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Three Megatrends
Generating Challenges and Opportunitiesin Drug Research
Christian R. NoeUniversity of Vienna
Barcelona, February 23rd, 2010
„sociological“Service to society„From knowledge to advice“
The „peer review system“ will not be enough to secureprogress of science!
Activities of a Scientist
Get out of the ivory tower!
„ideological“Progress of the research field„From hypothesis to knowledge“
„technological“Transfer of knowledge into society„From knowledge to innovation“
„sociological“Service to society„From knowledge to advice“
Long term scientific planning has to consider long term trends!
3 Megatrends
„ideological“Progress of the research field„From hypothesis to knowledge“
„technological“Transfer of knowledge into society„From knowledge to innovation“
The Fading Power of Innovation
The Loss of Fundaments of Co-operation
Robots and Humansin Scientific Work
Innovation is the majordriver of economicprogress!
Joseph Alois Schumpeter(1883 – 1950)
The Theory of Innovation
Kondratjew – Schumpeter cycles
Biotech
The Discreditation of Entrepreneurhip
• Communism: Dual system of workerand capitalist;
• No discrimination betweenentrepreneur and feudalist/ capitalist;.
• Entrepreneur is class enemy.
• Financial feudalism developed and took over power in the shadow of entrepreneurship..
• Entrepreneurship is still discredited as capitalism.
Spirited entrepreneurship is the source of innovation. It has to be promoted!
Alois Schumpeter:The entrepreneurdrives innovation!
Capitalist and entrepreneur
is not the same!
Prototypes of Human Behaviour
Has goals, takes risks,creates innovation
Operates
Entrepreneur Worker Feudalist
Individual characteristis are at the same time socioeconomic prototypes.
Exerts power via assets, usesand imitates innovation
Animal Farm
Funding of Innovation
Low expectation for public researchfunding due to budget constraints.
Mergers „destroy“ technology andinnovation: 2 x 100% = 1 x 150%
Governments have littleroom to move financially.
„Big pharma companies“ are playersin a finance feudalism driven world.
Neither the public sector nor industry may be expectedto secure long term funding of research and innovation.
Innovation and Imitation
High level innovation NDAs:Still funded by „Big Pharma“
Zero innovation generics:Strong support for imitationby Public Sector
Medium level innovation:No chance forimplementation
The antiinnovative character of generics will hamper pharmaceutical innovation more and more.
Funding of drug discovery and early development
Translation of projects into enterprises
Reducing cost of clinical development
New business models for new therapies
Sustainable SMEs
PPP-Systems
Finding alternatives for “generics” regulations
Supporting Innovation
The concept of „evolution“ dominated the thinkingof society in the 19th and 20th century by
suggesting „development“ and „progress“.
It included also the „progress of science“.
Charles Darwin1809 - 1882
Jean Baptiste de Lamarck: 1744 - 1829
Gregor Mendel1822 - 1884
The work for a better world - For a long time theethic fundament of scientific cooperation
An amazing amalgam of Kantian idealism and Darwinian rationalismreplaced religion basedethical concepts of co-operation.
Fundaments of cooperation
Immanuel Kant1724 - 1804
Decoding of thehuman genomeG. Venter, 2000
Milestone of greatexpectations and at thesame time end odexaggeratedexpectation.
A Great Goal
Also „positive“ singular events may contribute to thedistruction of the trust in progress.
The End of „Darwinian Idealism“
„Individualistic“ conceptsprevail.
The fundamental trust of scientists in the usefulness of their work as a contribution to the progress of mankind
is exstinugished.
Pseudodarwinism:„Selection“ suggests an active process.„Survival of the fittest“ isactively pursued.
An ethical crisis of science?
The Prisoners´ DilemmaInvented by Merill Flood and
Melvin Dresher, 1950
It is advantageous to be a defector(in a defect system)!
Oscar Morgenstern
John von Neumann
Theory of Gamesand Economic
Behaviour1944
The Evolution of Co-operation
1981
Robert AxelrodtWilliam Hamilton
The world is not only „struggle“ and „war for survival“.
Kinship
Reciprocity
Indirect Reciprocity
Network Reciprocity
Group Selection
Natural selection favours co-operation!
