Jeffrey S. Barrett, Kalpana Vijayakumar, Sundararajan Vijayakumar, Dimple Patel, Mahesh Narayan, Bhuvana Jayaraman, Erin Cummings, Steven Douglas

1
Managing Disparate Data Generated from
Translational Research Activities iClinical
Implementation to Support Data Integration and
Sharing for the IPCP
Jeffrey S. Barrett, Kalpana Vijayakumar,
Sundararajan Vijayakumar, Dimple Patel, Mahesh
Narayan, Bhuvana Jayaraman, Erin Cummings, Steven
Douglas Laboratory for Applied PK/PD, Division of
Clinical Pharmacology, The Childrens Hospital of
Philadelphia, PA
BACKGROUND
DESIGN AND METHODS
DESIGN AND METHODS
RESULTS
Pilot data from IPCP Projects 1 2 are analyzed
in iClinical and accessible via the web.
Figure 2 IPCP-iClinical Overview

• Sharing data is an essential part of current
  multidisciplinary research program supported by
  the NIH and has defined the need for integrated
  data solutions for many academic medical research
  programs.
• The Integrated Preclinical Clinical Program
  (IPCP) is an NIH grant mechanism that supports
  preclinical to clinical investigations for the
  discovery and development of new therapeutics
  targets for HIV infection.
• The program consists of three cores with four
  inter-related projects. Core A serves as an
  administrative core. Core B provides HIV
  antiretroviral drug susceptibility and drug
  Interactions. Core C provides biostatistics and
  pharmacology.
• The four projects to identify neurokinin-1
  receptor (NK1- R) antagonist for HIV therapy are
  recognized by number.
• Project 1 investigates the mechanisms involved
  in the NK-1R substance P (SP) preferring
  receptor, antagonist-mediated anti-HIV activity
  in human immune cells.
• Project 2 investigates the anti-viral, molecular
  and cellular immunologic mechanisms.
• Project 3 investigates the SIV disease
  progression, effects of SP level, and
  neurophysiologic and neurobehavioral studies in
  Rhesus.
• Project 4 investigates the safety, viral
  suppressive potential, pharmacokinetics in
  HIV-infected individuals and immune modulatory
  effects of treatment with aprepitant.
• The program generates a large number of
  translational data from all four projects
  including basic science, PK/PD, safety and
  efficacy, laboratory, protocol, and in vitro/in
  vivo data in addition to reports and documents
  obtained from these experiments.
• To comply with NIH data sharing requirements, an
  integrated data environment, iClinical, has been
  developed as a web-based tool to provide secure
  data storage, data sharing, analysis, and
  reporting capabilities.

iClinical is an integrated data solution that can
accommodate in-vitro, in-vivo and human clinical
trial data at multiple levels of granularity and
organization. Figure 1 shows the various
component tiers. Figure 2 shows the summary of
IPCP project data flow diagram into iClinical
System and shows the modules for input of new
data to pull together loosely coupled study data
or supportive experimental data, and results from
mathematical data analysis and predictive
modeling.
Figure 1 iClinical Component Tiers

• The system integrates and cross-links data from
  the above streams and making them available for
  drill down analysis within a study, meta-analysis
  across studies, data summarization and generation
  of tables and listings for reports.
• Data imported into iClinical is defined by
  association with corresponding data items in the
  central data dictionary. This allows users the
  ability to map multiple user defined dictionaries
  into one central dictionary or to external
  standards such as the CDISC.
• Benefits include the ability to define derived
  columns, unit standardization, enforcement of
  allowed values, translation of data coding tables
  into allowed values and most importantly enabling
  meta-analysis across studies and predictive
  results.
• The data that is mapped into the dictionaries can
  then be summarized and viewed via any number of
  canned reporting templates or one created on the
  fly.

Various views of the IPCP data in iClinical
system
CONCLUSIONS

• The IPCP-iClinical web interface will serve as a
  common platform to bring translational research
  data across projects from different institutions
  and streamlines the data capture process and
  efficiently stores research data for public data
  sharing, analysis and reporting.
• Centralized data and document storage, automated
  routing for review, correction and approval,
  capture interim data status, export raw data and
  import analytical results, gate access to entire
  study or sub-domain of study while limiting user
  operations on such data.
• iClinical thus promotes collaborative study
  engagement both within the organization and
  external collaborators, including project
  sponsors such as the NIH and gated access to the
  broader research community.
• The solution is easily accessible over the web
  and provides secure and encrypted access for both
  internal users and external sponsors,
  collaborators and the NIH.

RESULTS
Figure 3. Users have an option to filter by
dataset with variable of interest and stratify
plot types on available subgroups.
Figure 4. Shows the plots by selected drug and
subgroups.
Using iClinical
OBJECTIVES

• Preparing the data files is as simple as
  preparing data in ASCII comma separated value
  (.csv) or MS Excel file.
• The uploaded files are captured as-is with
  appropriate date/time stamps, data ownership and
  versioning as well as parsed into the central
  data mart for easy searching. Access to data
  within iClinical is password protected and made
  available for browsing, updating, reporting,
  exporting or importing based on the roles granted
  to each user.
• iClinical is accessible via the web in a secure
  encrypted session fully compliant with the Code
  of Federal Regulations, 21-CFR-11.

• To effectively capture IPCP translational data,
  protocols and define the framework for
  comprehensive data collection.
• Enforce metadata and dictionary standards right
  at the source and/or allow them to be mapped
  between systems
• Route the data for review and approval and
  notify data consumers and project team alike
• Standardize the data and results to enable
  meta-analysis across studies
• Provide analysis ready datasets and track results
  from analysis.