Development and Validation of a Stability-Indicating UPLC-PDA Method for Fruquintinib: LC-QTOF-MS/MS-Based Structural Characterization of Degradation Products and In Silico ADMET Prediction.

RATIONALE: Fruquintinib (FRQ) is a recently approved drug for the treatment of metastatic colorectal cancer. To date, only a limited number of studies have investigated the stability-indicating analysis of FRQ. Understanding its degradation pathways is essential for ensuring drug quality and safety, for regulatory compliance and for providing valuable insights into pharmaceutical development and quality control.

METHODS

FRQ was subjected to acid, base, oxidative, neutral, thermal, and photolytic stress as per ICH Q1A(R2). A stability-indicating UPLC-PDA method was developed on a Waters BEH C18 column (2.1 × 50 mm, 1.7 μm) with gradient elution using a mobile phase of 10 mM ammonium acetate (pH 3.5) and acetonitrile. Degradants were characterized by LTQ-XL ion-trap MS and LC-QTOF-MS/MS, followed by silico ADMET prediction.

RESULTS

The method is developed and validated in accordance with the ICH Q2(R2) guidelines. Forced degradation of FRQ under acidic, basic and oxidative stress conditions generated two common degradation products, DP-1 (m/z 207.0756) and DP-2 (m/z 221.0900), common to all three stress conditions, in addition to one degradation product specific to the oxidative pathway; FRQ was stable under neutral, thermal and photolytic conditions. The two major degradation products are identified and characterized by LC-QTOF-MS/MS. Out of two DPs, the DP-2 product has not been reported previously. ADMET prediction indicated acceptable pharmacokinetic properties but flagged potential drug-drug interaction risk.

CONCLUSIONS

The developed UPLC-PDA method provides a robust stability-indicating method for FRQ and enables comprehensive characterization of its degradation products. The discovery of the novel degradation product DP-2 expands current knowledge of FRQ degradation chemistry, whereas the combined analytical and in silico findings support pharmaceutical quality assessment, regulatory evaluation, and future safety investigations.