Studien syftar till att öka förståelsen för de utmaningar som läkemedelstillverkare står inför när manuellt arbete i befintliga tillverkningsprocesser ska förbättras. För att uppnå syftet utfördes en fallstudie vid ett av AstraZenecas produktionsavsnitt, med målet att undersöka hur manuellt arbete konkret kan och bör förbättras samt identifiera de faktorer som begränsar förbättringsarbetet. Studien identifierade både risker och potentiella förbättringsområden. För att förebygga risker presenterade studien följande rekommendationer: gå från att agera reaktivt till att agera proaktivt, riskbedöm alla aktiviteter och arbetsmoment, se över arbetsställningar och arbetsrörelser samt visualisera detta, inför specifika ergonomirutiner, utbilda personalen i grundläggande ergonomi och arbetsteknik, kompetensutveckla personalstyrkan, anställ ergonom internt, implementera studiens designförslag och utred möjligheten att förändra arbetsstationens utformning ytterligare. Studiens resultat indikerar att det är svårt att förändra arbetsstationernas utformning, således rekommenderas främst insatser på organisations- och individnivå. Faktorer som särskilt hindrade förbättringsarbetet kopplades till rådande regelverk, men fabrikens nuvarande förutsättningar påverkade också. Dessa hinder medförde att förvaltning av befintlig produktion prioriterades över faktisk förbättring. Studien bekräftade således att läkemedelstillverkare tenderar att ha en restriktiv syn på förändring. Föråldrade anläggningar och en ergonomisk eftersatt produktionsmiljö identifierades som de främsta konsekvenserna av detta synsätt. Slutsatsen av denna studie är att manuellt arbete i den studerade processen kan behöva förbättras och att förbättringsarbete generellt är utmanade. De framtagna rekommendationerna har anpassats efter en produktionsmiljö där teknik och designförändringar är svåra att genomföra. För att råda bukt på industrins restriktiva syn på förändring kan även en attitydförändring avseende förbättring av befintlig produktion krävas. / The purpose of this study is to enhance knowledge about the challenges pharmaceutical manufacturers face when trying to optimize manual work in manufacturing processes. To examine this, a case study of a production section at AstraZeneca was conducted. As part of the study, the factors that limit the improvement of manual work were identified, as well as in which ways work could and should be improved. The study identified both ergonomic hazards and potential areas for improvement. To prevent risks, the study presented the following recommendations: React proactively instead of reactively, ensure all activities and work steps are risk-adjusted, review work postures and movements, introduce routines with a focus on ergonomic inspectors, training staff in basic ergonomics and work techniques, develop the skills of the workforce, hire an ergonomist internally, implement the new design proposal and try to radically change workstation design. Study results indicated that changing the workstation layout and design was challenging. In this regard, the recommendations primarily address initiatives at the individual and organizational levels. Regulatory restrictions, as well as prevailing conditions in the factory, limit improvement. Additionally, the study confirms pharmaceutical manufacturers' conservative approach to change, identifying outdated facilities and a neglected ergonomic environment as primary reasons for their inaction. In conclusion, manual work in the studied process can be improved; however, it is challenging to do so. This is why the recommendations have been adjusted to fit a production environment where technology and design changes are difficult to implement. In other words, they are tailored to suit the needs of regulated industries. Furthermore, it may be necessary to overhaul the industry's attitude toward improving existing production as well.

<p dir="ltr">The dissolution profile is one of the most important critical quality attributes (CQAs) for pharmaceutical solid oral dosage forms, as failure to meet the dissolution specification can impact bioavailability. Dissolution tests are essential to assess lot-to-lot product quality and guide the development of new formulations. Therefore, predictive dissolution reduced-order models (ROM) are crucial for the successful implementation of any real-time release testing (RTRT) strategy. Mechanistic and semi-mechanistic ROMs of tablet dissolution for realizing quality by control (QbC) and RTRT frameworks in continuous manufacturing are still scarce or nonexistent. Moreover, realizing the underlying coupled mechanics of wetting, swelling, disintegration, and dissolution is still an open question. This dissertation contributes to developing a mechanistic ROM of pharmaceutical tablet dissolution for the design, optimization, and control of manufacturing processes. We follow several steps towards the progression of the mechanistic model development. First, we develop a semi-mechanistic ROM to capture the relationship between critical process parameters (CPPs), critical material attributes (CMAs), and dissolution profiles. We demonstrate the versatility and the capability of the semi-mechanistic ROM to estimate changes in dissolution due to process disturbances in tablet porosity, lubrication conditions, and moisture content in the powder blend. Next, to understand the underlying coupled mechanism of wetting, swelling, disintegration, and dissolution, we use dynamic micro-computed tomography (micro-CT) with a high temporal resolution to visualize water penetration through the porous network of immediate-release tablets. We couple liquid penetration due to capillary pressure described by the Lucas-Washburn theory with the first-order swelling kinetics of the excipients to provide a physical interpretation of the experimental observations. From the mechanistic understanding of the water penetration kinetics using the micro-CT tests, we propose a two-stage mechanistic ROM, which is comprised of (i) a mechanistic dissolution model of the active pharmaceutical ingredient (API) that solves a population balance model (PBM) for a given API crystal size distribution and dissolution rate coefficient, and (ii) a tablet wetting function that estimates the rate at which the API is exposed to the buffer solution. These two sub-models are coupled by means of convolution in time to capture the start time of the API dissolution process as water uptake, swelling, and disintegration take place. Finally, we demonstrate the versatility and the capability of the mechanistic API dissolution model and the two-stage tablet dissolution ROM to represent the dissolution profile of different pharmaceutical formulations and its connection with CMAs, CPPs, and other CQAs, namely initial API crystal size distribution, porosity, composition, and dimensions of the tablet. In all of the cases considered in this work, the estimations of the model are in good agreement with experimental data. </p>