Martin Novak Carl Sigmund
Bio-mathematical Models
DrugDiscovery
PreclinicalPhase ´Clinical Development
DrugReg. Marketing
2009 2011 2013 2018 2019 2020
Pharmacodynamics, -kinetics
Toxicology
Galenic Development
Chemical Development
New TargetLead - First Claims
L. OptimisationDrug Candidate
Phase 1Phase 2
Phase 3
NDA FirstIncome
First marketing activities
TRANSLATIONAL STEPS – „CULTURES“
A Biologist - Designer T.B Pharmacologist - Designer T.C Academia - Industry T.D Lab – Plant T.E Industry – Regulatory T.F Bench – Bed T.G Industry – Health System T.H Doctor – Patient T.
CC
FF
GGEE HH
BB
BB
DD
BB
AA
Target Search
Evolution favours co-operating groups ! (from project team to scientific communitiy and society)
To define and restructure “pharma” sciences
To optimise science communication at all levels
To reorganise education fundamentally
To promote specific professional profiles
To connect training courses and curricula
To promote regulatory-industrial-academic co-operation
To support financially European Scientific Organisation
To build up thematic networks
To establish a European grant system
Measures to promote co-operation
Ars ()
Scientia
Investigatio
Notitia
„Technique“ isTechnology
Collection of Knowledge
ExperimentalResearch
Awareness of a Problem
around 1750 around 1850
Pharmaceutical industry evolved out of emerging chemicalindustry (frequently based on pharmacies)
Experimental sciences led to innovation andthe „First Industrial Revolution“.
Reductionistic Research:To know more and more about less and less.
Technology Transfer
Collection of Knowledge
InstrumentalResearch
Awareness of a Problem
„Technique“ isTechnology
Collection of Knowledge
ExperimentalResearch
Awareness of a Problem
around 1850 from 1950
LHC-CERN
The „Revolution of the Thinking Machines“
Technology Transfer
Collection of Knowledge
ExperimentalResearch
Awareness of a Problem
TranslationalSciences
KnowledgeManagement
AutomationRobotics
Awareness of a Problem
2009: Robot Scientist Adam
Functional genomicsof orphan genes
Robot Scientist EveDrug screening
from 1950 2009
TranslationalSciences
ArtificialIntelligence
Automation Robotics
Awareness of a Problem
TranslationalSciences
KnowledgeManagement
AutomationRobotics
Awareness of a Problem
2009 around 2050
Artificial and humanintelligence
Tasks for future scientists: To identify problems – To translate knowledge
Bioinformatics allow theinterpretation of huge amounts of data from
genomics, transcriptomics, proteomics, metabolomics and…..
The central task of the scientist also in future: „Develop hypotheses! – Look for answers!“
Robot Scientists can do a lot, but not everything!
Systems Biology: The Fundament of Metabolic Network based Drug Research
Contains a total of 219 reactions and 322 species.
Established by help of CellDesigner ver. 2.0 http://www.systems-biology.org/002/
Oda et al., Molecular Systems Biology 1 doi:10.1038/msb4100014 publishedonline: 25 May 2005
The EGFR Pathway Map
From subtypespecificity to multi target design and to metabolic network based strategies
Would be only of value, if basedon reliable data
From Reductionisms to Systems Analysis
„Systems Biology“ expands to „Science of Systems of Life“
„General Pharmaceutical Systems Biology“Questions are inbedded into a „system“, whenever feasible.
„Holistic“ - „Systemic“ - „Translational“Research in „field“ und „lab“
Nomenclature and terminology
Harmonised Data Collection
Systems suited information on “old” drugs
“Systems” of particular interest for drug R&D
Disease simulations down to the molecular and metabolic level
Pilot studies with novel strategic avenues
Systems Biology and Drug Research
Dilemma: vast increase of R&D costassociated with decreased outputSolutions: New research strategies, but also critical analysis of the R&P process: „Critical Path“ bzw. „New Safe Medicines Faster“, now IMI
The „Implementation Gap“
Betz, U (2005) Drug Discovery Today 10(15):1057-63.
R&D Investments (US)
Registration of new APIs (global)
DrugDiscovery
PreclinicalPhase ´Clinical Development
DrugReg. Marketing
2009 2012 2014 2019 2020 2021
Pharmacodynamics, -kinetics
Toxicology
Galenic Development
Chemical Development
New TargetLead - First Claims
L. OptimisationDrug Candidate
Phase 1Phase 2
Phase 3
NDA FirstIncome
First marketing activities
TRANSLATIONAL STEPS – „PROJECT“
1 Target – Lead T.2 Discovery – Development T.3 API – Medicine T.4 Preclinical D. – Clinical D. T.5 Clinic – Market T.
T r a n s l a t i o n a l S c i e n c e s11
22
44
55
33
Target Search
Significant bottlenecks are generated in thetransfer of projects from one phase to the next.
