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At-Home Patients and AI: Q&A with Riccardo Butta

Feature
Article

Butta discusses how new technologies are being implemented in at-home devices.

Stevanato Group

Riccardo Butta
President of the Americas
Stevanato Group

New technologies have made at-home wearables much more effective at ensuring patients adhere to the medication and treatment plans their doctors put them on. Riccardo Butta, president of the Americas at Stevanato Group, discusses how technologies like AI are taking these devices even further.

Pharmaceutical Executive: What are the benefits of at-home wearables?
Riccardo Butta: There is currently a lot of effort being put into at-home wearables. This is largely due to providers' desire to improve patient adherence. This provides both benefits for the patient and treatment provider by reducing costs through fewer systems being used and allowing for patients to administer their treatments in the comfort of their home. Additionally, the growing number of therapies within immunology, oncology and pain management calls for better at-home wearables. Since these treatments often require larger volumes, which cannot be delivered through traditional methods, at-home wearables serve as a much-needed bridge within this space.

PE: How is AI being utilized in this space?
Butta: In short Artificial Intelligence (AI) is being used primarily for diagnostics application purposes to help with the diagnosis and treatment of patients. Additionally, AI is used to aid in the inspection and quality assurance of pharmaceutical products. This new technology has enabled us to create data sets which can then be utilized to improve manufacturing decisions.

AI has ultimately revolutionized the pharmaceutical industry, permeating every aspect from drug discovery to drug manufacturing. Due to its ability to analyze vast amounts of data, AI has become an invaluable tool in enabling the biopharma industry to bring safer and more reliable drugs to the market. Notable applications of AI in biomanufacturing include visual inspection equipment, deep learning, and digital twin technology–all of which provide drugmakers and biomanufacturers with increased productivity and improved efficiency.

While these technologies are redefining the standards of drug development and manufacturing, they also come with drawbacks that drug manufacturers must overcome to reap the ultimate benefits of adopting these cutting-edge technologies.

PE: What trends are you seeing in the at-home wearable market?
Butta: At-home wearables are trending towards the inclusion of larger volume capacities were once not possible with traditional devices such as injectors and pens. Another trend we’ve seen is the addition of digital connectivity within at-home wearable devices. This connectivity provides a level of visibility for providers into the patient's administration of their treatments. Additionally, there has been a move towards semi-reusable devices to prevent less waste and reduce costs.

PE: What makes this market attractive to investors?
Butta: What makes this attractive to investors is the ongoing developments and improvements in treatments due to new technologies and practices that enable the pharmaceutical manufacturing industry to thrive.

Take manual inspection for example. It continues to be the gold standard for detecting defects across the drug manufacturing lifecycle despite this process being largely time-consuming and labor-intensive. This can be challenging when the market continues to demand a higher volume output. To address these limitations, drug manufacturers are turning to automated inspection systems that incorporate advanced vision software and machine learning algorithms. The implementation of visual inspection equipment has significantly impacted the quality control process for drug manufacturers. These systems can efficiently inspect containers, detect defects, and ensure product integrity at high speeds, significantly increasing manufacturing capacity. Now, not only does the use of AI-powered vision equipment enhance the efficiency of defect detection but also allows for a more precise analysis of inspection data. By automating the inspection process, pharmaceutical manufacturers can improve productivity, reduce errors, and enhance overall product quality, all of which are exciting to hear for those looking to invest.

Additionally, we’re seeing a pattern of increasing automation, through the use of traditional rule-based algorithms and the application of deep learning models, as AI continues to permeate the industry. Deep learning is another exciting development that will have many investors' interested as it has emerged as a powerful tool within the biomanufacturing industry, offering substantial benefits in terms of quality control and inspection performance. Particularly, when applied to visual inspection, deep learning algorithms can significantly enhance accuracy and efficiency throughout the biomanufacturing lifecycle. By leveraging extensive training on large datasets, deep learning models can detect subtle defects and anomalies. This not only improves the overall product quality but also reduces the number of false rejects and eliminates the need for the manual re-inspection of grey items that do not meet quality standards. As a result, deep learning helps streamline the inspection process, saving both time and resources.

Furthermore, deep learning also leads to a reduction in the Total Cost of Ownership (TCO) for manufacturers. These robust systems can adapt to variations within production without the need to constantly adjust the inspection recipe, ensuring consistent performance and eliminating the costs associated with frequent modifications. With its ability to enhance quality, increase inspection performance, and reduce operational expenses, deep learning has proven to provide significant benefits to the pharmaceutical industry.

As innovations continue to skyrocket within this industry, more and more money will be coming out because of faster and more efficient processes proving to be a reliable and exciting market to invest in.

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