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 pdajpst.2022.012818
DO 10.5731/pdajpst.2022.012818
A1 Johnson, Candice
A1 Kiehl, Douglas
A1 Christiaens, Piet
A1 Sancho Jodar, Ferran
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 F&aacutetima Lucas, Maria
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 2023
UL http://journal.pda.org/content/early/2023/12/19/pdajpst.2022.012818.abstract
AB Leachables in pharmaceutical products may react with biomolecule Active Pharmaceutical Ingredients (APIs) e.g., mAb, peptide, RNA, potentially compromising product safety, 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, while 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 concluded that the sensitivity of an in silico screen using multiple methodologies was 80-90% and specificity 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 modelling and quality risk management.