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Predictive ADME and Toxicology Strategies
Challenges and opportunities for in vivo, in vitro and in silico predictive technologies
Publication Date January 2006
Publisher Business Insights
Product Type Strategic Report
Pages 149
ISBN Number not applicable
Product Code RBI101
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Summary
Researchers in the pharmaceutical and biotech industry have been developing tools over the years to maximize the efficacy of drugs while minimizing toxicity, and advances have been made. A decade ago, the number of drugs failing preclinically due to poor pharmacokinetics was upwards of 40%, but improved in vitro and animal models have reduced that rate to about 10%. Failures due to ADME and toxicology, however, are still in the 50% to 60% range, making it the number one reason for preclinical attrition. That disparity is likely due to outdated tools, says 'Innovation and Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products,' a white paper published last year by FDA: 'Despite some efforts to develop better methods, most of the tools used for toxicology and human safety testing are decades old. Although traditional animal toxicology has a good track record for ensuring the safety of clinical trial volunteers, it is laborious, time-consuming, requires large quantities of product, and may fail to predict the specific safety problem that ultimately halts development.' The white paper noted that one pharmaceutical company estimated that clinical failures based on liver toxicity cost them more than $2 billion over the last decade. Measuring the ADME/Tox properties early can be one method of minimizing failure. The process of drug discovery and development requires that a drug's behavior in the human body is modelled in other systems before being actually tested in humans. When considering the drug discovery process backwards, certain toxicities must be determined before long-term use is allowed. Prior to this, the correct starting dose for human tests must be estimated. This over simplification illustrates some of the reasons for the processes being analysed with ADME and toxicology for most drugs. This report provides an in-depth analysis of emerging and rapidly growing ADME/Tox screening technologies. The report focuses on emerging technologies, market drivers, restraints, challenges, and provides in-depth company profiles and key market engineering parameters for in vitro and in silico ADME/Tox screening markets.
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Content
- Executive Summary
- Introduction To Predictive Adme/Tox Screening
- Traditional Methods Of Adme/Tox Testing
- Novel In Vitro And In Vivo Predictive Models
- In Silico Adme/Tox
- Chapter 1 Introduction To Predictive Adme/Tox Screening
- Summary
- Objectives And Scope Of Report
- Overview
- A Historical Review Of The Drug Discovery Process
- Drug Attrition Rates In Preclinical And Clinical Testing Phases
- The Importance Of Adme/Tox Testing
- Early Adme Studies
- Current Market Landscape
- Market Challenges
- Market Drivers And Restraints
- Chapter 2 Traditional Methods Of Adme/Tox Testing
- Summary
- Introduction
- Absorption
- Distribution
- Metabolism
- Excretion
- Toxicity Testing
- Physiochemical Characterization And Stability
- Conventional In Vitro Adme/Tox Screening Methods
- Caco-2
- Mdck
- Cyp 450 And Hepatic Microsomes
- Paracellular Markers, Mtt Test, And Ldh Test
- Conventional In Vivo Adme/Tox Models
- Radiolabels
- Cassette Dosing
- Semi-Simultaneous Dosing
- Limitations Of Traditional In Vitro And In Vivo Adme/Tox Screening
- Methods
- Chapter 3 Novel In Vivo And In Vitro Predictive Models
- Summary
- Introduction
- In Vitro Screening
- Cultured Cells
- Membrane Vesicles (Bbmv, Blmv)
- Caco-2 Cells
- Madin-Darby Canine Kidney (Mdck) Model
- Ht29
- T84 And Iec-18 Cell Lines
- Immobilized Artificial Membrane (Iam) Columns
- Parallel Artificial Membrane Permeation Assay (Pampa)
- P-Glycoprotein (Pgp)-Drug Transporter
- Solubility
- Log P
- Plasma Protein Binding
- Toxicity
- Automated And High Throughput In Vitro Adme/Tox Screening
- Millipore 96-Well Filter-Based Assays And The Hamilton Microlab Star
- Liquid Handling Workstation
- Multiscreen Caco-2 Assay System
- Leadstream
- Hurel Microfluidic Circuit
- Nanostream Cl System
- Nanomate And Esi Chip System
- Rapidfire Lead Discovery System
- The Biomek Fx System
- B-Clear, Accutate And Accupro Systems
- Liquid Handling Systems
- In Vivo Models
- Drosophila
- C. Elegans
- Rodent Models
- Zebrafish Models
- Competitive Structure
- Company Profiles
- Aclara Biosciences
- Advion Biosciences
- Agilent Technologies
- Amphioxus Cell Technologies
- Applied Biosciences
- Bd Biosciences
- Biotrove Inc.
