A Method for Finding a Design Space as Linear Combinations of Parameter Ranges for Biopharmaceutical Control Strategies

According to ICH Q8 guidelines, the biopharmaceutical manufacturer submits a design space (DS) definition as part of the regulatory approval application, in which case process parameter (PP) deviations within this space are not considered a change and do not trigger a regulatory post approval procedure. A DS can be described by non-linear PP ranges, i.e., the range of one PP conditioned on specific values of another. However, independent PP ranges (linear combinations) are often preferred in biopharmaceutical manufacturing due to their operation simplicity. While some statistical software supports the calculation of a DS comprised of linear combinations, such methods are generally based on discretizing the parameter space - an approach that scales poorly as the number of PPs increases. Here, we introduce a novel method for finding linear PP combinations using a numeric optimizer to calculate the largest design space within the parameter space that results in critical quality attribute (CQA) boundaries within acceptance criteria, predicted by a regression model. A precomputed approximation of tolerance intervals is used in inequality constraints to facilitate fast evaluations of this boundary using a single matrix multiplication. Correctness of the method was validated against different ground truths with known design spaces. Compared to stateof-the-art, grid-based approaches, the optimizer-based procedure is more accurate, generally yields a larger DS and enables the calculation in higher dimensions. Furthermore, a proposed weighting scheme can be used to favor certain PPs over others and therefore enabling a more dynamic approach to DS definition and exploration. The increased PP ranges of the larger DS provide greater operational flexibility for biopharmaceutical manufacturers.Comment: 15 pages, 7 figures, 3 tables, research articl

Peanut‐induced anaphylaxis in children and adolescents: Data from the European Anaphylaxis Registry

Background Peanut allergy has a rising prevalence in high-income countries, affecting 0.5%-1.4% of children. This study aimed to better understand peanut anaphylaxis in comparison to anaphylaxis to other food triggers in European children and adolescents. Methods Data was sourced from the European Anaphylaxis Registry via an online questionnaire, after in-depth review of food-induced anaphylaxis cases in a tertiary paediatric allergy centre. Results 3514 cases of food anaphylaxis were reported between July 2007 - March 2018, 56% in patients younger than 18 years. Peanut anaphylaxis was recorded in 459 children and adolescents (85% of all peanut anaphylaxis cases). Previous reactions (42% vs. 38%; p = .001), asthma comorbidity (47% vs. 35%; p < .001), relevant cofactors (29% vs. 22%; p = .004) and biphasic reactions (10% vs. 4%; p = .001) were more commonly reported in peanut anaphylaxis. Most cases were labelled as severe anaphylaxis (Ring&Messmer grade III 65% vs. 56% and grade IV 1.1% vs. 0.9%; p = .001). Self-administration of intramuscular adrenaline was low (17% vs. 15%), professional adrenaline administration was higher in non-peanut food anaphylaxis (34% vs. 26%; p = .003). Hospitalization was higher for peanut anaphylaxis (67% vs. 54%; p = .004). Conclusions The European Anaphylaxis Registry data confirmed peanut as one of the major causes of severe, potentially life-threatening allergic reactions in European children, with some characteristic features e.g., presence of asthma comorbidity and increased rate of biphasic reactions. Usage of intramuscular adrenaline as first-line treatment is low and needs to be improved. The Registry, designed as the largest database on anaphylaxis, allows continuous assessment of this condition

