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Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes
Jiří Jaromír Klemeš, Petar Sabev Varbanov and Peng Yen Liew (Editors) Proceedings of the 24th European Symposium on Computer Aided Process Engineering – ESCAPE 24 June 15-18, 2014, Budapest, Hungary. Copyright © 2014 Elsevier B.V. All rights reserved.
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes Lukas G. Eberlea,*, Hirokazu Sugiyamab, Stavros Papadokonstantakisa, Andreas Graserc, Rainer Schmidtc, Konrad Hungerbühlera a
Institute for Chemical and Bioengineering, ETH Zurich, Wolfgang-Pauli-Strasse 10, 8093 Zurich, Switzerland b The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan b Parenterals Production, F.Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland [email protected]
Abstract Enhancing yield in production is paramount for success in the increasingly competitive pharmaceutical industry, especially for manufacturers of costly biopharmaceutical products. Effort and complexity of implementing enhancements are forcing decision makers to first identify and quantify potentials and opportunities and then trigger process reviews efficiently. Data for supporting such quantifications have been traditionally recorded in the industry; the trend towards better accessibility of production data now facilitates consulting them and the need for developing appropriate tools gets evident. We present a four-level approach to convert production data into serviceable information, supporting the prioritization of processes and assisting real-life changes based on information from analysing production conditions. The approach was applied at a drug product manufacturing plant on a sample of 43 batches; main loss causes were identified and quantified. The three dominant loss sources account for nearly two-third of losses and are largely batch-size independent. Keywords: Pharmaceutical Production, Industrial Application, Decision-making, Statistical Process Control, Multivariate Data Analysis.
1. Introduction Cutting costs of drug product manufacturing becomes a key element in the historically “spoiled” pharmaceutical industry to meet the public longing for cheaper medicinal treatments. Production of such drug products is performed by a chain of value-adding processes as shown in Figure 1, including Drug Substance Manufacturing, Drug Product Manufacturing, Drug Product Bulk Packaging and finally the Drug Product Distribution. The presented approach defines a generic tool for yield enhancements that is adoptable to any element of the value chain and applies it to the Drug Product Manufacturing of sterile drug products, so-called Parenterals. Parenterals production consists of the processes Compounding, Filling, Visual Inspection and the concluding Batch Record Review (BRR). Compounding blends Active Pharmaceutical Ingredient (API) with water-for-injection and excipients. Filling includes the sterile and particlefree filling of syringes or vials. Visual Inspection performs an extensive screening for imperfections and BRR conducts a review of production records to assure quality.
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L. Eberle et al.
Figure 1. Overview of the value-adding chain for Parenterals, adapted from Gernaey et al. (2012). The upper elements represent the overall Supply Chain, the lower indicate the main activities in Drug Product Manufacturing. In Compounding three different samples are taken for the validation of the process, microbiological burden and in-process control. Also the difference between material-in and -out unit is calculated (dead volume). In Filling, besides sampling and dead volume calculation, vials are rejected due to imperfections (e.g., under-filled products, unsealed products, etc.). In Visual Inspection further screening for imperfections and product handling is performed.
Losses may arise at any point during Parenterals production and appear in a variety of patterns. They can be caused inter alia by sampling, dead volumes or imperfect products, for instance scratched vials. Hence, losses emerge in multiple forms and are measured in various units ([g], [L], [#vials]), which makes it hard to establish an overview over the losses arisen in a facility. To make it even worse, losses are caused at multiple time points within one batch, possibly spread over weeks, which calls for a sophisticated pooling and extraction of loss data to standardize this error-prone process. Moreover, when performing an inter-batch comparison, additional obstacles have to be overcome with respect to batch size variability and fine-tuning of equipment. On the top of that, various performance indicators can be used in these comparisons. For instance, besides the two most established indicators, that is cost impact and mass of API lost, (Sugiyama and Schmidt, 2012) also implied GMP-risks should be considered. The mentioned factors impede drawing the overall picture about loss causes. Hence, prioritizing improvement efforts cannot be performed reasonably without applying a systematic data-based approach. The work of Schoot et al. (2007) on root causes of quality variability includes a differentiation of critical and noncritical disturbances and it was advanced by the efforts of Gins et al. (2011) with additional input parameters. Further efforts in this direction are, for example, those of Sugiyama and Schmidt (2012), which solely considered mass as a target parameter. Troup and Georgakis (2013) summarize the currently available process systems engineering tools for the pharmaceutical industry and present an optimisation approach, which is based on additional experimental data, hence, very costly for biopharmaceutical drug products. Latest efforts by Muñoz et al. (2014) align the varying quality of several reactor inputs to achieve enhanced overall product quality and thereby reducing the risk of batch rejections. However, conclusive research studies proposing pragmatic methodologies for pharmaceutical production are still missing (Kontoravdi et al., 2013). In this work, we first present a data-based, hierarchical four-level model tailored to batchwise production and then apply it to real-life production data to derive managerial implications.
