Transcript CUTEC_OpenInnovation_Team6Presentation

Building a Business Case for More
in silico Modelling in the
Pharmaceutical Industry
A. L. Eiden, G. Lever, J. Loh and A. Nicolas
CUTEC advisor: P. Zulaica
MedImmune Mentor: B. Agoram
Building a Business Case for More
in silico Modelling in the
Pharmaceutical Industry
A. L. Eiden, G. Lever, J. Loh and A. Nicolas
CUTEC advisor: P. Zulaica
MedImmune Mentor: B. Agoram
R&D Today: Escalating Costs, Need for Reshaping
• Evolution of New Molecular Entities (NME) cost
• Reasons for costs
• more stringent regulation
• tougher science
• economic change
• Over 90% of
compounds entering
the first stage of clinical
trials fail to become a
product
B. Munos, Nature Reviews Drug Discovery, (2009), 8, 959
R&D Today: Escalating Costs, Need for Reshaping
• Evolution of New Molecular Entities (NME) cost
• Reasons for costs
• more stringent regulation
• tougher science
• economic change
• Over 90% of
compounds entering
the first stage of clinical
trials fail to become a
product
• Predicted NME Cost
R&D Today: Escalating Costs, Need for Reshaping
• Reduction of costs
• use of simulations
• more efficient selection of
projects
• dramatically reduce attrition
rates
Presentation Outline
• Drug discovery
• In silico modelling: the state of the art
• 3 proposed strategies for implementation of in silico modeling in the
pharmaceutical industry
• Collaborations between Industry and Academia
• In-House simulation and development team
• Acquisition of existing companies
• Conclusions
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Clinical Trials
6 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Clinical Trials
6 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Clinical Trials
6 Years
Time and Processes Involved in Drug Discovery
Target Discovery
2.5 Years
Pre-clinical Development
4 Years
Clinical Trials
6 Years
FDA Approval
1.5 Years
Current Estimates of Timelines and Costs
$598 M
$220 M
$30 M
Target Discovery
2.5 Years
$26 M
Pre-clinical Development
4 Years
Clinical Trials
6 Years
FDA Approval
1.5 Years
Predicted Savings from in silico Modelling
• Simulation Filter
$ 48 M (indirectly)
• in silico savings $ 27 M
$6M
$550 M
$193 M
$26 M
$24 M
Target Discovery
2 years
Pre-clinical Development
3.5 years
Clinical Trials
5 years
FDA Approval
1.5 Years
Projections taken from predictions in Indian Institute of Technology, Delhi 2005 study and Cambridge University Medicinal BioChem lecture notes
State of the Art
• Airlines
• ~ $100 million per airline per year*
• $7.9 billion average per company revenue†
• Semiconductor Industry
• ~ $1 billion per company per year*
• $9.9 billion average top 20 company revenue‡
* Horst D. Simon - Deputy Director Lawrence Berkeley National Laboratory
†iSuppli
‡US
Corporation supplied rankings for 2010 (Preliminary)
DOT Form 41 via BTS, Schedule P12
State of the Art
Length scale
Theoretical
approaches
Available
software
(company)
Molecules
Proteins
(Signalling
pathways)
- Density Functional
Theory
- Molecular Dynamics
- ONETEP
Accelrys
- QSAR,
Semi-Empirical
Methods
Cells, tissues
and organs
- Coarse Grained
Models
- PathwayLab InNetics - Simcyp Limited
AB
- Physiome Project
Test
animals
Humans
- Statistical and
Empirical methods
- PhysioLab Entelos
- Living Human Project
- SYBYL-X
Tripos
- Pharsight
Successful Case Studies - Pharma in silico Savings
• Virtual Patients: Entelos and Johnson & Johnson
• Design Phase I trial of novel treatment, simulated effects of
various dosing levels.
• Results: trial redesigned, with:
• 40% time saving
• 66% saving in number of patients
• Real-life trial confirmed the simulation result.
Pharma 2020: Virtual R&D, PricewaterhouseCoopers (2007). John Gartner, Wired (20 May 2005)
ErbB 1
ErbB 2
ErbB 3
ErbB 4
• ErbB protein Family
• Simulations required less than 24 hours on a desktop computer, data from
experiment required weeks at the bench
This image has been released into the public domain by its author, K.murphy at the wikipedia project. This applies worldwide.
B.S. Hendriks et. al. IEE Proc. Syst. Biol. (2006) 153, 22–33
Academic Collaboration
• Sponsor PhD projects at the University of Cambridge
• Harness the expertise available in academia
• Reciprocate the success found in similar endeavours
In-house simulation and development teams
• 20-50 percent savings
• Simulations cut down on trial and error
• Software created became industry standard
• There is not a single microelectronics company in the world that doesn’t use
their technique
Acquisition of Existing Companies
Acquisition of firms with in-silico modelling capabilities to enrich pipeline
Examples including:
• Ready-to-use methods & models Skilled manpower
No conflict of interest
Strategy Evaluation
Strategy
Evaluation
Academic
Collaboration
In-House
Development
Acquisition
Invest in Academics
Start Hiring
Start Negotiating
Timescale
Costs
Returns
Ease of
Implementation
Intellectual Property
Opportunities
Key Players
Recommendations
Conclusions
Need for radical changes in R&D
More predictive power in future methods
Filter out unreliable new drugs before costly clinical trials
Prompt more ideas for target-specific NMEs
Invest in in silico modelling
Acknowledgements:
MedImmune Mentor: B. Agoram
CUTEC advisor: P. Zulaica
Be green...
