Charles University in Prague, Faculty of Pharmacy in Hradec Králové Department of Pharmaceutical Technology Student: Vlachová Hana Consultant: Doc. RNDr. Milan Řehula, CSc. Determination of optimal compression forces and speed of compression for the production of aciclovir tablets. This thesis deals with the characterisation of impact compression process on hardness of the tablets. The theoretical part of the thesis describes the effective substance - aciclovir. It also describes compression equations which explain process of compression and factors influencing terminal weight and hardness of tablets. The aim of the experimental part of the work is to measure physical properties of the tablets and find compression forces and speed of compression which are suitable for compression tablets. Tablets were pressed using Kilian Synthesis 700 machine at six compression forces in the range from 4 kN to 14 kN. The height, weight and hardness of each tablet were measured immediately after dressing and note to help for later calculation. The result of the study was to determine the impact of compression forces and compression speed on tablet hardness and weight. Tablet weight decreses with increasing comppession forces. Hardness increases with increasing compression forces. The range of radial hardness...

>Magister Scientiae - MSc / The pharmaceutical manufacturing sector operates within a highly regulated environment, with companies accountable to South African statutory bodies. The responsible pharmacist (RP) is responsible for their company’s adherence to the legislation requirements. Whilst the Pharmacy and the Medicines Acts outline the RP’s, there is no mandatory training requirement prior to registration as an RP, nor thereafter. This study investigated the role and competencies required of newly registered RPs in meeting their professional responsibilities in the pharmaceutical manufacturing sector. An online survey questionnaire elicited responses from RPs (n=102) about views and perceptions pertaining to their role and responsibilities. In addition, semi-structured interviews were conducted with statutory (n=3) and non-statutory representatives (n=5). Survey findings indicated that the majority (89,5%) of RPs felt competent and that they possessed the necessary skills and training. Almost two-thirds of respondents (63,2%) were experienced RPs who shared some reservations, that RPs may be excluded from far-reaching decisions with potential consequences for the company and patients. They added that RP performance monitoring was not regular, which may indicate that some companies view the RP as an appointment of convenience. The majority of respondents (89,5 %) were in favour of the development of training guidelines Findings from the semi-structured interviews indicated that RPs were not fully aware of their scope of duties and the implications thereof. The interviewees were also concerned that some companies, by not giving the RP role the level of importance and authority it required, were practicing tokenism. Further, that not all RPs had the necessary in-depth knowledge of the applicable laws, regulations, guidelines and codes. A competency framework for newly appointed RPs is needed to streamline their roles and responsibilities in the pharmaceutical manufacturing sector

The theoretical part deals with the problems of cleaning validation as one of the basic principles of quality assurance, which should secure the production of safe, effective and quality medicines. Validation of cleaning processes is required by good manufacturing practice, particularly to prevent contamination of raw materials, intermediate products, products and other materials. This work deals with the legislative control of validation, its organization and formalities. It also deals with the issue cleanliness in manufacturing facilities and its evaluation. The experimental part was carried out in a pharmaceutical company Teva Czech Industries s.r.o in Opava. Analytical method for flutamide was developed and validated. The analytical method will be used to cleaning validation of the device in which it will be produced in the future. Validation of the analytical method included verification of validation characteristics such as accuracy, precision, specificity, linearity, detection and the limit of quantification and stability. Keywords: pharmaceutical manufacturing, validation, cleaning

This dissertation undertakes to cast a new and discerning eye on the continuing enigma of Canadian federal innovation policy. Towards that end the dissertation employs the Developmental Network State (DNS) framework and pursues the question of whether the DNS can be used to explain why Canada - and specifically federal innovation policy since 2000 - has seemingly been unable to increase national Research and Development investments and positively impact Canada’s rate of innovation. Theoretically the dissertation argues that the DNS framework can be used to mend the gap between National Innovation Systems literature with its proclivity for an undertheorization of the state and historical institutionalism, which while robustly conceptualizing the state, has been less engaged with the innovation problématique. While admittedly including some modest conceptual sharpening of the framework, the argument draws on four key pillars of the DNS: policy durability over time; targeted resourcing that can enable innovation; thickening of triadic networks among business, academe and government and incentives for capitalization. The research methodology encompasses both qualitative and quantitative (particularly in the sense of economic) techniques and includes 54 in-depth interviews conducted with innovation leaders across the nation. With respect to the evidence assembled around the health of the Canadian innovation