Symbiotic innovation 2013

Symbiotic Innovation (SI):

An optimized, project focussed anatomy for

sustainable value creation

developed by:

Thomas Wilckens, MD

With support of Sandrine Shepard, M.Sc. B. Eng

The routes and development of Symbiotic Innovation: Disruptive innovation always evolves from the convergence of technologies creating new business principles

Symbiotic Innovation evolves from:

• The analysis of contemporary literature (app 300 publications on value generation, innovation, economics,

learning concepts, etc.)

• www-based reviews and discussions on optimized value creation concepts over three years

• Incorporation of the expertise of opinion leaders in economic and biomedical sciences

• Interviews within pharmaceutical companies

• Personal entrepreneurial experiences with a virtual company over 10 years

Symbiotic Innovation - © Thomas Wilckens 2

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“Science Business”, Gari P. Pisano,

Harvard Business School Press, 2007

Can Science be a business? It would appear that the answer,

based on the experience to date would be no.

This answer is, however, only correct, if we take existing

organizational and institutional arrangements and existing

management technologies as given.

Symbiotic Innovation: Reshaping value creation in concert with Translational Sciences

Academia cannot replace early stage R&D but must establish essential fundaments.

Lessons from biotech should guide the creation of novel anatomies.

Symbiotic Innovation - © Thomas Wilckens

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Improving ROI from biomedical research forces radical changes Biomedical research consumed a disproportional amount of investment

Still low efficacy of industrial and academic R&D by over 80 Billion/year funding

Biotech companies show dramatically increased late-stage failures compared to Pharma; i.e. 95%

Unacceptable output for society/patients, tax payers and investors, i.e. all “stakeholders” in R&D

Late stage failure of NCEs compared to FDA approvals 1/2006-12/2007; Nature Rev. Drug Discovery March 2008

• Many concepts in discussion, some actually request paradoxically even more funding

• All current initiatives & concepts must be considered as experiments themselves, none is validated

• Most reflect the legitimate bias of the developers, related lobbies, regional and/or political interests

• None are based on specific analyses and expertise on economic, sociologic and scientific aspects

• No model has an optimal product centric focus with scientific leadership and cost effectiveness/ROI

The quest for the holy grail in value creation:


http://www.sos.ucsf.edu/investigator.html

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Technical challenges for the implementation of translational sciences:

Polypharmacology: Polypharmacology is a major challenge for the development for the gene-to-lead

drug discovery concept and clinically differentiated therapie;

Action: eliminate risk of cross-reactivity within/beyond target families,

implement a pharmacogenomics platform on a human physiology first strategy

The animal trap: observation in rodents may create a hype on a novel target (TEGENERO);

human research lacks behind drug discovery efforts;

inbred strains to often mimick a rare phenotype not found in outbred human biology

Action: Implement a human biology first translational strategy:

novel paradigms on diseases, pathologic mechanisms, experimental design

supported by comprehensive target validation starting from human observations

Lack of IT-data storage solutions: No standardized platform for communication, data wikis or similar that enable

and safe communication platforms: cooperation of internal and external experts to generate content and define roles as

well as access to the data and content; lack of concises definitions for contributors

roles & data rights along the value chain with respect to sensitive data, trade secrets

Action: Establish and evaluate „Trusted interface platform and IT in a pilot project“


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Selected challenges in R&D:

Lack of IT-data storage solutions: No standardized platform for communication, data wikis or similar that enable

optimized translation of data to content to actionable information.

Action: Establish and evaluate „data storage and analytics plattform in a pilot project“

Insufficient knowledge base: No company or institution can comprehensively represent leadership for any

target/pathology in solitude;

Action: Engage scientific and technological leadership for a focussed pathology project;

enable novel paradigms on diseases, pathologic mechanisms, experimental design

supported by comprehensive data validation starting from human observations

Diverting technologies The R&D process becomes increasing complex and costly

Action: Establish a symbiotic virtual competence focus

on specific a specific project using validated external technologies

in creating a competence hub

Lack of standards: Studies validity and reliability and cannot br compared;

i.e. 80% of academia led biomarker studies are not reproducible

Action: Project oriented, technology focus with stringent, standardized procedures

from sample collection over processing and data analysis


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Symbiotic Innovation: Reshaping value creation

Current financing and business concepts fail to catch optimized

value from omics and Big-Data analysis

Our proposal:

• Create a competence hub from combination of advanced big data

knowledge extraction and experimental omics methods

• A one-stop-shop pathology focus service company with an innovative

revenue sharing and project spin-off business model

• Establish integrated disease management IT platform including

support for diagnostic and therapeutic decision

• Deliver new targeted services/products for health and green markets


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SI: Overcoming the challenges of early stage drug discovery Implementing Translational Sciences and new value creation concepts

Symbiotic Innovation & Translational Sciences

Lack of ROI and selected challenges

Collective learning and a team/project strategy

The creation of Symbiotic Innovation and the www.

