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

Sarmadi, Morteza; Behrens, Adam M.; McHugh, Kevin J.; Contreras, Hannah T. M.; Tochka, Zachary L.; Lu, Xueguang; Langer, Robert; Jaklenec, Ana

doi:10.1126/sciadv.abb6594

Cited by 46 publications

(20 citation statements)

References 44 publications

(62 reference statements)

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“…The o / w continuous flow method (Figure A) was successfully used to prepare particles ranging from 50 to 180 μm in diameter (Figure S4). The small size range of these microparticles allows them to be used as an injectable drug delivery system or implanted during an operation . This optimized continuous flow method eliminates the harsh stirring conditions often observed in emulsification techniques, and after drying, prepared particles appeared free flowing .…”

Section: Resultsmentioning

confidence: 99%

Control of Drug Release from Microparticles by Tuning Their Crystalline Textures: A Structure–Activity Study

Miles

Bernstein

Popp

et al. 2021

ACS Appl. Polym. Mater.

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Predicting drug release profiles from polymer microparticles has proven challenging due to the numerous environmental and chemical factors that affect the device and influence the rate of drug release. By measuring the various polymer properties that can influence drug release, a predictive approach can be used to select polymers with specific properties that will lead to the desired release profile for the application. To illustrate this, a library of tyrosol-derived poly(ester-arylate)s, poly(amide)s, and poly(carbonate)s were used to evaluate the effects of physical (crystallinity, water accessibility, thermal, and hydrophobicity) and chemical (polymer−drug interactions) polymer properties on the release of a highly crystalline drug dexamethasone, which was loaded at a high weight percent (wt %) in microparticles. Nuclear magnetic resonance (NMR) experiments showed that the polymer and drug were not chemically interacting and instead exist as a physical mixture even after exposure to physiological conditions. Polymer crystallinity data revealed that crystallite size was strongly correlated with faster drug release, suggesting that larger crystallites reduce the tortuosity for dexamethasone to diffuse out of the particle matrix. This correlation observed in particles with and without the drug was reproduced with bulk polymers, indicating that crystallinity data from bulk polymers can be used to predict release profiles without having to prepare drug-loaded particles. Consistent with the crystallinity data, particle pore sizes of representative formulations showed that particles with larger pores resulted in faster dexamethasone release. Interestingly, thermal properties (glass transition temperature and melting temperature), polymer hydrophobicity, and molecular weight retention at the end of the 119-day release study did not show any correlation with drug release.

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Section: Resultsmentioning

confidence: 99%

Control of Drug Release from Microparticles by Tuning Their Crystalline Textures: A Structure–Activity Study

Miles

Bernstein

Popp

et al. 2021

ACS Appl. Polym. Mater.

View full text Add to dashboard Cite

show abstract

“…For microparticle formulations, the problems of needle blockages and microparticle residue caused by the micron level size and a faster sedimentation rate during injection is still a challenge today. Sarmadi et al [157] used an ANN (2-10-1) to predict the injectability of microparticles by understanding the key parameters (particle size, needle size and solution viscosity) affecting the transport of the microparticles and designing an injection device to improve the delivery of the drugs in subcutaneous administration. A BP with the LM algorithm was applied to train the network and 319 data points were selected as the inputs.…”

Section: Other Applications Of Artificial Neural Networkmentioning

confidence: 99%

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Wang¹,

Di²,

Wang

et al. 2022

Pharmaceutics

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During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

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“…Another important direction for future research would be in the elaborate design of microparticles for advanced applications where microcapsules remain intact only until stress above a predetermined threshold value is applied to mechanically trigger the release. Furthermore, adopting various computational approaches including machine learning 130 may lead to precise prediction of the microparticle deformation behavior, offering an effective route to design these mechanically tailored microparticles. AUTHOR CONTRIBUTIONS Eunseo Kim: Writingoriginal draft (lead).…”

Section: Summary and Future Outlookmentioning

confidence: 99%

Mechanical characterization of soft microparticles prepared by droplet microfluidics

Kim

Lee

2022

Journal of Polymer Science

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Droplet microfluidics is one of the most promising approaches that allows preparation of microparticles with tailored structure and composition. Various functional microparticles have been developed using microfluidics, where their controlled structure shows high potential for a wide range of applications. Among these, soft polymeric microparticles which exhibit low elastic modulus compared to ceramics and metals are extensively investigated in biomedical applications due to their biocompatibility, high surface‐to‐volume ratio, as well as soft and deformable nature. As the mechanical properties of soft microparticles play important role in determining how they function in each application, it is essential to adequately characterize them for the optimal design of functional microparticles. In this review, we mainly discuss the mechanical characterization methods of soft microparticles and their elastic property. A brief overview of the droplet microfluidics‐assisted fabrication of microparticles is also provided before discussing the mechanical characterization techniques. We then describe the general characterization methods and models employed to determine the elastic properties of microparticles. In addition, we discuss the relationship between the physical parameters (size, composition, and structure) and the elastic properties of the microparticles, followed by the role of elastic properties in various applications including microcarrier, bioink, and self‐healing to name a few.

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

Cited by 46 publications

References 44 publications

Control of Drug Release from Microparticles by Tuning Their Crystalline Textures: A Structure–Activity Study

Control of Drug Release from Microparticles by Tuning Their Crystalline Textures: A Structure–Activity Study

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Mechanical characterization of soft microparticles prepared by droplet microfluidics

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