Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiranim metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova

New Methods for ferrous raw materials characterization in electric steelmaking

425 p.In the siderurgical sector, the steel scrap is the most important raw material in electric steelmaking,contributing between 70% of the total production costs. It is well-known how the degree of which thescrap mix can be optimized, and also the degree of which the melting operation can be controlled andautomated, is limited by the knowledge of the properties of the scrap and other raw-materials in thecharge mix.Therefore, it is of strategic importance having accurate information about the scrap composition of thedifferent steel scrap types. In other words, knowing scrap characteristics is a key point in order to managethe steel-shop resources, optimize the scrap charge mix/composition at the electric arc furnace (EAF),increase the plant productivity, minimize the environmental footprint of steelmaking activities and tohave the lowest total cost of ownership of the plant.As a main objective of present doctoral thesis, the doctorate will provide new tools and methods of scrapcharacterization to increase the current recycling ration, through better knowledge of the quality of thescrap, and thus go in the direction of a 100% recycling ratio. In order to achieve it, two main workinglines were developed in present research. Firstly, it was analysed not only the different existingmethodologies for scrap characterization and EAF process optimization, but also to develop new methodsor combination of existing, Secondly, it was defined a general recommendations guide for implementingthese methods based on the specifics of each plant

Measurement, optimisation and control of particle properties in pharmaceutical manufacturing processes

Previously held under moratorium from 2 June 2020 until 6 June 2022.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material.The understanding and optimisation of particle properties connected to their structure and morphology is a common objective for particle engineering applications either to improve materialhandling in the manufacturing process or to influence Critical Quality Attributes (CQAs) linked to product performance. This work aims to demonstrate experimental means to support a rational development approach for pharmaceutical particulate systems with a specific focus on droplet drying platforms such as spray drying. Micro-X-ray tomography (micro-XRT) is widely applied in areas such as geo- and biomedical sciences to enable a three dimensional investigation of the specimens. Chapter 4 elaborates on practical aspects of micro-XRT for a quantitative analysis of pharmaceutical solid products with an emphasis on implemented image processing and analysis methodologies. Potential applications of micro-XRT in the pharmaceutical manufacturing process can range from the characterisation of single crystals to fully formulated oral dosage forms. Extracted quantitative information can be utilised to directly inform product design and production for process development or optimisation. The non-destructive nature of the micro-XRT analysis can be further employed to investigate structure-performance relationships which might provide valuable insights for modelling approaches. Chapter 5 further demonstrates the applicability of micro-XRT for the analysis of ibuprofen capsules as a multi-particulate system each with a population of approximately 300 pellets. The in-depth analysis of collected micro-XRT image data allowed the extraction of more than 200 features quantifying aspects of the pellets’ size, shape, porosity, surface and orientation. Employed feature selection and machine learning methods enabled the detection of broken pellets within a classification model. The classification model has an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets from three capsules. The combination of single droplet drying (SDD) experiments with a subsequent micro-XRT analysis was used for a quantitative investigation of the particle design space and is described in Chapter 6. The implemented platform was applied to investigate the solidification of formulated metformin hydrochloride particles using D-mannitol and hydroxypropyl methylcellulose within a selected, pragmatic particle design space. The results indicate a significant impact of hydroxypropyl methylcellulose reducing liquid evaporation rates and particle drying kinetics. The morphology and internal structure of the formulated particles after drying are dominated by a crystalline core of D-mannitol partially suppressed with increasing hydroxypropyl methylcellulose additions. The characterisation of formulated metformin hydrochloride particles with increasing polymer content demonstrated the importance of an early-stage quantitative assessment of formulation-related particle properties. A reliable and rational spray drying development approach needs to assess parameters of the compound system as well as of the process itself in order to define a well-controlled and robust operational design space. Chapter 7 presents strategies for process implementation to produce peptide-based formulations via spray drying demonstrated using s-glucagon as a model peptide. The process implementation was supported by an initial characterisation of the lab-scale spray dryer assessing a range of relevant independent process variables including drying temperature and feed rate. The platform response was captured with available and in-house developed Process Analytical Technology. A B-290 Mini-Spray Dryer was used to verify the development approach and to implement the pre-designed spray drying process. Information on the particle formation mechanism observed in SDD experiments were utilised to interpret the characteristics of the spray dried material

Engineering Sustainability for the Future

The 38th International Manufacturing Conference, IMC38, showcases current research in the field of "manufacturing engineering" undertaken in Ireland by postgraduate students and experienced researchers. Indicative topics, in line with the contents of these proceedings, include; sustainable and energy efficient manufacturing, additive manufacturing, Industry 4.0 and digital manufacturing, machine tool, automation and manufacturing system design, surface engineering, forming and joining process research. The IMC community is also involved in research aimed at improving the learning experience of undergraduate and graduate engineers and developing high level skills for the manufacturing engineer of the future. The theme for this year’s conference is Sustainable Manufacturing, with a particular emphasis on a) Digitalisation of Manufacturing – its impact on sustainability and b) Addressing sustainability in Engineering Education, Industrial Training and CPD.Science Foundation Irelan

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”