<p>To apply Industry 4.0 technologies and accelerate the modernization of continuous pharmaceutical manufacturing, digital twin (DT) and advanced process control (APC) strategies are indispensable. The DT serves as a virtual representation that mirrors the behavior of the physical process system, enabling real-time monitoring and predictive capabilities. Consequently, this facilitates the feasibility of real-time release testing (RTRT) and enhances drug product development and manufacturing efficiency by reducing the need for extensive sampling and testing. Moreover, APC strategies are required to address variations in raw material properties and process uncertainties while ensuring that desired critical quality attributes (CQAs) of in-process materials and final products are maintained. When deviations from quality targets are detected, APC must provide optimal real-time corrective actions, offering better control performance than the traditional open loop-control method. The progress in DT and APC is beneficial in shifting from the paradigm of Quality-by-Test (QbT) to that of Quality-by-Design (QbD) and Quality-by-Control (QbC), which emphasize the importance of process knowledge and real-time information to ensure product quality.</p> <p><br></p> <p>This study focuses on four key elements and their applications in a continuous dry granulation tableting process, including feeding, blending, roll compaction, ribbon milling and tableting unit operations. Firstly, the necessity of a digital infrastructure for data collection and integration is emphasized. An ISA-95-based hierarchical automation framework is implemented for continuous pharmaceutical manufacturing, with each level serving specific purposes related to production, sensing, process control, manufacturing operations, and business planning. Secondly, investigation of process analytical technology (PAT) tools for real-time measurements is highlighted as a prerequisite for effective real-time process management. For instance, the measurement of mass flow rate, a critical process parameter (CPP) in continuous manufacturing, was previously limited to loss-in-weight (LIW) feeders. To overcome this limitation, a novel capacitance-based mass flow sensor, the ECVT sensor, has been integrated into the continuous direct compaction process to capture real-time powder flow rates downstream of the LIW feeders. Additionally, the use of near-infrared (NIR)-based sensor for real-time measurement of ribbon solid fraction in dry granulation processes is explored. Proper spectra selection and pre-processing techniques are employed to transform the spectra into useful real-time information. Thirdly, the development of quantitative models that establish a link between CPPs and CQAs is addressed, enabling effective product design and process control. Mechanistic models and hybrid models are employed to describe the continuous direct compaction (DC) and dry granulation (DG) processes. Finally, applying APC strategies becomes feasible with the aid of real-time measurements and model predictions. Real-time optimization techniques are used to combine measurements and model predictions to infer unmeasured states or mitigate the impact of measurement noise. In this work, the moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework is utilized. It leverages the capabilities of MHE for parameter updates and state estimation to enable adaptive models using data from the past time window. Simultaneously, NMPC ensures satisfactory setpoint tracking and disturbance rejection by minimizing the error between the model predictions and setpoint in the future time window. The MHE-NMPC framework has been implemented in the tableting process and demonstrated satisfactory control performance even when plant model mismatch exists. In addition, the application of MHE enables the sensor fusion framework, where at-line measurements and online measurements can be integrated if the past time window length is sufficient. The sensor fusion framework proves to be beneficial in extending the at-line measurement application from just validation to real-time decision-making.</p>

