Merck has developed a revolutionary scientific modeling platform to support all aspects of drug discovery and development. This platform, called the Virtual Pipeline, was created over 10 years in collaboration with regulators. It has allowed Merck to fully simulate drug lifecycles, power strategic decision making like portfolio acquisitions, and is projected to reduce timelines by 40% and costs by 50%. The platform aggregates both internal and external data, builds models and simulations, and provides best practice workflows to researchers.
How to Create a Big Data Culture in PharmaChris Waller
A talk presented at the Big Data and Analytics conference in Boston on January 28, 2014. Emphasis on data and information sharing cultures in companies.
How to Create a Big Data Culture in PharmaChris Waller
A talk presented at the Big Data and Analytics conference in Boston on January 28, 2014. Emphasis on data and information sharing cultures in companies.
In the world of pharma precompetitive efforts are increasing. These developments have created a dynamic ecosystem with pharma as smaller nodes in a complex network, in which collaborations have become an important business model.
New Disruptive Technology Helps CROs and Pharma Accelerate Oncology-Focused C...Rafael Casiano
As clinical trial outsourcing to CROs continues to increase, competition among CROs is becoming fierce, and sponsors are under pressure to do more with less. To respond, experienced CROs understand they must exploit every opportunity to gain a competitive edge. And, while technology can be a key differentiator, simply being “data-driven” is not enough to win studies. CROs must find consistent and predictable methods to accelerate clinical trial recruitment.
Strand featured in CIO Review: Pharma and Life Science Special edition - July 2014
Strand Genomics Inc recognised by CIO Review as one among 20 most promising Tech solution providers to Pharma and Life science industry 2014
Integrate RWE into clinical developmentIMSHealthRWES
With greater application of RWE throughout the pharmaceutical
lifecycle, learnings are emerging that offer guidance for
approaches to derive the maximum value. This article captures
the author’s experience at a leading international biotech, with
insights for smoothing RWE assimilation into clinical
development and realizing the benefits it brings.
Technology is disrupting the process behind drug development. Growing realization that current clinical trial strategies are not sustainable or feasible means one thing - change. But, where do pharmaceutical companies go from here? An integrated clinical trial ecosystem will arise through leveraging emerging business technologies. But, are companies prepared to take advantage?
Clinical Research Informatics World 2015Jaime Hodges
Complementing their exceptional series of informatics programming in Boston this spring, Cambridge Healthtech Institute and Clinical Informatics News are proud to launch Clinical Research Informatics World. The event brings together industry leaders, innovative thinkers and decision makers in the areas of clinical operations, clinical trial management, clinical innovation, data analysis, clinical trial informatics, data management, clinical research IT, and clinical information systems for two days of dynamic discussions, expert-led presentations and invaluable networking.
The 2015 program featuring a plenary keynote session and two concurrent conference tracks provides coverage on such topics as big data use and analytics for advancing clinical research, data visualization and analysis trends, new technologies in use for clinical trials (including mobile technology, wearables and social media), and cross-industry data sharing. Learn more at http://www.clinicalinformaticsworld.com
A global leader in real world health intelligence, Analytica Laser is powered by a renowned scientific team of 140+ senior experts across eight offices. Our consulting, research and data services are employed by the leading biopharma and public health innovators in over 20 countries. Every day, our work directly benefits millions of patients in advancing access to new therapies that are safer, more convenient and more affordable.
Data-driven Healthcare for the Pharmaceutical IndustryLindaWatson19
The tremendous opportunity of a data-driven strategy is apparent to the pharmaceutical industry, as all these informational assets exhibiting volume, variety, and velocity need to be ingested and analyzed for enhanced insight leading to better business decisions to address proactively the needs of patient care, while getting to market cheaper, faster, with better products.
its not my personal work presentation but taken from lecture ppt from university of San Diego, california.
Its about the drug discovery process, its development and its commercialization.
In the world of pharma precompetitive efforts are increasing. These developments have created a dynamic ecosystem with pharma as smaller nodes in a complex network, in which collaborations have become an important business model.
New Disruptive Technology Helps CROs and Pharma Accelerate Oncology-Focused C...Rafael Casiano
As clinical trial outsourcing to CROs continues to increase, competition among CROs is becoming fierce, and sponsors are under pressure to do more with less. To respond, experienced CROs understand they must exploit every opportunity to gain a competitive edge. And, while technology can be a key differentiator, simply being “data-driven” is not enough to win studies. CROs must find consistent and predictable methods to accelerate clinical trial recruitment.
