Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release

Abstract

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions between) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and a large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Non-linear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in training data set. The models were shown to be robust after inclusion of these new formulations in that the new inclusions did not significantly change model regression. This approach provides the first steps towards development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics.

Biodistribution of degradable polyanhydride particles in Aedes aegypti tissues

“…Nano-and microparticle carriers improve payload activity by navigating challenging biological barriers and delivering their cargo to putative sites of action [15,[53][54][55][56][57]. We hypothesized that polyanhydride particles could function in the same way in arthropod systems to deliver insecticidal compounds.…”

“…Biodegradable polyanhydride particles [8,9], synthesized from copolymers of 1,8-bis(p-carboxyphenoxy)-3,6-dioxaoctane (CPTEG), 1,6-bis(p-carboxyphenoxy)hexane (CPH), and sebacic acid (SA) monomers, represent promising delivery technologies for novel insecticides. These biocompatible carriers [10,11] encapsulate small molecule, nucleic acid, or protein payloads within a hydrophobic (or amphiphilic) polymer matrix, react with water to hydrolyze their anhydride bonds, and gradually release their payload as the polymer degrades [12][13][14] The payload release kinetics of these carriers can be tuned from hours to several months based on polymer degradation kinetics, device size, and understanding of polymer-payload interactions [15][16][17][18][19].…”

Kinetics of Nanomedicine in Tumor Spheroid as an In Vitro Model System for Efficient Tumor-Targeted Drug Delivery With Insights From Mathematical Models

“…Numerous nanomedicine formulations like dendrimer, liposome, drug-polymer conjugates, nanoparticles, and polymeric micelles act as tumor-targeted drug delivery vehicles ( Yang et al, 2015 ). The design of biocompatible polymers abides by the guidelines of the FDA for biomedical applications of the polymeric micelle, where a minimum amount of polymer is administered to avoid unwanted toxicity in the body and activation of immune responses ( Ahmad et al, 2010 ; Sun et al, 2017 ; Mullis et al, 2019 ; Tan et al, 2020 ). Thus, a risk-free biodegradable polymer is designed and frequently used, which disintegrates into monomer once contributing its part, excreted from the body without accumulation and toxicity.…”

Proton switching molecular magnetoelectricity

“…2b, c . In the cases of T c , the regression is set between a nonlinear dimensionality reduction 30 , 31 and the quantitative property values, while in the other case a quantitative approach is applied to categorical data to predict the probability that the material can be proton-compatible aqueous processing. A challenge in data-driven design is the requirement of sufficient data.…”