Identification Of Critical Process Parameters Research Articles

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

ABSTRACT High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. To elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

Identification of critical process parameters for expansion of clinical grade human Wharton's jelly-derived mesenchymal stromal cells in stirred-tank bioreactors.

Cell therapies based on multipotent mesenchymal stromal cells (MSCs) are traditionally produced using 2D culture systems and platelet lysate- or serum-containing media (SCM). Although cost-effective for single-dose autologous treatments, this approach is not suitable for larger scale manufacturing (e.g., multiple-dose autologous or allogeneic therapies with banked MSCs); automated, scalable and Good Manufacturing Practices (GMP)-compliant platforms are urgently needed. The feasibility of transitioning was evaluated from an established Wharton's jelly MSCs (WJ-MSCs) 2D production strategy to a new one with stirred-tank bioreactors (STRs). Experimental conditions included four GMP-compliant xeno- and serum-free media (XSFM) screened in 2D conditions and two GMP-grade microcarriers assessed in 0.25 L-STRs using SCM. From the screening, a XSFM was selected and compared against SCM using the best-performing microcarrier. It was observed that SCM outperformed the 2D-selected medium in STRs, reinforcing the importance of 2D-to-3D transition studies before translation into clinical production settings. It was also found that attachment efficiency and microcarrier colonization were essential to attain higher fold expansions, and were therefore defined as critical process parameters. Nevertheless, WJ-MSCs were readily expanded in STRs with both media, preserving critical quality attributes in terms of identity, viability and differentiation potency, and yielding up to 1.47×109 cells in a real-scale 2.4-L batch.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Stability studies for the identification of critical process parameters for a pharmaceutical production of the Orf virus

A promising new vaccine platform is based on the Orf virus, a viral vector of the genus Parapoxvirus, which is currently being tested in phase I clinical trials. The application as a vaccine platform mandates a well-characterised, robust, and efficient production process. To identify critical process parameters in the production process affecting the virus’ infectivity, the Orf virus was subjected to forced degradation studies, including thermal, pH, chemical, and mechanical stress conditions. The tests indicated a robust virus infectivity within a pH range of 5–7.4 and in the presence of the tested buffering substances (TRIS, HEPES, PBS). The ionic strength up to 0.5 M had no influence on the Orf virus’ infectivity stability for NaCl and MgCl2, while NH4Cl destabilized significantly. Furthermore, short-term thermal stress of 2d up to 37 °C and repeated freeze-thaw cycles (20cycles) did not affect the virus’ infectivity. The addition of recombinant human serum albumin was found to reduce virus inactivation. Last, the Orf virus showed a low shear sensitivity induced by peristaltic pumps and mixing, but was sensitive to ultrasonication. The isoelectric point of the applied Orf virus genotype D1707-V was determined at pH3.5. The broad picture of the Orf virus’ infectivity stability against environmental parameters is an important contribution for the identification of critical process parameters for the production process, and supports the development of a stable pharmaceutical formulation. The work is specifically relevant for enveloped (large DNA) viruses, like the Orf virus and like most vectored vaccine approaches.

Design and Development of Oxyclozanide Chewable Tablet Formulation Employing Quality by Design Approach

Abstract: Background: The focus of this research was to identify undiscovered knowledge associated with the production of oxyclozanide tablets utilizing Quality by Design (QbD) in order to develop an ideal formulation that would guarantee constant product quality. The modern approach to formulation design and optimization essentially entails QbD which is a systemic method of pharmaceutical development and comprises of the design and development of formulations as well as manufacturing processes to meet the target product quality. Materials and Methods: Pre-formulation studies on excipient flow properties, compactibility and tabletability profiles, identification of Critical Process Parameters (CPP) and Critical Quality Attributes (CQA) were carried out. An experimental design was adopted to investigate the effect of formulation and process variables (compression force, super-disintegrant type and concentration) on the CQAs using Modde Pro 12.1. The obtained results were used to generate a Design Space (DS) based on the study data. Results: Preformulation studies carried out on the core excipients used, gave an understanding to their tabletability and compactibility. Process parameters such a compaction force and formulation variables such as super-disintegrant concentration were studied within the framework of Quality by Design (QbD) and the new optimized formulation derived was tested and confirmed to be within the design space generated. Conclusion: At the end of the study, the aim was achieved; which was to implement pre-formulation studies and QbD to design a formulation containing Oxyclozanide (API) with a combination of excipients. Keywords: Quality by Design (QbD), Oxyclozanide, Chewable tablet, Compactibility, Tabletability.