Strand featured in CIO Review: Pharma and Life Science Special edition - July 2014
Strand Genomics Inc recognised by CIO Review as one among 20 most promising Tech solution providers to Pharma and Life science industry 2014
Integrate RWE into clinical developmentIMSHealthRWES
With greater application of RWE throughout the pharmaceutical
lifecycle, learnings are emerging that offer guidance for
approaches to derive the maximum value. This article captures
the author’s experience at a leading international biotech, with
insights for smoothing RWE assimilation into clinical
development and realizing the benefits it brings.
Technology is disrupting the process behind drug development. Growing realization that current clinical trial strategies are not sustainable or feasible means one thing - change. But, where do pharmaceutical companies go from here? An integrated clinical trial ecosystem will arise through leveraging emerging business technologies. But, are companies prepared to take advantage?
Clinical Research Informatics World 2015Jaime Hodges
Complementing their exceptional series of informatics programming in Boston this spring, Cambridge Healthtech Institute and Clinical Informatics News are proud to launch Clinical Research Informatics World. The event brings together industry leaders, innovative thinkers and decision makers in the areas of clinical operations, clinical trial management, clinical innovation, data analysis, clinical trial informatics, data management, clinical research IT, and clinical information systems for two days of dynamic discussions, expert-led presentations and invaluable networking.
The 2015 program featuring a plenary keynote session and two concurrent conference tracks provides coverage on such topics as big data use and analytics for advancing clinical research, data visualization and analysis trends, new technologies in use for clinical trials (including mobile technology, wearables and social media), and cross-industry data sharing. Learn more at http://www.clinicalinformaticsworld.com
A global leader in real world health intelligence, Analytica Laser is powered by a renowned scientific team of 140+ senior experts across eight offices. Our consulting, research and data services are employed by the leading biopharma and public health innovators in over 20 countries. Every day, our work directly benefits millions of patients in advancing access to new therapies that are safer, more convenient and more affordable.
Data-driven Healthcare for the Pharmaceutical IndustryLindaWatson19
The tremendous opportunity of a data-driven strategy is apparent to the pharmaceutical industry, as all these informational assets exhibiting volume, variety, and velocity need to be ingested and analyzed for enhanced insight leading to better business decisions to address proactively the needs of patient care, while getting to market cheaper, faster, with better products.
its not my personal work presentation but taken from lecture ppt from university of San Diego, california.
Its about the drug discovery process, its development and its commercialization.
This is part of the MaRS BioEntrepreneurship series.
Speaker: Lynne Zydowsky, Ph.D., Managing Principal Zydowsky Consultants
* Explore the development of regulated drugs and devices
* Understand where and how value is generated in the pharmaceuticals industry
* Appreciate the interplay between science and business in a biotech company
To download a copy of the audio for this presentation, please go to:
http://www.marsdd.com/bioent/oct16
For the event blog and Q+A, please see:
http://blog.marsdd.com/2006/10/17/bringing-together-art-and-science/
Lantern Pharma is a clinical stage biotechnology company focused on leveraging artificial intelligence (“A.I.”), machine learning and genomic date to streamline the drug development process and to identify patients who will benefit from their targeted oncology therapies. Their portfolio of therapies consists of compounds that others have tried, but failed, to develop into an approved commercialized drug. Additionally, they develop new compounds with the assistance of their A.I. platform (RADR) and biomarker driven approach. The Company is currently developing four therapeutic programs.
GSK’S Andrew Witty: Addressing Neglected Tropical Diseases and global health ...Nejmeddine Jemaa
Every day, Non Governmental Organization NGOs is confronted with the lack of access to adequate or affordable medical tools in the field. They face two major challenges the high cost of existing medicines on the one hand, and the absence of appropriate or effective treatments for many of the diseases affecting our patients on the other, we are talking about Neglected Tropical Disease NTD in the Least developed Countries LDCs.
Andrew Witty, Chief Executive Officer of Glaxo Smith Klein (GSK) delivered a speech at the Harvard Business School in Boston on February 2009 entitled “Big pharma a catalyst for Change” focused on two issues: a) promoting innovation to prevent or treat NTDs in the world’s Least Developed Countries by creating a “pharmaceutical patent pool”; b) improving the access to medicine in the poorer countries by lowering the prices of GSK’s medicines.
