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1. Reducing Passive Drug Diffusion from Electrophoretic Drug Delivery Devices through Co‐Ion Engineering

   https://doi.org/10.1002/advs.202003995  

   Chen, Shao‐Tuan; Renny, Megan_N; C_Tomé, Liliana; Olmedo‐Martínez, Jorge_L; Udabe, Esther; Jenkins, Elise_P_W; Mecerreyes, David; Malliaras, George_G; McLeod, Robert_R; Proctor, Christopher_M (April 2021, Advanced Science)

   Abstract Implantable electrophoretic drug delivery devices have shown promise for applications ranging from treating pathologies such as epilepsy and cancer to regulating plant physiology. Upon applying a voltage, the devices electrophoretically transport charged drug molecules across an ion‐conducting membrane out to the local implanted area. This solvent‐flow‐free “dry” delivery enables controlled drug release with minimal pressure increase at the outlet. However, a major challenge these devices face is limiting drug leakage in their idle state. Here, a method of reducing passive drug leakage through the choice of the drug co‐ion is presented. By switching acetylcholine's associated co‐ion from chloride to carboxylate co‐ions as well as sulfopropyl acrylate‐based polyanions, steady‐state drug leakage rate is reduced up to sevenfold with minimal effect on the active drug delivery rate. Numerical simulations further illustrate the potential of this method and offer guidance for new material systems to suppress passive drug leakage in electrophoretic drug delivery devices. 

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2. Acoustofluidics for simultaneous nanoparticle-based drug loading and exosome encapsulation

   https://doi.org/10.1038/s41378-022-00374-2  

   Wang, Zeyu; Rich, Joseph; Hao, Nanjing; Gu, Yuyang; Chen, Chuyi; Yang, Shujie; Zhang, Peiran; Huang, Tony Jun (April 2022, Microsystems & Nanoengineering)

   Abstract Nanocarrier and exosome encapsulation has been found to significantly increase the efficacy of targeted drug delivery while also minimizing unwanted side effects. However, the development of exosome-encapsulated drug nanocarriers is limited by low drug loading efficiencies and/or complex, time-consuming drug loading processes. Herein, we have developed an acoustofluidic device that simultaneously performs both drug loading and exosome encapsulation. By synergistically leveraging the acoustic radiation force, acoustic microstreaming, and shear stresses in a rotating droplet, the concentration, and fusion of exosomes, drugs, and porous silica nanoparticles is achieved. The final product consists of drug-loaded silica nanocarriers that are encased within an exosomal membrane. The drug loading efficiency is significantly improved, with nearly 30% of the free drug (e.g., doxorubicin) molecules loaded into the nanocarriers. Furthermore, this acoustofluidic drug loading system circumvents the need for complex chemical modification, allowing drug loading and encapsulation to be completed within a matter of minutes. These exosome-encapsulated nanocarriers exhibit excellent efficiency in intracellular transport and are capable of significantly inhibiting tumor cell proliferation. By utilizing physical forces to rapidly generate hybrid nanocarriers, this acoustofluidic drug loading platform wields the potential to significantly impact innovation in both drug delivery research and applications. 

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3. Genetic algorithm-optimized machine learning models for drug loading prediction

   https://doi.org/10.1142/S1793604725500213  

   Du, Yuncheng; Cereceda, Cristina Sanchez; Hayles, Aidan J (May 2025, Functional Materials Letters)

   Metal-organic frameworks (MOFs), made from metal ions and organic linkers, are promising materials for drug delivery due to their porous morphology. These components significantly affect drug loading, but the wide variety of irons and linkers makes it challenging to systematically evaluate their drug loading capacities. Machine Learning (ML) provides predictive models for drug loading based on properties such as ion type, linker structure, and MOFs morphology (e.g. surface area). However, the accuracy of these models is affected by hyperparameters. To improve model performance, this work develops a genetic algorithm (GA)-based optimization approach to build ML models for predicting drug loading rates. Our results demonstrate the predictability and generalizability of this approach for estimating the drug-loading capacities of different material-drug combinations. 

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4. Energy-based generative models for target-specific drug discovery

   https://doi.org/10.3389/fmmed.2023.1160877  

   Li, Junde; Beaudoin, Collin; Ghosh, Swaroop (June 2023, Frontiers in Molecular Medicine)

   Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis. Computational approaches are widely applied to drug development because of the increasing availability of biological molecular datasets. Popular generative approaches can create new drug molecules by learning the given molecule distributions. However, these approaches are mostly not for target-specific drug discovery. We developed an energy-based probabilistic model for computational target-specific drug discovery. Results show that our proposed TagMol can generate molecules with similar binding affinity scores asrealmolecules. GAT-based models showed faster and better learning relative to Graph Convolutional Network baseline models. 

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5. Mechanistic Computational Modeling of Implantable, Bioresorbable Drug Release Systems

   https://doi.org/10.1002/adma.202301698  

   Giolando, Patrick_A; Hopkins, Kelsey; Davis, Barrett_F; Vike, Nicole; Ahmadzadegan, Adib; Ardekani, Arezoo_M; Vlachos, Pavlos_P; Rispoli, Joseph_V; Solorio, Luis; Kinzer‐Ursem, Tamara_L (September 2023, Advanced Materials)

   Abstract Implantable, bioresorbable drug delivery systems offer an alternative to current drug administration techniques; allowing for patient‐tailored drug dosage, while also increasing patient compliance. Mechanistic mathematical modeling allows for the acceleration of the design of the release systems, and for prediction of physical anomalies that are not intuitive and may otherwise elude discovery. This study investigates short‐term drug release as a function of water‐mediated polymer phase inversion into a solid depot within hours to days, as well as long‐term hydrolysis‐mediated degradation and erosion of the implant over the next few weeks. Finite difference methods are used to model spatial and temporal changes in polymer phase inversion, solidification, and hydrolysis. Modeling reveals the impact of non‐uniform drug distribution, production and transport of H⁺ions, and localized polymer degradation on the diffusion of water, drug, and hydrolyzed polymer byproducts. Compared to experimental data, the computational model accurately predicts the drug release during the solidification of implants over days and drug release profiles over weeks from microspheres and implants. This work offers new insight into the impact of various parameters on drug release profiles, and is a new tool to accelerate the design process for release systems to meet a patient specific clinical need. 

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