Machine learning-guided high throughput nanoparticle design

Ortiz-Perez, Ana; van Tilborg, Derek; van der Meel, Roy; Grisoni, Francesca; Albertazzi, Lorenzo

doi:10.1039/d4dd00104d

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Further, microfluidic production offers fine control over critical process parameters (CPPs) such as flow rates, pressure, and temperature, which are crucial for optimizing liposome size, lamellarity, encapsulation efficiency, and stability. 21–23 Despite these benefits, however, several challenges hinder clinical and industrial applications. The primary obstacle is translating bench-scale liposomal production to larger scales, as traditional methods often do not scale up seamlessly.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the application of machine learning (ML) and artificial intelligence (AI) in pharmaceutical sciences has gained traction due to their ability to model highdimensional problems. 21,22,[28][29][30][31] ML is valuable throughout the entire drug discovery and development pipeline, 21,32 from exploring chemical space to identify new hit compounds [32][33][34][35][36] , to predicting synthetic pathways, [37][38][39] Absorption-Distribution-Metabolism-Excretion Toxicity (ADMET) properties 40,41 and target activity. 42 However, many methods rely on external datasets often lacking quality.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging machine learning to streamline the development of liposomal drug delivery systems

Eugster,

Orsi,

Buttitta

et al. 2024

Preprint

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Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Eugster,

Orsi,

Buttitta

et al. 2024

Preprint

View full text Add to dashboard Cite

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Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Chem. Rev.

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Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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