Drug identification methods
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Overview of Drug Identification Methods
Drug identification methods are essential in both drug discovery and clinical practice. These methods help researchers and clinicians determine the targets of drugs, identify culprit drugs in adverse reactions, and detect counterfeit or prohibited substances. The main approaches can be grouped into experimental, computational, and analytical techniques, each with unique strengths and limitations Tabana2023Bose2022Fang2025+7 MORE.
Experimental Biological Assays for Drug Target Identification
Affinity-Based Pull-Down and Label-Free Methods
Affinity-based pull-down methods use tagged small molecules to isolate target proteins, while label-free methods analyze small molecules in their natural state. Both are widely used in drug discovery to identify protein targets, with the choice of method depending on the specific project needs. These approaches are crucial for understanding drug mechanisms and improving therapy success rates Tabana2023Lundstrom2017.
Cell-Impedance and Label-Free Technologies
Label-free, cell-impedance-based technologies, such as surface plasmon resonance and mass spectrometry, allow for high-throughput screening of drug candidates. These methods can monitor cellular responses and predict drug effects without the need for labeling, making them valuable for screening anticancer drugs and other therapeutic agents .
Microparticle Assisted Precipitation Screening (MAPS)
The MAPS method simplifies drug target identification by using microparticles to assist protein precipitation after drug binding. This approach is robust, minimizes sample loss, and is effective even with small protein amounts, making it suitable for high-confidence target screening .
Quantitative Chemical Proteomics
Quantitative chemical proteomics combines affinity enrichment, isotope labeling, and mass spectrometry to systematically identify and quantify drug-binding proteins. This method is effective for discovering primary drug targets and can be applied to a wide range of small molecules .
Analytical and Machine Learning-Based Drug Identification
X-ray Absorption Spectroscopy and Machine Learning
X-ray absorption spectroscopy (XAS) combined with machine learning algorithms, such as principal component analysis and extreme learning machines, enables accurate, non-destructive identification of prohibited drugs, including isomers. This technique offers rapid and reliable drug detection .
Near-Infrared Spectroscopy and Deep Learning
Near-infrared spectroscopy (NIR) paired with Siamese-network modeling allows for on-site identification of counterfeit drugs. This method is cost-effective, non-destructive, and highly accurate, even for unknown or unbalanced sample sets, outperforming traditional classification algorithms .
Clinical and Laboratory Methods for Culprit Drug Identification
Causality Assessment Methods (CAMs)
In clinical settings, especially for cutaneous drug eruptions, various causality assessment methods are used to identify culprit drugs. These include algorithms, probabilistic approaches, and expert judgment. Algorithms tend to have higher sensitivity, while probabilistic methods offer higher specificity. Combining different methods may improve accuracy, but no consensus guidelines currently exist Bose2022Bose2021.
Lab-Based Techniques
Lab-based CAMs, such as lymphocyte transformation tests, cytokine measurements, and HLA allele genotyping, are low-risk and can complement clinical assessments. These methods vary in sensitivity and specificity and are used at different stages of drug eruptions. More high-quality studies are needed to validate these tools for routine clinical use .
Computational and Network-Based Approaches
In Silico Target Identification
Computer-aided methods can predict drug targets and binding sites, reducing experimental costs and time. These approaches use databases and specialized tools to assess druggability and guide experimental validation. They are increasingly important in early-stage drug discovery .
Network-Based Methods
Network-based approaches analyze relationships between genes, proteins, and other biological data to identify drug targets and repurposing opportunities. These methods can handle complex disease mechanisms and are enhanced by artificial intelligence and knowledge graphs, although further development is needed for clinical impact .
Conclusion
Drug identification methods span a wide range of experimental, analytical, and computational techniques. Advances in label-free technologies, machine learning, and network-based approaches are improving the accuracy and efficiency of drug identification in both research and clinical settings. However, challenges remain, including the need for consensus guidelines and further validation of emerging methods. Continued innovation and integration of multiple approaches will be key to advancing drug discovery and patient safety Tabana2023Bose2022Fang2025+7 MORE.
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Most relevant research papers on this topic
Target identification of small molecules: an overview of the current applications in drug discovery
Affinity-based pull-down and label-free methods are effective approaches for identifying protein targets in drug discovery, with their limitations and advantages discussed.
Identification method for prohibited drugs based on x-ray absorption spectroscopy and machine learning
This study successfully identified prohibited drugs using X-ray absorption spectroscopy and machine learning algorithms, providing a quick, non-destructive method for drug detection.
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