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes
465
2. Methodology The 1st level of the methodology conducts a cross-product comparison of the end-to-end yield, namely the performance indicators API yield ([g-API/batch], YAPI), financial yield ([monetary units/batch], YF), and GMP-risk ([-/batch], YGMP) are determined for every product. Products are distinguished from each other by differing in at least one of the following key features: API identity, API concentration, filling volume or product type (e.g., prefilled syringes). YAPI is calculated as defined by Eq.(1). (1) where n is the amount of batches for a product [-], Ok is the output resulting from production [number of vials] per batch, ρ is the density of the solution [kg/L], v is the volume of solution filled into each vial [L/vial] and Ik is the quantity of all input materials of a drug solution [kg] per batch. As shown in Figure 2, the costs per vial accumulate during production and can be doubling between Compounding and BRRfor small-volume products. YF accounts for this trend and is determined according to Eq.(2). (2) where m are the loss sources (e.g. samples, scratched or under-filled vials), Lk,i is the quantity lost per loss source and batch [L or vial], Cg the cost of a vial that is released to the market and Ci the cost of losses per loss source [monetary units/(l or vial)]. The third indicator, YGMP, accounts for the fact that losses with low implied GMP-risks are to be treated differently than losses more likely to have an impact on the therapeutic effect, if they would pass Visual Inspection, and is defined by Eq.(3). (3) where Gi is the GMP-factor [-/vial] of a loss source i based on the categorization of loss sources detected in Visual Inspection for the hypothetical negative impact on the health of a patient. The categorization is influenced by collaboration with pharmaceutical industry and includes critical losses with a weighting factor of 9 (e.g. for underfilled or imperfectly sealed products), a factor of 3 for major losses (e.g., empty or overfilled vials) and a factor of 1 for minor losses (e.g., scratches or abrasion at the glass body). YGMP and YF are both aggregations of information from the second level of our method, as opposed to YAPI which is calculated with top level information. By managerial decision and based on the findings from the 1 st level, the manufacturing review on the 2nd level focuses on the product with highest priority and the corresponding production is split into processes. The contribution of processes to the overall loss is investigated as LAPI [g-API/batch], LF [monetary units/batch] and LGMP [/batch]; Eq.(4) shows the calculation of LX,i for these three performance indicators.
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L. Eberle et al.
(4) LX is determined as an average of n batches by multiplying quantity Lk,i [L] and impact factor Fi (i.e., grams of API lost per unit for LAPI [g/L], Ci for LF [monetary units/L] and Gi for LGMP [-/L]). These performance indicators quantify the impact of loss sources and facilitate Fault Tree Analyses (FTA) to list possible root causes for the most important loss factors, as described for Drug Product Bulk Packaging by Rivero et al. (2008). The FTA allows to disaggregate a parameter that cannot be controlled directly, such as share of scratched vials, into elements that can be controlled (e.g. stirring velocity, temperatures, etc.). The branches of the FTA are then either starched or dropped with respect to the information gained on the 3rd and 4th level. Reducing the scope of the FTA on the 3rd and 4th level is crucial, since the amount of information available per level is increasing while progressing in the approach. On the 3rd level, tools of Statistical Process Control (SPC) are applied (e.g., Chopra et al., 2012) for eliminating some of the root causes listed by FTA. To achieve that goal, trends are associated to production conditions, mechanisms triggering outliers are investigated and production parameters that are found to be constant are neglected as causes of production variation. In this way, whole clusters of potential loss causes can be discarded and further efforts can be directed towards identified decisive production parameters. The concluding 4th level of our approach is presented conceptually and will exercise multivariate data analysis to further investigate and reveal non-trivial dependencies of root causes from multiple data sources (Rathore, 2011). The concept is to consolidate the plant information (PI) recordings and similar data recording systems, and to correlate losses to equipment parameters. A detailed description of the 4th level and the application to real-life production challenges will be presented in a next publication.
