Chemistry Reference
In-Depth Information
time, but in our experience they effectively mitigate the risk of a failed animal study and
wasted time.
This rigorous approach also distinguishes TDD
vis-à-vis
Development 2.0. This
latter involves increased integration of development stage scientists into the discovery
phase
—
a crucial advance. However, under the usual 2.0 paradigm,
the studies
conducted tend to be high-throughput, database-building work,
in the hope of
generating predictive guidelines such as Lipinski
s Rule of 5. While guidelines
such as these have been useful to a generation of development scientists, the added
rigor of modeling and of derived measurements offers a higher resolution view of
compound performance and greater predictive power. For instance, by executing
extensive stability studies, which are semi-empirical in nature, in the best-case
scenario, one can determine whether the API in dispersions and tablets remains
amorphous (within the limit of detection) at expected storage and manufacturing
conditions. One can also measure and calculate the extent of a material
'
sstructural
relaxation as an index of molecular mobility. The input to these models is the amount
of relaxation at high-stress conditions of increased temperature and humidity; by
generating nonzero data point this way, we can estimate by extrapolation how long an
API is likely to remain amorphous at room temperature. In practice (and with the
proper stabilizing polymers), the timescale of crystallization is often less clinical than
cosmological
'
sometimes in the millions of years.
In describing our approach and the positive results it has garnered to date, we hope
that our recommendations prove useful to our colleagues across the industry. However,
we do not mean to presume that TDD is the last word in pharmaceutical development.
While TDD, or
—
is, we believe, a salutary and important step beyond
the
status quo
, much room for growth remains.
For instance, it will be crucial, in the near future, to build on recent advances to more
fully apprehend the structure
“
Development 3.0,
”
function relationships of amorphous materials. One
noteworthy 2012 paper described the paracrystalline structure of amorphous silicon [26].
Attaining the same level of understanding in pharmaceutical amorphous dispersions
would enable researchers to better predict a dispersion
-
s properties, such as its solubility
and crystallization kinetics,
in silico
. It would not be an exaggeration to suggest that this
degree of sophistication in modeling would help bring drug development into a new era.
The aerospace industry, like the pharmaceutical industry, operates with extremely long
product cycle times, but at the beginning of every decade-long development process is a
period of extensive modeling and simulation. A similar advance in the pharmaceutical
industry would move us away from much of our current
a posteriori
trial-and-error
empiricism and into a level of rigor we have come to expect in other technically intensive
sectors of society.
To reach this point, however, a great deal of work remains to be done. We hope that
it is always so. A
'
field that is constantly developing, growing more sophisticated and
advancing more powerful tools, is one that will attract strong scientists and continually
push past what were once taken to be its limitations. A stagnant
field will do neither. And
stagnation is particularly important
to avoid, and constant growth is particularly
important
to strive for,
in the pharmaceutical sciences
—
a
field that produces not
only knowledge but also proximate gains to the public good.