system, OECD indicators over the period of the 2000s generally show deteriorating trends, although investment in Higher Education R&D (HERD) is very much the stalwart exception. As for the four lines of inquiry investigated regarding federal policy, the issue of policy durability in large part reveals an increasing number of priorities overlaid at times with exceedingly ambitious objectives and the ongoing challenge of fashioning a more enduring federal strategy. As for targeted resourcing, while federal investments have been both sustained and substantive and while there has been particular attention to macroeconomic stability and research infrastructure, there has been a continuing pattern of oscillation between a focus on research and one on commercialization. With respect to triadic thickening of networks, the evidence continues to reveal the relatively shallow nature of collaboration - this despite robust funding, institutional mechanisms for networking and an ongoing priority on this issue by government. Here again however business financing of HERD stands out as the exception. In contrast, on incentives for capitalization there has been significant progress, although access to late-stage capital remains a challenge. Additionally, the study undertakes an examination of two sectors - Artificial Intelligence (AI) and pharmaceutical manufacturing, employing a “quasi-experimental” technique that examines impacts of policy initiatives over a period that dates back to 1987. In the case of AI, the enabling conditions of DNS do coalesce to form a robust innovation ecosystem. In the case of pharma, policy efforts have not been so sustained or holistic and innovation indicators duly tell this tale of deteriorating trends. In sum, what emerges is that federal innovation policy in the Canadian context has developed in rather truncated form - and it has tracked within the confines of more historically adept and enduring strategies such as upstream Research and Development and fiscal policy. Indeed, the pillars of the DNS framework serve to illuminate the dissonance between policy intent and impact, highlighting the unique nature of federal innovation policies as they have endeavoured to establish the agendas, funding, networks and capital that can provide a formula for advancing Canada on this front. This case study also suggests that the DNS itself may need honing - specifically that policy durability is not only a sufficient condition for innovation but in fact an anchoring one enabling other dimensions to follow suit.

Antibiotics, oncology drugs and other products, mainly for intravenous use, are produced in facilities with very high cleanroom demands. To achieve pharmaceutical class A and ISO 4, the equipment must meet the highest demands of cleanability, sanitation and sterilization. The purpose of this master thesis is to develop a concept of an autonomous gripper to operate together with a robot in a class A environments that would replace humans. Requirements such as wireless communication, internal energy storage as well as gripping force of at least 1000 N per finger are defined. Additional, all materials in contact with the cleanroom must withstand hydrogen peroxide that is used for sterilisation without releasing particles or gases. A full-scale prototype has been manufactured to evaluate the properties of the concept. Several different concepts of grippers have been generated in this paper. A concept with an angular gripper and a bayonet clutch were selected after the concepts were discussed with the costumer and evaluated in an evaluation matrix. The gripper is driven by a stepping motor and has two gears, one planetary gear and one worm gear which together achieves a total ratio of 980:1. The gripper is wireless controlled using a Raspberry Pi which is programmed is CODESYS. Methods such as CAD and FEM has been used in the design of the gripper. The measured mean gripping force of the gripper is 1206 N and the estimated battery life of the gripper in work is 43 minutes. The prototype has the outer dimensions 400∙170∙170 mm and the mass 10.9 kg. This thesis, combined with a number of suggestions for improvements can give the gripper potential for cleanroom classification ISO 4. / Antibiotika, cancerläkemedel och andra läkemedelsklassade produkter i huvudsak för intravenöst bruk tillverkas i anläggningar med mycket höga renrumskrav. För att uppnå renrumsklassificering ISO 4 och GMP EU A ställs krav på rengörbarhet, sanitet och sterilisering av utrustningen. Syftet med detta examensarbete är att ta fram ett koncept till ett autonomt gripdon som tillsammans med en robot ska kunna ersätta människor i renrumsklass ISO 4/klass A. Krav finns på bland annat trådlös kommunikation, intern energilagring samt en gripstyrka på minst 1000 N per finger. Dessutom behöver alla material i kontakt med renrummet kunna motstå väteperoxid som används vid sterilisering utan att släppa ifrån sig partiklar eller gaser. En prototyp i full skala har tillverkats för att utvärdera konceptets egenskaper. Flera koncept på gripdon har genererats vartefter ett koncept med en vinkelgripare och bajonettkoppling valdes efter att de olika koncepten diskuterades med kund och utvärderats i en utvärderingsmatris. Gripdonet drivs av en stegmotor och har två växlar, en planetväxel och en snäckväxel som tillsammans har en total utväxling på 980:1. Gripdonen styrs trådlöst av en Raspberry Pi som är programmerad i CODESYS. Metoder som CAD och FEM har använts för att detaljutveckla gripdonet. Den uppmätta medelgripkraften för gripdonet är 1206 N och den beräknade batteritiden för gripdonet i arbete är 43 minuter. Prototypen har yttermåtten 400∙170∙170 och väger 10.9 kg. Detta arbete i kombination med en rad förbättringsförslag kan ge gripdonet potential att efter vidareutveckling uppnå renrumsklass ISO 4.