Figure 2. Left: Comparison of YAPI, YF and YGMP for products A, B and C. The grey boxes indicate the location of the central 50 % of values from a sample of batches, the horizontal line within a box represents the median and the crosshairs the mean. The vertical line is 1.5 times the heights of the respective box-half. All performance indicators show highest improvement potential for product A. Right: Cost comparison within Parenterals production for products A, B and C(e.g., indicating an increase in costs per vial by about 30 % for product A).
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes
467
Figure 3. Left: LAPI for product A, highlighting hardware losses (HwF, HwC) and samples as most decisive. Right: LGMP highlights three challenges associated with container of Product A.
3. Case Study The data-based approach was applied at a Parenterals manufacturing facility of Roche in Switzerland, which produces injectables with biopharmaceutically produced API for the fight against deadly diseases such as cancer. The study is based on 43 batches from commercial production of three drug products with a total of 2.3 million units and considers 58 loss causes. As indicated on the left side of Figure 2, three products were compared by their YAPI, YF and YGMP on the 1st level of our approach. All indicators rank the products in the order A, B and C, that is the same ascending order as their batch-sizes. The manufacturing of product A, identified as the process with the highest improvement potential was selected for further examination. The cost development within manufacturing of products A, B and C is reported on the right half of Figure 2, indicating that costs for a unit of product A are increasing roughly by 30 % from the ready-to-fill product solution to the inspected ready-to-inject drug product. Then, on the 2nd level, the indicators LAPI, LF and LGMP were determined for product A. As shown in Figure 3, the loss sources Hardware: Filling (HwF) and Hardware: Compounding (HwC) are the two most important loss causes in terms of LAPI. Both are not measured values but calculated as the difference of material in- and output from the Filling and Compounding unit, respectively. For enhancing the yield, the hardware setting will have to be modified, for instance tubes have to be shortened and diameters reduced. Furthermore, a request for increased batch-sizes was submitted to relevant health authorities and will take effect shortly after this study. Further data analysis will be executed after the production of product A in the updated setting.
Figure 4. The appearance of scratches (left) was associated to production parameters (right) to investigate root causes for the creation of scratches.
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L. Eberle et al.
As sketched in Figure 4, the 3rd level of the methodology was performed on scratches, the second most decisive GMP-loss because the most dominant loss source (i.e. glass splinters) was caused to a very large extent by a single event. An initial screening of production parameters that are associated by expert knowledge to scratches was performed graphically. Hence, a more rigorous data analysis with tools of multivariate data analysis is needed, which will be performed by executing the 4 th level of the presented approach.
4. Conclusions In this work we present a data-based four-level approach for efficient yield enhancement in drug product manufacturing. Based on three performance indicators, first product and second loss sources are prioritized in the order of their improvement potential. Possible root causes are then listed by performing a Fault Tree Analysis before reducing the root cause list with statistical analysis. The case study identified the product with the highest improvement potential in manufacturing processes and quantified the most decisive loss sources. For financial and API yield, hardware losses during pumping processes and quality control samples cause nearly two-thirds of losses, which are largely batch-size independent. In terms of implied GMP-risks, the three dominant factors are associated to the glass container of the drug product (i.e. glass splinters, scratches and glass failures). For future research, the third and fourth level of our approach will be advanced and applied to more production data. Also the GMP indicator can be further developed to support the mitigation of GMP-risks in pharmaceutical production.
