EY Drug and R&D: Big DATA for big returns

Drug R&D: big data for bigreturnsTodd Skrinar, Thaddeus WolframOctober 24, 2014

Drug R&D: big data for big returns

There are multiple factors currently limiting thediscovery and approval of new medicines

Discover and developinnovative new productsThe low-hanging fruit hasalready been picked andorganizations are pressed todisplay significantdifferentiation

Respond to changingpatient needs whilemaintaining a competitivepositionThe race for novel therapiesputs an even greater focuson R&D organizations

Operate R&D efficientlywhile maintainingcomplianceRegulatory constraints havetightened, requiringincreased level of efficacyand safety at loweroverall costs

R&D challenges

Businesschallenges

Patient needs

Impact of newtechnology

Impact of newdata sources

Healthcarereform

Growing costpressure

Regulatoryframeworks


More clinical trialsIncreased pressure to developnovel therapies has resulted inmore clinical trials in R&DMore patientsLarger clinical trials todemonstrate significantdifferentiation result inincreased overall R&DexpendituresMore informationMore generated, collectedand analysed data requiredfor payer approvals andreimbursement

Historically, getting more from drug R&D has meantputting more money into it

How does one reduce clinical trial costs while still meeting the rising demands of regulators and payers formore data that demonstrate that the drug is a significant improvement over current standards of care?

Clinical trialresultsThe model has reliablyled to much highercosts, but not improvedoutcomes


A more disruptive solution is urgently needed toincrease productivity and reduce costs

Potential advantages of big data:► Bringing more information and potential

for insight rather than time and volumeof resources

► Providing the robust data required for bothdrug approval and reimbursement

► Speeding the discovery and approval of newmedicines while lowering costs

► Helping R&D organizations ask the rightbusiness questions and then seek answersin the data

► Supporting more efficient clinical trial designand innovation in clinical trial approach, andhelps recognize research failures faster

Big data“Omics”

Technology

Dosing

Population

Disease

Social media Diagnostics

Biomarkers

Big data as a solution in R&D


When applied effectively, the use of big data and analytics areadding value to R&D in a growing number of ways

Targeting specific patientprofiles for a clinical trial

Making patient recruitment easyand efficient

Clinical trial

Advancing genomic analysis from the research stage tothe point where it is used in treatment decisions

Genomic analysisIdentifying and validatingassociations betweengenes and humandiseases

Drug repurposing by discovering new therapeutic uses forexisting molecules through efficacy prediction

Better clinical trial management through the query, analysisand visualization of drug discovery and development data

Utilizing data analytics to generate real world evidence,understanding patient needs and the effectiveness oftreatments to improve patient outcomes


Start with the big data of today and be ready for thebig data of tomorrow

Today► Assess the current accessible forms of data and

data access points (i.e., claims data, electronichealth records, clinical studies, social media)

► Develop partnerships that allow access to theright data

► Understand where data is coming from andwhat classifications it carries such as privateand public data pools

Tomorrow► Defining the data technology capabilities of the

future to align with the value opportunitiesof today

► Understanding the evolution in data access andhow best to tap into this information, includinguse of non-traditional and unstructured sourcesof data (e.g., from online patient comments andpatient advocacy groups)

► Addressing the challenges that come with patientprivacy rights, the transfer of high volumes ofdata, and interfacing with disparate data sources

Enabling big data across the R&D organization

Process

People Technology


► Step 1: Establish a clear analytics strategy► Step 2: Identify the most relevant sources of big data► Step 3: Master large-scale data management► Step 4: Pursue meaningful collaborations► Step 5: Optimize your analytics organization for

performance, value, and continuous learning► Step 6: Derive and define your value

How do we start when it comes to taking advantageof big data to improve the ROI of R&D?


Step 1: Establish a clear analyticsstrategy

► A strategy driven by the needs of thebusiness, not technology

► Define an analytics strategy and operatingmodel that includes a Center ofExcellence (COE)

► Strong collaboration with data scientistsand leaders who set strategic directionfor R&D

Step 2: Identify the most relevantsources of big data

► Leverage the defined analytics strategy(step 1) as initial guidance

► Apply greater focus to the accessibility ofdata, security requirements surroundingthe data, and the effort to make thedata usable

The first steps in incorporating big data involve alignment onstrategic direction as well as the most valuable and accessibledata sources

The R&D analytics strategyshould be driven by the needs ofthe business, not technology

Use case: Step 2

► R&D organizations usage of adiversity of sources – includingclaims data, electronic healthrecords (EHRs), clinical studies andsocial media


► Assess the current foundational IT andanalytical state to give a clear picture ofthe steps needed to reach theappropriate level of large-scale datamanagement

► Define processes to maintain data andenhance data quality

► Build capabilities necessary to access,pool, and maintain large volumes of datafrom varied sources

► Team with healthcare organizations inthe ecosystem that provide a “win/win” onintellectual property, risk, and resourcecommitments

► Select like-minded business partnersand using trusted third parties fordata-management challenges

Managing large and disparate amounts of data and avoiding dataoverload will be key to any R&D organization’s success

