Assay Results - Confirmation and Flexibility as a Key Component in the Value Chain.

For a small Biotech company, having all of their research data in a cleaned, validated and ready-to-use format is a critical part of their IP strategy. This organization counts a variety of chemical companies amongst its client base, and keeping their proprietary data secure yet complete and easy to disseminate is key to the value they provide their clients. Indeed, it’s key to the value of their company as a whole.

As their research teams generate new data, it’s captured by the Snthesis platform where it is aggregated, connected and harmonized before being exported into their in-house data science infrastructure in a clean and validated format. This approach frees their data science team to focus on delivering valuable insight instead of focusing on aggregating and validating data. The resulting flow of information is driven to reporting tools and dashboards for their closely-engaged Management and Sr. Leadership.

Using Snthesis has allowed them to evolve their experimental data pipelines without any outside assistance and gives them complete agency to add new classes of data or new data points as needed. Most importantly, the Snthesis platform allows them to quickly create new datasets and compile both recent and historical research data in support of the requirements of new clients. This reduction in time-to-value fosters a nimble and agile approach to growth in the biotech space.

The assays they have developed to assess whether a given sample is viable for a given purpose is part of their IP. Being able to aggregate, connect and harmonize all of the data generated by an assay or series of assays over time (including sample provenance and context of creation) is crucial in proving the validity of individual assays and the overall assay pipeline to potential partners and clients. In turn, being able to do that is crucial in proving the value of the organization to potential investors.

In some cases it’s not just “what you know”. It’s being able to quickly combine different assay results and surface them in a validated, secure and flexible environment so you can easily prove and share what you know.

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Controlling Variation in Sample Growth Conditions.

After years of sample-prep data and assay management data being collected in a variety of spreadsheets, a Life Sciences company realized they had a significant problem. They couldn’t always reproduce promising sample results.

Eventually, the culprit was determined to be slight variations in sample growth conditions that were missed. The different teams involved in the various projects generally kept their data in spreadsheets because of the flexibility, but over the years the inconsistency that resulted (such as different column labels or the occasional empty cell) had led to periodic variations in growth conditions.

The initial Snthesis Bio implementation aggregated, connected and harmonized several years’ worth of sample prep data. With invalid and anomalous data reviewed and corrected or dismissed, a firm and years-long baseline was established.

Today, data about growth conditions is collected by the teams that prepare samples for assays, including temperature, growth time, whether samples were shaken during growth, any modifications made to media, and other relevant growth parameters. This data is collected in spreadsheets or Electronic Lab Notebooks and fed into the Snthesis platform where it is aggregated and connected to previous data. Anomalies are identified immediately, before time is wasted on a sample with incomplete or inaccurate provenance.

Confirming consistency of this data is critical to ensuring predictability. When a promising sample is found, the growth conditions are now available in high fidelity. Being able to link how a sample was grown with its experimental results is critical in part because it enables scientists to analyze the effectiveness of particular growth methods and makes experiment results reproducible in later rounds of assays.

In this case, Snthesis Bio enables reuse of years of older data, ensures conformity in sample growth conditions going forward, and fosters continuous improvement in the sample growth process.

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Data Quality – Oversight & Validation

In the research lab of a mid-size research firm, data is being collected directly at the bench. This collection is either in paper notebooks or in spreadsheets because experimental endpoints evolve very quickly in this organization, and the freedom granted by a spreadsheet allows them to perform research at a fast and flexible pace.

As the Snthesis software captures the scientist’s spreadsheets the system automatically validates that the input meets the requirements for that experiment and confirms that assay data can be connected to sample preparation data. Any data quality issues the system identifies are corrected directly by scientists. The platform then consolidated these individual spreadsheets into a single master record of sample and assay data. This aggregated master record is downloaded and used by scientists directly to perform basic analysis on their local machines.

Importantly, all data required for the experiment at hand is validated. Any one-off metadata data is also captured and preserved in case there is a future need to add additional data points to a particular experimental workflow. Everything is captured and made available, often eliminating additional future work on that experiment.

Sometimes a single sample is used in multiple assays, or may be shipped to external partners for more specialized experiments. In those cases, the sample teams upload their data directly to the system as “standalone” data. Later, as results are produced, the scientists upload them independently. The Snthesis platform automatically makes the connection and links the sample data with the results data, eliminating the need to manually merge spreadsheets from collaborators.

As assays evolve, the team easily adjusts the metadata and linking requirements using tools built into the platform, enabling versioned tracking of data requirements. The system always validates and confirms data quality against the latest parameters as it’s collected. Older data can be automatically up-converted to the newest version, from renaming columns to populating missing values from new fields. The aggregated data (old and new) is always in sync and mirrors the latest set of data requirements.

The analysis can only be as good as the data that flows into it. Snthesis Bio provides oversight and validation to ensure that the data feeding your analysis is clean, connected and complete.

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