barriers in access to personalised medicine denis horgan...

Barriers in Access to Personalised Medicine

Denis Horgan,

EAPM Director

www.euapm.eu

Agenda •  PM & Barriers

•  Development of an index

–  Literature review

–  Stakeholder analysis

–  Priori?za?on

•  Index

•  Conclusions & Recommenda?ons

2

Preven?on Diagnosis

Personalised Medicine

Treatment

PM

Based on an individual’s specific profile

A targeted approach to:

Diagnosis

Treatment

BARRIERS pa?ent

Development of an Index

4

Literature reviews

Systematic literature review from scienti3ic articles

Gaps & Needs assessment and SWOT analysis

Review strategic reports

Stakeholder analyses

Survey research


Structured interviews

Prioritizing major areas within access to PM Roundtable discussions

Index for barriers to PM in the EU

Systematic literature review

Personalised Medicine and barriers

Care Treatment Clinical Legislation

Europe Patient Public Health Commercial

Ethics Biological Regulatory Industry

PubMed Web of Science Medline

Scientific articles

1543 articles 580 reviews 81 relevant

&

Gaps & Needs and SWOT-‐analysis

Strategic reports

GAPS flaws in the status quo acAng as barriers to PM

NEEDS necessary iniAaAves to bridge the gaps

+GAPS 40

NEEDS

101

39 13

44 60

Main areas of barriers

Section Subsection Science System biology and data Research Clinical study design Operational Information delivery Education and training Inform, educate, empower patients Regulations Economics Reimbursement Costs of testing EU-‐level Bio-‐banks Translation Reimbursement Legal and ethical

Framework summary points Patient

Healthcare professionals

Data collection

Data results CER/Biobanks

Regulations and guidelines

Information CDS

Reimbursement decision

PM-‐treatment

HTA-‐bodies

Government EU – national – regional

Insurance

Evaluation ACCE-‐framework

Industry Research

ICT-‐tools

Data complexity Research approaches Evidence

Privacy

Education


9

Literature reviews





Survey research





Stakeholder groups

Viewpoint Stakeholder group AmountBasic science 1.  ICT

2.  Industry56

Translational research 1. HTA2. Insurance

37

Regulatory field 1.  Member states 34Health systems 1.  Healthcare professionals

2.  Pathologists/oncologists72

Patient perspective 1.  Patient groups 35Total 234

Surveys

Group Sections Questions Insurance companies General information, PM, and reimbursement/ payment 61 ICT Experts General information, PM, data sharing, and ICT services 32 HTA Experts General information, PM, and HTA 76 Industry General information, cancer, PM, and infrastructure 81 Pathologists General information, genetic testing, cancer, infrastructure

and data sharing 105

Patient groups General information, cancer, PM, genetic testing, reimbursement/payment and others

80

Healthcare professionals General information, cancer, quality assurance, PM, payment/reimbursement, and infrastructure

113

Policy-‐makers General information, PM, genetic testing and payment/reimbursement

35

E-‐mail Website EAPM Snowball

General

Figure A. If participants were aware of any best practice guideline which promotes or plans to promote the concept of PM.

Figure B. If participants think funding is an issue with regard to PM- application

A

B

Basic science and translaAonal research

Are guidelines available re-‐use of samples?

TranslaAonal research and regulatory field

Who do you think influences decision-‐making processes in healthcare most?

Health systems

Healthcare professionals Pathologists/oncologists

How would you define PM?

PaAent perspecAve

Who do you think influences the promoAon and applicaAon of PM most?

&Gaps-‐and-‐needs assessment and SWOT-‐analysis

GAPS flaws in the status quo acAng as barriers to PM

NEEDS necessary iniAaAves to bridge the gaps

+GAPS 63

NEEDS

91

71 73

79 73


Gaps

Gaps from the interviews Patients Access to information

Funding Education via media

Basic research Basic research and PM too far apart Transparency knowledge transfer in preclinical research

Translational research Awareness of needs in society Not enough interdisciplinary translational centers Focus on knowledge and interpretation

Regulation bodies Concept of PM and its application Approval of CDx Regulations and legislation of PM

Health system Ethical issues Oversight versus research Healthcare decision making and delivery

Gaps Current lack of trained people looking beyond single fields

Lack of companion diagnosAcs for many treatments

We do not have any data producAon structure so that today gene?c sequencing leading to a clinical decision is restricted to cancer and liver-‐toxicity. A fruiUul cooperaAon

between stakeholders needs to be fostered. We should inform them, imply them in op?ons as well as choices.

