Biomedical Engineering Reference
In-Depth Information
repeated over and over throughout the industry. While this problem of data
reconciliation and reformatting is time consuming and error prone in the
chemical synthesis domain, this problem is often even more exacerbated in
the biological domain.
Often pharmaceutical companies will have outsourcing relationships with
contract laboratories that perform assays on compounds owned by the client.
These assays could be standard assays that are outsourced for cost effi ciencies
or proprietary assays that are otherwise not available to the pharmaceutical
client. As with compound registration systems, the outsource partner that runs
the assays will likely have internal protocol registration and biological assay
data management systems to capture the data. These systems will be built to
suit the needs of the internal processes within the contract laboratory, so that
they can properly manage, interpret, and report on their assay results. However,
most pharmaceutical companies like to import the assay results into the phar-
maceutical company's internal assay data management system. This would
enable the pharmaceutical scientists to interpret the outsourced assay data
side by side with all of the other data generated on that proprietary compound.
With every partner that generates assay data related to a compound, there is
an ongoing, complicated effort to properly format and transmit the data such
that the scientists in the pharmaceutical company can understand the nature
of the assay and accurately interpret the results. Too often, many days are
wasted merely explaining differences between internal and external assay
results. Especially with high-throughput or high-content biological assays,
there are a signifi cant number of attributes of the experimental design that
are important to account for in the data interpretation. For example, which
cell line was used? Was it a single-point assay or a dose-response? What was
the detection mechanism; fl uorescence, phosphorescence, and so on?
Furthermore, there are many cases where the proprietary assay platform gen-
erates data that have a unique structure.
Perhaps the assay is a high-throughput, low-resolution format, in which case
the raw numeric output must be binned into low-medium-high categories and
only the binned values are reported to the client, yet the client has stringent
data quality, numbers-only rules to which the contract laboratory cannot
adhere. Perhaps the assay has a cutoff at a reading threshold, causing the result
to be reported as a range instead of an explicit number. Perhaps there is a
nonlinear response that requires special curve-fi tting software to calculate the
half maximal inhibitory concentration (IC
50
) value. There are many nuances
and subtleties to biological assay data, and a large amount of metadata is
required to properly describe the experimental method. This must be under-
stood by the scientist who is using that assay data to make design or synthesis
decisions for the next molecule. As such, it is important for the contract labora-
tory to deliver the full experimental description of its data and for the phar-
maceutical customer to ingest and report all of that description to its scientists.
Again, as with compound synthesis, if this assay data generation was done with
a single partner, then a manual process with signifi cant interactions between
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