In deed, we are assisting a radical change in pharma Business model, we are moving from conflict to collaboration through the Medicines Patent Pool in the hope that it speed up access to newer medicines, and boost initiatives that make use of alternative financing mechanisms in order to develop new, more appropriate treatments that respond to medical needs.
On the other hand the pricing strategy dilemma facing the generic manufacturers and the non inclusion of HIV which is a major neglected disease in LDCs in the patent pool may compromise the success of such business model.
In order to deal with that two issues, GSK should include HIV drugs in their patent pool as other manufacturers and NGO are doing, and concerning the pricing strategy they should emphasize on the high quality of the original drug mandatory to eradicate this NTDs and communicate more on the fact that GSK will invest 20% of these drugs profit to improve the infrastructure of these LDCs.
Lantern Pharma is a clinical stage oncology-focused company who is harnessing the power of Artificial Intelligence and Genomics to develop precision cancer therapies. By leveraging RADR®, Lantern's proprietary big data engine, more effective cancer treatments can be developed and delivered to the right group of patients at a faster rate and for a fraction of the cost. RADR® identifies genetic signature patterns that underpin the identification of patients for whom Lantern's portfolio of oncology drug candidates is likely to have the greatest therapeutic effect. This is the future of precision medicine and Lantern is at the forefront of AI driven transformation that will change the lives of cancer patients.
Lantern Pharma is a clinical stage oncology-focused company who is harnessing the power of Artificial Intelligence and Genomics to develop precision cancer therapies. By leveraging RADR®, Lantern's proprietary big data engine, more effective cancer treatments can be developed and delivered to the right group of patients at a faster rate and for a fraction of the cost. RADR® identifies genetic signature patterns that underpin the identification of patients for whom Lantern's portfolio of oncology drug candidates is likely to have the greatest therapeutic effect. This is the future of precision medicine and Lantern is at the forefront of AI driven transformation that will change the lives of cancer patients.
Lantern Pharma is a clinical stage oncology-focused company who is harnessing the power of Artificial Intelligence and Genomics to develop precision cancer therapies. By leveraging RADR®, Lantern's proprietary big data engine, more effective cancer treatments can be developed and delivered to the right group of patients at a faster rate and for a fraction of the cost. RADR® identifies genetic signature patterns that underpin the identification of patients for whom Lantern's portfolio of oncology drug candidates is likely to have the greatest therapeutic effect. This is the future of precision medicine and Lantern is at the forefront of AI driven transformation that will change the lives of cancer patients.
Biotech revolution changed the pharmaceutical industry, triggering a wave of risky collaborations between rivals. Based on the research findings, we answer the question why cooperation in the field of immuno-oncology is a better strategy for Pfizer and Merck KGaA, which aim to achieve competitive advantage quickly and with minimum effort. Combining their assets and core expertise companies realize benefits of greater size and variety in the conduct of research, development and commercializing of their new breakthrough therapy for cancer treatment.
1. Building a Culture of Model-
Driven Drug Discovery
Chris L. Waller, Ph.D.
2. Forward-Looking Statement
This presentation includes “forward-looking statements” within the meaning of the safe harbor provisions of the United
States Private Securities Litigation Reform Act of 1995. Such statements may include, but are not limited to, statements
about the benefits of the merger between Merck and Schering-Plough, including future financial and operating results, the
combined company’s plans, objectives, expectations and intentions and other statements that are not historical facts.
Such statements are based upon the current beliefs and expectations of Merck’s management and are subject to
significant risks and uncertainties. Actual results may differ from those set forth in the forward-looking statements.
The following factors, among others, could cause actual results to differ from those set forth in the forward-looking
statements: the possibility that all of the expected synergies from the merger of Merck and Schering-Plough will not be
realized, or will not be realized within the expected time period; the impact of pharmaceutical industry regulation and
health care legislation in the United States and internationally; Merck’s ability to accurately predict future market
conditions; dependence on the effectiveness of Merck’s patents and other protections for innovative products; and the
exposure to litigation and/or regulatory actions.
Merck undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information,
future events or otherwise. Additional factors that could cause results to differ materially from those described in the
forward-looking statements can be found in Merck’s 2011 Annual Report on Form 10-K and the company’s other filings
with the Securities and Exchange Commission (SEC) available at the SEC’s Internet site (www.sec.gov).
3. Thoughts on Strategy and Culture
• “Culture eats strategy for breakfast.”
