Machine learning models for the prediction of pharmaceutical powder properties

Error on title page – year of award is 2023.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development. The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction. Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%. Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%. Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements.Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to the development of robust products and production routes. Tablet formation can be achieved by several techniques however, direct compression (DC) and granulation are the most widely used in industrial operations. DC is of particular interest as it offers lower-cost manufacturing and a streamlined process with fewer steps compared with other unit operations. However, to achieve the full potential benefits of DC for tablet manufacture, this places strict demands on material flow properties, blend uniformity, compactability, and lubrication, which need to be satisfied. DC is increasingly the preferred technique for pharmaceutical companies for oral solid dose manufacture, consequently making the flow prediction of pharmaceutical materials of increasing importance. Bulk properties are influenced by particle attributes, such as particle size and shape, which are defined during crystallization and/or milling processes. Currently, the suitability of raw materials and/or formulated blends for DC requires detailed characterization of the bulk properties. A key goal of digital design and Industry 4.0 concepts is through digital transformation of existing development steps be able to better predict properties whilst minimizing the amount of material and resources required to inform process selection during early- stage development. The work presented in Chapter 4 focuses on developing machine learning (ML) models to predict powder flow behaviour of routine, widely available pharmaceutical materials. Several datasets comprising powder attributes (particle size, shape, surface area, surface energy, and bulk density) and flow properties (flow function coefficient) have been built, for pure compounds, binary mixtures, and multicomponent formulations. Using these datasets, different ML models, including traditional ML (random forest, support vector machines, k nearest neighbour, gradient boosting, AdaBoost, Naïve Bayes, and logistic regression) classification and regression approaches, have been explored for the prediction of flow properties, via flow function coefficient. The models have been evaluated using multiple sampling methods and validated using external datasets, showing a performance over 80%, which is sufficiently high for their implementation to improve manufacturing efficiency. Finally, interpretability methods, namely SHAP (SHapley Additive exPlanaitions), have been used to understand the predictions of the machine learning models by determining how much each variable included in the training dataset has contributed to each final prediction. Chapter 5 expanded on the work presented in Chapter 4 by demonstrating the applicability of ML models for the classification of the viability of pharmaceutical formulations for continuous DC via flow function coefficient on their powder flow. More than 100 formulations were included in this model and the particle size and particle shape of the active pharmaceutical ingredients (APIs), the flow function coefficient of the APIs, and the concentration of the components of the formulations were used to build the training dataset. The ML models were evaluated using different sampling techniques, such as bootstrap sampling and 10-fold cross-validation, achieving a precision of 90%. Furthermore, Chapter 6 presents the comparison of two data-driven model approaches to predict powder flow: a Random Forest (RF) model and a Convolutional Neural Network (CNN) model. A total of 98 powders covering a wide range of particle sizes and shapes were assessed using static image analysis. The RF model was trained on the tabular data (particle size, aspect ratio, and circularity descriptors), and the CNN model was trained on the composite images. Both datasets were extracted from the same characterisation instrument. The data were split into training, testing, and validation sets. The results of the validation were used to compare the performance of the two approaches. The results revealed that both algorithms achieved a similar performance since the RF model and the CNN model achieved the same accuracy of 55%. Finally, other particle and bulk properties, i.e., bulk density, surface area, and surface energy, and their impact on the manufacturability and bioavailability of the drug product are explored in Chapter 7. The bulk density models achieved a high performance of 82%, the surface area models achieved a performance of 80%, and finally, the surface-energy models achieved a performance of 60%. The results of the models presented in this chapter pave the way to unified guidelines moving towards end-to-end continuous manufacturing by linking the manufacturability requirements and the bioavailability requirements

Prediction of powder flow of pharmaceutical materials from physical particle properties using machine learning

Understanding powder flow and how it affects pharmaceutical manufacturing process performance remains a significant challenge for industry. This work aims to improve decision making for manufacturing route selection, achieving the key goal of digital design within Industry 4.0 of being able to better predict properties whilst minimizing the amount of material required and time to inform process selection during early-stage development. A Machine Learning model approach is proposed to predict the flow properties of new materials from their physical properties. The model’s implementation will enhance manufacturing quality by taking advantage of the data generated throughout the manufacturing process

Prediction of powder flow of pharmaceutical materials using machine learning

The lack of understanding of powder flow adds cost and time to the development of robust production routes and compromises manufacturing process performance in the pharmaceutical industry. In this work, implementing machine learning models enables rapid decision-making regarding manufacturing route selection, thus, minimizing the time and amount of material required. This work focuses on using ML models to predict powder flow behavior of pharmaceutical materials for routine, widely available materials

