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Market Research Report

Managing Toxicology for the Future: Improving Predictive Power Through Integrated Technologies, 2nd Edition

Published by Drug & Market Development Publications Contact us : +1-860-674-8796
Published 2006/08 Content info 226 pages
Product code DM44535
Price From  US $ 4950 Order/Price list
US $ 4950 PDF by E-mail (Single User License)
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Description TOC

Abstract

Summary

About $70 million (20%) of the total R&D costs per drug are spent on ADME/Tox failures.

Preclinical toxicity is one of the major bottlenecks in drug development. In particular, the ability to predict the safety of a drug in the preclinical stage, prior to human testing, has been one of the major bottlenecks in drug development. Current approaches have serious limitations in their ability to predict toxicity in humans, and thus invite substantial risk not only in terms of patient welfare, but also in terms of the cost of late-stage clinical trial failure. The integration of novel and traditional approaches to preclinical toxicity assessment will have a major impact on the ability to predict compound behavior in humans, reduce clinical trial failure, and cut both risk and cost in drug development.

Scope

D&MD's Market Analysis Report, Managing Toxicology for the Future: Improving Predictive Power Through Integrated Technologies, discusses the critical challenges associated with ADME/Tox testing during pharmaceutical R&D, including the requirements for:

  • New strategies to minimize toxicity-related drug failures aimed at improving R&D productivity and pharmaceutical risk management.
  • Breath of expertise in multiple disciplines to deal with a complex and fragmented technology landscape, such as "omics" technologies and in silico approaches.
  • Integrative activities that combine new and old approaches to assess drug safety in a highly regulated and risk-aversive environment.
  • Industry-wide collaborative efforts to build the necessary research infrastructure for toxicology, including toxicology databases and computational models.
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