– Peter Drucker and Mark Fields, Ford
• “Culture eats strategy for lunch.”
– Dick Clark, Merck
• “Culture eats strategy for dinner.”
– Chris Waller, Merck
• Peter Drucker often argued that a companies culture would trump any attempt
to create a strategy that was incompatible with it's culture.
• “Company cultures are like country cultures. Never try to change one. Try,
instead, to work with what you’ve got.”
– Peter Drucker
5. Cost to Develop and Win Marketing Approval
for a New Drug Is Increasing!
BOSTON – Nov. 18, 2014 – Developing a new prescription medicine that gains marketing approval, a process often lasting longer than a decade, is estimated to cost $2,558 million, according to a new study
by the Tufts Center for the Study of Drug Development.
The $2,558 million figure per approved compound is based on estimated:
Average out-of-pocket cost of $1,395 million
Time costs (expected returns that investors forego while a drug is in development) of $1,163 million
Estimated average cost of post-approval R&D—studies to test new indications, new formulations, new dosage strengths and regimens, and to monitor safety and long-term side effects in patients required by
the U.S. Food and Drug Administration as a condition of approval—of $312 million boosts the full product lifecycle cost per approved drug to $2,870 million. All figures are expressed in 2013 dollars.
The new analysis, which updates similar Tufts CSDD analyses, was developed from information provided by 10 pharmaceutical companies on 106 randomly selected drugs that were first tested in human
subjects anywhere in the world from 1995 to 2007.
“Drug development remains a costly undertaking despite ongoing efforts across the full spectrum of pharmaceutical and biotech companies to rein in growing R&D costs,” said Joseph A. DiMasi, director of
economic analysis at Tufts CSDD and principal investigator for the study.
He added, “Because the R&D process is marked by substantial technical risks, with expenditures incurred for many development projects that fail to result in a marketed product, our estimate links the costs of
unsuccessful projects to those that are successful in obtaining marketing approval from regulatory authorities.”
In a study published in 2003, Tufts CSDD estimated the cost per approved new drug to be $802 million (in 2000 dollars) for drugs first tested in human subjects from 1983 to 1994, based on average out-of-
pocket costs of $403 million and capital costs of $401 million.
The $802 million, equal to $1,044 million in 2013 dollars, indicates that the cost to develop and win marketing approval for a new drug has increased by 145% between the two study periods, or at a
compound annual growth rate of 8.5%.
According to DiMasi, rising drug development costs have been driven mainly by increases in out-of-pocket costs for individual drugs and higher failure rates for drugs tested in human subjects.
Factors that likely have boosted out-of-pocket clinical costs include increased clinical trial complexity, larger clinical trial sizes, higher cost of inputs from the medical sector used for development, greater focus
on targeting chronic and degenerative diseases, changes in protocol design to include efforts to gather health technology assessment information, and testing on comparator drugs to accommodate payer
demands for comparative effectiveness data.
Lengthening development and approval times were not responsible for driving up development costs, according to DiMasi.
“In fact,” DiMasi said, “changes in the overall time profile for development and regulatory approval phases had a modest moderating effect on the increase in R&D costs. As a result, the time cost share of total
cost declined from approximately 50% in previous studies to 45% for this study.”
The study was authored by DiMasi, Henry G. Grabowski of the Duke University Department of Economics, and Ronald W. Hansen at the Simon Business School at the University of Rochester.
6. Progressive, Unsustainable Decline in Productivity
Reported by Matthew Herper, Forbes 5/22/2014 “Who’s the best in drug research…”
http://www.forbes.com/sites/matthewherper/2014/05/22/new-report-ranks-22-drug-companies-based-on-rd/
2014 New Drug Approvals Hit 18-Year High
2014 was a good year for pharmaceutical
innovation – the best, in fact, since the
industry’s all-time record of 1996. FDA
approved a total of 44 drugs –
7. The productivity crisis in pharmaceutical R&D
Fabio Pammolli, Laura Magazzini & Massimo Riccaboni
Nature Reviews Drug Discovery 10, 428-438 (June 2011)
28,000 compounds from Pharmaceutical Industry Database
We are unable to predict success.
Failure Rates Increasing at all Stages of R&D
9. Press Release v1 (Merck BHAG Realized)
Merck’s revolutionary model-driven approach to drug development leads to breakthrough therapies in Oncology and Neuroscience.
