© 2020 The Author(s) Background and Objective: Cancer tumors constitute a complicated environment for conventional anti-cancer treatments to confront, so solutions with higher complexity and, thus, robustness to diverse conditions are required. Alternations in the tumor composition have been documented, as a result of a conventional treatment, making an ensemble of cells drug resistant. Consequently, a possible answer to this problem could be the delivery of the pharmaceutic compound with the assistance of nano-particles (NPs) that modify the delivery characteristics and biodistribution of the therapy. Nonetheless, to tackle the dynamic response of the tumor, a variety of application times of different types of NPs could be a way forward. Methods: The in silico optimization was investigated here, in terms of the design parameters of multiple NPs and their application times. The optimization methodology used an open-source simulator to provide the fitness of each possible treatment. Because the number of different NPs that will achieve the best performance is not known a priori, the evolutionary algorithm utilizes a variable length genome approach, namely a metameric representation and accordingly modified operators. Results: The results highlight the fact that different application times have a significant effect on the robustness of a treatment. Whereas, applying all NPs at earlier time slots and without the ordered sequence unveiled by the optimization process, proved to be less effective. Conclusions: The design and development of a dynamic tool that will navigate through the large search space of possible combinations can provide efficient solutions that prove to be beyond human intuition.
Tsompanas, M. A., Bull, L., Adamatzky, A., & Balaz, I. (2020). In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times. Computer Methods and Programs in Biomedicine, 200, https://doi.org/10.1016/j.cmpb.2020.105886