<p>In recent years, the pharmaceutical industry has embarked on an extensive program to modernize its manufacturing resources. Recognizing the limitations of traditional mass manufacturing, the industry is now focused on developing new production systems that can deliver high-quality medicines with enhanced efficiency, flexibility, agility, and reliability. To realize this vision, innovations in three key areas are being pursued: continuous processing, personalized medicine, and Industry 4.0.</p> <p>This thesis contributes towards the industry’s goal by focusing on the development of an advanced system to manufacture solid oral drug products. This system is centered around a pharmaceutical additive manufacturing technology called drop on demand (DoD) printing. This technology is highly effective in making personalized drug products, which allow for customizing dose attributes such as drug loading, release behavior, formulation type, and dosage form, based on patient requirements. To develop the DoD printer into an advanced production system, technologies such as end-to-end continuous processing, real time quality assurance, and automated operation need to be incorporated into it. </p> <p>The studies presented in this thesis implement different aspects of these technologies in the printer. To enable end to end operation, a novel solvent switch process called three phase settling is developed to integrate the DoD system with upstream steps for synthesizing the active ingredient. To facilitate automated processing of formulations with different active ingredients, excipients, and particle concentrations, a model framework is developed to recommend operating conditions for the DoD platform that can deliver on-spec printer operation. To expand the range of personalized dosage forms offered by the system, the manufacturing of a new category of drug products, called mini-tablets, is demonstrated. To provide reconfigurability and quality assurance capabilities in the platform, modular design and process monitoring tools are implemented. To aid optimization and control of drug production processes, a digital twin is developed by combining the models developed for DoD, solvent switch, synthesis, and crystallization operations. </p> <p>The research presented in this thesis lays the foundation for developing the next generation of manufacturing systems for drug products. Incorporation of scalability, autonomous operation, and real time release prediction are critical steps in facilitating the next phase of its development – deployment in real-world manufacturing scenarios.  </p>

<p>In this dissertation, numerical analysis frameworks and software tools for digital design of process systems are developed. More specifically, these tools have been focused on digital design within the pharmaceutical manufacturing space. Batch processing represents the traditional and still predominant pathway to manufacture pharmaceuticals in both the drug substance and drug product spaces. Drug substance processes start with raw materials or precursors to produce an active pharmaceutical ingredient (API) through synthesis and purification. Drug product processes take this pure API in powder form, add excipients, and process the powder into consumer doses such as capsules or tablets.  Continuous manufacturing has allowed many other chemical industries to take advantage of real-time process management through process control, process optimization, and real-time detection of off-spec material. Also, the possibility to reduce total cleaning time of units and encourage green chemistry through solvent reduction or recycling make continuous manufacturing an attractive alternative to batch manufacturing. However, to fully understand and take advantage of real-time process management, digital tools are required, both as soft sensors during process control or during process design and optimization.  Since the shift from batch to continuous manufacturing will proceed in stages, processes will likely adopt both continuous and batch unit operations in the same process, which we will call {\em hybrid} pharmaceutical manufacturing routes. Even though these processes will soon become common in the industry, digital tools that address comparison of batch, hybrid, and continuous manufacturing routes in the pharmaceutical space are lacking. This is especially true when considering hybrid routes. For this reason, PharmaPy, an open-source tool for pharmaceutical process development, was created to address rapid in-silico design of hybrid pharmaceutical processes.  Throughout this work, the focus is on analyzing alternative operating modes within the drug substance manufacturing context. First, the mathematical models for PharmaPy's synthesis, crystallization, and filtration units are discussed. Then, the simulation capabilities of PharmaPy are highlighted, showcasing dynamic simulation of both fully continuous and hybrid processes. However, the technical focus of the work as a whole is primarily on optimization techniques for pharmaceutical process design. Thus, many derivative-free optimization frameworks for simulation-optimization were constructed and utilized with PharmaPy performing simulations of pharmaceutical processes.  