Save mice...
Invest in in silico modelling in the pharmaceutical industry !
Conclusions
Need for radical changes in R&D
More predictive power in future methods
Filter out unreliable new drugs before costly clinical trials
Prompt more ideas for target-specific NCEs
Invest in in silico modelling
Any Questions ?
Acknowledgements:
MedImmune Mentor: B. Agoram
CUTEC advisor: P. Zulaica
Conclusions
Need for radical changes in R&D
More predictive power in future methods
Filter out unreliable new drugs before costly clinical trials
Prompt more ideas for target-specific NCEs
Invest in in silico modelling
Any Questions ?
Acknowledgements:
MedImmune Mentor: B. Agoram
CUTEC advisor: P. Zulaica
Reviews of Problems
• A PricewaterhouseCoopers study
form 2008 identified some key areas
in which the pharma industry could
become more innnovative thus
reducing its R&D costs
• These included developing a
comprehensive understanding of how
the human body works at the
molecular level along with a much
greater use of new technologies in
Source: FDA CDER, PhRMA and PricewaterhouseCoopers analysis
order to “virtualise” the research
process thereby accelerating clinical
development
• The virtual vermin* implementation, allowing researchers studying Type I diabetes to
simulate the effects of new medicines including different dosing levels and regimens on
different therapeutic targets
* Developed by The American Diabetes Association and US Biopharma company Entelos
28
Current Ideas for types of modelling
• Bioinformatics creating the “virtual man” but requiring a significant global effort
comparable to that of the Human Genome Project
• Parametric empirical models based on existing experimental data such as the virtual
vermin, or the creation of 3D images from experimental data to enable closer scrutiny.
A study conducted at the Supercomputing Facility for Bioinformatics &
Computational Biology Indian Institute of Technology, Delhi in 2005
concluded that in silico intervention in drug discovery can save up to ~
15% of time and cost which could be significant for life threatening
diseases.
29
The Current Research Process
Source: PricewaterhouseCoopers
• The Step Consortium - investigating the human body as a single complex system
• The Living Human Project - an in silico model of the human musculoskeletal apparatus
• The Physiome Project - a computational framework toward understanding the
integrative function of cells, organs and organisms
• Model Trial - Entelos have developed their virtual research laboratory
30
Airline design in silico
Boeing invested more than $1 Billion (and insiders say much more) in CAD infrastructure for
the design of the Boeing 777. Boeing reaped huge benefits from design automation. The
more than 3 million parts were represented in an integrated database that allowed designers
to do a complete 3D virtual mock-up of the vehicle.
They could investigate assembly interfaces and maintainability using spatial visualizations of
the aircraft components to develop integrated parts lists and detailed manufacturing process
and layouts to support final assembly. The consequences were dramatic. In comparing with
extrapolations from earlier aircraft designs such as those for the 757 and 767, Boeing
achieved :
• Elimination of > 3000 assembly interfaces, without any physical prototyping
• 90% reduction in engineering change requests (6000 to 600)
• 50% reduction in cycle time for engineering change request
• 90% reduction in material rework
• 50x improvement in assembly tolerances for fuselage
31
Simulation: The Third Pillar of Science
•
Traditional scientific and engineering paradigm:
•
Do theory or paper design.
•
Perform experiments or build system.
•
Limitations:
•
Too difficult -- build large wind tunnels.
•
Too expensive -- build a throw-away passenger jet.
•
Too slow -- wait for climate or galactic evolution.
•
Too dangerous -- weapons, drug design, climate experimentation.
•
Computational science paradigm:
•
Use high performance computer systems to simulate the phenomenon
•
Base on known physical laws and efficient numerical methods.
32
Industries Making Use of Simulation
• Science
•
BusinessFinancial and economic
modelingTransaction processing, web
services
•
Search enginesDefenseNuclear weapons
-- test by simulationsCryptography
• Global climate modeling
• Astrophysical modeling
• Biology: genomics; protein folding; drug design
• Computational Chemistry
• Computational Material Sciences and Nanosciences
• Engineering
• Crash simulation
• Semiconductor design
• Earthquake and structural modeling
• Computational fluid dynamics
• Combustion
33
The Future for Predictive Rigorous Accurate
Quantum Mechanics based Methods
34
State of the Art - The Pharmaceutical Industry
Length scale
Theoretical
approaches
Available
software
(company)
Molecules
- DFT
- MD
- ONETEP
(Accelrys)
- SYBYL-X
(Tripos)
Proteins
(Signalling
pathways)
- Less
accurate MD
- QSAR
Cells, tissues
and organs
Test
animals
Humans
- Coarse Grained
- Finite Element
Models
Analaysis
- PhysioLab (Entelos) - PhysioLab
- SRS (BioWisdom)
(Entelos)
- PathwayLab (InNetics - Simcyp Limited
AB)
- Physiome Project
- HepatoSys
- BioSim
- Pharsight
- SysMo
- PhysioLab (Entelos)
- STEP Consortium,
Visual Physiological
Human
- Living Human Project
State of the Art - Other Industries
Airlines
~ $100 million per airline
per year
Semiconductor Indsutry
~ $1 billion per company per year
Automotive Design
~ $1 billion per company
per year
Securities Indsutry
~ $15 billion per year for U.S.
home mortgages