45 High-Throughput Prediction of
Physiochemical and Biochemical Processes from Structure Using Calculated
Abraham Solvation Descriptions
Dr. Michael H. Abraham, Department of Chemistry, University College
London
Transport properties of nonelectrolytes can be satisfactorily correlated
and predicted by multiple linear free energy relationships that use the
Abraham solvation descriptors as the independent variables. Recently, we have
calculated the descriptors from structure, using a group contribution scheme,
so that predictions from structure can now be made. This presentation will
discuss the results of those calculations.
4:15 Computational Approaches to Lead
Optimization
Dr. Timothy Carlson, Director, Drug Metabolism and
Pharmacokinetics, Camitro Corporation
Computational simulations of ADME/Tox properties of compounds can be used
by medicinal chemists and ADME/Tox scientists to facilitate the design and
selection of drug candidates with optimal ADME/Tox profiles. Camitro's
predictive computational models, developed in collaboration with select
pharmaceutical partners, require no experimental data but rather use compound
structure information directly to predict the ADME/PK properties of drug
molecules. This approach permits the high-throughput analysis of virtual
compounds or virtual compound libraries before synthesis at very early stages
in the drug development process and thus obviates the need for iterative
compound synthesis and experimentation. Camitro's models are also tightly
integrated, thereby enabling drug development scientists to evaluate and
optimize multiple ADME properties of drug candidates simultaneously and to
design drug candidates with not just adequate, but optimal, ADME/PK profiles.
4:45 Panel Discussion with All Afternoon
Speakers
5:15 Close of Conference
TRAVEL INFORMATION
Please call Great International and National Travel at 617-527-0800 and
ask for Joyce Dunn or e-mail her at jdunn@greatintltravel.com.
HOTEL INFORMATION
The Ritz-Carlton Hotel, Tysons Corner
1700 Tysons Boulevard
McLean, VA 22102
T: 703-506-4300
F: 703-506-2694
Room Rate: $185
Cut-off Date: February 25, 2001
Please call the hotel directly to make your room reservation. Identify
yourself as a Cambridge Healthtech Institute conference attendee to receive
the reduced room rate. Reservations made after the cut-off date or after the
group room block has been filled (whichever comes first) will be accepted on a
space-and-rate-availability basis. Rooms are limited, so please book early.
CALL FOR EXHIBITORS
Companies with products or services related to toxicology, ADME,
pharmacogenomics, productivity improvement as well as other technologies used
to select lead candidates for drug development will not want to miss this
meeting. Please contact Jim MacNeil at 617-630-1341 to obtain an exhibitor
package or to inquire about becoming a corporate sponsor of this event.
These companies have already confirmed their
space:
Arqule; Asinex; Camitro; Chemspeed, Inc.; Genevac; Trega Biosciences
CALL FOR POSTERS
Cambridge Healthtech Institute encourages attendees to gain further exposure
by presenting their work in the poster sessions. Please fill out the
registration form, with the poster title and primary author. To ensure
inclusion in the conference binder, a one-page summary must be submitted and
registration must be paid in full by February 9, 2001.
POSTER
INSTRUCTIONS
Phone: 617-630-1300, Fax: 617-630-1325
Email: chi@healthtech.com
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