A Novel Hybrid LC-MS/MS Methodology for the Quantitative Bioanalysis of Antibody-siRNA Conjugates

Small interfering RNA (siRNA) is a new class of oligonucleotide therapeutics that is rapidly growing in drug research and development for the treatment of various diseases. One major challenge for siRNA therapeutics is their inefficient delivery to target tissues by systemic administration. Antibody-siRNA conjugates (ARC) are being developed as a promising approach to enhancing the selectivity and delivery of siRNA payloads to target tissues. A sensitive, selective, and reliable method to quantify ARCs in biological samples is critical to understand their pharmacokinetics, pharmacodynamics, toxicity, and biodistribution properties. There are very limited methods reported for the quantification of ARCs, including reverse transcription-quantitative polymerase chain reaction (RT-qPCR) or hybridization enzyme linked immunosorbent assay (hELISA); however, these methods were still proof-of-concept or suffered from poor specificity or assay performance. LC-MS/MS has been used for the quantification of various modalities, including siRNA or oligonucleotides. The technique offers unique advantages of high specificity, fast method development, and robust assay performance. However, no LC-MS/MS-based method has been reported for the quantitative bioanalysis of ARCs. In this work, we developed a novel hybrid LC-MS/MS methodology for the quantification of ARCs using Ab-siRNA01, an antibody-siRNA conjugate, as the test compound. An antihuman IgG antibody was used as the capture antibody to selectively extract the target ARC analyte from the serum samples. The optimized hybrid LC-MS/MS method was successfully qualified for the quantitation of Ab-siRNA01 in mouse serum over the range of 6.70-3350 ng/mL. The developed method was used to support pharmacokinetic studies of Ab-siRNA01 in mice. This work is the first instance where a hybrid LC-MS/MS method was developed and applied to the quantification of antibody-siRNA conjugates. This novel methodology can also be applied to other ARCs and significantly facilitates their research and development.