A Molecular Communication-Based Simultaneous Targeted-Drug Delivery Scheme

Abstract

This paper considers simultaneous drug-delivery (SDD) in molecular communication-based (MC-based) targeted drug-delivery systems. In a realistic scenario, the drug-carrying nanomachines are randomly placed close to the infected site. Due to the random propagation delays in the MC channel, the drugs from multiple drug-carrying nanomachines may, therefore, not arrive simultaneously at the infected site, leading to low efficacy and resulting in drug-delivery-time errors. To overcome this error and to administer the drugs simultaneously at the infected site, we use an internal controller nanomachine to control the release times of the drug-carrying nanomachines, with consideration of the propagation delay, to achieve SDD. In this regard, we propose two SDD schemes, namely, the direct trigger estimate SDD (DTE-SDD) scheme and the indirect trigger estimate SDD (iDTE-SDD) scheme. The difference between these schemes is that in the iDTE-SDD scheme, to estimate the propagation delay, the internal controller nanomachine depends on the drug-carrying nanomachines, while in the DTE-SDD scheme, it does not. Furthermore, to study the errors theoretically, we derive the analytical model of delivery-time error, and this is validated with simulation results. We perform intensive evaluations to understand the system's behavior under different channel conditions, such as the number of molecules released and the distance. The simulation results highlight the proposed scheme's energy efficiency and robustness to the large propagation delay, reducing the delivery-time error to improve the accuracy of the SDD.

In-Body Sequential Multidrug Delivery Scheme Using Molecular Communication

“…Therefore, the value of E con can be obtained as follows where v_cap is capacity of the vesicle. The values of the parameters in ( 26) are identical to those found in [32], [45], and [25], [26]. Fig.…”

“…Fig. 11 shows the energy cost, E cost , versus the number of drug releaser, N , based on the energy model in [32], [45], and [25]. The total energy cost was calculated using the following formula:…”

“…They found that the extension of the lifetime of TDD can to some degree overcome the constraints of the reservoir capacity of a drugcarrying nanomachine. Another study proposed the coordination of multiple drug-carrying nanomachines for single drug delivery [25], [26], where an internal controller nanomachine controls the release time of the drug-carrying nanomachines based on the estimated propagation delay between the controller and the drug-carrying nanomachine. However, these aforementioned studies focused on different aspects of MC-based TDD systems, whereas no study has investigated the control of the DTI.…”

“…However, these aforementioned studies focused on different aspects of MC-based TDD systems, whereas no study has investigated the control of the DTI. Therefore, this study focused on the control of DTI at the infected site for multiple drugs delivery while considering a similar coordination approach that used in [26] [25] to reduce the DTI error, by considering a dedicated in-body-located control nanomachine. We note that in this paper, due to the system's internal control ability, we refer to the MC-based TDD system as an advanced TDD system (ATDD).…”