Aysun Karatas MedicReS World Congress 2015

Dr. Aysun Karatas

New York, 20.10.2015

A non-profit sponsor organisation

for investigator initiated oncologic trials

in Germany and Europe

German Cancer Society (DKG)

~1900 Foundation by Prof. Dr. Ernst von Leyden

1900 – 1933 German Central Committee for Cancer Research

1933 – 1945 German Reich Committee for Cancer Control

several Jewish and Marxist members were excluded, as well as others who were not „in line“ with

the National Socialists

1951 Foundation of the German Cancer Society (DKG)

The DKG is the biggest German

scientific-oncologic society

Today: 6600 members

Working Group of Medical Oncologist

1976 Foundation of the AIO in Essen

2004 Realization of the first AIO Autumn Symposium

2007 Foundation of the AIO-Studien-gGmbH non-profit Sponsor Organisation

AIO is the biggest group

within the DKG

Today: 1350 members

AIO –Arbeitsgemeinschaft Internistische Onkologie

in der Deutschen Krebsgesellschaft e.V.Chairs: Prof. Dr. Volker Heinemann, PD. Dr. Helmut Oettle

Board: Prof. Dr. Viktor Grünwald, Prof. Dr. Ralf Hofheinz, PD. Dr. Martin Reck

Coopted Members: Prof. Dr. Thomas Seufferlein, Prof. Dr. Martin Fassnacht, Prof. Dr. Rudolf M. Huber

AIO-Studien-gGmbH

CEO

Dr. Aysun Karatas

AIO-WorkgroupsCUP-Syndrome

Endocrine Tumors

Geriatric Oncology

Hepatobiliary Tumors

IAG-N - Interdisciplinary Workgroup Renal Cell Carcinoma

Interdisciplinary Workgroup Testicular Tumors

Colon-/Rectum-/Small Intestine Carcinoma (8 „Task Forces“)

Head-Neck-Tumors

Breast Cancer and Gynecological Tumors

Neuroendocrine Tumors

Esophagus-/Gastric-Carcinoma

Pancreatic carcinoma

Thoracic Oncology

Urogenital Tumors

Soft Tissue Sarcoma

CNS-Meningeosis

Communication

QoL and PRO

Oncological Rehabilitation

Oncological Therapy Protocols in the Internet

Supportive Therapy

Translational Research

Drug Development / Phase-I Studies / Early Phase-II Studies

Young medical oncologists

The mission of the AIO-workgroups includes

Scientific development

Coordination and

Quality Control

Therapy Optimization

Standard care guidelines

The AIO-Studien-gGmbH serves as

Sponsor for Clinical Trials incl. but not limited to:

Project development, protocol writing, CRO

assignment and -controlling, Data production,

budget negotiation and –controlling, site selection,

contracting, project management, publication

Assistant to Chairs / Administration Office: Kathrin Drischmann

Assistant Certification of Further Trainings: Kerstin Hofmann

Assistant Member Administration: Beatrice Grothe

Project ManagementRalph Keller / Katrin Krause / Helge Schröder /

Luisa Neubrandt / Saskia Schulze/Dr. Annette Hipper

AssistantsHendrik Kowalewski / Andrea Schwick /

Bukuri Quokai/ Tobias Meyer

Medical Writing

Dr. Martin Mänz

Actual Studies

Zusammenarbeit mit insgesamt 14 Arbeitsgruppen

16

11

6

6

4

3

3

2

3

2

2

1

1

1

Thorakale Onkologie

Kolon-/Rektum- Dünndarmtumoren

Ösophagus-/Magen-Karzinome

Pankreaskarzinom

Hepatobiliäre Tumoren

Nierenzellkarzinom – IAG-N

Supportive Therapie

Kopf-Hals-Tumoren

Weichteilsarkome

Lebensqualität und PRO

Young Medical Oncologist

Mammakarzinome / gyn. Tumoren

Neuroendokr. Tumore/Karzinoide

ZNS/Meningeosis

0 2 4 6 8 10 12 14 16 18

16

11

1

4

1

4

1111212

12

2

1

4

21 1 1 1

4SC

Amgen GmbH

Ariad

AstraZeneca

Baxter

Bayer AG

Boehringer-Ingelheim

Celgene International Sarl

Fresenius Biotech GmbH

Hexal

Lilly Deutschland GmbH

Medac GmbH

Merck

Mologen AG

MSD

Neovii Biotech

Nordic GmbH

Novartis Pharma GmbH

Pfizer

Roche Pharma AG

Sanofi-Aventis Deutschland GmbH

Sysmex

Taiho Pharmaceuticals CO. LTD

Terumo

Teva

Funding by Pharmaceutical Companies

Cooperating Countries in EU

• Austria

• Switzerland

• Belgium

• Netherlands

• Spain

• Italy

• France

• Denmark

• Sweden

• UK

• Israel

Intersection of big data and academic

oncology research – a reality check

AIO-Studien-gGmbH a creator of (big?)

data

A small diagnostic study

(100 patients, 3 yrs FU)

A mid-sized QoL study

(160 pat. 5 yrs FU)

10,000 data points

~107 data points [8 MB DB]

All our completed studies

combined (N=14) ~109 data points [~ 1 GB]

Realm of BigData

AIO-Studien-gGmbH as an indirect

contributor to BigData

Study participant

drug safety dataEudraVigilance

Electronic health records

Clinical trial data bases

(Public or propriatory)

Bio-bankingsamples

General expectations of the public with

regard to health care and oncology

Trained, compassionate health care providers

Cost effective treatments

Efficacious drugs with little side effects

Can big data deliver on any of these points?

