exhaustive investigation of drug delivery systems to achieve optimal condition of drug release using non-linear generalized artificial neural network method: feedback from the loading step of drug

Drug delivery systems are potential systems with ability to release drugs with a variety of mechanisms. some mechanisms consist of multiple steps, where the release rate of each step can vary from the others. moreover, a drug release is a kinetic process intrinsically and the initial amount of the loaded drug influence the amount of the release. therefore, to achieve the desirable release, the loading and release processes should be investigated simultaneously, while researchers have considered these processes independently so far. considering the fact that functional dependence between loading and release is not obvious, we proposed the combination of experimental design and powerful non-linear generalized artificial neural network (g-ann) methods for the simultaneous optimization of these processes. here, the functionalized pegylated kit-6 ([β-cd@pegylated kit-6]) nps and curcumin were selected as a nano-carrier and drug, respectively. the curcumin release was optimized by g-ann by considering the the feedback from the loading step. the obtained optimal parameters were as follows: (in the release process) 1.80 of the weight ratio of drug to nano-carrier, 5.70 of ph and 120 h of release time; and (in the loading process) 43 h of loading time and 2.2 of weight ratio of drug/nano-carrier.