A Novel Hybridization LC-MS/MS Methodology for Quantification of siRNA in Plasma, CSF and Tissue Samples
Bibliographic record
Abstract
Therapeutic oligonucleotides, such as antisense oligonucleotide (ASO) and small interfering RNA (siRNA), are a new class of therapeutics rapidly growing in drug discovery and development. A sensitive and reliable method to quantify oligonucleotides in biological samples is critical to study their pharmacokinetic and pharmacodynamic properties. Hybridization LC-MS/MS was recently established as a highly sensitive and specific methodology for the quantification of single-stranded oligonucleotides, e.g., ASOs, in various biological matrices. However, there is no report of this methodology for the bioanalysis of double-stranded oligonucleotides (e.g., siRNA). In this work, we investigated hybridization LC-MS/MS methodology for the quantification of double-stranded oligonucleotides in biological samples using an siRNA compound, siRNA-01, as the test compound. The commonly used DNA capture probe and a new peptide nucleic acid (PNA) probe were compared for the hybridization extraction of siRNA-01 under different conditions. The PNA probe achieved better extraction recovery than the DNA probe, especially for high concentration samples, which may be due to its stronger hybridization affinity. The optimized hybridization method using the PNA probe was successfully qualified for the quantitation of siRNA-01 in monkey plasma, cerebrospinal fluid (CSF), and tissue homogenates over the range of 2.00-1000 ng/mL. This work is the first report of the hybridization LC-MS/MS methodology for the quantification of double-stranded oligonucleotides. The developed methodology will be applied to pharmacokinetic and toxicokinetic studies of siRNA-01. This novel methodology can also be used for the quantitative bioanalysis of other double-stranded oligonucleotides.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".