The timeline of work originally began with derivative-based methods to solve mixed-integer programs (MIP) for water network sampling and security, as well as nonlinear programs (NLPs) and some mixed-integer nonlinear programs (MINLPs) for design space and feasibility analysis. Therefore, a method for process design that combines both the ease of implementation from a process simulator (PharmaPy) with the computational performance of derivative-based optimization was implemented. Recent developments in Pyomo through the PyNumero package allow callbacks to an input-output or black-box model while using {\sc Ipopt} as a derivative-based solver through the cyipopt interface. Using this approach, it was found that using a PharmaPy simulation as a black box within a derivative-based solver resulted in quicker solve times when compared with traditional derivative-free optimization strategies, and offers a much quicker implementation strategy than using a simultaneous equation-oriented algebraic definition of the problem.  Also, uncertainty exists in virtually all process systems. Traditionally, uncertainty is analyzed through sampling approaches such as Monte Carlo simulation. These sampling approaches quickly become computational obstacles as problem scale increases. In the 1980s, chemical plant design under uncertainty through {\em flexibility analysis} became an option for explicitly considering model uncertainty using mathematical programming. However, such formulations provide computational obstacles of their own as most process models produce challenging MINLPs under the flexibility analysis framework.  Specifically when considering pharmaceutical processes, recent initiatives by the FDA have peaked interest in flexibility analysis because of the so called {\em design space}. The design space is the region for which critical quality attributes (CQAs) may be guaranteed over a set of interactions between the inputs and process parameters. Since uncertainty is intrinsic to such operations, industry is interested in guaranteeing that CQAs hold with a set confidence level over a given operating region. In this work, the {\em probabilistic design space} defined by these levels of confidence is presented to address the computational advantages of using a fully model-based flexibility analysis framework instead of a Monte Carlo sampling approach. From the results, it is seen that using the flexibility analysis framework decreased design space identification time by more than two orders of magnitude.  Given implementation difficulty with new digital tools for both students and professionals, educational material was developed for PharmaPy and was presented as part of a pharmaceutical API process development course at Purdue. The students were surveyed afterward and many of the students found the framework to be approachable through the use of Jupyter notebooks, and would consider using PharmaPy and Python for pharmaceutical modeling and data analysis in the future, respectively.  Through software development and the development of numerical analysis frameworks, digital design of pharmaceutical processes has expanded and become more approachable. The incorporation of rigorous simulations under process uncertainty promotes the use of digital tools in regulatory filings and reduces unnecessary process development costs using model-based design. Examples of these improvements are evident through the development of PharmaPy, a simulation-optimization framework using PharmaPy, and flexibility analysis tools. These tools resulted in a computational benefit of 1 to 2 orders of magnitude when compared to methods used in practice and in some cases reduce the modeling time required to determine optimal operating conditions, or the design space of a pharmaceutical manufacturing process.</p>

Bakgrund: Outsourcing är inget nytt fenomen då transaktionskostnadsteorin formades 1937 som beskrev att företag bör vända sig till marknaden i de fall detta gynnade företaget ekonomiskt. Sedan dess har teorier kring outsourcing förändrats och i dag handlar outsourcing om att kunna fokusera på företagets kärnkompetens och vilka möjligheter och risker outsourcing medför. Läkemedelsbranschen har på senare år börjat outsourca forskning och produktutveckling, vilka ses som kärnprocesser. Det råder därför delade meningar om branschen faktiskt har en kärnprocess. Syfte: Syftet med denna uppsats är att förstå hur och varför läkemedelstillverkningsbranschen outsourcar, om de outsourcade processerna har koppling till företags kärnprocesser och hur outsourcing faktiskt påverkar företaget, både monetärt och icke-monetärt. Metod: Det empiriska materialet insamlades med både kvantitativ och kvalitativ metod. En enkätundersökning genomfördes för att få en generell bild av hur läkemedelstillverkningsbranschen outsourcar för att sedan genomföra tre intervjuer för att få djupare