IN THIS ISSUE - Drug Development & Delivery

MATHEMATICAL
M O D E L I N G
Mathematical Modeling for Faster
Autoinjector Design
By: Jonathan Wilkins, PhD, and Iain Simpson, PhD
INTRODUCTION
There is an increasing demand for
advanced injection devices that bring
benefits around self-administration and
ease of use and work with new biological
drugs, which are often required to be
delivered in larger volumes and/or at
higher viscosities than those for
conventional drugs. In order to reduce
development and manufacturing costs,
there is also a desire for platform
devices, which can meet a broader range
of drug and user requirements through
simple adaptation of a core design.
Unfortunately, a number of devices
currently in the market do not meet these
requirements and furthermore, there is
often a lack of a detailed understanding
of how the key design parameters affect
the overall device performance. In some
cases, this has led to product recalls and
in other cases, resulted in challenges in
adapting the designs to meet new needs.
Hence, there is a commercial need in the
injectables industry for better
understanding of how design
fundamentals affect the performance of
autoinjector devices.
Cambridge Consultants believe a
fast and effective approach to a better
injection device design is a novel
combination of mathematical modeling
on a desktop PC, and complementary
experimental data. The use of
mathematical modeling gives insight into
the physical behavior of the device, and
allows rapid prediction of the effect of
different parameters during the design
process. The more established
experimental approach provides
confirmatory data to support the
modeling. Cambridge Consultants has
deployed this methodology in a number
of development projects and achieved
benefits in terms of reduced
development time, more robust and
Schematic of the Autoinjector
F I G U R E 1
adaptable designs, and reduced product
cost.
Mathematical modeling allows for
quick simulations of device performance.
The effect of important design
parameters, such as injection time,
injection force, and shear stress on the
drug, can be predicted in real-time. This
allows the engineer to investigate the
design space and quickly optimize a
suitable set of components for a new
injection device. For example, the
mathematical model can guide the
designer in choosing the correct driving
spring, needle gauge, and plunger in
order to meet the device performance
Drug Development & Delivery July/August 2012 Vol 12 No 6
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