Systems Biology

Published by: Biopharm Knowledge Publishing

Published: Jul. 8, 2009 - 230 Pages

Table of Contents

EXECUTIVE SUMMARY

CHAPTER 1 DISCOVERY: NEW DEVELOPMENTS, TOOLS & LEAD OPTIMIZATION

Summary

Introduction

Potential Impact of Systems Biology on Pharma R&D & Cost-savings

Methodology

In silico

CHAPTER 2 DISCOVERY: TOWARDS VIRTUAL CHEMISTRY SCREENING

Summary

Introduction

Identifying targets & druggability space

Combinatorial chemistry Tools

Diversity Tools

Diversity-oriented synthesis and discovery

Differentially expressed proteins

Phage display

Chirality Tools

Asymmetrical synthesis

Homology Tools at Discovery

Homology modeling

Chemometrics

Cheminformatics

Fragment Based DD

Qualitative and quantitative screens and filters

Summary on chemistry tools at drug discovery: Key findings &opportunities

CHAPTER 3 BIOCHEMISTRY: PHASE 1 LEAD DISCOVERY & MOLECULAR INTERACTIONS

Summary

Introduction

Molecular screens: Receptor-ligand (R-L) interaction & molecular modeling

Molecular modeling

Quantum Chemistry

Molecular Mechanics

Molecular Dynamics

Receptor based QSAR methods

Biomimetics (BioMim)

Co-drugging: Multiple targets, combination therapy & multistage targeting

Multicomponent drugs

Multitarget approach

Multistage targeting

Collateral efficacy & permissive antagonism

Text mining for interactions

Employment of biochemical networks

Overview of deterministic models

Emergence

Reactome

Bioinformatics

Summary on biochemical tools: key findings & opportunities

CHAPTER 4 MOLECULAR BIOLOGY: PHASE 2 LEAD DISCOVERY & IN SILICO SCREENING

Summary

Introduction

Metabolomics

Metabolic profiling

Peptidome

Proteome analysis

Chemogenomics

Morphogenics

Minimal Phenotype & synthetic biology

Reconstructing biological networks

Summary on molecular biology tools: key findings & opportunities

CHAPTER 5 DISCOVERY: COMPUTATIONAL SYSTEMS BIOLOGY IN CELL BIOLOGY

Summary

Introduction

Cellular environment: network reconstruction & inference from experimental data

Reconstructing gene networks

Data mining

Analysis of disease correlation networkTM & concerted metabolic activation

Challenges for Stem Cells: Control

Emergent properties

Computational Systems Biology

Summary on quantitative CSB tools: Key findings & opportunities

CHAPTER 6 DEVELOPMENT: PHARMACOLOGY & PHARMOKINETICS

Summary

Introduction

In vivo Pharmacology

Animal disease models

Gene knockout animal models

Pheno- and genotyping

RNA interference

Pharmacogenomics

In silico Pharmacology: future

Summary on in vivo pharmacology: Key findings & opportunities

In vivo Pharmacokinetics - towards in silico systems PK & toxicology

Microdosing in PK

Adaptive trial design

Equilibrium vs. non-equilibrium PK models

Toxicity biomarkers

In silico toxicity prediction

Quantitative PKPD/tox modeling

Summary on PK quantitative simulation tools: key findings & opportunities

Multiscale CSB

Redefining (& discovering) emergent properties

Virtual organs, disease models, virtual patient

Population level model: towards individualized medicine

Targeting networks: towards organismic & full-scale quantitative design

Redefining traditional R&D paradigm

Summary on multiscale CSB: Key findings & opportunities

CHAPTER 7 DEVELOPMENT: DRUG FORMULATIONS & PRODUCTION

Summary

Introduction

Targeting concept & mechanisms

Nanoscale drug delivery systems

Summary on drug formulation: Key findings & opportunities

Metabolic engineering

Parallel production methods

Process/Equipment scale-up

Qualitative systems tools

Rule of thumb

Towards quantitative production design

Summary on design and production: key findings and opportunities

CHAPTER 8 DEVELOPMENT: PRECLINICAL MODEL BASED DRUG DEVELOPMENT

Summary

Introduction

Summary on clinical model: Key findings & opportunities

CHAPTER 9 SYSTEMS BIOLOGY: IMPACT ON PHARMA

Summary

Introduction

R&D Strategies & Investment

Blockbuster vs niche model

Pharma vs Biotech model

Outsourcing & in-licensing

Life cycle management growth

Diversification growth

Franchise development growth

New market entry growth

Summary on R&D strategies & investment: Key findings & opportunities

Alliances & collaborations: Key players

Summary on alliances & collaborators: Key findings & opportunities

Regulation & Intellectual property

Global regulation: Critical path initiative (FDA)

CHAPTER 10 REPORT CONCLUSIONS

Summary

Report Conclusions

Report Summary & suggestions

Bibliography & Endnote

List of Figures

Figure 1: The Industry’s Declining R&D Productivity

Figure 2: Overview of Employment of Systems Biology in Pharma

Figure 3: Systems Biology Paradigm #

Figure 4: Chemical Tools Typical For Drug Discovery

Figure 5: Number of Drug Targets Depicted in Venn Diagram

Figure 6: Biochemical Tools Typical For Drug Discovery

Figure 7: Molecular Biology Tools Typical For Drug Discovery

Figure 8: Mapping of Gene to Function and Emergence

Figure 9: Computational Systems Biology in Drug Discovery

Figure 10: Network Topology

Figure 11: In silico Pharmacology Tools in Drug Development

Figure 12: In silico Pharmacokinetics Tools in Drug Development

Figure 13: Flow Chart of Developing ADME/Tox & PBPK

Figure 14: Multiscale CSB Tools in Drug Discovery & Development

Figure 15: Systems Biology Paradigm

Figure 16: Systems Biology Paradigm

Figure 17: Modular Systems Biology at Correlation NetworkTM Inference (CNI)

Figure 18: Systems Biology Paradigm

Figure 19: Systems Biology Paradigm

Figure 20: Systems Biology Tools in Drug Development

Figure 21: Mind Map of Microbial/Mammalian Cell Bioreactor Scale-Up

Figure 22: R&D in Perspective

Figure 23: Systems Biology Paradigm

Figure 24: Different Soft R&D Strategies

Figure 25: Overview of Systems Biology Paradigms

Abstract

• Understand the nature and processes involved in Systems Biology
• See how its benefits relate to pharmaceutical innovation
• Delineate the costs and cost savings involved
• Understand why Systems Biology improves on current approaches to drug discovery
• Relate Systems Biology to advances in genetic profiling and personalised medicine

• Tools and lead optimisation
• Virtual chemistry screening
• Lead discovery and molecular interactions
• In silico screening
• Computational Systems Biology in cell biology
• Pharmacology and pharmacokinetics
• Formulation and production
• Model-based drug development

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