Diffusive Mobile MC for Controlled-Release Drug Delivery with Absorbing Receiver

Abstract

Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlledrelease drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

“…In the MC literature, different physical quantities have been modeled as the received signal. Important examples include i) the number of molecules observed at a given time within the volume of a transparent receiver [31], [32], [33], [34], [35], [36], [37], [38], [39], ii) the number of molecules bound at a given time to the receptors of a reactive receiver [60], [61], [74], iii) the accumulated number of molecules observed by a fully-absorbing receiver within a given observation time window [91], [73], [92], [93], [94], and iv) the arrival times of the molecules at a fully-absorbing receiver [95], [96], [97], [98], [99], [100]. In the following, we first provide a unified definition of the received signal of general MC receivers including the aforementioned special cases.…”

“…Non-Recurrent Accumulative-Molecule-Counting (nR-AMC) Receivers: The signal in this case is r(t m ) = n arv (t m )−n arv (t m −∆t) where n arv (t m ) ≥ n arv (t m −∆t) ≥ 0. For instance, for fully-absorbing receivers, r(t m ) denotes the number of molecules that have arrived within interval (t m − ∆t, t m ] [91], [73], [92], [93], [94].…”

“…The impact of the mobility of a point transmitter and a passive receiver on the CIRr(t, τ ) is studied in [102] and a stochastic model forr(t, τ ) is derived. Similarly, a stochastic model for mobile MC systems with a point transmitter and fully-absorbing receiver is derived in [94]. We note that mobile transceivers are relevant for many envisioned applications of synthetic MC systems such as targeted drug delivery and health monitoring [42], [43], [44], [45].…”

Ant-Behavior Inspired Intelligent NanoNet for Targeted Drug Delivery in Cancer Therapy

“…In passive targeting, by cloaking the drug-loaded nanoparticle with some sort of coating, the drugs are able to stay in circulation for a longer period of time. Some research might study the optimization issue of release pattern [9]. Active targeting of drug-loaded nanoparticles enhances the effects of passive targeting to make the nanoparticle more specific to a target site [10].…”

“…An example of lattice graph representing the path model. The "home" is at (2,2) and the "destination" is at(9,9).…”