Conferences and Events

Past conferences :

PhUSE US Connect-2021

June 2021

Integrating Clinical data for Translational Research

During development of biological or biomarker-based therapies, extracting insights from study data to assess efficacy for stratified cohort is a challenge because of the disparate nature of the data sources. The challenge is to use smart data transformation and machine learning algorithms to normalize and harmonize this disparate data to a single Universal Data Model (UDM), that supports any further analysis, visualization, or pattern recognition.

UDM can also enable integration of translational data such as biomarkers and assay data, with longitudinal subject data in legacy studies. Normalized and harmonized data in UDM is ready to be analyzed to gain insights on mechanisms, to perform cross-study analysis, and derive patient cohorts. In translational and precision medicine, these datasets can also be used as training sets for the machine learning algorithms designed to predict the outcomes of new drugs.

Integrating Clinical data for Translational Research
Integrating Clinical data for Translational Research using machine learning technique for integration and analysis for insights

Data transformation for normalization and harmonization

Data generated over a period in immunological studies/ cell/ gene therapy are very large and are often stored in many places. To make it available for insight generation at the right time and right place will require tools to standardize and integrate these data for their respective clinical data.

  • Automation and recommendation engine using Machine Learning algorithms working on the principle of supervised learning will both speed up and standardize and harmonize the data to a chosen libraries/ontologies.
  • Validation of the data for transformation and consistency.
  • Indexed and stored in a customizable Universal Data Model.
  • Metadata capturing at various levels of the data and making it available for search and selection of cohort.

Derive metadata for analysis, insights, and cohort selection

  • Statistical tools for visualization and analysis
  • Summarization, pattern identification and making it available for reconciliation, ready referencing
  • Enabling cross study analysis between studies.
  • Saving analysis/insights report for presentation, publication, and collaboration
  • Biosample availability tracking for search and selection of biosamples for any new assay required and new assay demand generation
  • Data tracking and reconciliation of ordered assays on biosamples.

View Poster: Integrating Clinical data for Translational Research


Effective March 15, 2021: Support for SEND DART IG 1.1 per FDA Data Standards Catalog

FDA AnnouncementFDA Data Standards Catalog)

Our Software solution (Xbiom) and services are set up to accommodate DART IG 1.1, and we have been generating SEND datasets for DART studies including EFD, Fertility and Early Embryonic Development and Pre-Natal and Post Natal Development studies. We also have worked with multigeneration reprotoxic studies in the past. To learn more about our SEND conversion services, contact us at dsservices@pointcross.com.


Webinar Recording: Enabling Data Integration for Translational & Precision Medicine

May 2021

PointCross’s Xbiom Clinical Insights Module is designed for Translational Medicine Teams to curate, harmonize, and standardize disparate assay data and EDC study data from clinical trials, along with genotyping data, molecular, and other biomarker data. Our newly launched Data Concierge Service helps shoulder the burden of data wrangling by integrating directly with your CROs and external labs to help close the gap between these disparate data sources and rendering study data fit-for-use by scientists and researchers for longitudinal analysis, cross-domain search, and stratified cohort selection based on patient history, consent availability, specific molecular biomarkers, study type, and more.

Listen now

Click here to View Abstract


Sep 15 2021
September 15, 2021

FDA to enforce the Technical Rejection Criteria (TRC) beginning Sept 15th, 2021

Technical Rejection Criteria for Study Data)

The TRC has been added to the existing eCTD validation criteria to enforce compliance with the SEND requirements for study types modelled in an FDA supported SEND Implementation Guide (SENDIG) version. Any electronic submission submitted after Sept 15th, 2021 will be rejected if the submitted datasets don’t pass the same. Our PointCross Validation engine have already been extended to ensure that all datasets are checked against the Technical Rejection Criteria. You can verify your datasets and make sure that they do pass the TRC by using our eDataValidator.


