Optimizing Targeted Drug Delivery through Hierarchical Network-Based Molecular Communication System

We introduce an innovative Molecular Communication (MC) system designed for efficient targeted drug delivery, leveraging a hierarchical network-based mechanism. This approach revolutionizes how nanomedicines are delivered, transforming the nanoparticle's motions in blood vessel networks into an information transfer process within a virtual network. Given that much of the current work focuses on the physical layer of molecular communication, we focus more on the network layer. Our work abstracts the pharmacokinetics nature of drug delivery within complex vascular networks into a stratified routing network. This abstraction allows for the optimization of drug delivery by adjusting key parameters at the network application layer. Our robust model replicates the drug delivery process while effectively modeling the vascular network's complexity. Simulation results demonstrate the feasibility of applying topology inference algorithms to vascular networks. Through a defined parameter acquired from the physical layer, the successful transmission rate, we achieve the extraction of vascular network topology. Furthermore, our approach validates the potential of employing tomography techniques within a biological setting, viewed through the prism of a layered network. This advancement in MC opens new avenues for optimizing drug delivery systems, promising improvements in targeted therapeutic interventions.

A Simple Queuing Model for Molecular Communications Receivers

The complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A typical scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in the diseased area. In this paper, we propose a simple but reliable receiver model for diffusion-based molecular communication systems tackling the time needed for analyzing such a system. The proposed model consists of using an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. It takes into account not only the fact that the absorption of molecules can occur only through receptors, but also that absorption is not an instantaneous process and may require a significant time during which the receptor is not available to bind to other molecules. Our results, expressed in terms of number of absorbed molecules and average number of busy receptors, demonstrate that the proposed approach is in good agreement with results obtained through particle-based simulations of a large number of receptors, although the time taken for obtaining the results with the proposed model is an order of magnitudes lower than the simulation time. We believe that this model can be the precursor of novel class of models based on similar principles that allow realizing reliable simulations of much larger systems.

Efficient Framework Analysis for Targeted Drug Delivery Based on Internet of Bio-NanoThings

The Internet of Bio-NanoThings (IoBNTs) is a novel paradigm that derives from synthetic biology and advances in nanotechnology for controlling the embedded nanodevices in various medical applications. However, numerous studies have focused on communication efficiency among the nanodevices in a given network, the challenges such as the design and the development of the nanodevices, and the coordination of molecular communication within the wireless body area network (BAN), and the interface connection between the BAN and the Internet are yet to be addressed. Therefore, in this study, we present a framework analysis comprising of the compartmental model, for studying the effects and variances in drug concentration that occur inside intra-body nanonetworks through IoBNT, while taking into account the properties of target cells as well as the ligand-receptor binding mechanism. A performance analysis of the proposed framework for the forward link (i.e., from the Internet to the intra-body nanonetwork) and reverse link (i.e., from the intra-body nanonetwork to the Internet) is presented. The simulation results of the developed framework reveal its ability to enhance the delivery of therapeutic drugs to the target cell while minimizing the side effects in healthy cells.

dMole: A Novel Transreceiver for Mobile Molecular Communication Using Robust Differential Detection Techniques

This paper proposes two differential detection techniques for signal detection in mobile molecular communication (MMC) for targeted drug delivery (TDD) application. In MMC, a nano-transmitter and a nano-receiver are considered to be in Brownian motion in an extracellular fluid medium. Transmitter uses calcium molecules to communicate with the receiver. Detection is performed using concentration difference based detector (CDD) at the receiver which calculates the maximum absolute concentration difference of the received signal within the same bit interval to detect the bit. This improves the bit error rate (BER) performance in MMC. The performance is further enhanced using manchester coded transmission with differential detection (MCD). In MCD, Bit-1 is coded by the symbol [1 0] and Bit-0 is coded by the symbol [0 1] and the difference between peaks of signals received in consecutive bit duration is taken to detect the bit. Simulation results prove that the MCD technique is 3 dB less sensitive to inter symbol interference (ISI) than the CDD technique. The detection threshold is selected using maximum a posteriori probability (MAP) rule. The performance of these detectors is compared with other existing detection techniques. Results reveal that BER performance of the CDD and MCD better by at least 3 dB and 6 dB, respectively. The proposed CDD and MCD techniques perform better in different bit-sequence length, various initial distance and different bit duration than other existing techniques.

Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery

Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system's bioequivalence measure.

Cooperative Abnormality Detection via Diffusive Molecular Communications

In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects a number of molecules into the medium which is proportional to its sensing output. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, each sensor releases a different type of molecule (DTM), whereas in the second scheme, all sensors release the same type of molecule (STM). The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the tradeoff between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

Lifetime Improvement of a Multiple Transmitter Local Drug Delivery System Based on Diffusive Molecular Communication

In this paper, a multiple transmitter local drug delivery system associated with encapsulated drug transmitters is investigated. One of the limitations of drug delivery systems is the reservoir capacity. In order to improve the lifetime of drug transmitting nanomachines, and, hence, the longevity of drug delivery scenario, the system is associated with encapsulated drug transmitters. Encapsulated drugs are incapable of reaction with the environment unless they are unpacked in a drug transmitter nanomachine. Therefore, far-reaching transmitters do not have harmful effects on the healthy parts of the body. The advantage of this protocol is to increase the time interval between consecutive administrations without increased toxicity. As a result, it improves the mental health of patients and reduces the costs of treatment. The lifetime of this drug delivery system depends on the distribution and topology of encapsulated drug transmitters rather than their rates. Finally, a lower bound is derived on the expected lifetime of a Poisson distributed random network of nanomachines.

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.

Modeling Contrast-Imaging-Assisted Optimal Targeted Drug Delivery: A Touchable Communication Channel Estimation and Waveform Design Perspective

To maximize the effect of treatment and minimize the adverse effect on patients, we propose to optimize nanorobots-assisted targeted drug delivery (TDD) for locoregional treatment of tumor from the perspective of touchable communication channel estimation and waveform design. The drug particles are the information molecules; the loading/injection and unloading of the drug correspond to the transmitting and receiving processes; the concentration-time profile of the drug particles administered corresponds to the signaling pulse. Given this analogy, we first propose to use contrast-enhanced microwave imaging (CMI) as a pretherapeutic evaluation technique to determine the pharmacokinetic model of nanorobots-assisted TDD. The CMI system applies an information-theoretic-criteria-based algorithm to estimate drug accumulation in tumor, which is analogous to the estimation of channel impulse response in the communication context. Subsequently, we present three strategies for optimal targeted therapies from the communication waveform design perspective, which are based on minimization of residual drug molecules at the end of each therapeutic session (i.e., inter-symbol interference), maximization of duration when the drug intensity is above a prespecified threshold during each therapeutic session (i.e., non-fade duration), and minimization of average rate that a therapeutic operation is not received correctly at tumor (i.e., bit error rate). Finally, numerical examples are applied to demonstrate the effectiveness of the proposed analytical framework.