Semi-quantitative non-targeted screening of extractables and leachables (E&L) relies on surrogate standards and is therefore subject to quantification bias driven by the high variability of relative response factors (RRFs). Thus, an uncertainty factor (UF) is used to account for RRF variability, which consequently lowers the analytical evaluation threshold (AET). However, the statistical basis of the ISO 10993–18 formula, UFISO= 1/(1 − RSD), with RSD the relative standard deviation of the RRFs, has not been established.The ISO formula omits a 1/μ factor (μ, mean RRF) and is defined only for RSD < 1. A Monte Carlo simulation was developed using the US FDA Chemicals List for Analytical Performance (CLAP) RRF distributions for gas chromatography-mass spectrometry (GC/MS) and for liquid chromatography-mass spectrometry (LC/MS) in positive and negative ionization modes, combined with empirical extractable concentration distributions. Multiple UF- and RRF-based correction strategies were compared across four AET levels and three surrogate concentrations. Without correction, false-negative (FN) rates ranged from 41% to 95% across analytical methods. All three CLAP datasets had RSD > 1, outside the ISO domain.Conditional correction produced simulated FN rates below 3%. These rates should however be considered optimistic, as they assume complete identification and a reliable compound-specific RRF, conditions rarely met in routine screening. Percentile-based UFs bound FNs at the selected threshold without these assumptions, although the most protective levels lowered the AET to values that are not achievable in E&L screening. A method-specific percentile UF, selected in line with the analytical instrumentation limits, is therefore proposed.
Natural products are an important source of drug discovery; however, their chemical complexity poses significant challenges for the efficient screening of bioactive compounds. Conventional screening methods have certain limitations in accurately reflecting ligand–target interactions in biological systems. Biochromatography, based on the specific interactions of biologically active molecules, enables efficient screening and analysis of active compounds by immobilizing enzymes, receptors, or cell membranes, thereby significantly improving selectivity and screening efficiency. This review summarizes recent advances in the application of biochromatography for screening bioactive compounds from natural products, with a focus on cell membrane chromatography, receptor chromatography, artificial membrane chromatography, and lipid raft chromatography. The advantages and limitations of these techniques are discussed, and their potential integration with other methodologies in drug discovery is further analyzed.
α-Glucosidase (α-Glu) is a critical target for diabetes therapy, making the discovery of efficient inhibitors important for antidiabetic drug development. Herein, a bifunctional Ce-MOF nanozyme (CeFMA) was synthesized via a one-step coordination assembly at room temperature, which intrinsically integrates oxidase (OXD)-like and laccase (LAC)-like activities. This nanozyme enables a dual-signal colorimetric platform for screening α-Glu inhibitors through two independent catalytic channels. In the OXD channel, CeFMA catalyzes the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB). This process is suppressed by ascorbic acid (AA) generated from α-Glu-triggered hydrolysis of its substrate, and is restored upon inhibition of α-Glu. In the LAC channel, phenol (PhOH) released by α-Glu-mediated hydrolysis of a phenolic glycoside undergoes oxidative coupling with 4-aminoantipyrine catalyzed by CeFMA, producing a red quinone imine product, and inhibition of α-Glu decreases the signal. The two channels operate independently with opposite signal response directions toward inhibitor concentration, enabling a dual-signal readout that can enhance the reliability of inhibitor screening in complex matrices. The platform was successfully applied to evaluate acarbose and oleanolic acid, and further validated in real medicinal herb extracts. This dual-active Ce-MOF nanozyme strategy provides a robust and reliable platform for the screening of natural α-Glu inhibitors, holding promise for antidiabetic drug discovery.
