Lead Optimization Strategies
Future developments, threats and opportunities for big pharma, speciality pharma and biotech.
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| Overview: | |
| The pharmaceutical industry has undergone considerable consolidation during the last 20 years as companies attempt to maintain double-digit sales growth and shareholder value. Strategic mergers and acquisitions have resulted in companies revitalizing their drug pipelines to replace blockbuster drugs that face patent expiration. In many cases, whilst mergers cause a period of less productive transition, significant cost-savings can be reaped and overheads reduced. However, more and more companies are resorting to streamlining their internal programs, both pre-clinical and clinical, to improve productivity and the cost-effectiveness of bringing drugs to the market as well as in-licensing late-stage products and outsourcing screening programs which may be less economical to entertain in-house. |
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By Dr CL Barton / Publication Date: 1st September 2005
Contents:
Table of Contents
Lead Optimization Strategies
Executive Summary 10
The importance of lead optimization 10
Traditional big pharma strategies 11
Alternative strategies used by specialty pharma and biotech 12
Future developments in lead optimization 13
Commercial threats and opportunities 14
Chapter 1
The importance of lead
optimization 16
Summary 16
Introduction 17
Current issues facing the pharmaceutical industry 18
Introduction to lead optimization 22
Hit generation 23
Lead identification 23
Lead optimization 23
Lead optimization in the R&D process 24
Time and costs associated with lead optimization 25
Commercial and clinical importance of lead optimization 26
Lead optimization strategies 27
Traditional big pharma strategies 28
Alternative strategies used by specialty pharma and biotech 29
Differences in strategy 29
Chapter 2
Traditional big pharma strategies 32
Summary 32
Introduction 33
A typical screening cascade 34
Key strategic components in lead optimization 37
Team organization for lead optimization 37
Medicinal chemistry functionality 38
Sources of hits and leads 40
Proprietary collections 41
Commercial non-exclusive libraries 41
Natural products 42
Academic groups 43
Advantages and disadvantages of hit sources 43
Improving the quality of compound collections 44
Case study 1: AstraZeneca 44
Case study 2: GSK 45
Setting the criteria for hits, leads and candidate drugs 45
Key technologies involved in lead optimization 47
HTS technologies 49
NMR 50
Utility of modeling in lead optimization 52
In silico modeling 53
In vitro modeling 53
Computational chemistry 55
Virtual screening 56
Library design 58
HT protein crystallography 59
Application of molecular biology in lead optimization 61
ADMET screening 62
The role of DMPK in lead optimization 62
Metabolism and covalent binding 63
Toxicity 63
Assays for assessing toxicity 65
HT chemistry 68
Preparative LC-MS chromatography 68
Supercritical Fluid Chromatography (SFC) 68
Outsourcing lead optimization 69
Weaknesses of the traditional lead optimization model 71
Sequential data gathering 71
Single compound synthesis 72
Animal models 73
Conclusions 74
Chapter 3
Alternative strategies used by
specialty pharma & biotech 78
Summary 78
Introduction 79
Alternative lead optimization strategies 80
Mimicking big pharma cascades 81
Polypharmacology 82
Short/partial screening cascades 86
Case Study: ArQule 86
Computational approaches 89
Case study: Locus Pharmaceuticals 91
Alternative approaches to screening 93
HT crystallography 97
Nanotechnology 99
Scanning probe microscopy 100
Nanoparticle formulation 101
Outsourcing lead optimization 101
Lessons from specialty pharma/biotech strategies 103
Conclusions 104
Chapter 4
Future developments in lead
optimization 108
Summary 108
Introduction 109
Key improvement opportunities 109
Reductions in time 110
In silico models 110
Producing fewer compounds of higher quality more efficiently 111
Cheminformatics 111
Bridging the chemistry and target space gap 112
Miniaturization of chemistry 115
More efficient screening cascades 116
Systems biology 116
Zebrafish model 120