Anaphylaxis in Elderly Patients-Data From the European Anaphylaxis Registry

Background: Elicitors and symptoms of anaphylaxis are age dependent. However, little is known about typical features of anaphylaxis in patients aged 65 years or more. Methods: The data from the Network for Online Registration of Anaphylaxis (NORA) considering patients aged ≥65 (elderly) in comparison to data from adults (18–64 years) regarding elicitors, symptoms, comorbidities, and treatment measures were analyzed. Results: We identified 1,123 elderly anaphylactic patients. Insect venoms were the most frequent elicitor in this group (p < 0.001), followed by drugs like analgesics and antibiotics. Food allergens elicited less frequently anaphylaxis (p < 0.001). Skin symptoms occurred less frequently in elderly patients (77%, p < 0.001). The clinical symptoms were more severe in the elderly (51% experiencing grade III/IV reactions), in particular when skin symptoms (p < 0.001) were absent. Most strikingly, a loss of consciousness (33%, p < 0.001) and preexisting cardiovascular comorbidity (59%, p < 0.001) were more prevalent in the elderly. Finally, adrenaline was used in 30% of the elderly (vs. 26% in the comparator group, p < 0.001) and hospitalization was more often required (60 vs. 50%, p < 0.001). Discussion and Conclusion: Anaphylaxis in the elderly is often caused by insect venoms and drugs. These patients suffer more often from cardiovascular symptoms, receive more frequently adrenaline and require more often hospitalization. The data indicate that anaphylaxis in the elderly tends to be more frequently life threatening and patients require intensified medical intervention. The data support the need to recognize anaphylaxis in this patient group, which is prone to be at a higher risk for a fatal outcome

Integrated Process Modeling—A Process Validation Life Cycle Companion

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events

Data science workflows for biopharmaceutical manufacturing process validation stage 1

The biopharmaceutical market is innovative, well growing and delivering about 20% of all pharmaceutical product to patients. In order to consistently deliver high product quality the biopharmaceutical manufacturing process needs to be understood, controlled and effectively monitored. Those tasks are commonly addressed in manufacturing process validation, which is also requested from regulatory agencies due its importance in respect to patient risk. Especially the first step of achieving process knowledge by understanding and controlling potential sources of variance and risks is key to ensure successful routine manufacturing. Those activities are usually covered in process characterization studies (PCS) in industry. Within this thesis, an advanced data science workflow for PCS is presented that points towards a holistic risk awareness and control strategy via knowledge obtained from single unit operations. Major novelties described in this thesis ensure on the one hand that information from single unit operations such as fermentation processes are accurately extracted. Moreover, novel statistical power analysis methods are presented to ensure that no critical information or process parameter on product quality has been overlooked. On the other hand an integrated process model has been introduced that facilitates to combine this knowledge from single unit operation by means of Monte Carlo simulation. The integrated process model was successfully applied on a real industrial process to derive holistic risk awareness and a holistic control strategy. By applying this advanced workflow it is anticipated that variance in process output and product quality can be reduced and commensurately producers and patient risk is lowered.11

Holistic Design of Experiments Using an Integrated Process Model

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality

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

Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle

Bacterial Cellulose&mdash;Adaptation of a Nature-Identical Material to the Needs of Advanced Chronic Wound Care

Modern wound treatment calls for hydroactive dressings. Among the variety of materials that have entered the field of wound care in recent years, the carbohydrate polymer bacterial cellulose (BC) represents one of the most promising candidates as the biomaterial features a high moisture-loading and donation capacity, mechanical stability, moldability, and breathability. Although BC has already gained increasing relevance in the treatment of burn wounds, its potential and clinical performance for &ldquo;chronic wound&rdquo; indications have not yet been sufficiently investigated. This article focuses on experimental and clinical data regarding the application of BC within the indications of chronic, non-healing wounds, especially venous and diabetic ulcers. A recent clinical observation study in a chronic wound setting clearly demonstrated its wound-cleansing properties and ability to induce healing in stalling wounds. Furthermore, the material parameters of BC dressings obtained through the static cultivation of Komagataeibacter xylinus were investigated for the first time in standardized tests and compared to various advanced wound-care products. Surprisingly, a free swell absorptive capacity of a BC dressing variant containing 97% moisture was found, which was higher than that of alginate or even hydrofiber dressings. We hypothesize that the fine-structured, open porous network and the resulting capillary forces are among the main reasons for this unexpected result