Impact on organisational measure and long term value creation

Summary and conclusions


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Increased complexity requires matrix anatomies and Collective Learning The number of disciplines in Translational Sciences forces a reevaluation of or current anatomies

Adopted from FitzGerald (2005) Nat Rev Drug Dis 4:815

Each approach to drug discovery may require an individual specifically designed focussed anatomy (USP)

Problem-oriented knowledge management concepts must create TS networks that enable collective learning

Translational Sciences are essential to create knowledge-bases on specific topics (pathology, target,…)

PoC

Human

disease


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The creation of Symbiotic Innovation and the www




Open Innovation versus Symbiotic Innovation: Symbiotic Innovation creates Spinn-off opportunities for further development

11 Symbiotic Innovation © Dr. Thomas Wilckens

Open Innovation: Innovations are absporbed for internal

development to commericalisation,

Interal innovations are outsourced for

external value creaton

Symbiotic Innovation; SI is serving research-intensive industries for create innovation

externatly for internal absorption in a mature stage.

Promising ideas are developed in mangend growing networks

with competence building and a horiziontal value chaine

High-risk project will be subject to spin-off financing

Competence

hub

Innovation Proof of concept Products

Know-how, technologies, tacit knowledge, etc.

Innovation: Prototypte to marketable product.

Proprietary

databases biomarkers

Spillover for spinn-off Drug target, NME, nutraceutical

R&D

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Implementing Symbiotic Innovation to ensures scientific leadership

Adopted from FitzGerald (2005) Nat Rev Drug Dis 4:815

Project specific management teams, will orchestrate the virtual R&D process securing quality control

Scientific leaders to become part of a value chain and the ROI by participating in the project solving a task

Participation anticipates acceptance of a master-contract a priori, which eliminates endless negotiations

Sponsor

&

Publisher

Successful

PoC

additional

value

A project’s specific tasks are posted on the web to attract scientific leaders

Management team & WWW-platform


Selected key issues for early stage project selection process by a project team and sponsoring organisation:

• Knowledge & technology base, disease mechanism, biology/physiology: validated/innovative, expression profiles, ….

• Analysis for potential of undesired and/or off-target effects: identification of therapeutic windows,

• Drug delivery/Formulation/Pharmacology: Route of Admin; comfort versus efficacy: what drug, when & how ?

• Competing technologies/target approaches: requirement to develop a significant better drug to get approved

• Surrogate markers & animal models & human relevance (human first approach)

• General feasibility to ranking as judged by every project team member

• Sponsor’s portfolio, strategic and market interests secures a priori interest to enter early stage development

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Sponsor lead selection of Spin-off opportunities in a synergistic manner: Selecting/developing high risk high ROI projects requires pharma’s very early engagement a priori

Project 1

Project n

Predefined

therapeutic

goals


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SI replaces random collaborations with an a priori accepted focus

Trusted Interfaces generate value as result of Technology morphing and Collective Learning

NEW Business

IT-hub

Services

Products

Clinical

data

Ontologies

Proteomics

Clinical

chemistry

Antibody

analysis

Genomics

Literature

mining

• New horizontal/vertical synapses continue to create value beyond the completion of a single project

• SI creates long term interfaces between converging disciplines & technologies, i.e. fosters innovation

• Knowledge created by industry standards and collective learning will create a competitive USP

Realiability

Validity

Proprietary Data & Algorithms Tech. leadership

Unique Services


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SI: reduces risks, costs, time and increases the chance for success Successful product development will require sustainable support combined with iterative project evaluation

‘It is not the strongest of the species that survives,

nor the most intelligent,

but the one most responsive to change’

Charles Darwin

Imagine, what happened, if we additionally used our intelligence and resources optimally to adopt?

P. Talaga in Drug Discovery Today 9/2009, Share or die!:


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Future leaders in R&D management: career chameleons? Scientific training desired for translational R&D: Do we have qualified personal?