AMBIENTE DE TRABALHO SAUDÁVEL NA ATENÇÃO PRIMÁRIA À SAÚDE: REVISÃO INTEGRATIVA DA LITERATURA

Objetivo: identificar, na produção científica, elementos constituintes do ambiente de trabalho saudável na Atenção Primária à Saúde para a saúde dos trabalhadores. Método: revisão integrativa da literatura, realizada no período de junho a agosto de 2019, conforme protocolo de busca construído com parâmetros de elegibilidade para a definição de uma amostra de 35 estudos. Resultados: a análise resultou na elaboração das categorias acerca do adoecimento e do estresse como repercussões da vivência subjetiva do trabalho e das alternativas protetivas, de educação e de transformação dos ambientes de trabalho. Conclusão: na produção científica estudada, os elementos constituintes do ambiente de trabalho saudável na Atenção Primária à Saúde, relacionados à saúde dos trabalhadores, são geradores de adoecimento e estresse e estão em constante associação ao contexto desse nível de atenção, à gestão política e do processo de trabalho na saúde e às vivências subjetivas no trabalho

Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

Understanding powder flow in the pharmaceutical industry facilitates the development of robust production routes and effective manufacturing processes. In pharmaceutical manufacturing, machine learning (ML) models have the potential to enable rapid decision-making and minimise the time and material required to develop robust processes. This work focused on using ML models to predict the powder flow behaviour for routine, widely available pharmaceutical materials. A library of 112 pharmaceutical powders comprising a range of particle size and shape distributions, bulk densities, and flow function coefficients was developed. ML models to predict flow properties were trained on the physical properties of the pharmaceutical powders (size, shape, and bulk density) and assessed. The data were sampled using 10-fold cross-validation to evaluate the performance of the models with additional experimental data used to validate the model performance with the best performing models achieving a performance of over 80%. Important variables were analysed using SHAP values and found to include particle size distribution D10, D50, and aspect ratio D10. The very promising results presented here could pave the way toward a rapid digital screening tool that can reduce pharmaceutical manufacturing costs

Innovation and access to technologies for sustainable development: diagnosing weaknesses and identifying interventions in the Transnational Arena

Sustainable development – improving human well-being across present generations without compromising the ability of future generations to meet their own needs – is a central challenge for the 21st century. Technological innovation can play an important role in moving society toward sustainable development. However, poor, marginalized, and future populations often do not fully benefit from innovation due to their lack of market or political power to influence innovation processes. As a result, current innovation systems fail to contribute as much as they might to meeting sustainable development goals. This paper focuses on how actors and institutions operating in the transnational arena can mitigate such shortfalls. To identify the most important transnational functions required to meet sustainable development needs our analysis undertook three main steps. First, we developed a framework to diagnose blockages in the global innovation system for particular technologies. This framework was built on existing theory and new empirical analysis. On the theory side, we drew from the literatures of systems dynamics; technology and sectoral innovation systems, science and technology studies, the economics of innovation, and global governance. On the empirical front, we conducted eighteen detailed case studies of technology innovation in multiple sectors relevant to sustainable development: water, energy, health, food, and manufactured goods. We use the framework to analyze our case studies in the common language of (1) technology stocks, (2) non-linear flows between stocks substantiated by specific mechanisms, and (3) characteristics of actors and socio-technical conditions (STCs) which mediate the flows between stocks . We identify blockages in the innovation system for each of the cases, diagnosing where in the innovation system flows were hindered and which specific sets of STCs and actor characteristics were associated with these blockages. Figure E.1 displays the components of our framework and how they relate

Predicting pharmaceutical powder flow from microscopy images using deep learning

The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development

Telepsychiatry During the COVID-19 Pandemic: Development of a Protocol for Telemental Health Care

Background The rapid spread of the Coronavirus disease 2019 (COVID-19) has forced most countries to take drastic public health measures, including the closure of most mental health outpatient services and some inpatient units. This has suddenly created the need to adapt and expand telepsychiatry care across the world. However, not all health care services might be ready to cope with this public health demand. The present study was set to create a practical and clinically useful protocol for telemental health care to be applied in the context of the current COVID-19 pandemic. Methods A panel of psychiatrists from 15 different countries [covering all World Health Organization (WHO) regions] was convened. The panel used a combination of reactive Delphi technique and consensus development conference strategies to develop a protocol for the provision of telemental health care during the COVID-19 pandemic. Results The proposed protocol describes a semi-structured initial assessment and a series of potential interventions matching mild, moderate, or high-intensity needs of target populations. Conclusions Telemedicine has become a pivotal tool in the task of ensuring the continuous provision of mental health care for the population, and the outlined protocol can assist with this task. The strength of this protocol lies in its practicality, clinical usefulness, and wide transferability, resulting from the diversity of the consensus group that developed it. Developed by psychiatrists from around the globe, the proposed protocol may prove helpful for many clinical and cultural contexts, assisting mental health care providers worldwide