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Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

ABSTRACT: Inefficient injection of microparticles through conventional hypodermic needles can impose serious challenges on clinical translation of biopharmaceutical drugs and microparticle-based drug formulations. This study aims to determine the important factors affecting microparticle injectability and establish a predictive framework using computational fluid dynamics, design of experiments, and machine learning. A numerical multiphysics model was developed to examine microparticle flow and needle blockage in a syringe-needle system. Using experimental data, a simple empirical mathematical model was introduced. Results from injection experiments were subsequently incorporated into an artificial neural network to establish a predictive framework for injectability. Last, simulations and experimental results contributed to the design of a syringe that maximizes injectability in vitro and in vivo. The custom injection system enabled a sixfold increase in injectability of large microparticles compared to a commercial syringe. This study highlights the importance of the proposed framework for optimal injection of microparticle-based drugs by parenteral routes.

SUBMITTER: Sarmadi M

PROVIDER: S-EPMC7455482 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

Sarmadi Morteza M Behrens Adam M AM McHugh Kevin J KJ Contreras Hannah T M HTM Tochka Zachary L ZL Lu Xueguang X Langer Robert R Jaklenec Ana A

Science advances 20200708 28

Inefficient injection of microparticles through conventional hypodermic needles can impose serious challenges on clinical translation of biopharmaceutical drugs and microparticle-based drug formulations. This study aims to determine the important factors affecting microparticle injectability and establish a predictive framework using computational fluid dynamics, design of experiments, and machine learning. A numerical multiphysics model was developed to examine microparticle flow and needle blo ...[more]

PMID: 32923598

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Project description:Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.

Dataset Information

Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

Publications

Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

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