PT  - JOURNAL ARTICLE
AU  - Johnson, Candice
AU  - Kiehl, Douglas
AU  - Christiaens, Piet
AU  - Jodar, Ferran Sancho
AU  - Cuyvers, Ruud
AU  - Bassan, Arianna
AU  - Beilke, Lisa
AU  - Bercu, Joel P.
AU  - Costelloe, Thomas
AU  - Cross, Kevin P.
AU  - Feilden, Andrew
AU  - Filler, Ron
AU  - Lucas, Maria Fátima
AU  - Masuda-Herrera, Melisa J.
AU  - Moghimi, Mona
AU  - Morley, Nick
AU  - Paskiet, Diane
AU  - Pavan, Manuela
AU  - Pletz, Julia
AU  - Reddy, M. Vijayaraj
AU  - Waine, Christopher J.
AU  - Myatt, Glenn J.
TI  - <em>In Silico</em> Assessment of Biomolecule Reactivity with Leachables
AID  - 10.5731/pdajpst.2022.012818
DP  - 2024 May 01
TA  - PDA Journal of Pharmaceutical Science and Technology
PG  - 214--236
VI  - 78
IP  - 3
4099  - http://journal.pda.org/content/78/3/214.short
4100  - http://journal.pda.org/content/78/3/214.full
SO  - PDA J Pharm Sci Technol2024 May 01; 78
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.