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

B. Munos, Nature Reviews Drug Discovery, (2009), 8, 959

• 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

• Over 90% of compounds entering the first stage of clinical trials fail to become a product

R&D Today: Escalating Costs, Need for Reshaping

• Predicted NME Cost

• Evolution of New Molecular Entities (NME) cost

• Reasons for costs

• more stringent regulation

• tougher science

• economic change

• Reduction of costs

• use of simulations

• more efficient selection of projects

• dramatically reduce attrition rates

R&D Today: Escalating Costs, Need for Reshaping

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 Development4 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Clinical Trials6 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Clinical Trials6 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Clinical Trials6 Years

Time and Processes Involved in Drug Discovery

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Clinical Trials6 Years

FDA Approval1.5 Years

Current Estimates of Timelines and Costs

$30 M

$220 M

$598 M

$26 M

Target Discovery 2.5 Years

Pre-clinical Development4 Years

Clinical Trials6 Years

FDA Approval1.5 Years

Predicted Savings from in silico Modelling

$24 M

$193 M

$550 M

$26 M

Target Discovery2 years

Pre-clinical Development3.5 years

Clinical Trials5 years

FDA Approval1.5 Years

$ 6 M

$ 27 M

$ 48 M (indirectly)• Simulation Filter

• in silico savings

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

‡US DOT Form 41 via BTS, Schedule P12

†iSuppli Corporation supplied rankings for 2010 (Preliminary)

Theoreticalapproaches

Length scale

Available software (company)

Molecules

- Density Functional Theory - Molecular Dynamics

- QSAR, Semi-Empirical Methods

- Coarse Grained Models

- Statistical and Empirical methods

HumansTestanimals

Cells, tissues and organs

Proteins(Signallingpathways)

State of the Art

- PhysioLab Entelos

- Living Human Project

- ONETEP Accelrys

- SYBYL-XTripos

- Simcyp Limited- Physiome Project

- PathwayLab InNetics AB

- Pharsight

Successful Case Studies - Pharma in silico Savings

B.S. Hendriks et. al. IEE Proc. Syst. Biol. (2006) 153, 22–33This image has been released into the public domain by its author, K.murphy at the wikipedia project. This applies worldwide.

ErbB 1 ErbB 2 ErbB 3 ErbB 4

• 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 protein Family

• Simulations required less than 24 hours on a desktop computer, data from experiment required weeks at the bench

Academic Collaboration

• Sponsor PhD projects at the University of Cambridge

• Harness the expertise available in academia

• Reciprocate the success found in similar endeavours

• 20-50 percent savings

• Simulations cut down on trial and error

In-house simulation and development teams

• 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

StrategyEvaluation

Academic Collaboration

In-House Development

Acquisition

Timescale

Costs

Returns

Ease of Implementation

Intellectual Property Opportunities

Key Players

Recommendations Invest in Academics Start Hiring Start Negotiating

Acknowledgements:MedImmune Mentor: B. Agoram

CUTEC advisor: P. Zulaica

Conclusions

Need for radical changes in R&DMore predictive power in future methods

Filter out unreliable new drugs before costly clinical trialsPrompt more ideas for target-specific NMEs

Invest in in silico modelling

Be green... Save mice...

Invest in in silico modelling in the pharmaceutical industry !

Acknowledgements:MedImmune Mentor: B. Agoram

CUTEC advisor: P. Zulaica

Conclusions

Need for radical changes in R&DMore predictive power in future methods

Filter out unreliable new drugs before costly clinical trialsPrompt 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&DMore predictive power in future methods

Filter out unreliable new drugs before costly clinical trialsPrompt more ideas for target-specific NCEs

Invest in in silico modelling

Any Questions ?

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• 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 order to “virtualise” the research process thereby accelerating clinical development

Source: FDA CDER, PhRMA and PricewaterhouseCoopers analysis

Reviews of Problems

• 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

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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.

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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

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Airline design in silico

• 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

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 :

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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.

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Industries Making Use of Simulation

• Science

• 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

• BusinessFinancial and economic modelingTransaction processing, web services

• Search enginesDefenseNuclear weapons -- test by simulationsCryptography

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The Future for Predictive Rigorous Accurate Quantum Mechanics based Methods

Theoreticalapproaches

Length scale

Available software (company)

Molecules

- DFT - MD

- Less accurate MD- QSAR

- Coarse Grained Models

- Finite Element Analaysis

HumansTestanimals

Cells, tissues and organs

Proteins(Signallingpathways)

State of the Art - The Pharmaceutical Industry

- PhysioLab (Entelos)- STEP Consortium, Visual Physiological Human- Living Human Project

- ONETEP (Accelrys)- SYBYL-X(Tripos)

- PhysioLab (Entelos)- Simcyp Limited- Physiome Project- BioSim- SysMo

- PhysioLab (Entelos)- SRS (BioWisdom)- PathwayLab (InNetics AB)- HepatoSys- Pharsight

State of the Art - Other Industries

Automotive Design~ $1 billion per company

per year

Airlines~ $100 million per

airline per year

Semiconductor Indsutry~ $1 billion per company per year

Securities Indsutry~ $15 billion per year for U.S.

home mortgages