Personalized Medicine - VDE der Technologieverband · Dr. Pablo Mentzinis, Director Government...

Dr. Pablo Mentzinis,

Director Government Relations, SAP SE

Courtesy by Dominik Bertram, Marc von der Linden,

Péter Adorján, SAP

June 2016

Personalized Medicine

© 2016 SAP SE or an SAP affiliate company. All rights reserved.

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1. Traditional medicine vs.

personalized medicine


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Personalized medicine is a paradigm change

Understand and target the biological root cause

Internal 3

FROM

• Descriptive

• Outcome based diagnosis

• Organ based

• Retrospective diagnosis

• Limitations for epidemiology

• Acute care

• Treatment for the „average" patient

TO

• Understand the disease mechanism

• Molecular diagnostics

• Fine molecular profile based groups

• Prospective diagnosis / Predisposition

• Environmental factors

• Prevention and early detection

• Individualized treatment


.

Internal

Concept of traditional medicineThe old way: Observe symptoms Classify disease Gold standard treatment

4

Antibiotics

High fever, cough, etc

Infection

High success rate


.

Internal

Concept of traditional medicineA more complex disease: Breast Cancer

5

Lumpectomy &

Radiotherapy

Lumps in breast

Breast Cancer

Low success rate, but

sometimes effective

Why?


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Concept of traditional medicineTreatment for the „average“ patient is not effective for several subgroups

6Internal


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Internal

Concept of personalized medicineA new approach: Determine individual root cause Targeted treatment

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Personalized

prevention

Better, but not perfect

Our understanding is incomplete

Gene Panel

Breast Cancer

Personalized

treatment

BRCA1/2

HER2+


.

Internal

Concept of personalized medicineAs our understanding improves, therapy will become more individualized

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High response ratePrecise analysis of disease cause

many different subtypes

Individualized treatment


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Concept of personalized medicineTargeting the individual root cause boosts treatment effectivity

9Internal


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The challenge: handle biological complexityMolecular root cause Personalized prevention, diagnosis and treatment

Internal 10

Amount of Data and Value of Information

DrugsCellular Pathways

GENOMICS

PROTEOMICS

METABOLOMICS

3.3 billion base pairs (haploid)

3-4 million variants per individual

~20000 protein-coding genes

~10.000 genetic disorders (WHO)

>5.000 known disorders (OMIM)

TRANSCRIPTOMICS

100000+ protein

variants

160 million data

points (2.4 GB) per

sample

7.6 TB raw

proteome data on

ProteomicsDB.org

~20000 protein-coding genes

53,000 non-coding RNAs

10000s of cellular

reactions

ProteinsDNAChromosomesCells RNA

PHARMACO-

GENOMICS

Millions of compounds


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2. Impact of personalized

Medicine on healthcare


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Internal

Why is it relevant? Low treatment efficacy

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Cancer: 75%

Percentage of

patients for whom

drugs are ineffective.

Alzheimer`s: 70%

Arthritis: 50%

Diabetes: 43%

Depression: 43%


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The challenge: from data to actionable information

Bridge the gap: advanced analytics, leverage network effects and digitize operations

Internal 13

Number of nucleotides sequenced / $1,000

2000 2002 2004 2006 2008 2010 2012 2014 2016

1,000,000,000

10,000

10,000,000,000

100,000,000

10,000,000

1,000,000

100,000

Moore’s law


.

Internal

Enabling personalized medicinePersonalized medicine turns healthcare into an information problem of enormous magnitude

14

Network

Create patient-centric

healthcare networks

Action

Make healthcare

processes more efficient

Insight

Gain precise

insights from

patient data

SAP Personalized Medicine

Solution Portfolio


.

Internal

Integrated view of “omics” data and patient information

• Empirical outcomes, symptoms

• Environmental factors & Lifestyle

• Wearable device data

• Genetics, genomics

• Transcriptomics

• Proteomics

• Metabolomics

• Microbiomics

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A true “-omics” integration and accessible analytics is

required to create deeper insights


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Internal

Many actors in healthcare – all work on their own

Patient centric information backbone and collaboration remains a major a challenge

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Chronic disease

care centers

Wearable devices

Cyber

physical

systems

Pharma/ R&D

Pharmacies

GP

PatientClinical

laboratories

Payers


.

Internal

Make healthcare processes more efficient

Clinical systems: from time and data sink to easy access support and information gold mine

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SAP Foundation for Health � Enablement of personalized medicine � Ability to analyze massive volumes of structured and unstructured data

(from patients, clinical, omics, third-parties, and so on) � Secure collaboration and sharing

Health Engagement � Open environment for customers and

partners to build care collaboration scenarios for:

� Engagement of the care network

� Motivation for behavioral change

� Prevention and risk detection

SAP Medical Research Insights � Faster building and validation of

hypotheses � Patient cohorts and research � Analytics (genome and patient data) � Patient-trial matching

SAP Patient Management application � Patient experience and clinical delivery � Ability to drive operational excellence – from admission to bill

Radical improvement through

• Modern software engineering

• User-centered design

• New technologies


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Digitalization of healthcare

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EMR adoption modelStage Cumulative capabilities

0

1

2

3

4

5

6

7

All three ancillaries not installed

Ancillaries – Lab, Rad, Pharmacy – All installed

CDR, Controlled Medical Vocabulary,

CDS, may have document imaging; HIE capable

Nursing/Clinical documentation (flow sheets),

CDSS (error checking), PACS available outside Radiology

CPOE, Clinical Decision Support (clinical protocols)