Boston, MA, November 4, 2024
In the last 12 months Merck has released breakthrough treatments for cancer and mental health in record time by using it’s revolutionary modeling platform for
human drug response.
By working with regulatory authorities world wide and leveraging public private partnerships, Merck has been able to develop deep models of human disease
allowing them to go straight to human trials. This has allowed them to greatly reduce the traditional timeline for drug development and by-pass controversial and
expensive animal trials.
Head of modeling Dr. Smith said that the approach was made possible by developing deep and accurate models of each individual in a clinical trial. “We actively
recruited patient populations and made use of sophisticated bio-sensors, nanotechnologies and real-time analysis to develop comprehensive predictive models of
their genetics, metabolism and disease”. Over a period of several years Merck modelers received constant streams of data from these volunteers giving them
unprecedented understanding of their disease. They combined this with large publicly funded datasets and crowd sourced and internal modeling methods.
“We are moving to a new paradigm in drug discovery where we enroll patients before we start therapeutic development” said Smith.
Merck believes that it’s modeling platform and methodology can be used to rapidly develop cures for other diseases and is actively seeking patients to donate
their health information as well as development partners to license this platform in new disease areas.
Note: This is completely fake and does not represent any forward looking statements on behalf of Merck.
10. Press Release v2 (Merck BHAG Realized)
Merck’s “Virtual PipelineTM” Powers Decision Making
Boston, MA, November 4, 2024
Merck released details today on a revolutionary platform that it created to support all aspects of the drug discovery and development process.
This 10 year journey began in 2014 with the acknowledgement that the pharmaceutical industry must transform in order to survive the mounting
financial and regulatory pressures.
In collaboration with regulatory agencies world-wide, Merck created the Virtual PipelineTM by adopting a Product Lifecycle Management (PLM)
mentality and completely and permanently altered the pharmaceutical research and development landscape.
“The existence of the Virtual PipelineTM and the ability to fully simulate the entire lifecycles of therapeutic agents allowed our business development
team to make an informed decision to acquire Iliad Pharmaceuticals’ entire portfolio with the intent to launch a drug that will see Merck re-enter the
infectious disease therapeutic area. It is our expectation that Merck will enter the market with First and Best-in-Class agents grossing in excess of
$10BN per annum.”, reported Dr. Hootie N.D. Blowfish, Head of Strategic Acquisitions.
While too early to verify, Merck projects that the Virtual PipelineTM will enable their research scientists to reduce the time from target identification
to product launch by as much as 40% with associated cost savings nearing 50%.
Note: This is completely fake and does not represent any forward looking statements on behalf of Merck.
11. Questions, questions, questions…
Research Development Commercial Medical
Drug Protein Target ResponseSystem Individuals PopulationsPathway
What entity should I make?
How active is my entity?
What other activities does my entity possess?
How can I make it?
Do I have the starting materials?
What dose is required?Is it likely to be metabolized?
Is clearance going to be a problem? What is the most effective formulation?
How can I make it in bulk?
What disease should I target?
What targets are involved?
What mechanisms are involved?
How are my competitors doing?
Is my compound more effective than comparators?
How much can I charge for this?
Can I patent this?