Reducing high attrition rates during lead optimization 121
The ‘omic revolution: transforming drug discovery 121
Technology developments 124
Lead optimization, 2010-2015 126
Conclusions 128
Chapter 5
Commercial threats and
opportunities 132
Summary 132
Introduction 133
Threats 133
Opportunities 134
Commercial threats 136
Industry consolidation 136
Increased drug safety requirements 138
Avoidance of untested innovative new technologies 139
Drive towards lower risk “me too” drugs 140
Commercial opportunities 142
The genomic revolution 142
Changes to orphan drug legislation 144
Changes in regulatory guidelines: Biomarkers helping to improve the chance
of new drug approvals 146
Combinatorial biomarkers aiding efficiency within the D&D process 148
In- and out-Licensing opportunities to create new revenues streams for all
players within the industry 149
Conclusions 152
Chapter 6
Appendix 154
Abbreviations 154
References 156
Index 162
List of Figures
Figure 1.1: Global pharmaceutical sales and growth rates, 1997-2004 18
Figure 1.2: The drug discovery process 22
Figure 2.3: A schematic of a typical screening cascade 35
Figure 2.4: Team organization within lead optimization 38
Figure 2.5: Technologies involved in lead optimization 48
Figure 2.6: Application of lead optimization technologies in drug development 49
Figure 2.7: Schematic representation of linked-fragment approaches 51
Figure 2.8: Temporal correlation between action potential duration and the QT interval on the
surface electrocardiogram 54
Figure 2.9: The role of HT protein crystallography in lead optimization 59
Figure 2.10: A) Antisense-directed mRNA degradation and B) Gene silencing by RNAi 61
Figure 2.11: The bacterial SOS assay to identify genotoxins with the umu response 65
Figure 3.12: No economies of scale in pharmaceutical R&D 79
Figure 3.13: Gleevec (imatinib) kinase activities 83
Figure 3.14: Screening the receptorome reveals multiple molecular targets implicated in
antipsychotic drug actions 85
Figure 3.15: Product portfolio of ArQule 87
Figure 3.16: Schematic of the ArQule preparative LC-MS purification system 88
Figure 3.17: The different methods and tools for virtual screening 89
Figure 3.18: The Locus Pharmaceuticals technology platform 91
Figure 3.19: Simulated annealing of the chemical potential of a fragment 92
Figure 3.20: Chemical structures and affinity fingerprints for met-enkephalin, vs. morphine, and
morphine vs. naltrexone 94
Figure 3.21: A schematic of the TRAP screening process 96
Figure 3.22: Fragment sets used for virtual library construction 98
Figure 3.23: Schematic of the technique of scanning probe microscopy 100
Figure 4.24: Schematic of the high throughput approaches in modern drug discovery 113
Figure 4.25: A microreactor chip 116
Figure 4.26: A schematic on the potential impact that systems biology could have on the drug
discovery process 118
Figure 4.27: Zebrafish Embryo 120
Figure 4.28: The future of lead optimization by 2015 126
Figure 5.29: Consolidation within the pharma industry, 1990-2005 137
Figure 5.30: Trends in R&D spend in Europe and the US, 1990-2000 138
Figure 5.31: Breakdown of portfolio by source of drug for the top 20 pharmaceutical companies,
2004 150
List of Tables:
List of Tables
Table 1.1: CDER approval times for priority and standard NMEs and BLAs, 1995-2004 19
Table 1.2: Sales from leading pharmaceuticals companies, 2003-2004 21
Table 1.3: R&D spend on drug development, 2003 26
Table 1.4: Constant dollar reduction in total cost per new drug, 2002 27
Table 2.5: Top 20 pharmaceutical drugs based on 2004 sales ($bn) 42
Table 2.6: Lipinski’s rule of 5 43
Table 2.7: Milestone criteria for hits and leads at AstraZeneca 46
Table 3.8: Efficiency of Locus Pharmaceuticals computational technology 93
Table 3.9: Leading CROs for lead optimization outsourcing 102
Table 4.10: Impact of systems biology on drug discovery and development costs 119
Table 4.11: Utilization and expenditures for key drugs metabolized by CYP2D6, 2003 123
Table 4.12: Potential cost savings of improvements in lead optimization timescale by 2015 127
Table 5.13: Commercial threats and opportunities for lead optimization 133
Table 5.14: Drug withdrawals, 1997-2005 139