Leveraging machine learning to streamline the development of liposomal drug delivery systems DOI
Remo Eugster, Markus Orsi,

Giorgio Buttitta

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Abstract 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 microfluidic manufacturing during COVID-19 pandemic. Despite its efficiency, liposomal production poses challenges, often requiring laborious, optimization on case-by-case basis. This is due lack comprehensive understanding robust methodologies, compounded by limited data varying lipids. Artificial intelligence offers promise predicting lipid behaviour production, still unexploited potential streamlining development. Herein we employ machine learning predict critical quality attributes process parameters for microfluidic-based liposome production. Validated models formation, size, parameters, significantly advancing our behaviour. Extensive model analysis enhanced interpretability investigated underlying mechanisms, supporting transition Unlocking drug development can accelerate pharmaceutical innovation, making more adaptable accessible.

Language: Английский

Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

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

Language: Английский

Citations

40
Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment DOI
Moein Shirzad,

Afsaneh Salahvarzi,

Sobia Razzaq

et al.

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104653 - 104653

Published: Feb. 1, 2025

Language: Английский

Citations

4
AI-Driven Innovations in Smart Multifunctional Nanocarriers for Drug and Gene Delivery: A Mini-Review DOI

H. Noury,

Abbas Rahdar, Luiz Fernando Romanholo Ferreira

et al.

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104701 - 104701

Published: March 1, 2025

Language: Английский

Citations

3
Nanotheranostics with Radionuclides for Cancer Diagnosis and Therapy DOI Open Access

Minhui Cui,

Mengmeng Zhu, Dongsheng Tang

et al.

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Abstract Anti‐tumor theranostic radionuclide nanosystems have gained significant attention as an emerging therapeutic strategy. This review systematically elucidates the concept and recent advances of anti‐tumor nanotheranostic systems with radionuclides, a focus on design nanocarriers, precise selection their advantages limitations in clinical translation. also explores integration imaging various treatment modalities, including photodynamic therapy, photothermal sonodynamic immunotherapy. Furthermore, combination therapy fluorescence magnetic resonance technologies, which broadens application nanotheranostics, is discussed. Finally, outlooks future development nanotheranostics radionuclides proposes key research focus.

Language: Английский

Citations

2
Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches DOI Creative Commons
Ravi Maharjan, Ki Hyun Kim, Kyeong Lee

et al.

Journal of Pharmaceutical Analysis, Journal Year: 2024, Volume and Issue: 14(11), P. 100996 - 100996

Published: May 8, 2024

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing microfluidic conditions and lipid mix ratios messenger RNA-lipid nanoparticles (mRNA-LNP). Different mRNA-LNP formulations (

Language: Английский

Citations

8
Traversing chemical space with active deep learning for low-data drug discovery DOI
Derek van Tilborg, Francesca Grisoni

Nature Computational Science, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

Language: Английский

Citations

5
Optimising the production of PLGA nanoparticles by combining design of experiment and machine learning DOI Creative Commons

Nidhi Seegobin,

Youssef Abdalla, Ge Li

et al.

International Journal of Pharmaceutics, Journal Year: 2024, Volume and Issue: 667, P. 124905 - 124905

Published: Nov. 2, 2024

Language: Английский

Citations

5
Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia DOI Creative Commons
Mehrnoosh Rafiei, Akbar Shojaei, Ying Chau

et al.

Drug Delivery, Journal Year: 2025, Volume and Issue: 32(1)

Published: March 3, 2025

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains challenge. This study investigates modified lipid nanoparticles (LNP) mRNA hyperactivated microglia, particularly those with pro-inflammatory characteristics, utilizing supervised machine learning (ML) classifiers. We developed screened library of 216 LNP formulations varying compositions, N/P ratios, hyaluronic acid (HA) modifications. The transfection efficiency eGFP was assessed in the BV-2 murine cell line under different immunological states, including resting activated conditions (LPS-activated IL4/IL13-activated). ML-guided morphometric analysis tracked phenotypes various subtypes before after transfection. Four ML classifiers were investigated predict phenotypic changes based on design parameters. Multi-Layer Perceptron (MLP) neural network emerged as best-performing model, achieving weighted F1-scores ≥0.8. While it accurately predicted responses from LPS-activated cells, struggled IL4/IL13-activated cells. MLP model validated by predicting performance four unseen delivering BV2 HA-LNP2 optimal formulation target IL10 mRNA, effectively suppressing phenotypes, evidenced shifts morphology, increased expression, reduced TNF-α levels. also evaluated human iPSC-derived confirming its efficacy modulating responses. highlights potential tailored techniques enhance therapy neuroinflammatory disorders leveraging carrier's immunogenic properties modulate microglial

Language: Английский

Citations

0
Nanoparticles with “K-edge” Metals Bring “Color” in Multiscale Spectral Photon Counting X-ray Imaging DOI
Nivetha Gunaseelan, Pranay Saha,

Nada Maher

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Preclinical and clinical diagnostics depend greatly on medical imaging, which enables the identification of physiological pathological processes in living subjects. It is often necessary to use contrast agents complement anatomical data with functional information or describe disease phenotypically. Nanomaterials are used as many advanced bioimaging techniques applications because their high payload, physicochemical properties, improved sensitivity, multimodality. Metals k-edge energy within X-ray bandwidth respond photon counting spectral imaging. This Perspective examines progress made emerging area nanoparticle-based agents. These nano

Language: Английский

Citations

2
Leveraging machine learning to streamline the development of liposomal drug delivery systems DOI
Remo Eugster, Markus Orsi,

Giorgio Buttitta

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Abstract 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 microfluidic manufacturing during COVID-19 pandemic. Despite its efficiency, liposomal production poses challenges, often requiring laborious, optimization on case-by-case basis. This is due lack comprehensive understanding robust methodologies, compounded by limited data varying lipids. Artificial intelligence offers promise predicting lipid behaviour production, still unexploited potential streamlining development. Herein we employ machine learning predict critical quality attributes process parameters for microfluidic-based liposome production. Validated models formation, size, parameters, significantly advancing our behaviour. Extensive model analysis enhanced interpretability investigated underlying mechanisms, supporting transition Unlocking drug development can accelerate pharmaceutical innovation, making more adaptable accessible.

Language: Английский

Citations

0