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Many drugs bind to human plasma proteins in variable degrees of association, and plasma protein binding is the reversible interaction of a drug with the proteins of the plasma, which has a significant impact on the pharmacokinetic properties, such as volume of distribution, clearance and elimination, as well as the drug pharmacological effect. As a result, it is reasonable to assume that medications with high protein binding tend to have larger half-lives than drugs with low values. The less free drug is available for therapeutic effect, the more the drug binds to the plasma protein. Therefore, the prediction of PPB is of great significance in the pharmacokinetic characterization of drugs. In silico methods to predict the plasma protein binding of drugs is an attractive approach because they save money and time, and can handle a large number of compounds simultaneously. Based on the advanced AI-assisted platform, Creative Biolabs can accurately predict the ADMET properties and help customers accelerate the drug screening process and reduce R&D costs.
Plasma protein binding (PPB) is the reversible binding of compounds to plasma proteins, so there is a balance between bound and unbound forms. Because PPB is strongly related to the uptake, distribution, metabolism, excretion, and toxicity of such compounds, the fraction bound to plasma protein at equilibrium (fb) is a crucial pharmacokinetic property. Most of the time, only the unbound part of the compound can be distributed into tissues, where it then interacts with the target protein and is finally excreted from the blood. At later stages of drug discovery, candidate compounds that do not have appropriate PPB values are discarded. There are varieties of in vitro assays that can be utilized to determine the extent of plasma protein binding, including ultrafiltration, equilibrium dialysis, ultracentrifugation, fluorescence spectroscopy, chromatographic methods, circular dichroism, ultraviolet spectroscopy, nuclear magnetic resonance spectroscopy, and capillary electrophoresis.
Fig.1 Analytical methods of plasma protein binding. (Seyfinejad, et al., 2021)
Experimental measurements of PPB are expensive and time-consuming, and the dropout of candidate compounds also increases the development costs in the later stage. Therefore, it is necessary to develop reliable in silico approaches that can handle the large amounts of data used for screening and can predict the plasma protein binding of virtual compounds. The use of this technique can avoid the synthesis of chemicals that do not have the potential to be approved drugs. Quantitative Structure-Activity Relationship (QSAR) analysis is one such technique to estimate plasma protein binding levels based on molecular and physicochemical properties of compounds. A number of attempts have been made to understand the molecular factors that influence binding to human plasma proteins. As for small molecules, computational methods for the PPB prediction include machine learning methods and docking-based methods. Machine learning methods were built on the basis of a large training set comprising more than thousands of compounds to represent large structural diversity. In docking-based methods, the PPB value is predicted based on the pose in which drugs dock to the plasma protein and the molecular docking score.
Fig.2 Prediction of plasma protein binding by QSAR models and machine learning. (Sun, et al., 2018)
As a reliable partner to the world's leading pharmaceutical companies and research institutions, Creative Biolabs brings together the market-leading in silico expertise to build our AI-assisted platform that can provide custom professional plasma protein binding prediction.
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