Ideal Profile

Expertise in physiology, physical chemistry, biology,

informatics, therapeutics, medicine,

understanding of drug discovery, development, …

Chemical Sciences

Medicinal chemistry-cross over into biology

Chemists with core skills (broadly based synthetic

organic chemists) rather than specific areas of the

process

(e.g. combinatorial chemistry) for large pharma

Chemists with specialized skill sets in smaller pharma

and biotech

Biological & Medical Sciences

In vivo/vitro physiology and pharmacology

Molecular biology; biochemistry; cell biology

Bioinformatics

Pathophysiology

Drug metabolism

Pharmacogenetics

Clinical Pharmacology

Managerial skills

Creation of project teams

Addressing scientific needs in a development process

towards a product

Orchestration of virtual projects with multiple

Defining and reaching goals & milestons

Generation of industrial standard decision matrices

Leadership

SI provides new career tracks for entrepreneurial scientists & managers in teams, where

expertises are casted and focussed according to each specific projects requirements


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Big-Pharma: Translational clinical

development; internal

leadership in drug delivery,

formulation technologies,

med. Chem., etc.

Symbiotic innovation:

Virtual preclinical/PoC

R&D creates a

sustained pipeline

Project 1 Project 2 Project n

Interactive knowledge transfer

Pharma‘s and academia‘s translational science plattform:

• in silico services, eBiology; animal or other disease models

• research tools (NCEs, siRNA,antibodies,...)

• expression profiling, assay development, imaging, tissues,...

Interdependent external

and internal review and

risk assessment

Symbiotic Innovation: Optimizing a global R&D organization for Innovation: Adopting entrepreneurial experience and spirits: CAVEAT project competition obviates collective learning

Input on formulation/drug

delivery/PK etc for

project review

Scientific unit

Scientist manger

Team leader

Knowledge base Technology base

Sponsor

Institution A Institution B Institution C Institution D Institution E Institution n


Symbiotic Innovation: Facilitating CRO business model Selected problems in technology driven CRO business models

Management spends disproportional time/money to identify and negotiate collaboration deals with an

academic leaders and the relevant technology transfer offices or biotech/pharma partners

Discussion on fictive values are waste of time before completing a PoC or Phase II

Neither scientific leadership, nor optimal motivation or critical assessment of the project are secured in

consulting, collaboration or “simple” MTT agreements

Single technology CRO deals usually create suboptimal value and knowledge

Knowledge is dispersed and not part of a growing proprietary competence hub

Discovery platform companies face a conflict in resource/financing allocation between the discovery

platform and more mature product opportunities

Optimized value growth requires complex technologies along the value chain and collective learning

difficult to establish within one single company without a concise focus

Emerging discovery technologies show significant deficits in reliability and validity due to a lack of

standardized procedures for sample collection and processing of both, sample and data.

…

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New anatomies foster innovation and enable Symbiotic Innovation Only engaging all leaders (academia, industry, politics) will allow to create of value from science

Innovation

&

Progress

Strategic alliances

• Optimally aligned strategic and

organizational goals and metrics

Exploit funding of common goals

• Public-private-partnerships

Patients, Pharma, Academia

Long term horizon of expectation

• NMEs entering clinical development not

sole performance criterion

• Integration of scientist thinking, motivation

and interest for recognition/reward

Dogmas, Dinosaurs & Dynasties

• Challenge research anatomies,

concepts, collaborations,

technologies, alliances, …

Creation of leadership

• Creative teams generate innovative

concepts and ideas

• Team leaders implement team ideas

Entrepreneurship & motivation

• Engage entrepreneurial leaders

and create related incentives

• Foster talents and related career

tracks in academia & industry

Exploitation

www.based

opportunities

Creation of a

competitive edge

in globalization


ROI

REWARD

Long term value

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How and why should Symbiotic Innovation work and will be adopted? Current pressure is immense and evolution cannot be stopped

Incentive & Motivation

• New story; soap effect ?

• New revenue stream

• Long term business model?

• It makes sense

Pharmaceutical Industry

• Why should the pharmaceutical industry

engage in this concept?

• Why should the pharmaceutical industry

provide funding and resources?

Incentive & Motivation:

• Early access to innovation

• Diversified risk, decreased costs

• Quality control, absorptive science

• Optimized use of resources & time

• It makes sense

Academic institutions

• What is the incentive for scientists to

engage in these kind of projects

• Why should tech-transfer accept master-

contracts?

Incentive & Motivation

• Access to cutting edge converging

science and resources

• Because Asia doesn’t sleep

• More opportunities for higher ROI

• It makes sense

Media partner/publisher

• For the pilot program scientist

must be continuously attracted

• Why will publishers endorse SI?

Innovate & improve,

deliver ROI

Publish & survive;

generate USP

for global competition


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Facing the opportunities of omics and Big-data the transitions that take place,

innovation & value creation for society and industry appears a feasible goal

Symbiotic Innovation: Fostering value creation from sciences

Big-Data

*omics data Clinical data

Precision

Medicine


SI unchains the value of emerging data sets