kunskap om varför företag gjort de angivna val kring outsourcing. De intervjuade företagen valdes ut genom ett typiskt urval där företag valdes som svarat utifrån vissa bestämda kriterier. Slutsats: Uppsatsen har belyst att den vanligaste processen att outsourca är tillverkning, men att även forskning och produktutveckling outsourcas till viss del. Detta berodde på att företag upplevde resurs- och kunskapsbrist inom de outsourcade processerna. Studien fann även att sambandet mellan anledningen till och utfallet av outsourcing var svagt. Däremot hamnade den ökade lönsamheten sist inom båda kategorierna. Det påvisades också att det är svårt att definiera en kärnprocess till branschen med anledning av den diversifiering som i dag karaktäriserar branschen. Däremot framkom produktutveckling som den vanligaste kärnprocessen inom företag, där det visade sig att företag inte tenderar att outsourca sin kärnprocess. / Background: Outsourcing is not a new phenomenon. Transaction cost theory was formed in 1937, which described that companies should transfer activities externally when this benefited companies financially. Since then, theories of outsourcing have changed and today, it has been switched to outsourcing of core competencies and a discussion of both opportunities and risks. In recent years the pharmaceutical industry has started to outsource their research and product development, which is considered a core process. Due to this there is a debate whether or not the industry has a core process. Purpose: The purposes of this thesis are to understand how and why the pharmaceutical manufacturing industry is outsourcing, how the outsourced processes relate to their core processes and how outsourcing affects the companies, both monetary and non-monetary. Method: The empirical evidence was gathered with both quantitative and qualitative methods. Firstly, a survey was conducted to get a general picture of how the pharmaceutical manufacturing industry outsources which was then followed by three interviews to gain an in-depth knowledge of why companies have made specified decisions regarding outsourcing. The companies were selected according to a number of specific criteria. Conclusion: The thesis has highlighted that manufacturing is the most common process of outsourcing, but also that research and product development is outsourced to some extent. This was due to the fact that companies experienced resource and knowledge shortage in the outsourced processes. The study also found that the relation between the reason and the outcome of outsourcing was weak. However, the increased profitability ended last in both categories. It was also found that it is difficult to define a core process within the industry due to the diversification that today characterizes the industry. On the other hand, product development appear to be the most common core process within companies, where it also shows that companies tend not to outsource their core process.

Solvents play a crucial role in the Active Pharmaceutical Ingredient (API) manufacturing and are used in large quantities. Most of the industries incinerate the waste solvents or send it to waste management companies for destruction to avoid waste handling and cross-contamination. It is not a cost effective method and also hazardous to the environment. This study has been performed at AstraZeneca’s API manufacturing plant at Sodertalje, Sweden. In order to find a solution, a solvent recovery system is modeled and simulated using ASPEN plus and ASPEN batch modeler. The waste streams were selected based on the quantity and cost of the solvents present in them. The solvent mixture in the first waste stream was toluene-methanol in which toluene was the key-solvent whereas in the second waste stream, isooctane-ethyl acetate was the solvent mixture in which isooctane was the key-solvent. The solvents in the waste stream were making an azeotrope and hence it was difficult to separate them using conventional distillation techniques. Liquid-Liquid Extraction with water as a solvent followed by batch distillation was used for the first waste stream and Pressure Swing Distillation was used for the second waste stream. The design was optimized based on cost analysis and was successful to deliver 96.1% toluene recovery with 99.5% purity and 83.6% isooctane recovery with 99% purity. The purity of the solvents was decided based on the quality conventions used at AstraZeneca so that it can be recovered and recycled in the same system. The results were favorable with a benefit of €335,000 per year and preventing nearly one ton per year carbon dioxide emissions to the environment. A theoretical study for the recovery system of toluene-methanol mixture was performed. The proposed design was an integration of pervaporation to the batch distillation. A blend of polyurethane / poly(dimethylsiloxane) (PU / PDMS) membrane was selected for the separation of methanol and toluene mixture. The results of preliminary calculations show 91.4% toluene recovery and 72% methanol recovery with desired purity.

