Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field’s short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module

Mixed-monolayer functionalized gold nanoparticles for cancer treatment: Atomistic molecular dynamics simulations study.

Gold nanoparticles (AuNPs) are employed as drug carriers due to their inertness, non-toxicity, and ease of synthesis. An experimental search for the optimal AuNP design would require a systematic variation of physico-chemical properties which is time-consuming and expensive. Computational methods provide quicker and cheaper approach to complement experiments and provide useful guidelines. In this paper, we performed atomistic molecular dynamics simulations to study how the size, hydrophobicity, and concentration of the drug affect the structure of functionalized AuNPs in the aqueous environment. We simulated two groups of nano-systems functionalized with a zwitterionic background ligand, and a ligand carrying a drug (Quinolinol or Panobinostat). Results indicate that in the case of a hydrophobic drug (Quinolinol), the hydrophobicity drives the conformation changes of the coating layer. The tendency of the hydrophobic drug to reduce its solvent-accessible surface results in a decrease of the coating thickness and the overall NP size. Although the amount of accessible drug can be increased by increasing its initial concentration, it will compromise the solubility of the system. In the case of a hydrophilic drug (Panobinostat), the ligand in excess has a dominant influence on the final structure of the coating conformations. The percentage of accessible drug is significantly higher than in the hydrophobic systems for any given ratio. It implies that for hydrophilic systems we can generally expect higher biological efficiency. Our results highlight the importance of taking into account physico-chemical properties of drugs and ligands when developing gold-based nanosystems, especially in the case of hydrophobic drugs

Evolutionary algorithms designing nanoparticle cancer treatments with multiple particle types

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

Novelty search employed into the development of cancer treatment simulations

Conventional optimization methodologies may be hindered when the automated search is stuck into local optima because of a deceptive objective function landscape. Consequently, open ended search methodologies, such as novelty search, have been proposed to tackle this issue. Overlooking the objective, while putting pressure into discovering novel solutions may lead to better solutions in practical problems. Novelty search was employed here to optimize the simulated design of a targeted drug delivery system for tumor treatment under the PhysiCell simulator. A hybrid objective equation was used containing both the actual objective of an effective tumor treatment and the novelty measure of the possible solutions. Different weights of the two components of the hybrid equation were investigated to unveil the significance of each one

In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times

© 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

Metameric representations on optimization of nano particle cancer treatment

In silico evolutionary optimization of cancer treatment based on multiple nano-particle (NP) assisted drug delivery systems was investigated in this study. The use of multiple types of NPs is expected to increase the robustness of the treatment, due to imposing higher complexity on the solution tackling a problem of high complexity, namely the physiology of a tumor. Thus, the utilization of metameric representations in the evolutionary optimization method was examined, along with suitable crossover and mutation operators. An open-source physics-based simulator was utilized, namely PhysiCell, after appropriate modifications, to test the fitness of possible treatments with multiple types of NPs. The possible treatments could be comprised of up to ten types of NPs, simultaneously injected in an area close to the cancerous tumour. Initial results seem to suffer from bloat, namely the best solutions discovered are converging towards the maximum amount of different types of NPs, however, without providing a significant return in fitness when compared with solutions of fewer types of NPs. As the large diversity of NPs will most probably prove to be quite toxic in lab experiments, we opted for methods to reduce the bloat, thus, resolve to therapies with fewer types of NPs. Namely, the bloat control methods studied here were removing types of NPs from the optimization genome as part of the mutation operator and applying parsimony pressure in the replacement operator. By utilizing these techniques, the treatments discovered are composed of fewer types of NPs, while their fitness is not significantly smaller

Harnessing adaptive novelty for automated generation of cancer treatments

© 2020 The Authors Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However, it also leads to a rich design space which is difficult to investigate and optimize. An additional problem emerges when they are applied to cancer treatment. A heterogeneous and highly adaptable tumour can quickly become resistant to primary therapy, making it inefficient. To automate the design of potential therapies for such complex cases, we propose a computational model for fast, novelty-based machine learning exploration of the nanoparticle design space. In this paper, we present an evolvable, open-ended agent-based model, where the exploration of an initially small portion of the given state space can be expanded by an ongoing generation of adaptive novelties, whenever the simulated tumour makes an adaptive leap. We demonstrate that the nano-agents can continuously reshape themselves and create a heterogeneous population of specialized groups of individuals optimized for tracking and killing different phenotypes of cancer cells. In the conclusion, we outline further development steps so this model could be used in real-world research and clinical practice