Characterizing Nanoscale Transient Communication

Overcoming Channel Uncertainties in Touchable Molecular Communication for Direct-Drug-Targeting-Assisted Immuno-Chemotherapy

OBJECTIVE

The performance of targeted immuno-chemotherapy of tumor is highly exposed to drug absorption in systemic circulation, which reduces its efficiency and increases side-effects. Direct drug targeting (DDT) combined with immuno-chemotherapy has the potential to mitigate the undesired systemic exposure, by using drug-loaded nanorobots to target cancer cells with the shortest possible physiological routes. This process can be modeled by the "touchable" (i.e., externally controllable and trackable) molecular communication system. However, such a complex process still suffers from various pharmacokinetic uncertainties caused by diffusion, degeneration, and branching of nanorobots (DDT pharmacokinetic uncertainties), as well as tumor/immune system modeling errors. The current work aims at identifying optimal drug administration plans by overcoming such challenges.

METHODS

A revisited tumor-immune interaction model is proposed to incorporate randomness of the drug concentration in the tumor site. Then, a robust multiple model predictive control (MMPC) scheme for the proposed tumor-immune interaction model is designed that uses multiple system models and an adaptive switch to identify the optimal plans for mixed drug administration via drug-loaded nanorobots. Furthermore, a wide range of prediction horizons under different loss scenarios of drug-loaded nanorobots and system model mismatches have been investigated in order to identify safe operating regions. From the molecular communications paradigm, this can be considered as a more robust information transmission system with feedback of channel state information to the transmitter implemented in the control unit.

RESULTS

The efficacy of the proposed MMPC is illustrated through identification of globally optimized drug administration schedules subject to various controller operation imperfections, which lead to successful cancer treatment as demonstrated through computational experiments.

CONCLUSION

By combining DDT with conventional mixed immunotherapy and chemotherapy, the proposed robust MMPC offers promising performance in controlling tumor growth while keeping the immune cell density higher than a specific level in the presence of both DDT pharmacokinetic uncertainties and system model mismatches.

SIGNIFICANCE

We believe that the proposed design framework represents an important first step towards clinically relevant DDT in the combined immunotherapy and chemotherapy of tumor given its robust performance.

Overcoming Channel Uncertainties in Molecular-Communication-Inspired Direct Drug Targeting

Recent progress in the development of new methods for cancer treatment has shown advantages of multiple therapies over mono-therapy. In particular, direct drug targeting (DDT) combined with mixed immunotherapy and chemotherapy has the potential to mitigate the undesired side-effects allied with conventional therapies, where nanorobots in DDT carry therapeutic agents through the blood vessel channel in order to localize and target diseased tissue with a safe drug interaction. This process can be modeled by a "touchable" (i.e., externally controllable and trackable) molecular communication (MC) system. However, in such a complex process overcoming unavoidable vascular channel uncertainties remains a great challenge. In this paper a multiple model predictive controller (MMPC) is proposed, which is robust against random channel uncertainties. The efficacy of the proposed method is illustrated through identification of globally optimized drug administration schedules. Furthermore, we introduce upper and lower bounds on the inputs and outputs which lead to clinically realistic constraints on the system. Simulation results demonstrate the promising performance of proposed MMPC to control tumor growth in presence of vascular channel uncertainties in MC-inspired DDT for cancer treatment.

Touchable Computing: Computing-Inspired Bio-Detection

We propose a new computing-inspired bio-detection framework called touchable computing (TouchComp). Under the rubric of TouchComp, the best solution is the cancer to be detected, the parameter space is the tissue region at high risk of malignancy, and the agents are the nanorobots loaded with contrast medium molecules for tracking purpose. Subsequently, the cancer detection procedure (CDP) can be interpreted from the computational optimization perspective: a population of externally steerable agents (i.e., nanorobots) locate the optimal solution (i.e., cancer) by moving through the parameter space (i.e., tissue under screening), whose landscape (i.e., a prescribed feature of tissue environment) may be altered by these agents but the location of the best solution remains unchanged. One can then infer the landscape by observing the movement of agents by applying the "seeing-is-sensing" principle. The term "touchable" emphasizes the framework's similarity to controlling by touching the screen with a finger, where the external field for controlling and tracking acts as the finger. Given this analogy, we aim to answer the following profound question: can we look to the fertile field of computational optimization algorithms for solutions to achieve effective cancer detection that are fast, accurate, and robust? Along this line of thought, we consider the classical particle swarm optimization (PSO) as an example and propose the PSO-inspired CDP, which differs from the standard PSO by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the PSO-inspired CDP for different blood flow velocity profiles caused by tumor-induced angiogenesis. The proposed TouchComp bio-detection framework may be regarded as one form of natural computing that employs natural materials to compute.