References V. Chopra, M. Bairagi, P. Trivedi, M. Nagar, 2012, A case study: application of statistical process control tool for determining process capability and sigma level, PDA J. Pharm. Sci. Tech., 66, 2, 98-115. K. Gernaey, A. Cervera-Padrell, J. Woodley, 2012, A perspective on PSE in pharmaceutical process development and innovation, Comput. Chem. Eng., 42, SI, 15-29. G. Gins, J. Valaer, J. Van Impe, 2011, Discriminating between Critical and Noncritical Disturbances in (Bio)Chemical Batch Processes Using Multimodel Fault Detection and EndQuality Prediction, Ind. Eng. Chem. Res., 51, 38, 12375-12385. C. Kontoravdi, N. Samsatli, N. Shah, Development and design of bio-pharmaceutical processes, Curr. Opin. Chem. Eng., 2, 4, 435-441. S. Muñoz, V. Padovani, J. Mercado, 2014, A computer aided optimal inventory selection system for continuous quality improvement in drug product manufacture, Comput. Chem. Eng., 60, 396-402. A. Rathore, 2011, Quality by Design for Biologics and Biosimilars, Pharm. Technol., 35, 3, 64-68. A. Rivero, N. Prado, Y. Molina, I. de Zayas, 2008, Packing-Line Improvement Based on a FaultTree Analysis Approach, Pharm. Technol., 3, 32, 106-126. S. Schoot, B. Nuijen, A. Huitema, J. Beijnen, 2007, Assessment of Performance of Manufacturing Procedures in a Unit for Production of Investigational Anticancer Agents, Using a Mixed Effects Analysis, Pharm. Res., 24, 3, 605-612. H. Sugiyama, R. Schmidt, 2012, Model-based Optimization in Pharmaceutical Technical Operations – Yield Measurement and Increase in Roche’s Parenterals Production Kaiseraugst, Comput. Aided Chem. Eng., 30, 577-581. G. Troup, C. Georgakis, 2013, Process systems engineering tools in the pharmaceutical industry, Comput. Chem. Eng., 51, 157-171.
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes Lukas G. Eberlea,*, Hirokazu Sugiyamab, Stavros Papadokonstantakisa, Andreas Graserc, Rainer Schmidtc, Konrad Hungerbühlera a
Institute for Chemical and Bioengineering, ETH Zurich, Wolfgang-Pauli-Strasse 10, 8093 Zurich, Switzerland b The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan b Parenterals Production, F.Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland [email protected]
Abstract Enhancing yield in production is paramount for success in the increasingly competitive pharmaceutical industry, especially for manufacturers of costly biopharmaceutical products. Effort and complexity of implementing enhancements are forcing decision makers to first identify and quantify potentials and opportunities and then trigger process reviews efficiently. Data for supporting such quantifications have been traditionally recorded in the industry; the trend towards better accessibility of production data now facilitates consulting them and the need for developing appropriate tools gets evident. We present a four-level approach to convert production data into serviceable information, supporting the prioritization of processes and assisting real-life changes based on information from analysing production conditions. The approach was applied at a drug product manufacturing plant on a sample of 43 batches; main loss causes were identified and quantified. The three dominant loss sources account for nearly two-third of losses and are largely batch-size independent. Keywords: Pharmaceutical Production, Industrial Application, Decision-making, Statistical Process Control, Multivariate Data Analysis.
1. Introduction Cutting costs of drug product manufacturing becomes a key element in the historically “spoiled” pharmaceutical industry to meet the public longing for cheaper medicinal treatments. Production of such drug products is performed by a chain of value-adding processes as shown in Figure 1, including Drug Substance Manufacturing, Drug Product Manufacturing, Drug Product Bulk Packaging and finally the Drug Product Distribution. The presented approach defines a generic tool for yield enhancements that is adoptable to any element of the value chain and applies it to the Drug Product Manufacturing of sterile drug products, so-called Parenterals. Parenterals production consists of the processes Compounding, Filling, Visual Inspection and the concluding Batch Record Review (BRR). Compounding blends Active Pharmaceutical Ingredient (API) with water-for-injection and excipients. Filling includes the sterile and particlefree filling of syringes or vials. Visual Inspection performs an extensive screening for imperfections and BRR conducts a review of production records to assure quality.
