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The Challenge of Transforming Scientific Innovation into Commercial Success. Dr. Norbert Riedel Kellogg Biotech Symposium April 16, 2004. Healthcare is Changing Rapidly. Increasing age of world population Increased prevalence of life-threatening conditions

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slide1

The Challenge of Transforming Scientific Innovation into Commercial Success

Dr. Norbert Riedel

Kellogg Biotech Symposium

April 16, 2004

healthcare is changing rapidly
Healthcare is Changing Rapidly
  • Increasing age of world population
  • Increased prevalence of life-threatening conditions
  • Cost-containment pressure from governments and other payors
  • Expanding patient knowledge and empowerment
  • More diverse sources of innovation
  • Personalized Medicine
  • Rapid technological change
slide3

Predicted 2003 and 2007 sales in the top nine markets in Europe and the top five in selected other regions at ex-manufacturers’ prices using forecast exchange rates. Source: IMS Market Prognosis Global

slide5

Leading Major Companies by Profitability

(profit as % of sales) in 2002

slide8

BIO’s Biotech Drug Approval List

Also approved in 2003 were 13 additional recombinant proteins, monoclonal antibodies, small-molecule products and selected tissue-engineered products. Biotechnology Industry Organization (BIO) considered relevant to the sector. With the exception of FluMist and Advate, all these products were transferred from the Center for Biologics Evaluation and Research (CBER) to CDER, but were not included on CDER’s list of NME approvals for 2003.

slide10

With only 31 new active substances last year, the much-vaunted revolution in drug development still seems a long way off.

increase in life threatening conditions presents major growth opportunities
Blood, immune and autoimmune disorders

Cancer

CNS Disorders

Infectious diseases, including biodefense

Tissue engineering

Surgical situations

Trauma

Increase in Life-Threatening Conditions Presents Major Growth Opportunities

Small molecules, biopharmaceuticals and vaccines for:

what s next in technology
Bioinformatics

Blood safety (prions)

Drug delivery

Homecare

Medication error prevention

Personalized Medicine

Internet-based clinical trials

Minimally invasive surgery

Nanotechnology

Proteomics

Stem cells

Cell therapy

Tissue engineering

Organ replacement

Telemedicine/remote patient monitoring

What’s Next in Technology?
technological changes are creating new possibilities
Technological Changes areCreating New Possibilities

Contemporary

1920s

1950s

1980s

Major New Drug Classes

Combinatorial

small molecules,

designer peptides

Recombinant human

proteins, monoclonal

antibodies,

nucleic acids

Antibiotics

Cardiovascular drugs,

antihypertensives

psychotropics

Combinatorial chemistry,

combinatorial peptides

Genetic engineering,

tissue culture

New Drug Sources

Proteins

Genomics, proteomics,

functional genomics,

high-throughput screening,

transgenic/knockout

model systems,

transcript profiling

New Target Identifaction Tools

Molecular biology,

molecular genetics,

PCR, rDNA

Biotechnology, cell biology

Targets

Genes

Genes, genetic regulatory elements, signal transduction pathways, protein-protein and protein-macromolecular interactions

Human

subjects

Microorganisms, animal models

Enzymes,

receptors

Sources: Pharmaceutical Research and Manufacturers of America, ING Baring Furman Setz

future mandate for r d
Future Mandate for R&D
  • Shorten discovery timelines
  • Strengthen development efforts
  • Increase research productivity
slide15

Timelines in drug development

Targets selected

Lead series

selected

Candidate leads selected

IND

approval

NDA

approval

Lead selection

Lead optimisation

(in vitro ADMET)

Target identification

Target selection

Preclinical evaluation

(in vivo ADMET)

Clinical evaluation

Safety/Efficacy

Marketing & sales

Monitoring-ADR

OLD PARADIGM

Lead identification

1.5-4 yr

1.5-1 yr

1-2 yr

1-2 yr

8-10 yr

0.5 yr

0.5 yr

1-1.5 yr

1-1.5 yr

7-9 yr

Increased efficiency in drug development

Technological drivers:

Genomics, proteomics, metabonomics, disease models, combinatorial chemistry, HTS, bioinformatics…

Increased lifecycle of new drugs

NEW PARADIGM

2-6 yr

slide16

35%

30%

35%

Even small improvements in compound selection will have huge effects on profits

With up to 70% of clinical trial spending wasted on trials of failed compounds…

Annual spending in clinical trials

…a 1 percent improvement in selection can save $7 million from each $1 billion of an annual development budget.

