Chemical Characterization of Leachables in Catheter Device

Identification of nonvolatile organic compounds (NVOCs) in biopharmaceuticals through non-target analysis and quantification using complexation-precipitation extraction

Single-use systems in biopharmaceutical manufacturing can potentially release chemical constituents (leachables) into drug products. Prior to conducting toxicological risk assessments, it is crucial to establish the qualitative and quantitative methods for these leachables. In this study, we conducted a comprehensive screening and structure elucidation of 23 leachables (nonvolatile organic compounds, NVOCs) in two antibody drugs using multiple (self-built and public) databases and mass spectral simulation. We identified 7 compounds that have not been previously reported in medical or medicinal extractables and leachables. The confidence levels for identified compounds were classified based on analytical standards, literature references, and fragment assignments. Most of the identified leachables were found to be plasticizers, antioxidants, slip agents or polymer degradants. Polysorbate (namely Tween) is commonly used as an excipient for protein stabilization in biopharmaceutical formulations, but its ionization in liquid chromatography-electrospray ionization mass spectrometry can interfere with compound quantification. To address this, we employed a complexation-precipitation extraction method to reduce polysorbate content and quantify the analytes. The developed quantitative method for target NVOCs demonstrated high sensitivity (limit of quantification: 20 or 50 μg/L), accuracy (recoveries: 77.2 to 109.5 %) and precision (RSD ≤ 8.2 %). Overall, this established method will facilitate the evaluation of NVOC safety in drug products.

A Chemical Characterization Workflow for Nontargeted Analysis of Complex Extracts from Polymer Based Medical Device Using High Resolution LC/MS

The chemical characterization of extractables and leachables (E&Ls) is an important aspect of biosafety and biocompatibility assessment in medical device industry. The advent of the body-contact use of medical devices in patient treatment has introduced a potential source for extractables and leachables as these medical devices are comprised of various polymeric materials. Several industry working groups, the FDA and USP, have recognized the guidance for chemical characterizations and nontargeted analysis of medical device extracts, such as ISO 10993-18:2020. The MS application of nontargeted analysis has played a critical role in understanding the E&Ls from medical device extracts. However, there have been very few reports about the MS based workflow with nontargeted analysis for medical device extracts and there is little guidance about the exact methodologies which should be used, even though there is an urgent need for a clearly defined process for the identification of medical device extracts. In this study, we demonstrated an analytical LC/MS (liquid chromatography/mass spectrometry) workflow using high resolution Exploris120 Orbitrap instrument for data acquisition and Compound Discoverer 3.3 intelligent software for data processing to profile the polymer related E&Ls from a balloon dilation catheter device extracted with 40% ethanol. An E&L ID workflow combining LC separation, data-informed MS acquisition strategy, MS information mining (including adduct ions, MS information from both electrospray ionization (ESI) (+) and ESI (-), in-source fragmentation, common fragment ions (CFIs), common neutral losses (CNLs), and in silico MS simulation was described with intelligent software processing and manual data interpretation. The workflow developed in this study was proven to be effective to provide a comprehensive profile of polymer related degradation products, polymer impurities and additives including surfactants, UV curing agent, antioxidants, and plasticizers for the device analyzed. The classification of E&L compounds using CFIs and CNLs was very effective to facilitate the identification of polymer related impurities and extract the polymer related impurities with common structures in a large data result set.