464
L. Eberle et al.
Figure 1. Overview of the value-adding chain for Parenterals, adapted from Gernaey et al. (2012). The upper elements represent the overall Supply Chain, the lower indicate the main activities in Drug Product Manufacturing. In Compounding three different samples are taken for the validation of the process, microbiological burden and in-process control. Also the difference between material-in and -out unit is calculated (dead volume). In Filling, besides sampling and dead volume calculation, vials are rejected due to imperfections (e.g., under-filled products, unsealed products, etc.). In Visual Inspection further screening for imperfections and product handling is performed.
Losses may arise at any point during Parenterals production and appear in a variety of patterns. They can be caused inter alia by sampling, dead volumes or imperfect products, for instance scratched vials. Hence, losses emerge in multiple forms and are measured in various units ([g], [L], [#vials]), which makes it hard to establish an overview over the losses arisen in a facility. To make it even worse, losses are caused at multiple time points within one batch, possibly spread over weeks, which calls for a sophisticated pooling and extraction of loss data to standardize this error-prone process. Moreover, when performing an inter-batch comparison, additional obstacles have to be overcome with respect to batch size variability and fine-tuning of equipment. On the top of that, various performance indicators can be used in these comparisons. For instance, besides the two most established indicators, that is cost impact and mass of API lost, (Sugiyama and Schmidt, 2012) also implied GMP-risks should be considered. The mentioned factors impede drawing the overall picture about loss causes. Hence, prioritizing improvement efforts cannot be performed reasonably without applying a systematic data-based approach. The work of Schoot et al. (2007) on root causes of quality variability includes a differentiation of critical and noncritical disturbances and it was advanced by the efforts of Gins et al. (2011) with additional input parameters. Further efforts in this direction are, for example, those of Sugiyama and Schmidt (2012), which solely considered mass as a target parameter. Troup and Georgakis (2013) summarize the currently available process systems engineering tools for the pharmaceutical industry and present an optimisation approach, which is based on additional experimental data, hence, very costly for biopharmaceutical drug products. Latest efforts by Muñoz et al. (2014) align the varying quality of several reactor inputs to achieve enhanced overall product quality and thereby reducing the risk of batch rejections. However, conclusive research studies proposing pragmatic methodologies for pharmaceutical production are still missing (Kontoravdi et al., 2013). In this work, we first present a data-based, hierarchical four-level model tailored to batchwise production and then apply it to real-life production data to derive managerial implications.
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes
465
2. Methodology The 1st level of the methodology conducts a cross-product comparison of the end-to-end yield, namely the performance indicators API yield ([g-API/batch], YAPI), financial yield ([monetary units/batch], YF), and GMP-risk ([-/batch], YGMP) are determined for every product. Products are distinguished from each other by differing in at least one of the following key features: API identity, API concentration, filling volume or product type (e.g., prefilled syringes). YAPI is calculated as defined by Eq.(1). (1) where n is the amount of batches for a product [-], Ok is the output resulting from production [number of vials] per batch, ρ is the density of the solution [kg/L], v is the volume of solution filled into each vial [L/vial] and Ik is the quantity of all input materials of a drug solution [kg] per batch. As shown in Figure 2, the costs per vial accumulate during production and can be doubling between Compounding and BRRfor small-volume products. YF accounts for this trend and is determined according to Eq.(2). (2) where m are the loss sources (e.g. samples, scratched or under-filled vials), Lk,i is the quantity lost per loss source and batch [L or vial], Cg the cost of a vial that is released to the market and Ci the cost of losses per loss source [monetary units/(l or vial)]. The third indicator, YGMP, accounts for the fact that losses with low implied GMP-risks are to be treated differently than losses more likely to have an impact on the therapeutic effect, if they would pass Visual Inspection, and is defined by Eq.(3). (3) where Gi is the GMP-factor [-/vial] of a loss source i based on the categorization of loss sources detected in Visual Inspection for the hypothetical negative impact on the health of a patient. The categorization is influenced by collaboration with pharmaceutical industry and includes critical losses with a weighting factor of 9 (e.g. for underfilled or imperfectly sealed products), a factor of 3 for major losses (e.g., empty or overfilled vials) and a factor of 1 for minor losses (e.g., scratches or abrasion at the glass body). YGMP and YF are both aggregations of information from the second level of our method, as opposed to YAPI which is calculated with top level information. By managerial decision and based on the findings from the 1 st level, the manufacturing review on the 2nd level focuses on the product with highest priority and the corresponding production is split into processes. The contribution of processes to the overall loss is investigated as LAPI [g-API/batch], LF [monetary units/batch] and LGMP [/batch]; Eq.(4) shows the calculation of LX,i for these three performance indicators.