The biggest gap lies in sharing of the interpretaAon and knowledge. There is currently more effort on data sharing, but not yet on interpreta?on, knowledge management and knowledge sharing across private/public boundaries.

A policy gap: laws and regula?ons by EU and na?onal governments focus on data protec?on. One has to move away from protec?on and privacy towards control of one’s own data and thus true empowerment of the ciAzen.

The legal part of PM might be easier when working cross borders. Maybe by the EC.

Needs

Needs from the interviews Patients Complementing issues in stakeholders agendas

Representatives with aggregated knowledge Systematic involvement

Basic research Long-‐term policies to integrate genomics/clinical data Modelling of different types of quantitative –omics

Translational research New research instead of rebranding existing research to PM Train healthcare professionals to translate to patient Involve professionals in innovations and implementation plans

Regulatory bodies Evidence on link between speci3ic tests and treatment outcomes Review current business models Sustainable study designs

Health system Health literacy Information systems PM in education healthcare professionals

Needs BeWer links between funding bodies, industry and the technology plaXorms

Long term funding for cri?cal resources and infrastructures.

Decrease the complexity and access barrier to technology for normal general hospitals. This is not only about the technology but also about decision support systems from data.

To set up mulAdisciplinary teams with basic researchers and clinicians to work together in the implementaAon of biomarkers in the field.

Decision making to tackle the connecAon between genomic and clinical “worlds”.

A smart implementa?on strategy is needed. New ways of innovaAon in health care systems.

Partnership between several stakeholders. Data providers and technology manufacturers agreement. Public and private engagement.

SWOT Strengths Weaknesses Opportunities Threats

Basic science Better rational treatment

Individual parameters do not give the full story

Combined efforts of the pharmaceutical and diagnostic industry

Premature implementation will lack credibility

Translation Data from large cohorts directly used to inform decision making

Lack of connection between genomic research and clinical practice

Exploit PM for a better understanding of diseases and patient treatment, using genomic data

Har dto introduce new elements, removing outdated or less useful technology is hard

Regulatory Organized discussion on guidelines

Pharmaceutical companies like PM to avoid required evidence based research

Informed drug development leads to cost efficient drug development

Patient groups may become impatient due to the long way

Health system Help in the sustainability of healthcare systems

Healthcare systems rigid May be useful for broader public health interventions

New drug, similar outcome, higher costs


23

Literature reviews





Survey research





PrioriAzaAon of barriers by stakeholders

Stakeholder group Expertise Mield Amount

Basic Science Universities, research institutes, industry 19

T r a n s l a t i o n a l

research

HTA-‐bodies, research networks 18

Regulatory Mield Governmental institutes, EU-‐member states,

European commission

21

Health systems Healthcare professionals' associations, medical

faculties

22

Total 82

PerMed-‐mee?ng roundtable sessions 27 -‐ 28 March 2014

Basic science

Access to data

In the healthy; in the diseased Longitudinal studies 'Mobile' data Accounting for heterogeneity Standardisation of methodology and quality Quality assurance underpins everything

Measurable outcomes

Imaging Inter-‐operability Genomics (exome -‐ WGS) Clinical validation Other data (omics, mass spec, data in exhaled breath, urine, saliva)

Making sense of it New statistical techniques Multi-‐scale data integration Modelling More than bioinformatics alone required Danger of confounding error by combining multiple 3lawed datasets

B

TranslaAonal research

Citizen

Data ownership and custodianship Training in literacy Education Trials; Cohorts Registries

Data

Sharing/harmonization/standardisation Policy Infrastructure Paper to electronic

Partnerships

Public-‐private: IMI for PM Basic-‐, clinical-‐, and industry researchers

Funding

New funding models Developing partnerships Brokering partnerships Derisking projects Coordination

