Rapid Oligonucleotide Drug Impurity Determination By Direct Spectral Comparison Of Ion-Pair Reversed-Phase High-Performance Liquid Chromatography Electrospray Ionization Mass Spectrometry Data

RationaleQuantitative ion-pair (IP) high-performance liquid chromatography/mass spectrometry (HPLC/MS) methods are employed to determine the complex impurity profiles of oligonucleotide therapeutics. While impurities that co-elute with the main product are routinely monitored, the large number of early and late eluting impurities makes their individual measurements tedious and time-consuming. An improved method is needed for routine analyses.MethodsA Combined Ranking (CR) index is developed to provide a composite value for both qualitative and quantitative impurity changes. Positive and Negative Impurity Change (PIC/NIC) indices are developed to determine the degree and direction of change. Optimized experimental conditions are determined for the characterization of trace levels of impurities. Replicate analysis, blank subtraction, and signal processing approaches are used to enhance the signal-to noise (S/N). Dot-product and Euclidean distance equations monitor spectral changes.ResultsSpiking experiments with individual and complex impurity mixtures show the method can distinguish among samples differing in impurity content by 0.2% wt. The method has been applied to monitor changes in impurity profiles among different batches of the same oligonucleotide and changes in the same batch over time (stability analysis). The method permits rapid determination of changes in the types and amounts (increases and decreases) of impurities present.ConclusionsA novel approach for the rapid determination of changes in the impurity profile of oligonucleotide therapeutics has been developed. The straightforward data treatment and the speed and simplicity of the approach make the method easy to implement and use. Possible quality control applications include drug substance and drug product stability studies, and the assessment of batch-to-batch variability.