Retrospective quality by design r(QbD) for lactose production using historical process data and design of experiments

Quality by Design (QbD) is a popular formal approach for designing, upscaling and optimizing industrial production facilities towards guaranteed quality. To avoid the many costly experiments required for QbD, historical production data may be exploited for optimization instead in what is known as a retrospective QbD (rQbD) study. Current rQbD literature does limitedly discuss data-driven identification of Critical Process Parameters (CPPs) to optimize limited process knowledge availability, and does not cover situations where technical operation limits have not yet been fully explored and/or where parallel equipment (lines) are used. This work presents a new rQbD strategy that addresses these challenges by balancing knowledge that can be obtained from statistical analysis of historical data, together with process experts with a carefully designed set of plant-scale experiments within current operational limits. This novel strategy is demonstrated on a long-running industrial lactose production facility. By digitally and experimentally exploring historical operation variability, we found new operational regimes for this production that may lead to up to 7% product quality improvement, reduced energy consumption and increased process understanding. Although optimizing a specific process by necessity requires a process-specific approach, the way in which we systematically optimize the current process with Hybrid AI (combining available knowledge with new insights from historical data) shows that approaches that are currently used in prospective process upscaling may be modified to be invaluable for optimization of full-scale processes with a long operational history.

Volumetric Scalability of Microfluidic and Semi-Batch Silk Nanoprecipitation Methods.

Silk fibroin nanoprecipitation by organic desolvation in semi-batch and microfluidic formats provides promising bottom-up routes for manufacturing narrow polydispersity, spherical silk nanoparticles. The translation of silk nanoparticle production to pilot, clinical, and industrial scales can be aided through insight into the property drifts incited by nanoprecipitation scale-up and the identification of critical process parameters to maintain throughout scaling. Here, we report the reproducibility of silk nanoprecipitation on volumetric scale-up in low-shear, semi-batch systems and estimate the reproducibility of chip parallelization for volumetric scale-up in a high shear, staggered herringbone micromixer. We showed that silk precursor feeds processed in an unstirred semi-batch system (mixing time > 120 s) displayed significant changes in the nanoparticle physicochemical and crystalline properties following a 12-fold increase in volumetric scale between 1.8 and 21.9 mL while the physicochemical properties stayed constant following a further 6-fold increase in scale to 138 mL. The nanoparticle physicochemical properties showed greater reproducibility after a 6-fold volumetric scale-up when using lower mixing times of greater similarity (8.4 s and 29.4 s) with active stirring at 400 rpm, indicating that the bulk mixing time and average shear rate should be maintained during volumetric scale-up. Conversely, microfluidic manufacture showed high between-batch repeatability and between-chip reproducibility across four participants and microfluidic chips, thereby strengthening chip parallelization as a production strategy for silk nanoparticles at pilot, clinical, and industrial scales.

QbD-guided pharmaceutical development of Pembrolizumab biosimilar candidate PSG-024 propelled to industry meeting primary requirements of comparability to Keytruda®

