[Thesis]. Manchester, UK: The University of Manchester; 2012.
Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascularinjury
are dependent on or aggravated by inflammation. Their prevention and treatment isarguably
one of the greatest challenges to medicine in the 21st century. The pleiotropic,proinflammatory
cytokine; interleukin-1 beta (IL-1b) is a primary, causative messenger ofinflammation.
Lipopolysaccharide (LPS) induction of IL-1b expression via toll-likereceptor 4 (TLR4)
in myeloid cells is a robust experimental model of inflammation and isdriven in large
part via p38-MAPK and NF-kB signaling networks. The control ofsignaling networks involved
in IL-1b expression is distributed and highly complex, so toperturb intracellular
networks effectively it is often necessary to modulate several stepssimultaneously.
However, the number of possible permutations for intervention leads to acombinatorial
explosion in the experiments that would have to be performed in a completeanalysis.
We used a multi-objective evolutionary algorithm (EA) to optimise reagentcombinations
from a dynamic chemical library of 33 compounds with established orpredicted targets
in the regulatory network controlling IL-1Î˛ expression. The EAconverged on excellent
solutions within 11 generations during which we studied just 550combinations out of
the potential search space of ~ 9 billion. The top five reagents with thegreatest
contribution to combinatorial effects throughout the EA were then optimised pairwisewith
respect to their concentrations, using an adaptive, dose matrix search protocol.A
p38a MAPK inhibitor (30 Â± 10% inhibition alone) with either an inhibitor of IkB kinase(12
Â± 9 % inhibition alone) or a chelator of poorly liganded iron (19 Â± 8 % inhibitionalone)
yielded synergistic inhibition (59 Â± 5 % and 59 Â± 4 % respectively, n=7, pâ‰¤0.04 forboth
combinations, tested by one way ANOVA with Tukeyâ€™s multiple test correction) ofmacrophage
IL-1b expression.Utilising the above data, in conjunction with the literature, an
LPS-directed transcriptionalmap of IL-1b expression was constructed. Transcription
factors (TF) targeted by thesignaling networks coalesce at precise nucleotide binding
elements within the IL-1bregulatory DNA. Constitutive binding of PU.1 and C/EBP-b
TFâ€™s are obligate for IL-1bexpression. The findings in this thesis suggest that PU.1
and C/EBP-b TFâ€™s form scaffoldsfacilitating dynamic control exerted by other TFâ€™s,
as exemplified by c-Jun. Similarly,evidence is emerging that epigenetic factors, such
as the hetero-euchromatin balance, arealso important in the relative transcriptional
efficacy in different cell types.Evolutionary searches provide a powerful and general
approach to the discovery of novelcombinations of pharmacological agents with potentially
greater therapeutic indices thanthose of single drugs. Similarly, construction of
signaling network maps aid theelucidation of pharmacological mechanism and are mandatory
precursors to thedevelopment of dynamic models. The symbiosis of both approaches has
provided furtherinsight into the mechanisms responsible for IL-1b expression, and
reported here provide aplatform for further developments in understanding NCDâ€™s dependent
on or aggravated byinflammation
A computer program which can select effective drug combinations from a billion others
isset to improve our understanding of cancer, heart disease, stroke and many other
diseases.Inflammation-best known in arthritis, is common to many chronic diseases.
Inflammationcauses the release of a whole array of molecules that help our bodies
fight infection but canalso be very damaging in long term diseases. A key molecule
called IL-1 is produced byblood cells called macrophages and these cells can be studied
in the laboratory. Only 30drugs are required to generate over a billion possible combinations,
so it would take over100 years to test all possible combinations even with the assistance
of the lab robot!We bypassed this problem with a computer program: an evolutionary
algorithm (EA) thatcompared the results of a few combinations that we tested in the
lab, and selected thosethat were most effective in blocking IL-1 production. We repeated
this cycle until the bestcombination was found, but in 10 weeks rather than 100 years!
(see Figure)Although the promise of future remedies is a distant one, using this approach,
of computerprogram with experiments will vastly speed up the search for remedies and
provide a betterunderstanding of chronic diseases such as cancer, cerebrovascular
injury and heart disease.