A new paradigm for innovation
Growth in the pharmaceutical market has slowed - almost to a standstill. One reason is that governments and other payers are cutting costs in a faltering world economy.
But a more fundamental problem is the failure of major companies to discover, develop and market new drugs. Major drugs losing patent protection or being withdrawn from the market are simply not being replaced by new therapies - the pharmaceutical market model is no longer functioning effectively and most pharmaceutical companies are failing to produce the innovation needed for success.
This new report looks at a vital strategy which can bring innovation to a market in need of new ideas and new products - Systems Biology.
Systems Biology employs a rational approach, via a mix of analytical and systemic routes, to delineate the emerging properties of biological networks. It aims to explain and predict, quantitatively, molecular, cellular, tissue, organ and whole-body processes. By using multi-scale models in silico, Systems Biology is expected to bring numerous benefits to pharmaceutical discovery and development as the properties of a system can be studied over a wide range of length and time scales.
Systems Biology can reduce the number of compounds that need to be synthesised in discovery by using refined algorithms to weed out compounds with poor pharmacokinetic of toxicology profiles. And it will save time and money by selecting the drugs which are more likely to succeed in clinical development.
This new 200 page report is written by industry and academic expert Dr Ales Prokop, one of the leading researchers in Systems Biology. It will enable you to:
- 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
Contents include:
- 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
Systems Biology is essential reading for corporate management and those in product, process and analytical discovery and development, product quality and characterisation, R&D, and regulatory affairs. It is aimed at biotech, big pharma, producers of biosimilars, contract manufacturers, providers of analytical services, and clinical research organizations. This report gives an authoritative, detailed and clear explanation of the issues surrounding Systems Biology, its implications for the market and for the biotech and pharmaceutical industries.
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- 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
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