Market Report Source

Overview

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 That disparity is likely due to outdated tools, says “Innovation and Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products,” a white paper published last year by FDA: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.

Table of Contents

Table of Contents

Predictive ADME and Toxicology Strategies

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

iv

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

v

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

vi

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

vii

Simulations Plus

Market analysis

Chapter 5 Appendix

Research methodology

Abbreviations and acronyms

Index

viii

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 140