The goal of a drug delivery system (DDS) is to convey a drug where the medication is needed, while, at the same time, preventing the drug from affecting other healthy parts of the body. Drugs composed of micro- or nano-sized particles (particulate DDS) that are able to cross barriers which prevent large particles from escaping the bloodstream are used in the most advanced solutions. Molecular communication (MC) is used as an abstraction of the propagation of drug particles in the body. MC is a new paradigm in communication research where the exchange of information is achieved through the propagation of molecules. Here, the transmitter is the drug injection, the receiver is the drug delivery, and the channel is realized by the transport of drug particles, thus enabling the analysis and design of a particulate DDS using communication tools. This is achieved by modeling the MC channel as two separate contributions, namely, the cardiovascular network model and the drug propagation network. The cardiovascular network model allows to analytically compute the blood velocity profile in every location of the cardiovascular system given the flow input by the heart. The drug propagation network model allows the analytical expression of the drug delivery rate at the targeted site given the drug injection rate. Numerical results are also presented to assess the flexibility and accuracy of the developed model. The study of novel optimization techniques for a more effective and less invasive drug delivery will be aided by this model, while paving the way for novel communication techniques for Intrabody communication networks.

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Particulate Drug Delivery Systems (PDDS) are therapeutic methods that use nanoparticles to achieve their healing effects at the exact time, concentration level, and location in the body, while minimizing the effects on other healthy locations. The Molecular Communication (MC) paradigm, where the transmitted message is the drug injection process, the channel is the cardiovascular system, and the received message is the drug reception process, has been investigated as a tool to study nanoscale biological and medical systems in the recent years. In this paper, the various noise effects that cause uncertainty in the cardiovascular system are analyzed, modeled, and evaluated from the information theory perspective. Analytical MC noise models are presented to include all the end-to-end noise effects, from the drug injection, to the absorption of the drug nanoparticles by the diseased cells, in the presence of a time-varying and turbulent blood flow. The PDDS capacity is derived analytically including all these noise effects and the constraints on the drug injection. The proposed MC noise model is validated by using the Kinetic Monte-Carlo simulation technique. Analytical expressions of the noise and the capacity are derived, and MC is presented as a framework for the optimization of particulate drug delivery systems.

Targeted drug delivery systems (TDDSs) are therapeutic methods based on the injection and delivery of drug-loaded particles. The engineering of TDDSs must take into account both the therapeutic effects of the drug at the target delivery location and the toxicity of the drug while it accumulates in other regions of the body. These characteristics are directly related to how the drug-loaded particles distribute within the body, i.e., biodistribution, as a consequence of the processes involved in the particle propagation, i.e., pharmacokinetics. In this paper, the pharmacokinetics of TDDSs is analytically modeled through the abstraction of molecular communication, a novel paradigm in communication theory. Not only is the particle advection and diffusion, considered in our previous study, included in this model, but also are other physicochemical processes in the particle propagation, such as absorption, reaction, and adhesion. In addition, the proposed model includes the impact of cardiovascular diseases, such as arteriosclerosis and tumor-induced blood vessel leakage. Based on this model, the biodistribution at the delivery location is estimated through communication engineering metrics, such as channel delay and path loss, together with the drug accumulation in the rest of the body. The proposed pharmacokinetic model is validated against multiphysics finite-element simulations, and numerical results are provided for the biodistribution estimation in different scenarios. Finally, based on the proposed model, a procedure to optimize the drug injection rate is proposed to achieve a desired drug delivery rate. The outcome of this study is a multiscale physics-based analytical pharmacokinetic model.

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