Modeling of Modulated Exosome Release From Differentiated Induced Neural Stem Cells for Targeted Drug Delivery

Optimization of Extracellular Vesicle Release for Targeted Drug Delivery

Targeted drug delivery is a promising approach for many serious diseases, such as glioblastoma multiforme, one of the most common and devastating brain tumor. In this context, this work addresses the optimization of the controlled release of drugs which are carried by extracellular vesicles. Towards this goal, we derive and numerically verify an analytical solution for the end-to-end system model. We then apply the analytical solution either to reduce the disease treatment time or to reduce the amount of required drugs. The latter is formulated as a bilevel optimization problem, whose quasiconvex/quasiconcave property is proved here. For solving the optimization problem, we propose and utilize a combination of bisection method and golden-section search. The numerical results demonstrate that the optimization can significantly reduce the treatment time and/or the required drugs carried by extracellular vesicles for a therapy compared to the steady state solution.

COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration

Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.

Estimating Time Window to Administer Anti-Cytokine Therapeutic Drugs to COVID-19 Patients

The severe COVID-19 infection often leads to "Cytokine Release Syndrome (CRS)", which is a serious adverse medical condition causing multiple organ failures. Anti-cytokine therapy has shown promising results for the treatment of the CRS. As part of the anti-cytokine therapy, the immuno-suppressants or anti-inflammatory drugs are infused to block the release of cytokine molecules. However, determining the time window to infuse the required dose of drugs is challenging due to the complex processes involving the release of inflammatory markers, such as IL-6 and C-reactive protein (CRP) molecules. In this work, we develop a molecular communication channel to model the transmission, propagation, and reception of cytokine molecules. The proposed analytical model can be used as a framework to estimate the time window to administer anti-cytokine drugs to get successful outcomes. Simulation results show that at a 50 s-1 release rate of IL-6 molecules, the cytokine storm is triggered at ~ 10 hours, and consequently, the CRP molecules reach the severe level of 97 mg/L at ~ 20 hours. Further, the results reveal that with one-half of the release rate of IL-6 molecules, the time to observe the severe level of 97 mg/L CRP molecules increases by 50%.

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, and fewer sensors. There is, hence, higher dependence on swarm sensing and perception of many nanomachines. Here, taking hardware and software bioinspiration, we propose Chemo-Mechanical Cancer-Inspired Swarm Perception (CMCISP) based on online nano fuzzy haptic feedback for early disease diagnosis and targeted therapy. Particularly, we use epithelial cancer cell's scaffold as a carrier, its properties as a distributed perception mechanism, and its motility patterns as the swarm movements such as anti-durotaxis, blebbing, and chemotaxis. We implement the in-silico model of CMCISP using a hybrid computational framework of the cellular Potts model, swarm intelligence, and fuzzy decision-making. Furthermore, the target convergence of CMCISP is analytically proved using swarm control theory. Finally, several numerical experiments and validations for cancer target therapy, cellular stiffness measurement, anti-durotaxis movement, and robustness analysis are also conducted and compared with a mathematical chemotherapy model and authors' previous works on targeted therapy. Results show improvements of up to 57.49% in early cancer detection, 26.64% in target convergence, and 68.01% in increased normoxic cell density. The study also reveals the strategy's robustness to environmental/sensory noise by applying six SNR levels of 0, 2, 5, 10, 30, and 50 dB, with an average diagnosis error of only 0.98% and at most 2.51%.

A Neuron-Glial Model of Exosomal Release in the Onset and Progression of Alzheimer's Disease

Exosomes are nano-sized extracellular vesicles that perform a variety of biological functions linked to the pathogenesis of various neurodegenerative disorders. In Alzheimer's disease (AD), for examples, exosomes are responsible for the release of Aβ oligomers, and their extracellular accumulation, although the underpinning molecular machinery remains elusive. We propose a novel model for Alzheimer's Aβ accumulation based on Ca²⁺-dependent exosome release from astrocytes. Moreover, we exploit our model to assess how temperature dependence of exosome release could interact with Aβ neurotoxicity. We predict that voltage-gated Ca²⁺ channels (VGCCs) along with the transient-receptor potential M8 (TRPM8) channel are crucial molecular components in Alzheimer's progression.