Pharmaceutical development of biosimilars is primarily focused on meeting the regulatory requirements for analytical comparability of the product's critical quality attributes (CQAs), concerning safety and efficacy, to those of the originator drug of interest. To this end, the early adoption of a systematic science-based approach, as guided by quality-by-design (QbD) principles, is crucial due to the blind starting point where the same insights of an originator developer into the challenges of a given biopharmaceutical and its manufacturing process are lacking. In this study, we devised a pharmaceutical QbD-guided approach to undertake the biosimilar development of Pembrolizumab (Keytruda®), the ace of therapeutic monoclonal antibodies (mAbs) in terms of approved indications and market sales, and its manufacturing process development. Quality target product profile (QTPP) for Pembrolizumab biosimilar product was assembled using publicly available information on Keytruda®. Upon preliminary analyses of four different lots of Keytruda®, the product CQAs and their acceptable ranges of specification were determined via risk assessment based on the relevant pharmaceutical development quality guidelines, particularly those of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). The development and clone selection of Chinese Hamster Ovary (CHO) DG44 cell line was performed using DHFR expression vectors and Methotrexate (MTX) selective pressure. The CHO clone stably expressing relatively higher mAb titer (∼1200 mg/l) in small-scale shake-flask cultures, with the highest similarity of the CQAs charge variants contents (CVCs), N-glycan profile, and biological potency to those of Keytruda® reference standard was selected as the lead clone and the produced Pembrolizumab candidate was named PSG-024. The upstream process (USP) and downstream process (DSP) developments for production were started with the process evaluation screening experiments for the identification of critical process parameters (CPPs) founded upon the prior knowledge on different process stages, input process parameters (iPPs), output process parameters (oPPs), and their impacts on product CQAs. Thereby, screening experiments of USP fed-batch cell culture in 5-liter bioreactor resulted in improvement of PSG-024 expression titer to 2060 ± 70 mg/l and selection of the iPPs feed amount (A), glucose setpoint (B), culture temperature (C), and agitation rate (D) for the optimization design of experiments (DoEs) mainly focused on the CQA acidic CVC and the oPPs mAb expression yield. The USP optimization DoEs using response surface methodology (RSM) yielded valid prediction models and optimal conditions of A = 35%, B = 4.5 g/l, C = 37 °C, and D = 160–220 rpm, which resulted in the final PSG-024 expression titer of 3170 ± 40 mg/l without an excessive rise in acidic CVC. The DSP screening experiments led to achieving the mAb recovery rates of 94% ± 3% and 71.5% ± 3.5% for affinity (capture) and cation-exchange (polishing) chromatography stages, respectively. The capture eluate buffer and viral inactivation conditions were optimized to prevent mAb eluate turbidity and protein aggregation. Moreover, the polishing stage optimization DoEs via one-factor-at-a-time method focused on wash and elution steps for control of the acidic CVC CQA and achieving >80% mAb recovery rate. By shifting to Step elution from the primary salt gradient method and considering an additional intermediate wash step, the maximum mAb recovery of 87% ± 1.5% was achievable while maintaining the CQA acidic CVC within the acceptable range. The consistency of final analytical comparability of PSG-024 demonstrated the effectiveness of the adopted pharmaceutical QbD approach for Pembrolizumab biosimilar development, paving the way for the technology transfer to the client to proceed further development.

Application of a Statistical Approach to Process Development of Futibatinib by Employing Quality-by-Design Principles. Part 1: Identification of Critical Process Parameters for Impurities

Application of a Statistical Approach to Process Development of Futibatinib by Employing Quality-by-Design Principles. Part 1: Identification of Critical Process Parameters for Impurities

An Improved Impact Ratio for Identifying Critical Process Parameters in Pharmaceutical Manufacturing Processes.

The identification of critical process parameters in biologics and small molecule process development is a key element of quality by design. The objectivity and consistency of procedures to identify critical process parameters can be improved with the use of impact ratios. Impact ratios quantify a process parameter's practical effect on a critical quality attribute relative to the critical quality attribute's acceptance limits. If the impact ratio is large, i.e., exceeds a predefined impact ratio threshold, the recommendation is to classify the process parameter as a critical process parameter. This article introduces an improved and mathematically well-defined impact ratio. Benefits of this impact ratio are a consistent interpretation for many scenarios commonly encountered in practice, high suitability to automation, and the possibility of standardizing on a single impact ratio definition for pharmaceutical manufacturing.

Identification of critical process parameters of Jinqing alcohol precipitation of Reduning Injection by big data

Lonicerae Japonicae Flos and Artemisiae Annuae Herba(LA or Jinqing) alcohol precipitation has various process parameters and complex process mechanism, and is one of the key units for manufacturing Reduning Injection. In order to identify the critical process parameters(CPPs) affecting the weight of the extract produced from the alcohol precipitation process, 259 batches of historical production data from 2017 to 2018 were collected, with a total of 829 318 data points. These data showed characteristics of large data, such as a large data volume, a low value density, and diverse sources. The data cleaning and feature extraction were first performed, and 48 feature variables were selected. The original data points were reduced to 9 936. Then, a combination of Pearson correlation analysis and grey correlation analysis were used to screen out 15 potential critical process parameters(pCPPs). After that, the partial least squares(PLS) was used in prediction of the weight of the extract, proving that the performance of predictive model based on 15 pCMAs is equivalent to that of predictive model based on 48 feature variables. The variable importance in projection(VIP) index was used to identify 9 CPPs, including 2 alcohol precipitation supernatant volume parameters, 4 initial extract weight parameters and 3 added alcohol volume parameters. As a result, the number of data points was 1 863, accounting for 0.28% of the original data. The big data analysis approach from a holistic point of view can effectively increase the value density of the original data. The critical process parameters obtained can help to accurately describe the quality transfer mechanism of the Jinqing alcohol precipitation process.

