ArticleBiosensors & bioelectronics2023
Microfluidic nanodevices for drug sensing and screening applications.
Article in Biosensors & bioelectronics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 47 citations in OpenAlex.
- An Energy Autonomous Microneedle Array-Based Sensing System for Continuous Biomarker Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in Biosensor Technology for Illicit Drug Detection Enable Effective Wastewater Surveillance.Chem & bio engineering · 2026Review
- Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration.Biosensors · 2026Review
- Microfluidics-based Polymeric Micro/Nanocarriers for Drug Delivery in Liver Cancer Treatment: Recent Advances, Outlooks, and Progress.Current topics in medicinal chemistry · 2026Review
- Aging on Chip: Harnessing the Potential of Microfluidic Technologies in Aging and Rejuvenation Research.Advanced healthcare materials · 2025Review
- Microfluidic platforms for monitoring cardiomyocyte electromechanical activity.Microsystems & nanoengineering · 2025Review
- A systematic review of sensors to combat crime and routes to further sensor development.Frontiers in chemistry · 2025Review
- Biomimetic Materials to Fabricate Artificial Cells.Chemical reviews · 2024Review
- Rapid and Online Microvolume Flow-Through Dialysis Probe for Sample Preparation in Veterinary Drug Residue Analysis.Sensors (Basel, Switzerland) · 2024Article
- Microfluidic strategies for biomimetic lung chip establishment and SARS-CoV2 study.Materials today. Bio · 2024Review
- Organic Electronics-Microfluidics/Lab on a Chip Integration in Analytical Applications.Sensors (Basel, Switzerland) · 2023Review
- Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays.Micromachines · 2023Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of pandemics (e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 in 2019), influenza A viruses (H1N1 in 2009), etc.), and worldwide spike in the aging population have created unprecedented urgency for developing new drugs to improve disease treatment. As a result, extensive efforts have been made to design novel techniques for efficient drug monitoring and screening, which form the backbone of drug development. Compared to traditional techniques, microfluidics-based platforms have emerged as promising alternatives for high-throughput drug screening due to their inherent miniaturization characteristics, low sample consumption, integration, and compatibility with diverse analytical strategies. Moreover, the microfluidic-based models utilizing human cells to produce in-vitro biomimetics of the human body pave new ways to predict more accurate drug effects in humans. This review provides a comprehensive summary of different microfluidics-based drug sensing and screening strategies and briefly discusses their advantages. Most importantly, an in-depth outlook of the commonly used detection techniques integrated with microfluidic chips for highly sensitive drug screening is provided. Then, the influence of critical parameters such as sensing materials and microfluidic platform geometries on screening performance is summarized. This review also outlines the recent applications of microfluidic approaches for screening therapeutic and illicit drugs. Moreover, the current challenges and the future perspective of this research field is elaborately highlighted, which we believe will contribute immensely towards significant achievements in all aspects of drug development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.