Abstract
Fibrosis is a progressive disease
leading to organ failure through the accumulation of scar tissue which induces
morphological changes in organ structure in response to a variety of
physiological and environmental insults. The measurement of key fibrosis
biomarkers through high content imaging is often used for the pre-clinical
assessment of candidate compound’s anti-fibrotic efficacy in a higher
throughput manner than
in vivostudies.
In vitro models, however,
are poor predictors of
in vivo efficacy due to the complicated disease provenance and pathophysiology
of fibrosis. There is growing support that 3-dimensional (3D) cellular models
may be more predictive by effectively recapitulating the increased complexity
of the physiological environment of the disease tissue. In this poster, we
present the development of a 3D
in vitro rat kidney spheroid assay to improve
translation to
in vivo models.
The optimization of the 3D model by evaluating the
contribution of various assay parameters One Factor at a Time (OFAT) was time
prohibitive due to the protracted nature of fibrosis assays. Therefore, we utilized
a Design of Experiment (DoE) approach to evaluate 8 different parameters
simultaneously using statistical modelling software Design Expert. We developed
a D-optimal Response Surface model to identify parameters essential to the
development of a fibrotic phenotype in NRK-49F spheroids and subsequently
explore potential synergistic relationships between these parameters. Our
approach tested 200 conditions across 5 numeric and 3 categorical parameters
and progression of fibrosis was quantified via high content imaging. This
approach led to the optimisation of a TGF beta 1 induced fibrotic rat kidney 3D model
to allow direct screening of small molecules. The spheroid model provided an
overall improvement in assay quality and response window in comparison to the 2D
assay setup using adherent NRK-49F fibroblasts. The model was subsequently validated
against well-known anti-fibrotic TGF beta 1 signalling inhibitors.
Overall DoE provided a valuable approach to assay
optimization by allowing a quantitative assessment of significant assay
parameters, as well as, any potential non-linear relationship between these
parameters within the scope of a few experiments. This resulted in a
comprehensive evaluation of the entire assay design space with significant
reduction in time to assay optimization in comparison to conventional OFAT
optimization based approaches.