Good modeling practice for industrial chromatography: Mechanistic modeling of ion exchange chromatography of a bispecific antibody

In the biopharmaceutical industry, development and characterization of chromatography processes is typically based on statistical models. Although these approaches are easy to apply, the resulting models may fail to predict non-linear behavior in preparative chromatography with complex protein feed streams. An alternative to empirical methods are mechanistic models. In chemical engineering, mechanistic modeling has been a standard method for decades. As mechanistic models continue their advance in the biopharmaceutical industry, this study underlines the need of a standardized methodology for mechanistic model calibration.A lumped rate model was applied to the polishing chromatography of a bispecific antibody. Following guidelines for good modeling practice, the model was thoroughly analyzed. Potential limitations such as over-parameterization, parameter correlations, imprecise parameter estimates or systematic errors were considered by evaluation of parameter confidence intervals, visual sensitivity analysis and model validation across different scales. Application of simulations for identification of critical process parameters will be discussed.

Aeration and Shear Stress Are Critical Process Parameters for the Production of Oncolytic Measles Virus.

Oncolytic Measles virus is a promising candidate for cancer treatment, but clinical studies have shown that extremely high doses (up to 1011 TCID50 per dose) are required to effect a cure. Very high titers of the virus must therefore be achieved during production to ensure an adequate supply. We have previously shown that Measles virus can be produced in Vero cells growing on a Cytodex 1 microcarrier in serum-containing medium using a stirred-tank reactor (STR). However, process optimization and further process transfer or scale up requires the identification of critical process parameters, particularly because the use of STRs increases the risk of cell damage and lower product yields due to shear stress. Using a small-scale STR (0.5 L working volume) we found that Measles virus titers are sensitive to agitator-dependent shear, with shear stress ≥0.25 N m−2 reducing the titer by more than four orders of magnitude. This effect was observed in both serum-containing and serum-free medium. At this scale, virus of titers up to 1010 TCID50 mL−1 could be achieved with an average shear stress of 0.1 N m−2. We also found that the aeration method affected the virus titer. Aeration was necessary to ensure a sufficient oxygen supply to the Vero cells, and CO2 was also needed to regulate the pH of the sodium bicarbonate buffer system. Continuous gassing with air and CO2 reduced the virus titer by four orders of magnitude compared to head-space aeration. The manufacture of oncolytic Measles virus in a STR can therefore be defined as a shear-sensitive process, but high titers can nevertheless be achieved by keeping shear stress levels below 0.25 N m−2 and by avoiding extensive gassing of the medium.

QFD-Taguchi based hybrid approach in die casting process optimization

Abstract The present research work emphasized on identifying and optimizing various significant process parameters of high pressure die casting by using QFD-Taguchi based hybrid approach in order to yield the optimum casting density of the A380 alloy. Identification of critical process parameters, selection of appropriate orthogonal array, analysis of means and analysis of variance are employed to study the performance characteristic of the die casting process. The most critical process parameters identified and optimized by QFD-Taguchi based hybrid approach, such as the injection pressure, the molten metal temperature, the plunger velocity (first and second stage) and the die temperature were explored in the experimental work. The results show that injection pressure is the most significant factor among the selected parameters. The contribution of the injection pressure to the variation of mean casting density is around 61.483%. Confidence interval (CI) has also been estimated as 0.000718 for 95% consistency level to validate the predicted range of optimum casting density of aforesaid alloy.

Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1.

Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety.

Alginate coated chitosan nanogel for the controlled topical delivery of Silver sulfadiazine

Burn wounds environment favors the growth of micro-organisms causing delay in wound healing. The traditional treatment with antimicrobial creams offer inaccurate doses.The aim of the present study is to formulate and evaluate different silver sulfadiazine loaded nanogel formulations. A factorial design experiment was used for the identification of critical process parameters and for the optimization of the respective process conditions. The prepared drug loaded nanogels were characterized for their particle size, zeta potential, entrapment efficiency and swelling index in order to demonstrate their physicochemical properties, in addition, FTIR, TEM, SEM and in vitro release were used for characterization. The release profile of all tested nanogels showed an initial burst followed by a slow and continuous release rate. An optimum nanogel formulation was predicted by the JMP® software according to the stated prediction expressions and was composed of 0.4% sodium alginate (ALG) and 0.414% Silver sulfadiazine (SSD). The optimized formulation showed higher therapeutic efficacy in vivo when compared to market product.

