Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials DOI Creative Commons
Ozlem Ozbek,

Destina Ekingen Genc,

Kutlu Ö. Ülgen

и другие.

ACS Pharmacology & Translational Science, Год журнала: 2024, Номер 7(8), С. 2251 - 2279

Опубликована: Июль 12, 2024

Nanoparticles (NPs) have been widely used to improve the pharmacokinetic properties and tissue distribution of small molecules such as targeting a specific interest, enhancing their systemic circulation, enlarging therapeutic properties. NPs unique complicated in vivo disposition compared molecule drugs due complex multifunctionality. Physiologically based (PBPK) modeling has powerful tool simulation absorption, distribution, metabolism, elimination (ADME) characteristics materials, it can be characterization prediction disposition, toxicity, efficacy, target exposure various types nanoparticles. In this review, recent advances PBPK model applications related nanoparticles with properties, dispositional features biological systems, ADME characteristics, description transport processes model, challenges development are delineated juxtaposed those encountered models. Nanoparticle related, non-nanoparticle-related, interspecies-scaling methods applied reviewed. vitro extrapolation (IVIVE) being promising computational provide predictions from results silico studies discussed. Finally, advancement ML/AI-based approaches estimation parameters (PK) analysis introduced.

Язык: Английский

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches DOI
Abhinav Vashishat, Preeti Patel, Ghanshyam Das Gupta

и другие.

Stem Cell Reviews and Reports, Год журнала: 2024, Номер 20(4), С. 881 - 899

Опубликована: Март 1, 2024

Язык: Английский

Процитировано

27

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

Afsaneh Salahvarzi,

Sobia Razzaq

и другие.

Critical Reviews in Oncology/Hematology, Год журнала: 2025, Номер unknown, С. 104653 - 104653

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

5

Advances in medical devices using nanomaterials and nanotechnology: Innovation and regulatory science DOI

Chubing Lin,

Xin Huang, Yueguang Xue

и другие.

Bioactive Materials, Год журнала: 2025, Номер 48, С. 353 - 369

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

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

и другие.

Critical Reviews in Oncology/Hematology, Год журнала: 2025, Номер unknown, С. 104701 - 104701

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

4

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice DOI Creative Commons
Qiran Chen, Long Yuan, Wei-Chun Chou

и другие.

ACS Nano, Год журнала: 2023, Номер 17(20), С. 19810 - 19831

Опубликована: Окт. 9, 2023

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of “Nano-Tumor Database”, which increases the number time-dependent concentration data sets for different nanoparticles (NPs) tumors from previous 376 with 1732 points 200 studies to current 534 2345 297 published 2005 2021. Additionally, database includes 1972 five major organs (i.e., liver, spleen, lung, heart, and kidney) total 8461 points. Tumor organ distribution are calculated using three pharmacokinetic parameters, including efficiency, maximum concentration, coefficient. The median 0.67% injected dose (ID), low but consistent studies. Employing best regression model we generate hypothetical scenarios combinations NP factors that may lead higher >3%ID, requires further experimentation confirm. In healthy organs, highest accumulation liver (10.69%ID/g), followed by spleen 6.93%ID/g kidney 3.22%ID/g. Our perspective on how facilitate design clinical translation presented. substantially expanded Database” several statistical models help nanomedicine future.

Язык: Английский

Процитировано

26

Artificial Intelligence in nanotechnology for treatment of diseases DOI

Soroush Heydari,

Niloofar Masoumi, Erfan Esmaeeli

и другие.

Journal of drug targeting, Год журнала: 2024, Номер 32(10), С. 1247 - 1266

Опубликована: Авг. 19, 2024

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading diverse applications across different diseases. However, complexity, cost time-consuming nature of laboratory processes, large volume data, in data analysis prompted integration artificial intelligence (AI) tools. AI has been employed designing, characterising manufacturing nanosystems, well predicting treatment efficiency. AI's potential personalise based on individual patient factors, optimise formulation design predict properties highlighted. By leveraging datasets, developing safe effective can be accelerated, ultimately improving outcomes advancing pharmaceutical sciences. This review article investigates role development nano-DDSs, a focus their applications. The use revolutionise optimisation improve care.

Язык: Английский

Процитировано

18

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials DOI Creative Commons
Weihao Tang, Xuejiao Zhang, Huixiao Hong

и другие.

Nanomaterials, Год журнала: 2024, Номер 14(2), С. 155 - 155

Опубликована: Янв. 10, 2024

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and environment may impede advancement novel materials. Traditionally, can be evaluated by experimental methods such as environmental field monitoring animal-based toxicity testing. However, it is time-consuming, expensive, impractical evaluate risk increasingly large number with methods. On contrary, artificial intelligence machine learning, silico recently received more attention assessment ENMs. This review discusses key progress computational nanotoxicology models assessing ENMs, including material flow analysis models, multimedia physiologically based toxicokinetics quantitative nanostructure-activity relationships, meta-analysis. Several challenges are identified a perspective provided regarding how addressed.

Язык: Английский

Процитировано

13

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models DOI Creative Commons
Kun Mi, Wei-Chun Chou, Qiran Chen

и другие.

Journal of Controlled Release, Год журнала: 2024, Номер 374, С. 219 - 229

Опубликована: Авг. 16, 2024

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low efficiency (DE) to tumor site. Understanding impact of NPs' physicochemical properties on target tissue distribution and DE help improve design nanomedicines. Multiple machine learning artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, deep neural networks (DNN), were trained validated predict based therapeutic strategies with dataset from Nano-Tumor Database. Compared other DNN model had superior predictions tumors major tissues. The determination coefficients (R

Язык: Английский

Процитировано

11

Two heads are better than one: Unravelling the potential Impact of Artificial Intelligence in nanotechnology DOI Creative Commons
Gaurav Gopal Naik,

Vijay A. Jagtap

Nano TransMed, Год журнала: 2024, Номер 3, С. 100041 - 100041

Опубликована: Июль 9, 2024

Artificial Intelligence (AI) and Nanotechnology are two cutting-edge fields that hold immense promise for revolutionizing various aspects of science, technology, everyday life. This review delves into the intersection these disciplines, highlighting synergistic relationship between AI Nanotechnology. It explores how techniques such as machine learning, deep neural networks being employed to enhance efficiency, precision, scalability nanotechnology applications. Furthermore, it discusses challenges, opportunities, future prospects integrating with nanotechnology, paving way transformative advancements in diverse domains ranging from healthcare materials science environmental sustainability beyond.

Язык: Английский

Процитировано

10

Computer-aided nanodrug discovery: recent progress and future prospects DOI Creative Commons
Jia‐Jia Zheng, Qiao-Zhi Li, Zhenzhen Wang

и другие.

Chemical Society Reviews, Год журнала: 2024, Номер 53(18), С. 9059 - 9132

Опубликована: Янв. 1, 2024

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation the 1990s. Substantial efforts been made to develop nanodrugs for overcoming limitations of conventional drugs, such as low targeting efficacy, high dosage toxicity, potential drug resistance. Despite significant progress that has nanodrug discovery, precise design or screening with desired biomedical functions prior experimentation remains a challenge. This is particularly case regard personalised precision nanodrugs, require simultaneous optimisation structures, compositions, surface functionalities nanodrugs. The development powerful computer clusters algorithms it possible overcome this challenge through

Язык: Английский

Процитировано

10