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
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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.
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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。
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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
Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups. Pre-Posterior Distributions in Drug Development and Their Properties. Beyond the Fragility Index. A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data.
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