A novel methodology to study polymodal particle size distributions produced during continuous wet granulation

It is important during powder granulation to obtain particles of a homogeneous size especially in critical situations such as pharmaceutical manufacture. To date, homogeneity of particle size distribution has been defined by the use of the d50 combined with the span of the particle size distribution, which has been found ineffective for polymodal particle size distributions. This work focuses on demonstrating the limitations of the span parameter to quantify homogeneity and proposes a novel improved metric based on the transformation of a typical particle size distribution curve into a homogeneity factor which can vary from 0 to 100%. The potential of this method as a characterisation tool has been demonstrated through its application to the production of granules using two different materials. The workspace of an 11mm twin screw granulator was defined for two common excipients (α-lactose monohydrate and microcrystalline cellulose). Homogeneity of the obtained granules varied dramatically from 0 to 95% in the same workspace, allowing identification of critical process parameters (e.g. feed rate, liquid/solid ratio, torque velocities). In addition it defined the operational conditions required to produce the most homogeneous product within the range 5μm–2.2mm from both materials.

Impact of cell lysis on the description of cell growth and death in cell culture.

The primary task of process development is a process design that guarantees product quality and maximizes product quantity. One part of the process development is the identification of critical process parameters. Especially in cell culture processes, unwanted cell damage as critical process parameter is still challenging in stirred tank reactors and needs therefore to be considered. Nevertheless, this topic and its effects on process performance are currently not well discussed and not verified in literature until now. The process of cell damage or lysis can be defined as the loss of integrity of the cells. For the investigation of this phenomenon, a model-based designed fed-batch cultivation with Chinese hamster ovary cells was performed. Besides measurements of viable and dead cell concentration, lysed cell count was determined by DNA measurements. Based on these analytics, different hypotheses, characterizing the cell death, were compared. From this, four main statements could be derived: (i) consideration of lysis in cell culture processes is of great importance for the description of the living biomass population in terms of growth and dying; (ii) a higher effort in process monitoring facilitates significantly model development; (iii) in contradiction to existing models from literature, our verification approach demonstrated a direct correlation of lysis with viable cell concentration and therefore with productivity; and (iv) lysis could be effectively described by only one model parameter, the specific lysis rate from viable cells to lysed cells. All these statements could be accurately proven by statistical methods. Our results enable future detailed investigations of the causes of cell damage and the influences of cell lysis on product quality and quantity. Potential application fields are scale-up issues, process optimization, and impurity screening.

The TOP‐REF Approach for Identification of Critical Process Parameters for Process Monitoring and Optimization

Chemie Ingenieur TechnikVolume 88, Issue 9 p. 1377-1377 Vortrag The TOP-REF Approach for Identification of Critical Process Parameters for Process Monitoring and Optimization H. Radatz, Corresponding Author H. Radatz heiko.radatz@bci.tu-dortmund.de TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanyTU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this authorT. Hellenkamp, T. Hellenkamp INOSIM Software GmbH, Joseph-von-Fraunhofer-Straße 20, 44227 Dortmund, GermanySearch for more papers by this authorDr.-Ing. C. Bramsiepe, Dr.-Ing. C. Bramsiepe TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this authorProf. Dr.-Ing. G. Schembecker, Prof. Dr.-Ing. G. Schembecker TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this author H. Radatz, Corresponding Author H. Radatz heiko.radatz@bci.tu-dortmund.de TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanyTU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this authorT. Hellenkamp, T. Hellenkamp INOSIM Software GmbH, Joseph-von-Fraunhofer-Straße 20, 44227 Dortmund, GermanySearch for more papers by this authorDr.-Ing. C. Bramsiepe, Dr.-Ing. C. Bramsiepe TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this authorProf. Dr.-Ing. G. Schembecker, Prof. Dr.-Ing. G. Schembecker TU Dortmund, Lehrstuhl für Anlagen- und Prozesstechnik, Emil-Figge-Straße 70, 44227 Dortmund, GermanySearch for more papers by this author First published: 29 August 2016 https://doi.org/10.1002/cite.201650310AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume88, Issue9Special Issue: ProcessNet-Jahrestagung und 32. DECHEMA-Jahrestagung der Biotechnologen 2016September, 2016Pages 1377-1377 RelatedInformation

Quality by Design in Action 2: Controlling Critical Material Attributes during the Synthesis of an Active Pharmaceutical Ingredient

Quality by Design (QbD) is of paramount importance not only for patient safety but also for the timely and uninterrupted supply of products at affordable prices into the market. Both of these objectives can be achieved only through a robust process, and one of the major obstacles for developing a robust process is the quality of input materials and reagents used for the manufacture of active pharmaceutical ingredients (APIs). This article demonstrates the use of QbD methodology to optimize the quality of input materials and make the process more consistent, thereby reducing the variation in the quality of API produced. This article highlights the use of failure mode and effect analysis (FMEA) for the unbiased identification of critical process parameters and critical material attributes associated with the manufacturing of key starting materials, which are later used as input for the design of experiments (DoE) study that is used for the optimization.