  The effects of molecularly targeted drug perturbations on cellular activities
and fates are difficult to predict using intuition alone because of the complex
behaviors of cellular regulatory networks. An approach to overcoming this
problem is to develop mathematical models for predicting drug effects. Such an
approach beckons for co-development of computational methods for extracting
insights useful for guiding therapy selection and optimizing drug scheduling.
Here, we present and evaluate a generalizable strategy for identifying drug
dosing schedules that minimize the amount of drug needed to achieve sustained
suppression or elevation of an important cellular activity/process, the
recycling of cytoplasmic contents through (macro)autophagy. Therapeutic
targeting of autophagy is currently being evaluated in diverse clinical trials
but without the benefit of a control engineering perspective. Using a nonlinear
ordinary differential equation (ODE) model that accounts for activating and
inhibiting influences among protein and lipid kinases that regulate autophagy
(MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal
control strategies, we find optimal drug dosing schedules (open-loop
controllers) for each of six classes of drugs and drug pairs. Our approach is
generalizable to designing monotherapy and multi therapy drug schedules that
affect different cell signaling networks of interest.
