Nanosuspension Innovations: Expanding Horizons in Drug Delivery Techniques

Pharmaceutics, Journal Year: 2025, Volume and Issue: 17(1), P. 136 - 136

Published: Jan. 19, 2025

Nanosuspensions (NS), with their submicron particle sizes and unique physicochemical properties, provide a versatile solution for enhancing the administration of medications that are not highly soluble in water or lipids. This review highlights recent advancements, future prospects, challenges NS-based drug delivery, particularly oral, ocular, transdermal, pulmonary, parenteral routes. The conversion oral NS into powders, pellets, granules, tablets, capsules, incorporation film dosage forms to address stability concerns is thoroughly reviewed. article summarizes key stabilizers, polymers, surfactants, excipients used formulations, along ongoing clinical trials patents. Furthermore, comprehensive analysis various methods preparation provided. also explores vitro vivo characterization techniques, as well scale-down technologies bottom-up preparation. Selected examples commercial products discussed. Rapid advances field could resolve issues related permeability-limited absorption hepatic first-pass metabolism, offering promise based on proteins peptides. evolution novel stabilizers essential overcome current limitations stability, bioavailability, targeting ability, safety profile, which ultimately accelerates application commercialization.

Language: Английский

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Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment

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

Published: Feb. 1, 2025

Language: Английский

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Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Toxicological Sciences, Journal Year: 2022, Volume and Issue: 191(1), P. 1 - 14

Published: Sept. 22, 2022

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model requires the collection species-specific physiological, chemical-specific absorption, distribution, metabolism, excretion (ADME) parameters, which can be a time-consuming expensive process. This raises need to create computational capable predicting input parameter values for models, especially new compounds. In this review, we summarize an emerging paradigm integrating modeling with machine learning (ML) or artificial intelligence (AI)-based methods. includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches predict (3) incorporate ML/AI into summary statistics (eg, area under curve maximum plasma concentration). We also discuss neural network architecture "neural ordinary differential equation (Neural-ODE)" that is providing better predictive capabilities than other ML methods when used directly time-series profiles. order support applications development, several challenges should addressed as more become available, it important expand training set by including structural diversity compounds improve prediction accuracy models; due black box nature many lack sufficient interpretability limitation; Neural-ODE has great potential generate profiles limited information, but its application remains explored. Despite existing challenges, will continue facilitate efficient robust large number

Language: Английский

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Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Pharmacological Research, Journal Year: 2023, Volume and Issue: 189, P. 106706 - 106706

Published: Feb. 20, 2023

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In past decade, breakthroughs in field artificial intelligence (AI) have inspired development algorithms cancer setting. A growing body recent studies evaluated machine learning (ML) and deep (DL) for pre-screening, diagnosis management liver patients through diagnostic image analysis, biomarker discovery predicting personalized clinical outcomes. Despite promise these early AI tools, there is a significant need to explain 'black box' work towards deployment enable ultimate translatability. Certain emerging fields such as RNA nanomedicine targeted therapy may also benefit from application AI, specifically nano-formulation research given that they still largely reliant on lengthy trial-and-error experiments. this paper, we put forward current landscape along with challenges management. Finally, discussed future perspectives how multidisciplinary approach using could accelerate transition medicine bench side clinic.

Language: Английский

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“Targeting Design” of Nanoparticles in Tumor Therapy

Pharmaceutics, Journal Year: 2022, Volume and Issue: 14(9), P. 1919 - 1919

Published: Sept. 11, 2022

Tumor-targeted therapy based on nanoparticles is a popular research direction in the biomedical field. After decades of and development, both passive targeting ability inherent properties NPs active ligand receptor interaction have gained deeper understanding. Unfortunately, most targeted delivery strategies are still preclinical trial stage, so it necessary to further study biological fate particles vivo mechanism with tumors. This article reviews different NPs, focuses physical chemical (size, morphology, surface intrinsic properties), ligands (binding number/force, activity species) receptors (endocytosis, distribution recycling) other factors that affect particle targeting. The limitations solutions these discussed, variety new schemes introduced, hoping provide guidance for future design achieve purpose rapid transformation into clinical application.

Language: Английский

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Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Advanced Drug Delivery Reviews, Journal Year: 2023, Volume and Issue: 199, P. 114974 - 114974

Published: June 24, 2023

Language: Английский

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Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(13), P. 8575 - 8637

Published: June 1, 2023

Decades of nanotoxicology research have generated extensive and diverse data sets. However, is not equal to information. The question how extract critical information buried in vast streams. Here we show that artificial intelligence (AI) molecular simulation play key roles transforming nanotoxicity into information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, elucidating toxicity-related mechanisms. For AI realize their full impacts this mission, several obstacles must be overcome. These include paucity high-quality nanomaterials (NMs) standardized data, lack model-friendly databases, scarcity specific universal nanodescriptors, inability simulate NMs at realistic spatial temporal scales. This review provides a comprehensive representative, but exhaustive, summary current capability gaps tools required fill these formidable gaps. Specifically, discuss applications simulation, which can address large-scale challenge for research. need powerful new modeling approaches, mechanism analysis, design next-generation are also critically discussed. Finally, provide perspective on future trends challenges.

Language: Английский

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Recent progress of 4D printing in cancer therapeutics studies

SLAS TECHNOLOGY, Journal Year: 2023, Volume and Issue: 28(3), P. 127 - 141

Published: Feb. 17, 2023

Cancer is a critical cause of global human death. Not only are complex approaches to cancer prognosis, accurate diagnosis, and efficient therapeutics concerned, but post-treatments like postsurgical or chemotherapeutical effects also followed up. The four-dimensional (4D) printing technique has gained attention for its potential applications in therapeutics. It the next generation three-dimensional (3D) technique, which facilitates advanced fabrication dynamic constructs programmable shapes, controllable locomotion, on-demand functions. As well-known, it still initial stage requires insight study 4D printing. Herein, we present first effort report on technology This review will illustrate mechanisms used induce management. recent be further detailed, future perspectives conclusions finally proposed.

Language: Английский

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Plasma medicine: The era of artificial intelligence

Plasma Processes and Polymers, Journal Year: 2023, Volume and Issue: 20(12)

Published: July 18, 2023

Abstract The current trends that incorporate artificial intelligence (AI) and medicine have created new opportunities for improvement in both early diagnosis treatment of diseases. In this framework, AI might also the potential to significantly revolutionize way we approach field plasma medicine, an area is quickly growing uses cold atmospheric (CAP) address a variety medical conditions. Plasma offers promising therapeutic alternatives conditions widely ranging from cancer wound healing antimicrobial applications, but complexity sources huge number parameters may be overwhelming determination underlying mechanisms understanding effect source. This where steps in, provide strong tools modeling, evaluating, controlling CAPs. By harnessing power AI, researchers area, are now able evaluate massive volumes data, enhance their protocols, predict results with level precision never possible before. Hereby, emphasized further utilization light fascinating recent developments cooperation. New encouraging, limitations, ethical issues, model transparency, generalizability should considered. Regardless, possibilities endless, future looking brighter than ever implementation AI.

Language: Английский

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Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice

ACS Nano, Journal Year: 2023, Volume and Issue: 17(20), P. 19810 - 19831

Published: Oct. 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.

Language: Английский

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