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developed, known as Molecular Communications. This new emerging area uses elements of classical communication systems to molecular signalling and communication found inside and outside biological systems, characterizing the signalling processes between cells and viruses. In this paper, we provide an extensive and detailed discussion on how molecular communications can be integrated into the viral infectious diseases research, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on molecular communications models for viral infection (intra-body and extra-body), a deep analysis on their effects on immune response, how experimental can be used by the molecular communications community, as well as open issues and future directions.

Targeted Drug Delivery for Cardiovascular Disease: Modeling of Modulated Extracellular Vesicle Release Rates

Invasive and medical therapy has led to major improvements in cardiovascular disease management, but important challenges remain open. The discovery of a nano-sized system of extracellular vesicles (EVs) is opening new possibilities for reprogramming malfunctioning cells and indicates that EVs can be employed in therapeutic biomedical applications as engineered drug vehicles. Molecular communication (MC) has applications for treating cells with directed drug delivery, employing special targeting transmembrane proteins. In this paper, we propose a novel drug delivery system for cardiovascular diseases using an EV-mediated MC platform and exemplify the potential use in hypertrophic cardiomyopathy. We utilize intracellular calcium signaling as a natural mediator of EVs released from synthetic cells and model the release rate. We propose to use the cells as a therapeutic release system with a control signal input which modulates the EVs release rate as the output signal. We also study the frequency domain of the proposed model and estimate the transfer function of the therapeutic release system model numerically where the root-mean-square error for two separate estimated output signals are 0.0353 and 0.0124. The proposed EV-mediated targeted drug delivery system can make breakthroughs in future healthcare, in cardiovascular and other diseases where targeting is required.

A Voxel Model to Decipher the Role of Molecular Communication in the Growth of Glioblastoma Multiforme

Glioblastoma Multiforme (GBM), the most malignant human tumour, can be defined by the evolution of growing bio-nanomachine networks within an interplay between self-renewal (Grow) and invasion (Go) potential of mutually exclusive phenotypes of transmitter and receiver cells. Herein, we present a mathematical model for the growth of GBM tumour driven by molecule-mediated inter-cellular communication between two populations of evolutionary bio-nanomachines representing the Glioma Stem Cells (GSCs) and Glioma Cells (GCs). The contribution of each subpopulation to tumour growth is quantified by a voxel model representing the end to end inter-cellular communication models for GSCs and progressively evolving invasiveness levels of glioma cells within a network of diverse cell configurations. Mutual information, information propagation speed and the impact of cell numbers and phenotypes on the communication output and GBM growth are studied by using analysis from information theory. The numerical simulations show that the progression of GBM is directly related to higher mutual information and higher input information flow of molecules between the GSCs and GCs, resulting in an increased tumour growth rate. These fundamental findings contribute to deciphering the mechanisms of tumour growth and are expected to provide new knowledge towards the development of future bio-nanomachine-based therapeutic approaches for GBM.

Visualizing Extracellular Vesicles and Their Function in 3D Tumor Microenvironment Models

Extracellular vesicles (EVs) are cell-derived nanostructures that mediate intercellular communication by delivering complex signals in normal tissues and cancer. The cellular coordination required for tumor development and maintenance is mediated, in part, through EV transport of molecular cargo to resident and distant cells. Most studies on EV-mediated signaling have been performed in two-dimensional (2D) monolayer cell cultures, largely because of their simplicity and high-throughput screening capacity. Three-dimensional (3D) cell cultures can be used to study cell-to-cell and cell-to-matrix interactions, enabling the study of EV-mediated cellular communication. 3D cultures may best model the role of EVs in formation of the tumor microenvironment (TME) and cancer cell-stromal interactions that sustain tumor growth. In this review, we discuss EV biology in 3D culture correlates of the TME. This includes EV communication between cell types of the TME, differences in EV biogenesis and signaling associated with differing scaffold choices and in scaffold-free 3D cultures and cultivation of the premetastatic niche. An understanding of EV biogenesis and signaling within a 3D TME will improve culture correlates of oncogenesis, enable molecular control of the TME and aid development of drug delivery tools based on EV-mediated signaling.