Feb 17 2021
February 17, 2021

Molecular Medicine Tri-Con

Feb 17th 2021

Translational and precision medicine development in immuno and gene therapies rely on biomarker data from assays including genomics, proteomics, IHC, Flow cytometry and cell phenotyping data. Biomarkers are not only generated from the patient Bio samples, but also from the biomanufacturing sites such as in adoptive immune cell biomanufacturing for novel immunotherapies. Extracting valuable insights from these disparate data sources and integrating it to clinical data to relate to patient outcome and/or discover and validate relevant biomarkers are met with challenges of ingestion, harmonization and integration of disparate data with the clinical data. Key decisions and ideas that impact study design of future clinical trials depend on gaining insights rapidly from such integrated data on patients or stratified cohorts. Systematic curation with self-validation for completeness and consistency is time consuming and difficult without technology. Xbiom, built on Machine Learning and Universal Data Modelling effectively solves these challenges of disparate, big and varied data sources. Xbiom's Smart Transformation platform, can make ingestion, curation, and harmonization process automatic and is also capable of processing both streamed data as well as in batch mode.

Poster: A Universal Data Model for Longitudinal Integration of Disparate Biomarker and In-Life Patient Data Augmented by Machine Learning
Click here to View Abstract


Sep 21 2020
September 21, 2020

PhUSE CSS 2020 Poster

PhUSE CSS 2020 September 21 – 23, 2020

“A Process for Automated Reconciliation of SEND Datasets with Study Reports for Confident Submission and Review.”

Abstract

SEND datasets and study reports are created by different processes, with study reports having predefined groupings, terminologies, and reporting conventions. In our opinion, regulatory reviewers trust reports because they fall under GLPs, are audited by QA, and signed by the Study Director. To reconcile SEND datasets against study reports, we propose a process in which study report tables are converted to a machine-readable columnar format that includes all summary tabulations and subject level listings. 100% automated reconciliation of tables in study reports with regenerated summaries from SEND is then possible. Automated “reconcilers” can use semantic enrichment for controlled terminologies and compare mapping between predefined cohorts in the study report to those in the SEND trial design. We propose that this model will demonstrate to reviewers that SEND datasets accurately represent data in study reports.

  1. Extract study report summary tables and subject-level data
    into digital machine readable form

Extract study report summary tables and subject-level data into digital machine readable form

2) Generate Trial Summary and Trial Design ONLY from Study Report using automated tools

Generate Trial Summary and Trial Design ONLY from Study Report using automated tools

3) Regenerate group summary data from SEND datasetRegenerate group summary data from SEND dataset

At this point in the process, there are several sets of machine readable files:
1. One set of summaries extracted from PDF study report
2. Another set of summaries regenerated from SEND dataset
3. TS and Trial domains generated from PDF study report
4. TS and Trial domains from SEND dataset

4) Use Comparator/ Reconciler tool to compare study report data with regenerated summary from SEND and SEND Trials and TS domains

Use Comparator or Reconciler tool to compare study report data with regenerated summary from SEND and SEND Trials and TS domains

Notes:
• During steps 1 & 2, Study Report page # & section # are extracted for traceability of Study Report to it’s digital representation in Study Report Reference files.
• Additional information is extracted from Study Report text that is needed
for SEND but not part of Study Report summary tables.
• Subject level data can be extracted from Study Report appendices if necessary to resolve significant issues in SEND datasets.
• The output of this process includes human-readable files that identify inconsistencies between SEND dataset & Study Report, correction
instructions, notes for nSDRG & complete traceability between SEND dataset & Study Report.

CONCLUSIONS

According to FDA’s Technical Conformance Guide (Section 8.3), traceability allows the reviewer to understand and trace relationships between analysis results, single animal listings in the Study Report, and the tabulation data sets. Since SEND is generated independently of study reports, we believe that systematic and complete reconciliation with study reports is necessary. We have presented an automated method for 100% reconciliation that does not require any proprietary tools. This process can assure sponsors and reviewers that SEND datasets accurately represent data in the final study report

PointCross presented and was awarded Best Poster In the Industry category for our poster. Click the link below to view the poster.

PhUSE CSS final poster
Laura Kaufman certificate PhUSE CSS


Sep 20 2020
September 20, 2020

PHUSE US Connect 2020

(Conference cancelled, but presentations are available on PhUSE website)
Presentation Title: DH12: Validation Consistency and Conformance Checking of SEND Datasets