<p>  </p> <p>In the pharmaceutical industry, special care must be taken by companies to guarantee high quality medications that are free from byproducts and impurities. The development process involves various considerations including solvent selection, solubility screening, unit operation selection, environmental, and health impact evaluations. Traditionally, pharmaceutical manufacturing consisted of large, centralized facilities to meet pharmaceutical demands; however, there has been a recent shift toward distributed manufacturing. With distributed manufacturing platforms, rapidly changing supply chain needs can be met regionally in addition to supplying small-volume medications and personalized medicines to hospitals and pharmacies. To produce quality pharmaceuticals, distributed manufacturing platforms should integrate digital design, novel unit operations, and process analytical technology (PAT) tools for quality monitoring and control. In this dissertation, a process design and development framework is proposed and implemented for a small-scale pharmaceutical manufacturing platform: MiniPharm.</p> <p>Various approaches to process design are detailed in this dissertation, which include heuristic-based and digital or simulation-based design. For heuristic-based design, the knowledge of the researchers was utilized to provide unit operation evaluation and screening of process alternatives. In cases when unit operations were highly complex, digital or simulation-based design was utilized to conduct sensitivity analyses and simulation-based design of experiments. With the implementation of simulation-based design, material and time needs were reduced while gaining knowledge about the system. The integration of various unit operations comes with increased understanding of start-up dynamics and operational constraints. What was found to be the most successful approach was the combination of heuristics and digital design to combine researcher knowledge and experience with the information gained from process modeling and simulation to create process alternatives that utilized system dynamics to reach desired process outcomes. </p> <p>Additionally, MiniPharm was used for process model development at the small-scale. Various software packages have been made commercially available that focus on production scale; however, models for small-scale operations are not typically implemented in these packages. Models for unit operations were fit with collected experimental data to estimate model parameters for small-scale synthesis, liquid-liquid extraction, and crystallization unit operations. The models were implemented to better capture the heat and mass transfer of the milli-fluidic scale platform, which consist of unit operations housed within microchannels. MATLAB was utilized for estimation of parameters such as kinetic rate constants and overall mass transfer coefficients. These parameters were used for design space determination and process disturbance simulation. The exploration of the impact of various process parameters on quality attributes helps researchers gain a deeper understanding about the manufacturing process and helps to demonstrate how to control the process. </p> <p>An important aspect of MiniPharm is the process development progress that has been demonstrated. With the construction of a modular and reconfigurable platform, various process alternatives can now be experimentally validated. The integration of unit operations operated at a decreased scale makes MiniPharm an example of process intensification. The implementation of integrated unit operations decreases handling time of intermediates and reduces the overall footprint for manufacturing. Designed to allow for increased flexibility of operation, perfluoroalkoxy alkane (PFA) tubing was used for synthesis and purification. With PFA tubing clean in place procedures can be implemented using continuous solvent flow or the low cost, PFA tubing can be replaced. The modular nature of the platform also allows for the investigation of individual unit operations for performance evaluation. </p> <p>Finally, a novel continuous solvent switch distillation unit operation was designed and constructed along with customized reactor and crystallizers for process alternative screening for the synthesis and purification of two compounds: Diphenhydramine hydrochloride and Lomustine. Diphenhydramine hydrochloride is a low-value, high volume allergy medication commonly found in Benadryl and Lomustine is a high-value, low volume cancer medication used to treat glioblastoma and Hodgkin Lymphoma. The production of the compounds demonstrated the flexibility of the manufacturing platform to produce both a generic and a specialty medication. A versatile platform is needed for distributed manufacturing because of quickly changing supply chain needs. Overall, this dissertation successfully demonstrates the process design, development, and simulation for small-scale manufacturing.</p>

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>

The traditional quality culture in the pharmaceutical industry is driven by the regulatory process of marketing authorisation and manufacturing authorisation. These components of the South African regulatory control system are exclusively technically-orientated, with no managerial focus. This study identifies several quality management principles which could find general application in the pharmaceutical industry. The research compares the current regulatory control system with the total quality concept, and highlights the positive contribution which the total quality approach is able to make in terms of its field of reference; the strategic business value of quality; quality policy formulation; the quality organisational structure; enhanced operations management; and management's control over quality costs, in particular / Economic & Management Sciences / M.Com. (Business Economics)

The traditional quality culture in the pharmaceutical industry is driven by the regulatory process of marketing authorisation and manufacturing authorisation. These components of the South African regulatory control system are exclusively technically-orientated, with no managerial focus. This study identifies several quality management principles which could find general application in the pharmaceutical industry. The research compares the current regulatory control system with the total quality concept, and highlights the positive contribution which the total quality approach is able to make in terms of its field of reference; the strategic business value of quality; quality policy formulation; the quality organisational structure; enhanced operations management; and management's control over quality costs, in particular / Economic and Management Sciences / M.Com. (Business Economics)