Evolutionary Algorithms Designing Nanoparticle Cancer Treatments with Multiple Particle Types [Application Notes]

There is a rich history of evolutionary algorithms tackling optimization problems where the most appropriate size of solutions, namely the genome length, is unclear
a priori
. Here, we investigated the applicability of this methodology on the problem of designing a nanoparticle (NP) based drug delivery system targeting cancer tumors. Utilizing a treatment comprised of multiple types of NPs is expected to be more effective due to the higher complexity of the treatment. This paper begins by using the well-known NK model to explore the effects of fitness landscape ruggedness on the evolution of genome length and, hence, solution complexity. The size of novel sequences and variations of the methodology with and without sequence deletion are also considered. Results show that whilst landscape ruggedness can alter the dynamics of the process, it does not hinder the evolution of genome length. On the contrary, the expansion of genome lengths can be encouraged by the topology of such landscapes. These findings are then explored within the aforementioned real-world problem. Variable sized treatments with multiple NP types are studied via an agent-based open source physics-based cell simulator. We demonstrate that the simultaneous evolution of multiple types of NPs leads to more than 50% reduction in tumor size. In contrast, evolution of a single NP type leads to only 7% reduction in tumor size. We also demonstrate that the initial stages of evolution are characterized by a fast increase in solution complexity (addition of new NP types), while later phases are characterized by a slower optimization of the best NP composition. Finally, the smaller the number of NP types added per mutation step, the shorter the length of the typical solution found.