On Drug Delivery System Parameter Optimisation via Semantic Information Theory

TL;DR

This paper applies semantic information theory to optimize drug delivery systems by modeling them as molecular communication channels, providing a quantitative framework for design and parameter tuning in therapeutic contexts.

Contribution

It introduces a novel semantic information framework for DDS, linking system-environment correlations to therapeutic effectiveness and enabling optimization under constraints.

Findings

Quantitative analysis of DDS as a molecular communication channel.

Framework for optimal DDS parameter selection based on semantic information.

Insights into the relationship between system correlations and therapeutic outcomes.

Abstract

We investigate the application of semantic information theory to drug delivery systems (DDS) within the molecular communication (MC) framework. To operationalise this, we observe a DDS as a molecular concentration-based channel. Semantic information is defined as the amount of information required for a DDS to achieve its therapeutic goal in a dynamic environment. We derive it by introducing interventions, defined as modifications to DDS parameters, a viability function, and system-environment correlations quantified via the channel capacity. Here, the viability function represents DDS performance based on a drug dose-response relationship. Our model considers a system capable of inducing functional changes in a receiver cancer cell, where exceeding critical DDS parameter values can significantly reduce performance or cost-effectiveness. By analysing the MC-based DDS model through a semantic information perspective, we examine how correlations between the internalised particle concentration $(Y)$ and the particle concentration in the extracellular environment $(X)$ evolve under interventions. The final catalogue of results provides a quantitative basis for DDS design and optimisation, offering a method to determine optimal DDS parameter values under constraints such as chemical budget, desired effect and accuracy. Thus, the proposed framework can serve as a novel tool for guiding DDS design and optimisation.