TargetDiscovery
LeadDiscovery
LeadOptimisation
„Holistic DrugDiscovery“
IMI-JU(New Safe Medicines Faster,
Critical Path)
Early LADME Research, Biomarker..
Forward translational
DRUG DISCOVERY
DRUG DEVELOPMENTMarketed
Drug
Reverse translational
IMI GovernanceIMI is composed by the IMI Joint Undertaking and it
has two External Advisory Bodies
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IMI will fund research recommendedin its Research Agenda.
2000
2002
2004
2007
2008
• The IMI Research Agenda describes the research bottlenecks in drug development and recommendations how to solve those.
• These recommendations represent the outcome of an extensive consultation between Europe’s key stakeholders under the lead of EFPIA, organised within the European Technology Platform on Innovative Medicines Initiative
• The review of the IMI Research Agenda will start in 2010 with the IMI Scientific Committee.
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IMI Research Agenda - Pillars
EFFICACY
KNOWLEDGE MANAGEMENT EDUCATION & TRAINING
SAFETY
Source: EFPIA 3838
To reduce the medical need
To support breakthrough of novel therapeutic avenues
To harmonise reductionistic and systemic approaches in drug research
To optimise the Drug R&D Process
To implement new techniques and technologies
Great Tasks
“Extracorporal” Therapies
Immunotherapies – Vaccines
Nucleic Acid Therapies
Nuclear Medicine and Imaging Based Approaches
The Three D´s: Drugs – Diagnostics - Devices
Novel Therapeutic Avenues
Gene TherapyMany unmet promises, huge research investments, still little success!Nevertheless many opportunities!
Gene SurgeryPrediction: The greatest challenge and medium term most promisingopportunity of pharmaceutical sciences!
From molecular to cellular concepts!
http://www.biochem.arizona.edu/classes/bioc471/pages/Lecture25/Lecture25.htm
Stem Cell ResearchFashion – Fund Raising Machinery - Challenge – Important Approach
Adult stem cells contain the patient´s genome.Techniques for their in vivo activation will come up.There are already now therapeutic applications.
Adult stem cells will probably outperform embryonic stemcells in therapeutic applications,
Tissue Engineering
Medium term challenges:Organ reconstructionNeuronal regeneration
Tissue engineering is an opportunity to expand the scopeof pharmaceutical sciences
NF
P
MRP
PP SM
DNA
mRNA
TS TL
AS ODNAS ODN
NR
CellCell AnswerAnswer
New Paradigm: „Block the receptor or prevent its formation!„
Nucleic Acids The next big thing to come! (Scrip Magazine)
PET Imaging
Optical Imaging
MR Imaging
ImagingAn obvious opportunity not only fordiagnostics and tissue research, but also forpharmacokinetic studies in drug discovery and development and aCornerstone in Theragnostics
I m a g i n g
Ravindra K. Padney,Roswell Park Cancer Institute (above);
Vladimir P. Torchilin, NortheasternUniversity, Boston (right above);
Siemens (right).
T h e N a n o s c a l a r C h a l l e n g e
A G a p i n U n d e r s t a n d i n g
m o r e t h a n j u s t
N a n o t e c h n o l o g y
Chemical Biology(Synthetic Biology, Biological Chemistry)
An obvious opportunity and avenue intolife sciences for scientists from „old“ disciplines
Many challenges bothfor „cutting edge“scientistsand engineers
Giambattista Vicoaround 1750
Recurrent events
Hiddenopportunities
Charles Darwinaround 1850
Linear evolution
Obviousopportunities
Bertrand Russellaround 1900
Singular events
Unpredictableopportunities
Strategic Planning and Elements of Time
Recurrent events:IMI is an implementation tool for theIndustrial revolution of „biotech“!
Linear evolution:The translational character of IMI will helpto close the „implementation gap“ in Drug R&D!
Singular events:The PPP concept of IMI will hopefully inducenovel models of research co-operation!
Ambitious hopes for IMI