PT  - JOURNAL ARTICLE
AU  - Banton, Dwaine
AU  - Vacante, Dominick
AU  - Bulthuis, Ben
AU  - Goldstein, Josh
AU  - Wineburg, Mike
AU  - Schreffler, John
TI  - The Use of Bayesian Hierarchical Logistic Regression in the Development a Modular Viral Inactivation Claim
AID  - 10.5731/pdajpst.2019.010116
DP  - 2019 Jan 01
TA  - PDA Journal of Pharmaceutical Science and Technology
PG  - pdajpst.2019.010116
4099  - http://journal.pda.org/content/early/2019/05/28/pdajpst.2019.010116.short
4100  - http://journal.pda.org/content/early/2019/05/28/pdajpst.2019.010116.full
AB  - Low pH inactivation of enveloped viruses has historically been shown as an effective viral inactivation step in biopharmaceutical manufacturing. To date, most statistical analyses supporting modular low pH viral inactivation claims have used descriptive statistical analyses, which in many cases do not allow for probabilistic characterization of future experimental log10 reduction value (LRV). Using Bayesian hierarchical logistic regression modeling, probability statements regarding the likelihood of successful low pH viral inactivation, based only on certain process parameter settings, can be derived. This type of analysis also permits statistical modeling in the presence of historical data from different experiments and right-censored data, hitherto, two issues that have not been dealt with satisfactorily in the literature. The characterization of probability of successful inactivation allows creation of a modular claim stating future LRV will be greater than or equal to some critical value, based only on certain process parameter settings of the viral inactivation unit operation. This risk-based approach, when used in conjunction with traditional descriptive statistics, facilitates coherent and cogent decision-making about modular viral clearance LRV claims.