466
L. Eberle et al.
(4) LX is determined as an average of n batches by multiplying quantity Lk,i [L] and impact factor Fi (i.e., grams of API lost per unit for LAPI [g/L], Ci for LF [monetary units/L] and Gi for LGMP [-/L]). These performance indicators quantify the impact of loss sources and facilitate Fault Tree Analyses (FTA) to list possible root causes for the most important loss factors, as described for Drug Product Bulk Packaging by Rivero et al. (2008). The FTA allows to disaggregate a parameter that cannot be controlled directly, such as share of scratched vials, into elements that can be controlled (e.g. stirring velocity, temperatures, etc.). The branches of the FTA are then either starched or dropped with respect to the information gained on the 3rd and 4th level. Reducing the scope of the FTA on the 3rd and 4th level is crucial, since the amount of information available per level is increasing while progressing in the approach. On the 3rd level, tools of Statistical Process Control (SPC) are applied (e.g., Chopra et al., 2012) for eliminating some of the root causes listed by FTA. To achieve that goal, trends are associated to production conditions, mechanisms triggering outliers are investigated and production parameters that are found to be constant are neglected as causes of production variation. In this way, whole clusters of potential loss causes can be discarded and further efforts can be directed towards identified decisive production parameters. The concluding 4th level of our approach is presented conceptually and will exercise multivariate data analysis to further investigate and reveal non-trivial dependencies of root causes from multiple data sources (Rathore, 2011). The concept is to consolidate the plant information (PI) recordings and similar data recording systems, and to correlate losses to equipment parameters. A detailed description of the 4th level and the application to real-life production challenges will be presented in a next publication.
Figure 2. Left: Comparison of YAPI, YF and YGMP for products A, B and C. The grey boxes indicate the location of the central 50 % of values from a sample of batches, the horizontal line within a box represents the median and the crosshairs the mean. The vertical line is 1.5 times the heights of the respective box-half. All performance indicators show highest improvement potential for product A. Right: Cost comparison within Parenterals production for products A, B and C(e.g., indicating an increase in costs per vial by about 30 % for product A).
Data-Based Tiered Approach for Improving Pharmaceutical Batch Production Processes
467
Figure 3. Left: LAPI for product A, highlighting hardware losses (HwF, HwC) and samples as most decisive. Right: LGMP highlights three challenges associated with container of Product A.
3. Case Study The data-based approach was applied at a Parenterals manufacturing facility of Roche in Switzerland, which produces injectables with biopharmaceutically produced API for the fight against deadly diseases such as cancer. The study is based on 43 batches from commercial production of three drug products with a total of 2.3 million units and considers 58 loss causes. As indicated on the left side of Figure 2, three products were compared by their YAPI, YF and YGMP on the 1st level of our approach. All indicators rank the products in the order A, B and C, that is the same ascending order as their batch-sizes. The manufacturing of product A, identified as the process with the highest improvement potential was selected for further examination. The cost development within manufacturing of products A, B and C is reported on the right half of Figure 2, indicating that costs for a unit of product A are increasing roughly by 30 % from the ready-to-fill product solution to the inspected ready-to-inject drug product. Then, on the 2nd level, the indicators LAPI, LF and LGMP were determined for product A. As shown in Figure 3, the loss sources Hardware: Filling (HwF) and Hardware: Compounding (HwC) are the two most important loss causes in terms of LAPI. Both are not measured values but calculated as the difference of material in- and output from the Filling and Compounding unit, respectively. For enhancing the yield, the hardware setting will have to be modified, for instance tubes have to be shortened and diameters reduced. Furthermore, a request for increased batch-sizes was submitted to relevant health authorities and will take effect shortly after this study. Further data analysis will be executed after the production of product A in the updated setting.