This dissertation is devoted to the modeling, analysis and development of solution approaches for some optimization-related problems encountered in industrial and manufacturing settings. We begin by introducing a special type of traveling salesman problem called "High Multiplicity Asymmetric Traveling Salesman Problem" (HMATSP). We propose a new formulation for this problem, which embraces a flow-based subtour elimination structure, and establish its validity for this problem. The model is, then, incorporated as a substructure in our formulation for a lot-sizing problem involving parallel machines and sequence-dependent setup costs, also known as the "Chesapeake Problem". Computational results are presented to demonstrate the efficacy of our modeling approach for both the generic HMATSP and its application within the context of the Chesapeake Problem. Next, we investigate an integrated lot-sizing and scheduling problem that is encountered in the primary manufacturing facility of pharmaceutical manufacturing. This problem entails determination of production lot sizes of multiple products and sequence in which to process the products on machines, which can process lots (batches) of a fixed size (due to limited capacity of containers) in the presence of sequence-dependent setup times/costs. We approach this problem via a two-stage optimization procedure. The lot-sizing decision is considered at stage 1 followed by the sequencing of production lots at stage 2. Our aim for the stage 1 problem is to allocate batches of products to time-periods in order to minimize the sum of the inventory and backordering costs subject to the available capacity in each period. The consideration of batches of final products, in addition to those for intermediate products, which comprise a final product, further complicates the lot-sizing problem. The objective for the stage 2 problem is to minimize sequence-dependent setup costs. We present a novel unifying model and a column generation-based optimization approach for this class of lot-sizing and sequencing problems. Computational experience is first provided by using randomly generated data sets to test the performances of several variants of our proposed approach. The efficacy of the best of these variants is further demonstrated by applying it to the real-life data collected with the collaboration of a pharmaceutical manufacturing company. Then, we address a single-lot, lot streaming problem for a two-stage assembly system. This assembly system is different from the traditional flow shop configuration. It consists of m parallel subassembly machines at stage 1, each of which is devoted to the production of a component. A single assembly machine at stage 2, then, assembles products after components (one each from the subassembly machines at the first stage) have been completed. Lot-detached setups are encountered on the machines at the first and second stages. Given a fixed number of transfer batches (or sublots) from each of the subassembly machines at stage 1 to the assembly machine at stage 2, our problem is to find sublot sizes so as to minimize the makespan. We develop optimality conditions to determine sublot sizes for the general problem, and present polynomial-time algorithms to determine optimal sublot sizes for the assembly system with two and three subassembly machines at stage 1. Finally, we extend the above single-lot, lot streaming problem for the two-stage assembly system to multiple lots, but still, for the objective of minimizing the makespan. Due to the presence of multiple lots, we need to address the issue of the sequencing of the lots along with lot-splitting, a fact which adds complexity to the problem. Some results derived for the single-lot version of this problem have successfully been generalized for this case. We develop a branch-and-bound-based methodology for this problem. It relies on effective lower bounds and dominance properties, which are also derived. Finally, we present results of computational experimentation to demonstrate the effectiveness of our branch-and-bound-based methodology. Because of the tightness of our upper and lower bounds, a vast majority of the problems can be solved to optimality at root node itself, while for others, the average gap between the upper and lower bounds computed at node zero is within 0.0001%. For a majority of these problems, our dominance properties, then, effectively truncate the branch-and-bound tree, and obtain optimal solution within 500 seconds. / Ph. D.