Education

Citizens/practitioners/ researchers/patients Continued learning and development Behaviour change/social sciences T

Regulatory field

Systematic early dialogue

Decision maker (DSM) – HTA Manufacturer – HTA Manufacturer – HTA – DSM Manufacturer – Manufacturer

Data

Ownership; access Purposes/users Bottom-‐up policy-‐making

Dynamics of information

No prediction of risk No prediction of phenotype No indication No validation

Regulations Product vs. process: process is not under legislation

Outcome data

Feedback data from market to DSM

R

Health systems

Communication/dissemination

Public debate on PM Stakeholder participation

DeMine PM

Proof of concept

Legal and ethical

Sensitive issue Inequalities in access

Information & health literacy Support patients/citizens CDS/EMR

Outcome data

Feedback data from market to DSM

H


29

Literature reviews





Survey research





Comparing results

Stakeholder analyses Literature review

Priori?za?on

Index

PM treatment and care for paAents in EU

Healthcare system

Data and research Data collecAon

Data interpretaAon

Data evaluaAon

Funding

EU-‐level policy making

Stakeholder involvement

StandardizaAon

Interoperable infrastructure

Conclusions and recommendaAons Barrier Recommendations Involved stakeholders Stakeholder involvementMismatch in needs and provided information between clinical practice and research

Facilitate stakeholder involvement in policy making an research implementation by:- Early dialogue- PPPs- Public debate- Bottom-up policy making

From all fields

Lack of awareness and knowledge on PM in clinical practiceStandardizationNational focus for research grants

Standardization of methods across EU by:- Evaluation research grants- Guidelines on data collection, interpretation, quality assurance- Information on reimbursement decisions

From all fields

Lack of comparable data and information in databasesDifferences in reimbursement decisions and implementation of CDx in healthcare systems

Interoperable infrastructureLack of ICT-support tools for data- and information-sharing

Develop ICT-tools for data- and information sharing Should include information on:-  Biobanks-  Data-accessibility-  Information on best practice guidelines

From all fields

Conclusions and recommendaAons Barrier Recommendations Involved stakeholdersPolicy-makingScattered policies and legislation across EU

Update and/or develop regulations and legislation on EU-level, such as:- Best practice guidelines (research, reimbursement, implementation, CDS)- Harmonize legislation for diagnostics and treatment; development processes

Basic science e.g. from industry Translation e.g. HTA-expertsRegulatory e.g. member statesHealthcare systems e.g. representatives from associations

Funding Lack of funding for PM-research based on biomarkers to develop CDx

Adjustment of funding models- Best practice guidelines- Conditional reimbursement- Adaptive licensing

Basic science e.g. from industry Translation e.g. HTA-expertsRegulatory e.g. government bodies

Conclusions and recommendaAons Barrier Recommendations Involved stakeholdersData and researchLack of relevant data Point-of-care research (CER)

- Agreements on type of data, annotation and quality- Adjusted informed consent (EMR)

Basic science e.g. from industry Healthcare systems e.g. representatives from associations Patient groups e.g. representatives from associations

Lack of information to interpret health data for clinical practice

Integrated informational models- Harmonization of relevant data- Updated disease models

Basic science e.g. from industry Healthcare systems e.g. representatives from associations

Lack of information relevant for HTA-evaluation

Use ACCE-framework in CER:- Best practice guidelines

Basic science e.g. from industry Translation e.g. HTA-expertsRegulatory e.g. government bodiesHealthcare systems e.g. representatives from associations

Conclusions and recommendaAons Barrier Recommendations Involved stakeholdersHealthcare systemLack of awareness and knowledge of PM

Training and education- Workshops- Automated support tools- Curricula healthcare professionals

Basic science e.g. from industry Regulatory e.g. government bodiesHealthcare systems e.g. representatives from associations

Lack of support in clinical decision makingLack of uptake of PMPM to patientLack of awareness and knowledge of PM

Information about PM to patients, e.g. by:- Education programs by government- Communication by healthcare professionals

Regulatory e.g. government bodiesHealthcare systems e.g. representatives from associations Patient groups e.g. representatives from associations

barriers in access to personalised medicine denis horgan...

Documents