- Caliper Life Sciences
- Covance
- Daniolabs
- Eksigent Technologies
- Gene Logic
- Hurel Corporation
- Lgc Limited
- Nanostream Inc.
- Nemarx Pharmaceuticals Inc.
- Nimbus Biotechnology
- Novascreen
- Perkin Elmer
- Phylonix
- Qualyst
- Tecan
- Thermo Electron Corp.
- Xenogen
- Zygogen
- Zyomyx
- Market Analysis
- In Vitro Adme/Tox Screening Market
- In Vivo Adme/Tox Market
- Chapter 4 In Silico Predictive Models
- Summary
- Introduction
- Current Technologies
- Mathematical Methods
- Oral Bioavailability
- Solubility
- Blood-Brain Barrier Penetration
- Absorption/Membrane Permeability
- Toxicity
- Metabolism
- Physicochemical Properties
- "Global" And "Local" Models
- Commercially Available Products
- Gastroplus
- Admet Modeler
- Dddplus
- Admet Predictor
- Derek For Windows
- Meteor
- Vitic
- Accord For Excel
- Cerius2 Adme/Tox Package
- Acd/Logd Suite And Acd/Logd Sol Suite
- Metabolite Id
- Aurscope Adme/Ddi
- Bio-Loom
- Knowitall
- Pallas Software Family
- Hazardexpert
- Metabolexpert
- Toxalert
- Mdl Metabolite Database
- Mdl Toxicity Database
- Metadrug
- Volsurf
- Metasite
- Emerging Technologies
- Factors Limiting The Impact Of In Silico Adme/Tox Models
- Competitive Structure
- Company Profiles
- Accelrys
- Applied Biosystems
- Aureus Pharma
- Bg Medicine
- Biobyte Corporation
- Biokin Limited
- Bio-Rad Laboratories
- Chemsilico
- Chenomx
- Comgenex Inc.
- Compudrug
- Cyprotex
- Elsevier Mdl
- Genego
- Leadscope
- Lhasa Limited
- Molecular Discovery Ltd.
- Pharma Algorithms
- Simulations Plus
- Market Analysis
- Chapter 5 Appendix
- Research Methodology
- Abbreviations And Acronyms
- Index
- List Of Figures
- Figure 1.1: The Major Phases Of Drug Discovery
- Figure 1.2: The Drug Discovery Phase Of A Typical Project Aimed At Producing A Drug
- Figure 1.3: Typical Success Rates At Each Step Of Drug Discovery And Development
- Figure 1.4: Factors That Cause The Failure Of Potential Drug Candidates
- Figure 1.5: Adme/Tox Market Challenges
- Figure 1.6: Adme/Tox Market Challenges
- Figure 3.1: The Hamilton Microlab Star Liquid Handling System
- Figure 3.2: Multiscreen Caco-2 Assay System Components
- Figure 3.3: The Leadstream System
- Figure 3.4: The Nanostream Lc System
- Figure 3.5: Esi Biochip
- Figure 3.6: The Biomek Fx Laboratory Automation Workstation
- Figure 3.7: Vivovision System For Retn-Luc Expression In Male And Female Mice
- Figure 3.8: Utility Of The Zebrafish Model In Drug Design
- Figure 3.9: In Vitro Adme/Tox Revenue Forecasts, 2004-2012
- Figure 3.10: In Vivo Adme/Tox Revenue Forecasts, 2004-2012
- Figure 4.11: Admet Modeler Overview
- Figure 4.12: Admet Predictor Screen Shot
- Figure 4.13: Knowitall Concept
- Figure 4.14: In Silico Adme/Tox Revenue Forecasts
- List Of Tables
- Table 2.1: Typical Experiments To Assess The Adme Properties Of Potential Drug Candidates
- Table 2.2: Percentage Of Pharmacokinetic Studies By In Vivo Model
- Table 3.1: In Vitro And In Vivo Adme/Tox Companies And Products, A-M
- Table 3.2: In Vitro And In Vivo Adme/Tox Companies And Products, N-Z
- Table 3.3: In Vitro Adme/Tox Revenues, 2004-2012
- Table 3.4: In Vivo Adme/Tox Revenues, 2004-2012
- Table 4.5: Mathematical Models For Predictive Adme/Tox
- Table 4.6: Advantages And Disadvantages Of "Global" Predictive Models
- Table 4.7: Advantages And Disadvantages Of "Local" Predictive Models
- Table 4.8: Commercially Available In Silico Adme/Tox Products
- Table 4.9: In Silico Adme/Tox Revenues, 2004-2010
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| Level |
Management Strategy |
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| Data |
Broad Market Predictions |
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| Profiles |
Introduces Relevant Companies |
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| Features |
Identifies Hot Technologies |
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