Accelerate Your Regulatory Submissions - Today

Transform your raw clinical data into CDISC SDTM standards.

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Source to Submission (S2S)

A better option for SDTM transformations

Transform your raw clinical data to CDISC SDTM standard – fast and accurately – with the power of artificial intelligence (AI) and machine learning (ML). Eliminate manual, slow, and inefficient SDTM transformation processes while improving data quality and accelerating your time to market with Source to Submission (S2S).


Source to Submission (S2S)
Accelerate time
to submission

Automated data mapping eliminates slow, manual processes, saving time, especially with large or complex datasets.

Better utilization of
existing resources

Automating the SDTM transformation process allows clinical programmers and others involved in SDTM mappings to focus on higher-value activities.

No need to maintain
CDISC SDTM standards

CDISC standards are maintained and continually updated within S2S. This eliminates costly and resource-intensive processes, required to maintain CDISC standards.

How S2S Works

Accurate Mapping, AI Assistance, and Improved Efficiency

Accurate AI-driven mappings, out of the box
S2S applies advanced AI models to source data, making suggestions and assigning confidence scores to each mapping. Users can then approve or reject each mapping, keeping the human in the loop and ensuring accuracy. Mapping accuracy can increase when S2S is pre-trained on your organization’s own data.

Smart Data Quality (SDQ)
Data Hub

Up to 50% time savings for SDTM transformation


Optimize and accelerate SDTM transformations.

Automated data mapping

S2S uses artificial intelligence (AI) and machine learning (ML) models to automatically map data fields from various sources into a common model.

Source to Submission (S2S)
Train AI models on historical data

S2S’s AI models can be trained on your previous study data, increasing the accuracy of SDTM mappings.

Train AI on user inputs

S2S’s AI models continuously improve, becoming more accurate with each new deployment as they learn from previous mappings and user inputs.

Source to Submission (S2S)
Apply AI maps to future studies

When you use the auto mapping feature, new maps are created automatically. These AI-driven maps are saved in the global library and can be copied and applied to future studies.

Source to Submission (S2S)
Global metadata

A library of target metadata maintains conformance of submission data towards CDISC SDTM and sponsor standards and controlled terminology.

Source to Submission (S2S)
Global library

A library of reusable macros organized by therapeutic area, indication, program/project and study helps users create maps quicker.

Scheduled jobs

Data ingestion – from Saama’s Data Hub – and transformation jobs can be scheduled to run immediately or later.

Source to Submission (S2S)

Users can save a snapshot of their in-process work, and come back to it later. They can also save snapshots of their data to support interim analyses.

Python integration

Supports Python transformation languages and allows users to write their own Python code directly within the global library.

Submission KPG
Submission package

Enables export of SAS transport files for submission and integration with Pinnacle 21, to ensure submission compliance.

Office Workers

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