Full complement of radiology PACS

Physician documentation (structured templates), full CDSS

(variance & compliance), Closed Loop Medication Administration

Complete EMR; CCD transactions to share data; Data warehousing;

Data continuity with ED, ambulatory, OP


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3. Solution portfolio


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SAP Medical Research Insights

Challenges addressed

• Build patient cohorts

• Match patients for trials

• Explore clinical outcomes

Solution highlights

• Combines structured and unstructured patient data from

disjointed data sources

• On the fly cohort analysis with an award winning UI

• Flexible model for comprehensive clinical and genetic data

Internal 20

In depth analytics enabled for clinical research


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SAP Medical Research Insights

SAP Medical Research Insights 2.0 GA release highlights

Internal 21

• Enhanced configuration

• Flexible adaptation of MRI to different

users within your organization

Boxplot visualization

Drill down to get more insights on the

distribution behind the numbers

Genomics data

Analyzing Genomics variation of

cohorts and single patients.


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Clinical Measure Analytics

Effective clinical quality assessment is a challenge

• Complex quality rule sets are hard to implement and maintain

• Relevant clinical data are stored in multiple silos

• Labor intensive manual process to calculate quality measures

• Post hoc information – no chance for early intervention

Solution highlights

• On the fly dashboards for actionable measures and quality scores

• Flexible rule configuration and query engine

• Data sources are integrated in a clinical data warehouse

• Quality scores at multiple levels of granularity and time frames

• Scalable for hundreds of physicians and multi million patients

Turning quality assessment into immediate actions and excellence in patient care

Internal 22

Lab preview


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Clinical Genomics Services

Enable physicians to determine pathogenicity of genetic variants

• Integrate genomic, clinical and wearable device data

• Efficient semi-automated genetic report generation

• Large scale genomic services enabled

• Workflow support: change alerts, knowledge sharing, audit trail,

hand-overs and approvals

Features highlights

• Functional annotation of whole genome sequencing data

• Patients like yours?

• Patient dashboard – workflow support

• Clinical history timeline

• Interactive filtering and ranking of genetic variants

Generate actionable genetic insights in a clinical setting

Internal 23

Lab preview


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Proteomics DB

A central repository for browsing and analyzing proteome data

Internal 24

Offers a complete map of human proteins (proteome) to improve our

understanding of physiological processes

A public, free-of-charge platform powered by SAP HANA managing

terra-bytes of human proteomics data

Published in Nature May 2014 (selected as cover story)

https://www.proteomicsdb.org

http://www.proteomicsdb.org/


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Proteomics DB 2015 release highlightsExtended analytics and data exploration enabled

Internal 25

Transcriptomics data

Connecting proteomics and

transcriptomics data to obtain more

insights into molecular processes of the

human organism

Drug potency analysis

See the effect of drugs on proteins and at

the same time all potential side effects.

Experimental planning

Plan and store complex designs of

experiments.


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Proteome toolsTranslate proteome data into molecular and digital tools for drug discovery, personalized medicine, and life science research

Internal 26

• Synthesize and measure more then one million human peptides as a

reference standard on https://www.proteomicsdb.org

• Turning the data into improved hardware, novel software approaches, and

reagents for proteomics

• Provides a foundation for a new class of analytical approaches and

algorithms

• High-class spectral libraries to improve methods and algorithms in

proteomics

Lab preview

http://www.proteomicsdb.org/


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Machine learning in proteomicsSAP-funded PhD / Postdoctoral research projects at Technische Universität München

Metabolomics

Genomics

Transcriptomics

Clinic Pharma Biotech. Crop Sci.

Decoding of genomes

Decoding of proteomes

Proteomics

ADH5 MAP-K

Drug sensitivity / resistance

Make ProteomicsDB pharma-ready and

enable the generation of drug sensitivity

and resistance markers by considering

additional model organisms, e.g. mouse,

rat, and pig

Multi-Omics biomarkers

Leverage machine learning on top of

several omics-data sources to enable

molecular diagnostics and treatment

decision support

Proteomics analytics

Combine the data, to be generated in

ProteomeTools, with innovative

identification algorithms to establish

ProteomicsDB as a reference for

protein identification

Internal 27

Lab preview


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Medical Allround Care Service Solution

Remove roadblocks for high quality care

Low patient adherence

No triggers for preventive intervention

No patient centric view with multiple stakeholders

Connect all stakeholders and share information

Engage patients: mobile data exchange

Alerts enable preventive measures

Share medical data across stakeholders

Platform solution based on SAP HANA

Clinical data warehouse with unified ontologies

Strict access right management, patient remains data owner

Easy integration of applications and data services (e.g., NLP)

Health network = High quality patient care for chronic diseases

Internal 28

Patient-Front-Ends to

support therapy in

everyday life

Back-End / Middleware

Secure storage of medical data incl. management of

access privileges in SAP HANA

Data and services for

medical systems and

devices

Secure data platform at Charité Berlin

Pharmacy

Clinic

Doctor

Dialysis center

Patient

Mobile apps

Vital signs

Lab preview


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Health Engagement Solution

End-to-end solution concept – open for partners

Internal 29

Physician portal

Researcher portal

Patient app

Connected

devices

SAP

HANA

Cloud

Platform

Program manager

Personalized Medicine - VDE der Technologieverband · Dr. Pablo Mentzinis, Director Government...

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