Academic sponsor wish list (AIO-Studien-gGmbH)

High potential candidate

drugs and treatments

Feasible study designs

Fast and predictable site

and patient recruitment

Cost effective trials

Interpretable and relevant

results

skilled doctors and

nurses

Academic sponsor wish list (AIO-Studien-gGmbH)

High potential candidate

drugs and treatments

Feasible study designs

Fast and predictable site

and patient recruitment

Cost effective trials

Interpretable and relevant

results

skilled doctors and

nurses

Can BigData help us in

any way to fullfill our

wishes?

Candidate drugs in oncology IITs

Nilotinib – a tyrosine kinase inhibitor – and

its corresponding protein binding domain

[Source: wikipedia; UCSF]

Drug development

BigData technologies:

i.e. pharmacogenomics,

proteine modelling etc.

influence the process

during commercial development

Candidate A Candidate B Candidate C

Clinical testing of candidate drugs

Phase I trial

(drug A)

Recruit patients with

various cancer types

Treat & observe:

Safety & efficacy

failure

Phase II & III trials

(drug A)

Phase I trial

(drug B)

Recruit patients with

various cancer types

Treat & observe:

Safety & efficacy

Phase II & III trials

(drug B)

………

……

Process is very stereotypic and not very efficient

No BigData solution to this problem

Clinical trials are a validating step for assumptions made

BigData may speed up the process of identifying candidate drugs

BigData may reduce faillure rate Phase I/II trials

-drugs have been designed in smarter fashion

candidate drugs = safe drugs?

Anti-cancer drugs are toxic and have significant and

frequently serious side effects

Typical adverse events are: fatigue, diarrhea, loss of white

blood cells, hair loss, neuropathy …

Risk-Benefit-Assessmet

The assessment is done with safety information obtained with old-fashioned clinical trials!

Realm of BigData

AIO-Studien-gGmbH as source of drug

safety data

Study participant

drug safety dataEudraVigilance

Annual safety reports

and

new side effects (SUSARs)

EudraVigilance

Early detection of possible safety signals

Continual monitoring and evaluation of potential safety issues in relation to

reported adverse reactions.

We feed (indirectly through CA)

Eudravigilance with drug safety data:

annual safety reports and SUSARs!

Drug safety feed-back loop

Sponsor:

assessment

of risk/benefit

EudraVigilance:

analytics

and

signal detection

safety

information

communication

of

safety issues

Process generally works but is of very little consequence to us

The cost of clinical trials

A very simple rule:

more data = higher costs!

Concepts of BD and cost effective

trials are contradictory

IITs and data collection: The rule of parsimony

only collect data that is absolutely

necessary

very stringent sample size

calculations

rigorous feasibility assessments

[quality trumps quantity]

Big Data violates that

rule!

Big Data could lower costs by accessing DB repositories

and data warehouses

Access of BigData resources(to reduce cost of data acquisition)

Realm of BigData


(study site)

Realm of

Sponsor data

Electronic

Case Report Form

• Legal obstacles

• (ICH-GCP)

• Data privacy protection legislation

• Technical hurdles (lack of standardization)

Access of BigData resources(to reduce cost of the trial by faster recruitment)

Realm of BigData


(health insurance,

government health

care systems)

Realm of

Sponsor

Knowledge of

existing eligible

patients = faster enrolment

• In Germany there are more than 120 health insurance

companies & no centralized gov. health care system

• No legal basis for a data exchange

Access to BigData resources(to reduce cost of the trial by faster recruitment)

In UK a private business (CRO) can cooperate with a

NHS health care trust to identify and obtain direct

access to patients

CRO will „sell“ that information advantage to a

potential sponsor

Feasible clinical trials & interpretable results

Academic research is often reductionist

Clinical trials try to:

reduce complexity

eliminate confounders

be reproducible

interpretable results

a treatment in the first place

old fashioned

statistics

clinical judgement


Personalised medicine:

genetic testing (i.e. tumor and host

genome sequencing)

other biomarkers (cell surface

molecules)

epigenetic testing

……

A big data approach is holistic

better treatment?

personalised medicine?

AI

machine learning

pattern recognition


Big Data approaches increase

complexity:

genetic testing of biomarkers biomarkers result in additional

sub-populations of patients

additional testing costs time

(feasibility of a trial)

and money (cost effectivness)

sub-groups (sample size) may

become to small for a meaningful

statistical analysis (inconclusive

results)

new treatable tumor entities

more or bigger trials

General considerations

Can it get any more „personal“ than that?


personalised medicine


Big Data. Do we actually care how

„big“ the data really is?




Big Data. Do we actually know how

validated the data really is?






Big Data. Do we actually know how

validated the data really is?



In fact we want SMART DATA

with

human judgement

trained and skilled doctors and nurses

(improvement of quality)

example: ASCO CancerLinQ initiative

Conclusions

BigData (technologies) slowly but constantly seep into oncology

research

BigData may speed up the process of identifing candidate drugs,

but has so far little impact on the clinical development and

testing.

BigData may help us reduce the failure rate of phase I/II trials,

because the drugs have been designed in a smarter fashion.

BigData will be a success story, if it can either reduce the cost of

medical research or dramatically improve the quality of patient

care

Conclusions

BigData (technologies) slowly but constantly seep into oncology

research

BigData may speed up the process of identifing candidate drugs,

but has so far little impact on the clinical development and

testing.

BigData may help us reduce the failure rate of phase I/II trials,

because the drugs have been designed in a smarter fashion.

BigData will be a success story, if it can either reduce the cost of

medical research or dramatically improve the quality of patient

care

Whether this is true we will only know in the future but all the

promises have to be taken with a grain of salt

Thank you for your attention!

Dr. Aysun Karatas

[email protected]

Aysun Karatas MedicReS World Congress 2015

Health & Medicine

Transcript of Aysun Karatas MedicReS World Congress 2015