RT Journal Article SR Electronic T1 In Silico Assessment of Biomolecule Reactivity with Leachables JF PDA Journal of Pharmaceutical Science and Technology JO PDA J Pharm Sci Technol FD Parenteral Drug Association (PDA) SP 214 OP 236 DO 10.5731/pdajpst.2022.012818 VO 78 IS 3 A1 Johnson, Candice A1 Kiehl, Douglas A1 Christiaens, Piet A1 Jodar, Ferran Sancho A1 Cuyvers, Ruud A1 Bassan, Arianna A1 Beilke, Lisa A1 Bercu, Joel P. A1 Costelloe, Thomas A1 Cross, Kevin P. A1 Feilden, Andrew A1 Filler, Ron A1 Lucas, Maria Fátima A1 Masuda-Herrera, Melisa J. A1 Moghimi, Mona A1 Morley, Nick A1 Paskiet, Diane A1 Pavan, Manuela A1 Pletz, Julia A1 Reddy, M. Vijayaraj A1 Waine, Christopher J. A1 Myatt, Glenn J. YR 2024 UL http://journal.pda.org/content/78/3/214.abstract AB Leachables in pharmaceutical products may react with biomolecule active pharmaceutical ingredients (APIs), for example, monoclonal antibodies (mAb), peptides, and ribonucleic acids (RNA), potentially compromising product safety and efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film-forming agents, whereas specific chemical classes included epoxides, acrylates, and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data showed that the sensitivity of an in silico screen using multiple methodologies was 80%–90% and the specificity was 58%–92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modeling and quality risk management.