Total: $20 billion

$700 million potential improvement opportunity by improving selection process

Drugs that fall due to selection of wrong targets

For each $1 billion of an annual development budget…

Successful

drugs

…70% is spent on failed drugs

$70 million for a 10 percent improvement

Drugs that fall due to poor chemistry

drug discovery in the 21st century
Drug Discovery in the 21st Century

High Throughput Screening

Genomics/Proteomics Novel Targets

Informatics

Therapeutic Areas

Informatics

Informatics

Combinatorial Chemistry

slide18

Medicinal chemist

Pharmacologist/

toxicologist

Clinical

researcher

Molecular biologist/

bioinformatician

Characterize

mechanism

of action

Prioritize

lead

compounds

Characterize

‘off target’

effects

Develop

surrogate

markers

Understand

clinical responses

Discovery Preclinical Clinical

Target selection Lead optimization IND NDA

slide19

HIGH-EFFICIENCY PROTEOMICS

(applied to small molecule drug discovery)

Informatics

Proteomics

Sorting of cells from tissue, in vitro cell culture

Affinity chromatography (MDLC)

Quantitation & Identification (LC-MS, MS/MS)

Genome sequence (identification & in silico triage)

INPUT:

Normal &

Disease

Samples

(+ in vitro)

Affinity

capture of

protein class

(in vitro/invivo)

Proteomic

profiling of

differences

Validate with

class-specific

inhibitors

(in vitro/in vivo)

Structure-

based drug

design

OUTPUT:

Optimised

lead

X-ray crystallography

Focused library (+ biology)

Class-specific agonists/antagonists

Activity-based affinity reagents

Chemistry

slide20

QUANTITY

Library Generation

Medicinal Chemistry

QUALITY

TARGET

VALIDATION

cell-based

SAR

Functional Genomics

Proteomics

LEAD

PROFILING

ASSAY

DEVELOPMENT

MET/TOX

Primary Screening

HIT

VALIDATION

cell-based

key srategies for success
Key Srategies for Success

Industrialization of lead identification

Data warehousing and portals for knowledge access

Customer groups

Innovation

Commercialization

Internet-based clinical trials

Computer-aided trial design and simulation

slide22

IBM’s New Business Model for Pharma

Accurate Assessment of the Threshold of Innovation

Investment

New Products

Discovery

Marketing

Development

Manufacturing

Sales

Traditional

Products

High Density

Products

Targeted

Treatment

Solutions

New development approach

Adaptive trials and in-life testing

Rolling dossier

Outcomes – oriented marketing

EMRs

Smaller and smarter sales force

Integrated media

New disease-led approach

Multiple supply chain models

integrating into networks of innovation is a key growth strategy
Integrating Into Networks of Innovationis a Key Growth Strategy

Areas of Strength

Areas of Weakness

Academia

Preferred Flows

Biotech Start-up Companies

Large Healthcare Companies

Regulatory

Marketing & Sales

Exploratory Research

Development

Manufacturing

key business challenges for biotech
Key Business Challenges for Biotech
  • Create a strategic vision for “grown up” companies
  • Deliver earnings to support valuations
  • Structure value maximizing alliances with traditional pharmaceutical companies
  • Evaluate mergers and acquisitions
  • Build production capacity
challenge define strategic vision
Challenge: Define Strategic Vision
  • What is the appropriate vision?
    • Fully integrated pharmaceutical company or pure discovery engine?
  • What therapeutic / technology focus should a company have?
    • Diagnostics, delivery or drugs? Technology Play?
  • What supporting functions, processes are needed?
    • Discovery to development to portfolio management to marketing
challenge translate innovation into earnings
Challenge: Translate InnovationInto Earnings
  • How do companies maximize the value of their:
    • Technology platforms
      • Identify the appropriate targets and tools
    • Development candidates
      • Balance technical and commercial risk
    • Marketed products
      • Expand capacity and develop line extension strategies
challenge structure value maximizing alliances
Challenge: StructureValue-Maximizing Alliances
  • Alliances between biotechs and pharma firms will become even more important
    • How do biotechs balance short-term funding needs with long-term value maximization?
    • What are the elements of win-win alliances?
    • What strategic frameworks and processes can shape future alliances?
slide28

Some of the more significant deals signed between pharma and biotech companies last year. Valuations cannot be directly compared because

they have not been uniformly calculated.

slide29

Biotech Disappointments

  • Merck & Co drops Celltech compound
  • Novo Nordisk tries to stop Pfizer ending its hormone replacement therapy deal
  • Lilly drops Ono’s sivelestat
  • Lilly ends a deal to develop oral insulin with Generex
  • Biogen ends Icos deal
  • Bayer shelves key PPL project
  • Pfizer ends deal with Phytopharm’s P57 obesity project
  • AstraZeneca pulls out of a deal with NicOx
  • Cambridge Antibody Technology/Abbott deal goes sour
  • GlaxoSmithKline scraps its oral insulin deal with Nobex
  • Abbott scraps its diabetes deal with Karo Bio
  • Pfizer hands back Celltech’s antiheumatic
  • Pfizer returns product rights to Debiopharm
challenge evaluate m a
Challenge: Evaluate M&A
  • The biotech industry is still highly fragmented, and more consolidation is inevitable
  • Successful companies will proactively evaluate:
    • When is the right time for M&A?
    • What attributes make a candidate attractive?
    • What synergies exist, and how quickly can they be realized?
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