Post on 22-Jan-2018
transcript
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
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
Electronic health records
(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
Electronic health records
(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
Feasible clinical trials & interpretable results
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
Feasible clinical trials & interpretable results
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
personalised medicine
Can it get any more „personal“ than that?
Big Data. Do we actually care how
„big“ the data really is?
General considerations
personalised medicine
Can it get any more „personal“ than that?
Big Data. Do we actually know how
validated the data really is?
Big Data. Do we actually care how
„big“ the data really is?
General considerations
personalised medicine
Can it get any more „personal“ than that?
Big Data. Do we actually know how
validated the data really is?
Big Data. Do we actually care how
„big“ the data really is?
In fact we want SMART DATA
with
human judgement
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