12. Transform
Deliver
Aggregate
Access
Drug Protein Target Response
Answers, answers, answers…
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
14. Tools for Expert Modelers in Early Discovery
Model Generation and Capture
Automate model-building to drive consistency and share
best practices
Automate model capture and registration to ensure
consistent way to find and consume models
Automate updating of models to ensure latest data and
highest quality
Build QSAR Models Publish QSAR Models
15. Tools for Early Discovery Project Teams
Enrich Simple Drawn Compounds with Calculated Properties
Transform how chemists interact with their data
Transform how tools are delivered to the desktop
Transform how IT builds and supports applications
Two Clicks
Equally easy access to the same calculations from other familiar applications
16. Model Usage is Growing…
Compounds registered as ‘GENERAL_SCREENING’ excluded from analysis
17. Resulting in Higher Quality Compounds!
Descriptor Function X1 X2 X3 X4
QSAR_CLint_rat_hepatocyte Decreasing 45 100
QSAR_CLint_human_hepatocyte Decreasing 25 60
QSAR_Clearance_rat Decreasing 15 35
ClogD_pH_7.4 Hump Function 1.5 23 3 3.5
Polar_Surface Hump Function 65 75 125 140
Molecular_Weight Hump Function 420 475 530 580
Courtesy: Kerim Babaoglu
Multiparameter Optimization (MPO) Analysis Drives Design of More Desirable Compounds
More Desirable Compounds Display Lower (Better) Human Dose Calculations
(Scaled from Experimental Rat PK Data)
Design/Synthesis Cycle
DesirabilityScore
Legend:
Green = Good Dose
Yellow = Moderate Dose
Red = Poor Dose
19. Execution
Service
(AEP Runner)
JobsXMLDB
AEP Cluster
Runner
(Predict)
AEP Grid
(Build/Learn)
AEP Grid
(Build/Learn)
AEP Grid
Runner
(Build/Learn)
Publication Service
Checks new models for
validating, complete
metadata and assigns
identity. At the mid-term,
this service is embedded in
QSAR workbench only.
Execution Service
Launches job requests on
appropriate infrastructure.
This service is provided out-
of-the-box by AEP.
GEMS
Information Service
Returns a listing of published
services (models) the user is
allowed to see and run
QSAR Workbench ALDaS Insight / ADMET Workbench
SOAP/HTTP
SOAP / HTTP
ODBC /
JDBC
AEP standard functionality
Logical Architecture Overview
Information
Service
Publication
Services
Service
Metadata DB
PSN Project
Other Service Consumers
20. -
Extensible and Leveragable Informatics Platform
A Service Oriented Architecture (SOA) That Invokes Connectivity
Sharepoint (one.merck.com/cheminfo)Translational Solutions Architecture
Get Me The Data What Do I Make Next? Now, Help Me Make It
Sharepoint (one.merck.com/cheminfo)Service Oriented Architecture Framework for Reusable Services Development
Sharepoint (one.merck.com/cheminfo)Merck Master Data and Data Architecture
Sharepoint (one.merck.com/cheminfo)Transactional Applications and Data Repositories
Lead
Identification
Lead
Optimization
Preclinical
Candidate to
First in Human
First in Human
to
Phase 2B
Phase 3
to
File
Pre-Lead
Optimization
Lead
Optimization
Early
Development
PCC Phase IIb
Chemical Biology
(chemical probes
predict targets)
Systems Biology
(off target activity
prediction)
Clinical Trials
(ADMET predictions)
Chemical Pharmacology
(toxicity predictions)
Sharepoint (one.merck.com/cheminfo)
Local (Project Team) QSAR
Models
Sharepoint (one.merck.com/cheminfo)
Ligand-based Design
Support
Sharepoint (one.merck.com/cheminfo)
Structure-based Design
Support
Hit Lead
22. Drug Protein Target
Response
interacts
with
and elicits a
What is the Scope of Scientific Modeling?
distributes to
site of action
through a
in
System
IndividualsPopulations
Pathway
in a
within
that respond to
Each arrow represents an
opportunity to develop and utilize
a predictive model in lieu of more
resource and time-consuming
experimentation!
23. The Modeling and Simulation Landscape
Research Development Commercial Medical
Drug Protein Target ResponseSystem Individuals PopulationsPathway
A wide variety of solution providers…
NONMEM®
…incorporating a wide vide variety of technologies.
