Demands in Clinical database build for the trials of tomorrow

Moovendhan Devaraj

Associate Manager – Statistical programming

Accenture Solutions private limited

TABLE OF CONTENTS

Known Story Short

Advancements in Clinical database build

Advancements to be adapted in Clinical database build - IoT

Advancements to be adapted in Clinical database build - mHealth

Conclusion

Thanks

Cloud

Paper CRF

Computers

Internet

Data Base

Integrated database

Standardization

Secured ConnectivityEasy accessibility

Known Story Short

➢ Advancements in Clinical database build

➢ Advancements to be adapted in Clinical database build

Advancements in Clinical database build

Machine interpretable

Protocol Final eCRF design

eCRF / EC build

TA Specific eCRF Standards

?

ProtocolDraft eCRF

designFinal eCRF design eCRF / EC build

Traditional Approach

Automated Approach

Do we need eCRF Design for CDB?

WHY?

• Time and Events schedule( TE )

• Subject Population in study

• Inclusion Exclusion Criteria

• Site Information

• Time Points for Sample Collection

• Any other specific Requirements For Protocol

AI tool, enabled with Machine

learning and NLP

Machine interpretable protocol

TA Specific standard libraries

Advancements to be adapted in Clinical database build - IoT

Adaptations in SDTM

• SDTM device Sub team formed in 2006

• 7 SDTM domains created for submission of data on devices

National Evaluation System for health Technology • Generate better evidence for medical device evaluation • Regulatory decision-making• Collaborating with medical device stakeholders

In 2018, the US FDA approved 106 IoT medical devices in comparison to 25 devices in 2009

Advantages

• Faster data collection

•Higher data quality

• Shorter trial span

•Near virtual trials

Advantages•Data collection, monitoring, and project management in

real time

• Investigators were more likely to rely on transferring data by means of paper-to-computer when they captured clinical data, particularly in trials wherein data sources involved health records in Computer based trials

• Easy to access EDC and enter data

•Reduces trial conducting cost

• Increases patient retention

To be prepared for• Interface to handle data from smart devices to be

integrated in to CDB

•Optimal process and steps yet to be identified

REMOTE PATIENT MONITORING

DECENTRALIZED/ VIRTUAL

TRIAL

REAL WORLD OUTCOMES

DIGITAL THERAPY

PATIENT CENTRICITY

Advancements to be adapted in Clinical database build - mHealth

Conclusion

➢Ultimate Goal is patient safety and to bring drugs faster to patients➢By reducing cost and cycle time in clinical trials➢Adaptations to accomplish our goal

➢Prepare ourselves and the society for the better future

Trials without Animals and patients, and is currently happening.

Organ chips – with instrumentation and software to maintain the same physiological and biochemical environment of the organ system, enables monitoring and easy collection of data.

Thanks!!!

References

• IOT• https://dig.watch/updates/2018-us-fda-approved-106-iot-medical-devices-comparison-99-devices-2017

• mHealth• https://www.mhealth.org/discover-our-difference/clinical-trials

• http://mhealth.amegroups.com/

• Organ Chips• https://www.europeanpharmaceuticalreview.com/news/68630/fda-organs-chips/

• https://www.sciencedirect.com/science/article/pii/B9780124115514000131

• https://www.fda.gov/media/119946/download

Abstract

Methods and tools of collecting data and database build in clinical industry was differing across the ages, between various sponsors and trials, before the introduction of standards. CDASH played a major role in standardizing the eCRF data collection, across different sponsors, with meaningful variable names that could be related further to downstream SDTM and ADaM, helping in traceability and data integrity. Having robust standards across therapeutic areas along with standard machine interpretable protocols, will help in automating the complete Clinical data base build.

AI, Machine learning and NLP will play a major role in simplifying Clinical database build, with only minimal requirements as inputs. Clinical data bases will evolve to accommodate data entered from smart devices and validated using IOT. Use of Organ chips and its evolution in future will demand clinical databases to collect the data that’s exactly needed for analysis, that is the markers of indication and how it responds to the drug. We will discussing on these topics in this presentation.