Predictive Ppk calculations for biologics and vaccines using a Bayesian approach – a tutorial

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-04-11 DOI:10.1002/pst.2380
Jos Weusten, Jianfang Hu
{"title":"Predictive Ppk calculations for biologics and vaccines using a Bayesian approach – a tutorial","authors":"Jos Weusten, Jianfang Hu","doi":"10.1002/pst.2380","DOIUrl":null,"url":null,"abstract":"In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://en.wikipedia.org/wiki/Process_(engineering)\">process</jats:ext-link> to produce output within <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://en.wikipedia.org/wiki/Specification_(technical_standard)\">specification</jats:ext-link> limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15–30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre‐licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data‐driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian‐based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre‐defined commercialization stage‐gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2380","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

Abstract

In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15–30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre‐licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data‐driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian‐based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre‐defined commercialization stage‐gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用贝叶斯方法对生物制剂和疫苗进行预测性 Ppk 计算--教程
在制药行业,尤其是生物制剂和疫苗制造行业,对快速工艺开发的重视可能会导致工艺开发不充分,这往往会导致上市后的商业制造工艺不够稳健。工艺性能指数(Ppk)是对工艺在一段时间内按照规格限制生产产出的能力进行的统计测量。在生物制药生产中,工艺开发的进展基于关键质量属性是否满足其规格限制,而缺乏对工艺稳健性的深入了解。Ppk 通常在 15-30 个商业批次后进行估算,此时再进行工艺调整以提高稳健性可能为时已晚/过于复杂。贝叶斯统计法、先验知识和主题专家 (SME) 的意见为在开发周期内预测工艺能力提供了机会。开发一种标准方法来评估不同开发阶段的长期工艺能力有以下几个好处:提供早期洞察工艺漏洞的机会,从而能够在认证前解决问题;使用数据驱动方法确定工艺开发/工艺特征描述 (PC) 期间需要优先考虑和重点关注的工艺领域;以及最终在投产时获得更高的工艺稳健性/工艺知识。我们提出了一种基于贝叶斯的方法,用于在商业数据存在之前,在开发和商业化阶段预测制造工艺在全制造规模下的性能。在贝叶斯框架下,手头相关工艺的有限开发数据、类似产品的数据、中小企业的一般知识以及文献资料都可以被精心编制成信息丰富的前置条件。本文通过两个例子介绍了所建议方法的实施。为了在工艺开发过程中实现持续改进,我们建议在预先确定的商业化阶段,例如在工艺开发完成时、PC 完成之前、技术转让运行(工程/工艺性能鉴定,PPQ)之前以及商业规格制定之前,采用这种使用预测 Ppk 的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes. Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control. Treatment Effect Measures Under Nonproportional Hazards. Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment. PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1