Note: Illustrative Purposes Only
QSAR Workbench ModSpace
NavigatorInsight Analytics
GastroPlus
DDDPlus
ADMET Simulator
Phoenix
WinNonlin
SimCyp
Trial Simulator
Life Sciences
Data Hub
Foundation / PLP
Derek Nexus
…offering a wide variety of tools…
DILIsym
25. Data ingestion
transformation
DATA
Data integration
Warehousing
Data stores
Authoritative
Repositories
Client
tools
PRESENTA
TION
LOGIC
Data Access; Infrastructure Access (HPC); Access control
Private
Services
Domain
Users
Data Sources
Shared
Services
Scientific Information Management
Research Development Commercial Medical
Data
Delivery
Service
Data Platform
Data Mart or
View
Data Mart or
View
Data Mart or
View
Data Mart or
View
Note: Illustrative Purposes Only
D360
ChemCart Scientific Information Platform API
Scientific Information Platform API
Scientific Information Common Data Model
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
Data
Ingestion
Service
26. Model (Lifecycle) Management
Model ingestion
transformation
MODELS
Model integration
Warehousing
Model stores
Authoritative
Repositories
Client
tools
PRESENTA
TION
LOGIC
Model Access; Infrastructure Access (HPC); Access control
Private
Services
Domain
Users
Model Sources
Shared
Services
Model Repository
Note: Illustrative Purposes Only
File
System
Document
s
Transactiona
l DB
Transactiona
l DB
Transactiona
l DB
MLM
Service
MLM
Service
MLM
Service
MLM
Service
MLM
Service
Research Development Commercial Medical
D360
ChemCart
ADMET
Workbench
WebModel Mobile
Apps
Scientific Modeling Platform API
Scientific Modeling Platform API
Scientific Modeling Common Data Model
Model
Execution
Service
Model
Information
Service
Results
Presentation
Service
29. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Conceive Develop Realize Use
Research Development Commercial Medical
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
30. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
Learning Loops (DMAIC Cycles) within the functional domains of Pharma R&D Support:
• Adaptive Research Operating Plans
• Adaptive Clinical Trials
• Behavioral Modification…
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl
Design
Measure
Analyze
ImproveControl
31. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
Cross-domain Workflows…
32. Drug Protein Target Response
Pharma Product Lifecycle Management
System Individuals PopulationsPathway
Research Development Commercial Medical
Data
(Internal and
External, Structured
and Unstructured)
Models and
Simulations
(Data)
Workflows
(Best Practices)
Can we construct pan-R&D workflows that incorporate existing data, predictive models, and best practices
to drive design, predict full product lifecycle, and increase probability of success?
34. Level 4Level 3Level 2Level 1
Current State
EDDS
Data
EDDS
Models
PCD
Data
PCD
Models
Clinical
Data
Clinical
Models
Real
World
Data
Real
World
Models
Discovery Pre-clinical Clinical Real World
While we are beginning to see sharing of models and integration of data WITHIN
functional domains, we are still advancing sub-optimal POC entities.
Technology: Siloed information and model management solutions
Process: Siloed workflows
People: Siloed thinking
Root Causes
Source: The Pharmaceutical Industry Database (PhID), maintained by the IMT (Institutions, Markets, Technologies) Lucca, Italy, combines several sector-specific proprietary data sets regarding research and development (R&D) activity, collaborations and final drug markets. These data are collected from public sources and from companies (confidential information and press releases). Data collection started in 2000, financed by a grant from the Merck Foundation (EPRIS project). The PhID includes full text entries comprising more than 200,000 patent applications since the early 1970s (from the US Patent and Trademark Office, the European Patent Office and the World Intellectual Property Organization); detailed information about R&D projects spanning more than 28,000 compounds; 20,000 collaborative agreements; and sales figures on ~160,000 pharmaceutical products (both branded and generics) sold in the major markets (the United States, the 15 European Union countries (EU-15: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and the United Kingdom) and Japan) between 1996 and 2008 (Ref. 16). For each compound, information regarding the targeted therapeutic market, the timing of major development milestones, and the name and type of organizations involved is provided.
Project Background
Predictive Modeling efforts across MRL have fundamental needs which are not currently being adequately addressed by the IT infrastructure. Expert modelers need to integrate proprietary Merck and publicly available data across scientific and business disciplines to optimize efficiency and the quality of models. Expert modelers need to collaborate on and manage the process of methodology development to (a) eliminate duplicated efforts, (b) ensure everyone is using the latest data and methods, (c) establish standard libraries for methods, and (d) enable external collaborations in development of models. Expert modelers require capabilities to manage all aspects of predictive model construction to optimize efficiency and share collective knowledge across the modeling community. Delivery of a platform to support our collective predictive modeling efforts will increase utilization of predictive modeling across Merck improving probability of success from potency to dose, ID and selection of targets, drug re-purposing, trial design and outcomes research.
MLM Services include:
Data Access and Retrieval
Data Discovery
Data Set Creation
Data Quality Management
Data Visualization
Model Development
Data Traceability
Model Traceability
Process Traceability
Collaborative Development
Model Quality Management
Model Registration
Model Publishing
Model Discovery
Model Distribution
Model Sharing
Model Deployment
Model Analysis
Scenario Simulations
Visual Analysis
Statistical Analysis
Reporting
Model Governance and Stewardship
Metadata Management
Best-Practices
Standards
Project Objectives:
To optimize modeling and simulation impact on pharmaceutical research and development by:
enabling search, access, and integration of data, models, and knowledge (as best practices workflows and social scientific networks) across domains,
providing a collaborative platform to support predictive model methods development and utilization, and
developing a model-based pharmaceutical product lifecycle management platform.