The induction of particular brain dynamics via neural pharmacology involves the selection of particular agonists from among a class of candidate drugs and the dosing of the selected drugs according to a temporal schedule. Such a problem is made nontrivial due to the array of synergistic drugs available to practitioners whose use, in some cases, may risk the creation of dose-dependent effects that significantly deviate from the desired outcome. Here, we develop an expanded pharmacodynamic (PD) modeling paradigm and show how it can facilitate optimal construction of pharmacologic regimens, i.e., drug selection and dose schedules. The key feature of the design method is the explicit dynamical-system based modeling of how a drug binds to its molecular targets. In this framework, a particular combination of drugs creates a time-varying trajectory in a multidimensional molecular/receptor target space, subsets of which correspond to different behavioral phenotypes. By embedding this model in optimal control theory, we show how qualitatively different dosing strategies can be synthesized depending on the particular objective function considered.
Almost Optimal Construction of Functional Batch Codes Using Hadamard Codes
