CRISPR-Enabled Graphene-Based Bio-Cyber Interface Model for In Vivo Monitoring of Non-Invasive Therapeutic Processes.

In this paper, we present a model of the bio-cyber interface for the Internet of Bio-Nano Things application. The proposed model is inspired by the gains of integrating the Clustered Regularly Interspace Short Palindromic Repeats (CRISPR) technology with the Graphene-Field effect transistor (GFET). The capabilities of the integrated system are harnessed to detect nucleic acids transcribed by another component of the bio-cyber interface, a bioreporter, on being exposed to the signalling molecule of interest. The proposed model offers a label-free real-time signal transduction with multi-symbol signalling capability. We model the entire operation of the interface as a set of simultaneous differential equations representing the process's kinetics. The solution to the model is obtained using a numerical method. Numerical results show that the performance of the interface is influenced by parameters such as the concentrations of the input signalling molecules, the surface receptor on the bioreporter, and the CRISPR complex. The interface's performance also depends considerably on the elimination rate of the signalling molecules from the body. For multi-symbol molecular signalling, the rate of degradation of the transcribed RNAs influences the system's susceptibility to inter-symbol interference.

Dataset for discovering new hypertension small molecules using machine learning-aided computational fragment-based design.

This dataset demonstrates the use of computational fragmentation-based and machine learning-aided drug discovery to generate new lead molecules for the treatment of hypertension. Specifically, the focus is on agents targeting the renin-angiotensin-aldosterone system (RAAS), commonly classified as Angiotensin-Converting Enzyme Inhibitors (ACEIs) and Angiotensin II Receptor Blockers (ARBs). The preliminary dataset was a target-specific, user-generated fragment library of 63 molecular fragments of the 26 approved ACEI and ARB molecules obtained from the ChEMBL and DrugBank molecular databases. This fragment library provided the primary input dataset to generate the new lead molecules presented in the dataset. The newly generated molecules were screened to check whether they met the criteria for oral drugs and comprised the ACEI or ARB core functional group criterion. Using unsupervised machine learning, the molecules that met the criterion were divided into clusters of drug classes based on their functional group allocation. This process led to three final output datasets, one containing the new ACEI molecules, another for the new ARB molecules, and the last for the new unassigned class molecules. This data can aid in the timely and efficient design of novel antihypertensive drugs. It can also be used in precision hypertension medicine for patients with treatment resistance, non-response or co-morbidities. Although this dataset is specific to antihypertensive agents, the model can be reused with minimal changes to produce new lead molecules for other health conditions.

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.

Molecular Communication Model for Targeted Drug Delivery in Multiple Disease Sites With Diversely Expressed Enzymes.

Targeted drug delivery (TDD) for disease therapy using liposomes as nanocarriers has received extensive attention in the literature. The liposome's ability to incorporate capabilities such as long circulation, stimuli responsiveness, and targeting characteristics, makes it a versatile nanocarrier. Timely drug release at the targeted site requires that trigger stimuli such as pH, light, and enzymes be uniquely overexpressed at the targeted site. However, in some cases, the targeted sites may not express trigger stimuli significantly, hence, achieving effective TDD at those sites is challenging. In this paper, we present a molecular communication-based TDD model for the delivery of therapeutic drugs to multiple sites that may or may not express trigger stimuli. The nanotransmitter and nanoreceiver models for the molecular communication system are presented. Here, the nanotransmitter and nanoreceiver are injected into the targeted body system's blood network. The compartmental pharmacokinetics model is employed to model the transportation of these therapeutic nanocarriers to the targeted sites where they are meant to anchor before the delivery process commences. We also provide analytical expressions for the delivered drug concentration. The effectiveness of the proposed model is investigated for drug delivery on tissue surfaces. Results show that the effectiveness of the proposed molecular communication-based TDD depends on parameters such as the total transmitter volume capacity, the receiver radius, the diffusion characteristic of the microenvironment of the targeted sites, and the concentration of the enzymes associated with the nanotransmitter and the nanoreceiver designs.