Use case: Step 4

► Data partnerships with life sciencescompanies as well as academicinstitutions, providers, and payersare key to gaining access to thewidest range of big data

Step 3: Master large-scale datamanagement

Step 4: Pursue meaningfulcollaborations


Complacency around big data will lead to missed insights,over-looked efficiencies, and an inadequate analytics function

Step 5: Master large-scale datamanagement

► Strong governance and anorganizational structure that incentivizesthe right analytics behaviours andencourages learning

► Establish a continuous feedback loop tounderstand the results of analytics andapply them to future analytics efforts

Step 6: Pursue meaningfulcollaborations

► Utilize a balance approach to analyticswith near, mid-, and long-term metrics forassessing benefits and business impact

► Align results to specific targets to informaudiences of R&D value


These steps along with the right combination of people, process,and technology are the path to creating value from big data

Companies that master these steps will build a more sustainable approach to R&D and develop competitiveadvantages in the life sciences space

ROI from R&Dthrough bigdata

The single most important determinant of success for analytics projects is havingthe right people on the team driving towards the targeted value to be gained. Thisvalue may be linked to specific focused targets, or may derived more broadly forinstance from the collective intent of a particular ecosystem surrounding a diseasestate, care pathways and real world data.

Realizing the greatest value requires process that aligns to the desired outcomes.An example would be process implemented based on the goal of bridging theevidence gap for health authorities and payers. As superior efficacy in the clinic isnot the same as superior effectiveness in the real world, processes need to reflectnew evidence requirements for gaining approval and reimbursement of new drugs.

Taking advantage of the right technological advancements as a business drivenenabler provides the foundation for gaining the value that is now achievable fordrug companies. Innovative technologies and analytics capabilities allowgeneration of predictive models of the world, providing better inputs to R&Ddecisions and clearer association of R&D outcomes with real world value.

People

Process

Technology


Case studies


Case study 1:Comparative effectiveness and total cost of care models

Effe

ctiv

enes

s

MK-3102

Goal: predictive, personalized treatment algorithms for approved and experimental drugs usingsimulated patient populations leveraging real-world data

Data:► Real world dataPredictions:► Simulation of models built

from real world dataValidation:► These predictive models

show what diabetes drugswork for what subset ofpatients for multipleendpoints of interest


Case study 2:Prediction of the effects of novel Lymphoma drug combinations

Viability

Control+Drug1+Drug1 +Drug2

Goal: predicting differential effects of combinations based on treatment order

Data:► Multiple drugs tested in

multiple cell lines► Dose, viability and gene

expression dataPredictions:► Predicting the most effective

drug combination as well asthe order in which the drugs,when applied together, aremost efficacious

Validation:► Blinded combinations can

be validated in wet labexperiments


Case study 3:Prediction of immunology-related disease progression

Patient baseline

Patient-administeredsurvey

Annual in-personpatient visit

6 months

0 months

12 months

6 months

Goal: predict disease progression in patients with established disease from a multi-yearregistry dataset

Data:► Demographic/clinical information, functional status and other data over several years, with patients

entering the study at different points in time for over 1000 patients► Detailed information on medications taken over time


Case study 4:Translational R&D

Goal: Using clinical data in combination with experimental and laboratory data can lead to thediscovery of new targets and surrogate markers for improved disease understanding and treatment

Simulations predicted patient-specific mechanisms and targets

Big data analytics serviceData:► ~400 patients clinical trial data► SNPs, gene expression, protein biomarkers

and disease severity scoresPredictions:► Big data analytics platform identified

novel potential targets and surrogatebiomarkers (upstream and downstreamof endpoints, respectively)

Validation:► Multiple big data analytics platform-identified

surrogate biomarkers independently validatedin the literature


Appendix


The information in this presentation came from EY’s internal researchand the following articles by Todd Skrinar and Thaddeus Wolfram:► 6 steps for a sustainable approach to R&D through big data,

Life Science Leader, April 2, 2014► Drug R&D: big data for big returns, Genetic Engineering and

Biotechnology news, July 1, 2014

EY | Assurance | Tax | Transactions | Advisory

About EYEY is a global leader in assurance, tax, transaction andadvisory services. The insights and quality services wedeliver help build trust and confidence in the capitalmarkets and in economies the world over. We developoutstanding leaders who team to deliver on ourpromises to all of our stakeholders. In so doing, we playa critical role in building a better working world for ourpeople, for our clients and for our communities.

EY refers to the global organization, and may refer toone or more, of the member firms of Ernst & YoungGlobal Limited, each of which is a separate legal entity.Ernst & Young Global Limited, a UK company limited byguarantee, does not provide services to clients. For moreinformation about our organization, please visit ey.com.

Ernst & Young LLP is a client-serving member firm ofErnst & Young Global Limited operating in the US.

© 2014 Ernst & Young LLP.All Rights Reserved.

1410-1340693ED None

This material has been prepared for general informational purposesonly and is not intended to be relied upon as accounting, tax, or otherprofessional advice. Please refer to your advisors for specific advice.

ey.com

EY Drug and R&D: Big DATA for big returns

Health & Medicine

Transcript of EY Drug and R&D: Big DATA for big returns