Optical Screening and Classification of Drug-binding to Proteins in Human Blood Serum

Abstract

Protein-drug interactions in the human bloodstream are important factors in applications ranging from drug design, where protein-binding influences efficacy and dose delivery, to biomedical diagnostics where rapid, quantitative measurements could guide optimised treatment regimes. Current measurement approaches use multistep assays, which probe the protein-bound drug fraction indirectly and do not provide fundamental structural or dynamic information about the in vivo protein-drug interaction. We demonstrate that ultrafast 2D-IR spectroscopy can overcome these issues by providing a direct, label-free optical measurement of protein-drug binding in blood serum samples. Four commonly prescribed drugs, known to bind to human serum albumin (HSA), were added to pooled human serum at physiologically relevant concentrations. In each case, spectral changes to the amide I band of the serum sample was observed, consistent with binding to HSA, but which were distinct for each of the four drugs. A machine learning-based classification of the serum samples achieved a total cross-validation prediction accuracy of 92% when differentiating serum-only samples from those with a drug present. Identification on a per drug basis achieved correct drug identification in 75% of cases. These unique spectroscopic signatures of the drug-protein interaction thus enable detection and differentiation of drug containing samples and give structural insight into the binding process, as well as quantitative information on protein-drug binding. Using currently available instrumentation, the 2D-IR data acquisition required just 1 minute and 10 μL of serum per sample and so these results pave the way to fast, specific and quantitative measurements of protein-drug binding in vivo with potentially invaluable applications for the development of novel therapies and personalised medicine.