APR Sept/Oct 2023 - 97

« FORMULATION AND DEVELOPMENT
Target Selection and Assay Validation
For each disease target of interest, project teams must focus on one (or
more) biological targets whose function mitigates or eliminates disease
progression. The process of target selection starts with gaining a
fundamental understanding of the biochemical mechanism of disease
or infection. Data from systems biology experimentation (genetics,
proteomics, etc.) is carefully evaluated to select the most suitable
biological target. To efficiently discover the most suitable molecular
entity for target functional activity modulation, stakeholders must
develop a series of in-vitro and in-vivo biological assays. Consequently,
a series of assay engineering experiments are performed to assure that
functional activity modulation can be detected in a reliable manner.
Data from such experiments allow for a series of assay protocols to be
implemented. Moreover, assay validation experiments generate data
which allows for stakeholders to assure that data produced from such
assays can be used to rank-order prioritize molecules in terms of their
likely disease mitigating effect.
Molecular Entity Screening
Upon completion of assay validation, discovery project teams seek to
identify a series of molecules across a range of molecular modalities
which exhibit target function modulation (i.e., inhibition, agonism,
etc.). In many cases, millions of samples (or test articles) of molecules
are subjected to initial screening to determine minimal activity.
The data generated must be carefully evaluated using a variety of
chemically and statistically aware decision support software interfaces.
Additionally, a variety of so-called counter-screens are employed (and
datasets evaluated) to assure that any lead series molecules interact
specifically with the disease target while avoiding unintentional
interaction with the large variety of biological systems present in the
human body. Ideally, the results of evaluating datasets produced by
screening and counter-screening assays will reveal a number of lead
series molecules which can be further optimized with increasingly
realistic (and costly) in-vitro and in-vivo assay models.
Lead Optimization
Upon completion of lead series identification, discovery project
teams undertake the iterative lead optimization cycle, wherein the
team conducts a series of molecular optimization experiments via
a design-make-test (DMT) iterative cycle. Each step of this cycle
generates data that establishes quantitative (molecular) structureactivity
relationships. The goal is to gain a clear understanding of
the correlation between molecular identity and corresponding
test article assay performance. For cycles to produce key insights,
project teams must confirm that the substances produced for assays
are of the correct identity and sufficient purity. Medicinal chemists
producing test articles subject them to spectroscopic (usually NMR)
and chromatographic analysis to confirm identity and purity.
When considering the use of data during lead optimization, the
source and format of molecular design and preparation information
(i.e., synthetic route and purification method), identity and purity
measurements, and bioassay protocol and activity modulation
quantitative measurements must all be presented in the appropriate
decision support interface. These data are used to establish correlation
models between identity/composition attributes and therapeutic
attributes, assuring that the appropriate data governance models are
instituted to assure accurate correlations.
Clinical Candidate Selection
As performance testing during DMT cycles achieves important
performance and selectivity milestones, project teams must ultimately
select from a variety of substances to promote candidates for clinical
investigation. A variety of factors influence the outcome of candidate
nomination activities. The goal is to maximize the probability of
mitigating or eliminating disease progression while minimizing
adverse events. Project teams subject clinical candidates to a set of
confirmatory assays to predict the overall likelihood of successful
clinical outcomes.
Since such clinical trials (i.e., testing with human patient populations)
of therapies are regulated by global health authorities (FDA, eMEA,
etc.), confirmatory studies must adhere to good laboratory practices
(cGLP). The procedures executed and the data generated during these
experiments must also adhere to a firm's cGLP policies. Therefore,
data governance should also extend to overall quality assurance and
regulatory policies during this important phase of the lifecycle.
Finally, a key factor also considered when promoting a specific
candidate is projected manufacturing cost. All other things being
equal, project teams will nominate the candidate that can be made
for the lowest cost.
Clinical Testing and
CMC Development
Upon acceptance of a nominated candidate for drug development,
project teams (usually with a new composition of stakeholders)
must design and implement a clinical strategy to assess the viability
of a new therapy. The knowledge obtained during discovery unit
operations will be leveraged to prepare this clinical strategy. In
addition, such clinical investigation must follow good clinical practices
(cGCP) and good manufacturing practices (cGMP). Experiments to
design and implement processes to produce, formulate and test
clinical trial materials must follow such cGxP policies to mitigate risk.
But adherence to cGxP policy also helps to assure approval of clinical
testing applications (e.g., investigational new drug applications, as
mandated in the US by the code of federal regulations, specifically
21CFR312).
Therefore, a firm's data governance strategy should account for these
policies-an incremental consideration to efforts in discovery.
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