Figure 4. The appearance of scratches (left) was associated to production parameters (right) to investigate root causes for the creation of scratches.
468
L. Eberle et al.
As sketched in Figure 4, the 3rd level of the methodology was performed on scratches, the second most decisive GMP-loss because the most dominant loss source (i.e. glass splinters) was caused to a very large extent by a single event. An initial screening of production parameters that are associated by expert knowledge to scratches was performed graphically. Hence, a more rigorous data analysis with tools of multivariate data analysis is needed, which will be performed by executing the 4 th level of the presented approach.
4. Conclusions In this work we present a data-based four-level approach for efficient yield enhancement in drug product manufacturing. Based on three performance indicators, first product and second loss sources are prioritized in the order of their improvement potential. Possible root causes are then listed by performing a Fault Tree Analysis before reducing the root cause list with statistical analysis. The case study identified the product with the highest improvement potential in manufacturing processes and quantified the most decisive loss sources. For financial and API yield, hardware losses during pumping processes and quality control samples cause nearly two-thirds of losses, which are largely batch-size independent. In terms of implied GMP-risks, the three dominant factors are associated to the glass container of the drug product (i.e. glass splinters, scratches and glass failures). For future research, the third and fourth level of our approach will be advanced and applied to more production data. Also the GMP indicator can be further developed to support the mitigation of GMP-risks in pharmaceutical production.
References V. Chopra, M. Bairagi, P. Trivedi, M. Nagar, 2012, A case study: application of statistical process control tool for determining process capability and sigma level, PDA J. Pharm. Sci. Tech., 66, 2, 98-115. K. Gernaey, A. Cervera-Padrell, J. Woodley, 2012, A perspective on PSE in pharmaceutical process development and innovation, Comput. Chem. Eng., 42, SI, 15-29. G. Gins, J. Valaer, J. Van Impe, 2011, Discriminating between Critical and Noncritical Disturbances in (Bio)Chemical Batch Processes Using Multimodel Fault Detection and EndQuality Prediction, Ind. Eng. Chem. Res., 51, 38, 12375-12385. C. Kontoravdi, N. Samsatli, N. Shah, Development and design of bio-pharmaceutical processes, Curr. Opin. Chem. Eng., 2, 4, 435-441. S. Muñoz, V. Padovani, J. Mercado, 2014, A computer aided optimal inventory selection system for continuous quality improvement in drug product manufacture, Comput. Chem. Eng., 60, 396-402. A. Rathore, 2011, Quality by Design for Biologics and Biosimilars, Pharm. Technol., 35, 3, 64-68. A. Rivero, N. Prado, Y. Molina, I. de Zayas, 2008, Packing-Line Improvement Based on a FaultTree Analysis Approach, Pharm. Technol., 3, 32, 106-126. S. Schoot, B. Nuijen, A. Huitema, J. Beijnen, 2007, Assessment of Performance of Manufacturing Procedures in a Unit for Production of Investigational Anticancer Agents, Using a Mixed Effects Analysis, Pharm. Res., 24, 3, 605-612. H. Sugiyama, R. Schmidt, 2012, Model-based Optimization in Pharmaceutical Technical Operations – Yield Measurement and Increase in Roche’s Parenterals Production Kaiseraugst, Comput. Aided Chem. Eng., 30, 577-581. G. Troup, C. Georgakis, 2013, Process systems engineering tools in the pharmaceutical industry, Comput. Chem. Eng., 51, 157-171.