APR Sept/Oct 2023 - 98

» FORMULATION AND DEVELOPMENT
»
Commercialization and Supply Chain
Many have summarized the costs and risks associated with the
clinical development of new therapies. The process can be long and
relatively expensive. If such clinical studies confirm the therapeutic
benefits of an investigational new drug (IND), and health authorities
approve the commercialization of this new drug via an NDA, project
teams are faced with the task of transferring the various clinical
production, formulation, and testing processes to their commercial
supply chain teams.
An effective digital tech transfer process is essential to an efficient
supply of these new therapies to patients. Additionally, ongoing
testing within the commercial supply chain is essential to assure
quality and risk mitigation. Similar to the DMT cycles in discovery, the
data governance (for batch records and test result records generated
during production) must be carefully devised.
Pharma Data Utilization
Categories
A number of important data utilization categories must be considered
across this lifecycle when establishing a data governance model.
* Contemporaneous Decision Support-offering data access to
stakeholders just-in-time for any/every experiment/process
result. These interfaces support both experimentation-specific
decisions (i.e., the results of an experiment in discovery-is this
material the appropriate identity and composition to subject to
a bioassay?); and release decisions (i.e., for a collection of test
results from different experiments, is the material appropriate
for a downstream process operation?)
* Comparative Analysis-reference-type comparisons: being able
to compare data from a variety of experiments, samples, and
other variables is essential to glean various insights (longitudinal
and serial trends, standard-to-sample differences, etc.)
Attributes
Implementation of systems based on these use cases require the
following attributes to be carefully considered:
* Lowering Data Access Utilization Barriers-in addition to
the types of decision support use cases identified above,
establishing data governance which minimizes access barriers,
while maintaining data security is essential to overall success.
Stakeholders must consider:
· The distribution of data sources (data entry/capture
systems, data generating instruments and equipment);
just-in-time, network-based access to stream data from
the source to accessible repositories
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Conclusion
Stakeholders are challenged to implement strategies to support
effective data usage across the pharmaceutical lifecycle. While this
challenge can appear daunting, designing a data governance model
using the considerations described above can lead to the discovery
of better medicines, more efficient matriculation of R&D project
timelines, and ultimately implementation of predictive models
for the next generation of vital, life-saving therapies. Industry
stakeholders should also consider practical access, format, and
presentation requirements when developing such data governance
with particular emphasis on data conversion, normalization, and
presentation microservices implementation.
· The degree to which data sets are tagged to enable
facile queries (scientifically relevant tags including
chemical structure query, spectral feature-type queries)
* Support for Format and Technique Agnostic Tools-based on
the volume and variety of data formats from the collection
of data-producing sources, data governance must include
subsystems which serve as conversion and normalization
microservices. This allows for data consumers to rely on
access to datasets without having to depend on format and
technique specific access and presentation interfaces.
* Access Points and Consumer Demographics Accounting-when
implementing such access points, stakeholders should carefully
consider how data consumers will interact with data, upon
execution of queries.
Relevant priorities should include:
· The type of software interface (browser or federated
user interfaces); the structure and types of queries (SQL
and RestAPI-based, form-based, text/natural language
based)
· How results are displayed/presented
· Decisions made with such query output
Predictive Models Based on
Machine Learning
Finally, the most recent addition to most data governance strategies
is the consideration for systems to produce query outputs that serve
as training sets for predictive models. As data is produced by an
enterprise-wide collection of data sources, and each dataset is given
context (re: sample, experiment, and acquisition method), firms can
utilize the systems above to produce such training sets. This can be
accomplished by including RestAPI-based access to data utilization
systems and include a JSON-based conversion utility. This allows facile
incorporation into various ML systems-both on the cloud and onpremises
systems.
APR Sept/Oct 2023

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