Quantitative CPP Evaluation from Risk Assessment Using Integrated Process Modeling

Abstract

Risk assessments (RAs) are frequently conducted to assess the potential effect of process parameters (PPs) on product quality attributes (e.g., a critical quality attribute (CQA)). To evaluate the PPs criticality the risk priority number (RPN) for each PP is often calculated. This number is generated by the multiplication of three factors: severity, occurrence, and detectability. This mathematical operation may result in some potential errors due to the multiplication of ordinal scaled values and the assumption that the factors contribute equally to the PPs criticality. To avoid these misinterpretations and to assess the out of specification (OOS) probability of the drug substance, we present a novel and straightforward mathematical algorithm. This algorithm quantitatively describes the PPs effect on each CQA assessed within the RA. The transcription of severity and occurrence to model effect sizes and parameters distribution are the key elements of the herein developed approach. This approach can be applied to any conventional RA within the biopharmaceutical industry. We demonstrate that severity and occurrence contribute differently to the PP criticality and compare these results with the RPN number. Detectability is used in a final step to precisely sort the contribution of each factor. To illustrate, we show the misinterpretation risk of the PP critically by using the conventional RPN approach. of consequence and probability of occurrence [3]. Due to its systematic approach for risk management, this guideline additionally lays the basis for the quality by design approach [4][5][6], which has been implemented in the biopharmaceutical industry for years.As a RA method, a failure mode and effects analysis (FMEA) tool aims to determine how a process can fail and evaluate the effect of this failure on the product [7,8]. This is determined via the risk priority number (RPN) approach which ranks the assessed risk of potential failure mode. The RPN is calculated by multiplying three factors: severity (S), occurrence (O), and detectability (D). Where S represents the severity of failure mode, O the probability of the failure, and D the likelihood of detecting the failure [6]. Resulting from its extensive use for many different applications, the RPN approach has been the most used assessment approach for years [9,10].Although widespread in the industry, it is often reported that the RPN approach is not the best assessment tool for such an evaluation [10,11]. This statement is supported by the following reasons:Bioengineering 2019, 6, 114 3 of 16 the biopharmaceutical process. Such a simulation strategy is called an integrated process model (IPM) and enables the simulation of the process outputs from a holistic point of view across the unit operation.

Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications

“…By leveraging a digital copy of the process where simulations replace physical experiments limited only by computational power, process success can be maximized, and failures may be swiftly mitigated. For bioprocesses and bioproduct lifecycles, an IPM can substantially shorten development and improve quality both in terms of speed to market and manufacturing success rates [ 1 , 11 , 12 ].…”

Integrated Process Model Applications Linking Bioprocess Development to Quality by Design Milestones

“…Risk assessments such as the FMEA are perhaps the QbD deliverable most sensitive to the quality and quantity of available data, as they must overcome certain numerical [ 5 ] and psychological challenges [ 6 ]. Specifically, critical impact or severity on a CQA by a process parameter (which leads to the CPP designation) is determined through discussion and process expertise within the perspective of a single unit operation [ 7 ].…”