Identification, characterization, and in silico ADMET prediction of nirmatrelvir and its degradation products using HPLC-PDA and LC-QTOF-MS/MS

Rationale

Nirmatrelvir is a protease inhibitor that is essential for virus replication. Nirmatrelvir is indicated for the management of mild to severe cases of COVID-19 in individuals who are 12 years of age or older. Forced degradation studies of nirmatrelvir were carried out on the drug substance in solid and solution forms, subjecting it to various stress conditions according to International Conference on Harmonisation (ICH) Q1A(R2) and Q1B guidelines. The analytical method was validated as per the ICH Q2(R1) guidelines.

Methods

The drug substance (nirmatrelvir) was subjected to hydrolysis (acidic, alkaline, and neutral), thermal, photolytic, and oxidative stress conditions. Five degradation products (DPs) of nirmatrelvir formed under hydrolytic (acidic and alkaline) and oxidative (2,2-azobisisobutyronitrile) stress conditions. These degradation products were identified and separated using reverse-phase HPLC on a phenomenex kinetex C8 column (250 mm × 4.6 mm × 5 μm) with gradient elution. The mobile phase consisted of 0.1% formic acid and acetonitrile, and detection was carried out at a wavelength of 210 nm.

Results and conclusions

Nirmatrelvir and its five DPs were efficiently separated using reverse phase–HPLC. These five DPs were identified and characterized using LC-electrospray ionization (ESI)-Q-TOF-coupled mass spectrometry analysis in the ESI-positive ionization mode. The formation mechanisms of the DPs and the most probable mass fragmentation pathways for both nirmatrelvir and its DPs were elucidated. The developed method demonstrated selectivity, accuracy, linearity, and reproducibility, making it appropriate for quality control of nirmatrelvir and future research studies. Additionally, the physicochemical and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of nirmatrelvir and its DPs were predicted using ADMET predictor software. The toxicity profile revealed that DP2 and DP3 have teratogenic effects while DP1 and DP3 caused phospholipidosis.