Here we describe a serial IA-enrichment work flow that was applied for the sequential analysis of physiological turnover of 5 proteins in serum sample sets obtained from a D 3-leucine pulse-chase study in healthy human volunteers. A multitude of protein targets can be analyzed from a single sample set without compromising analytical sensitivity, which leads to improved usage of samples and more clinical utility while decreasing the relative cost-per-analyte from such studies. Turnover analysis using immunoaffinity enrichment and targeted mass spectrometry offers the possibility to reuse samples multiple times. Targeted mass spectrometry has previously been used in combination with immunoaffinity (IA)-enrichment strategies for the analysis of individual target proteins for half-life determination ( 8, 11– 14). However, in low-resolution mass spectrometry, i.e., multiple–reaction-monitoring (MRM) MS, signal interference occurs between the natural isotopic peak of the light peptide and the peak of the heavy isotopologue due to a mass shift of only 3 Da. Leucine (D 3-leucine) tracers are widely used in MS-based pulse-chase studies. The choice of tracer considers the metabolic and biochemical stability of the amino acid and its isotopically labeled moieties, as well as provision of a sufficient mass offset to enable resolving the peptide isotopologues by mass spectrometry ( 10). In clinical pulse-chase studies, the steady-state tracer concentration is typically targeted to be a fraction of the endogenous amino acid counterpart (for example, 10%), thereby only minimally altering the available amino acid pool.Ī variety of stable isotope-labeled amino acid tracers have been deployed in pulse-chase investigations for protein turnover or flux analysis. Clinical pulse-chase studies using a stable isotope-labeled amino acid combined with mass spectrometry analysis of metabolic incorporation of a tracer into target proteins have enabled physiologically relevant target-turnover measurements ( 8, 9). However, some biotherapeutics show nonlinear PK or reliable information on binding affinities and target concentrations may not be available, which complicates the estimation of target turnover ( 6).Īnalytical techniques that deliver reliable, accurate, and target-specific turnover measurements have recently emerged ( 6, 7). In some cases, target-turnover information can be indirectly derived from interventional studies monitoring the PK of the biotherapeutics and total or complexed target measurements. The accuracy of model predictions strongly depends on the accuracy of the input parameters, including the PK of the biotherapeutics, target expression levels, drug-binding affinity to the target, internalization rate of membrane-associated target proteins, and turnover rate of soluble targets ( 1, 4, 5).Īccurate experimental data can reduce assumptions and biases in PK/PD modeling approaches however, previous experimental information on target turnover has not been routinely available, frequently contributing to PK/PD modeling uncertainties ( 1). ![]() PK/PD modeling is also applied in early stages of drug discovery to aid in the selection and validation of new biotherapeutic targets or to determine an optimal affinity for target binding. Mechanistic models that mathematically describe the pharmacokinetic and pharmacodynamic (PK/PD) 3 relationship of biotherapeutics and their target proteins are used to project the dose levels and rationalize the dosing regimen for interventional clinical studies such as first-in-human clinical trials ( 1– 3).
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