Conferences and Events

( FDA Announcement; FDA 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.

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.

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Click here to View Abstract

( 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 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

Presentation Title:

PHUSE US CSS 2020 PP08 : Taking full advantage of the SEND data potential

Jointly published by Roche and PointCross Lifesciences
Click here to listen Virtual CSS

Tuesday 29 September 2020
Presentation Title: Value of Rapid Integration of Patient Immunoassays and eCRFs in Trials

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

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

3) Regenerate 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

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

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

Webinar: Additional Tools and QC checks to ensure SEND datasets are Fit For Review (FRR).

The recording of the webinar is available here. During the webinar, you will learn about an affordable way of meeting these new FFR recommendations by building on the automated checks, reconciliation tools, and processes that we presented during the March 17th webinar.