The Use of Bayesian Hierarchical Logistic Regression in the Development of a Modular Viral Inactivation Claim

Banton, Dwaine; Vacante, Dominick A.; Bulthuis, Ben; Goldstein, Josh; Wineburg, Michael; Schreffler, John

doi:10.5731/pdajpst.2019.010116

Cited by 2 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complete survey is available online as Supporting Information to this article. The survey included questions related to respondent 21 Gelman et al (2004) 22 Sivaganesan et al (2008) 23 Peterson and Yahyah (2009) Feng et al (2011) 25 Rozet et al (2011) 26 Novick et al (2012) 27 Lebrun et al (2013) 28 Fronczyk and Kottas (2014) 29 Rozet et al (2015) 30 Muleme et al (2016) 31 Sondag et al (2016) 32 Novick et al (2018) 33 Yang and Novick (2019) 34 Lebrun and Rozet (2020) 35 Process development Peterson (2004) 36 Miro-Quesada et al (2004) 37 Peterson (2008) 38 Peterson et al (2009) 39 Peterson and Lief (2010) 40 Mockus et al (2011) 41 Woodward (2011) 42 Lebrun et al (2012) 43 Hubert et al (2014) 44 Lebrun et al (2015) 45 Mockus et al (2015) 46 Lebrun et al (2018) 47 Boulanger and Mutsvari (2020) 50 Sano et al (2020) 48 Tabora et al (2019) 49 Yang and Novick (2019) 34 Process validation Yang (2013) 51 LeBlond and Mockus (2014) Faya et al (2017) 13 Yang and Novick (2019) 34 Peterson (2020) 53 Process control/manufacturing Mockus et al (2011) 54 Yang and Zhang (2016) 55 Novick et al (2017) 56 Faya et al (2017) 57 Yu et al (2017) 58 Banton et al (2019) 59 Overstall et al (2019)…”

Section: Bayesian Survey 21 | Backgroundmentioning

confidence: 99%

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

Faya

Sondag

Novick

et al. 2020

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

Summary The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA‐BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.

show abstract

Section: Bayesian Survey 21 | Backgroundmentioning

confidence: 99%

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

Faya

Sondag

Novick

et al. 2020

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some attempts have been made in the past to analyze such data with traditional statistical methods. 7 , 8 , 9 , 10 The current work focuses on evaluating the usefulness of machine learning methods to support the development of processes for viral clearance of biological therapeutic molecules. Specifically, a case study is presented for low pH viral inactivation unit operation.…”

Section: Introductionmentioning

confidence: 99%

“…Some attempts have been made in the past to analyze such data with traditional statistical methods 7‐10 …”

Section: Introductionmentioning

confidence: 99%

Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes

Panjwani

Cui

Spetsieris

et al. 2021

Biotechnology Progress

View full text Add to dashboard Cite

The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control such potential viral contamination is to ensure the manufacturing process can adequately clear the potential viral contaminants. Viral clearance for production of human monoclonal antibodies is achieved by dedicated unit operations, such as low pH inactivation, viral filtration, and chromatographic separation. The process development of each viral clearance step for a new antibody production requires significant effort and resources invested in wet laboratory experiments for process characterization studies. Machine learning methods have the potential to help streamline the development and optimization of viral clearance unit operations for new therapeutic antibodies. The current work focuses on evaluating the usefulness of machine learning methods for process understanding and predictive modeling for viral clearance via a case study on low pH viral inactivation.

show abstract

The Use of Bayesian Hierarchical Logistic Regression in the Development of a Modular Viral Inactivation Claim

Cited by 2 publications

References 10 publications

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group

Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes

Contact Info

Product

Resources

About