Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

Microfluidics for personalized drug delivery

This review highlights the transformative impact of microfluidic technology on personalized drug delivery. Microfluidics addresses issues in traditional drug synthesis, providing precise control and scalability in nanoparticle fabrication, and microfluidic platforms show high potential for versatility, offering patient-specific dosing and real-time monitoring capabilities, all integrated into wearable technology. Covalent conjugation of antibodies to nanoparticles improves bioactivity, driving innovations in drug targeting. The integration of microfluidics with sensor technologies and artificial intelligence facilitates real-time feedback and autonomous adaptation in drug delivery systems. Key challenges, such as droplet polydispersity and fluidic handling, along with future directions focusing on scalability and reliability, are essential considerations in advancing microfluidics for personalized drug delivery.

The role of artificial intelligence in generating original scientific research

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.

Ethical perspective on AI hazards to humans: A review

This article explores the potential ethical hazards of artificial intelligence (AI) on society from an ethical perspective. We introduce the development and application of AI, emphasizing its potential benefits and possible negative impacts. We particularly examine the application of AI in the medical field and related ethical and legal issues, and analyze potential hazards that may exist in other areas of application, such as autonomous driving, finance, and security. Finally, we offer recommendations to help policymakers, technology companies, and society as a whole address the potential hazards of AI. These recommendations include strengthening regulation and supervision of AI, increasing public understanding and awareness of AI, and actively exploring how to use the advantages of AI to achieve a more just, equal, and sustainable social development. Only by actively exploring the advantages of AI while avoiding its negative impacts can we better respond to future challenges.

Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.