6 Ways Artificial Intelligence Can Improve Pharmaceuticals
In the competitive arena of pharmaceuticals, the focus is always on increasing production and minimizing cost. What better way to increase productivity and further gains than by improving the quality of products and services offered to patients/end users? With some creative innovation and the advancement of technology, a significant game-changer in healthcare and pharmaceuticals is Artificial Intelligence (AI). AI is shepherding in imaginative ways to innovate and transform the way we assess drug development, manufacturing, and testing. Here we highlight six ways artificial intelligence can improve quality in pharmaceuticals.
1. Enhanced Drug Discovery and Development
The first significant way artificial intelligence can improve pharmaceuticals is through drug discovery and development. Drug discovery is a costly and time-consuming. Current approaches to creating new compounds typically involve trial-and-error. Artificial intelligence accelerates this process sifting through and analyzing vast amounts of data that help identify potential drug candidates. AI can also help to predict the behavior of drugs and their interactions with the body by providing more information about their potential risks and benefits.
2. Improved Manufacturing through Quality
Artificial intelligence also has the ability to refine the manufacturing process of pharmaceuticals. It is essential that companies manufacturing pharmaceuticals adhere to strict regulations and perform extensive quality control operations to ensure that the final product meets the required standards. Here at Quality Means Business we use artificial intelligence and quality intelligence to ensure that all critical-to-quality needs are met. Our Quality Intelligence Solution is a cloud-native solution powered by AI engines that enables end-to-end quality workflow automation and predictive insights paired with regulatory orchestration of Real-World Evidence. AI can also surveil and optimize manufacturing processes in real-time, improving overall efficiency. Through machine learning, AI is able to pinpoint potential issues in the manufacturing process before they become significant problems, a key feature in our Quality Intelligence Solution. This reduces the number of failed batches, minimize waste, and ensures increases in profitability.
3. Superior Quality Control
As mentioned above, QMB is a quality powerhouse and domain authority. Quality is not only a crucial part of the pharmaceutical industry, but is also a profit powerhouse, if done correctly. Not only are profits maximized, but overall patient quality of life is improved when quality is the focal point. AI can help to automate quality processes by improving workload efficiency and reducing errors. Machine learning’s ability to identify patterns through data can allow for production processes to meet the required standards, identify incoming potential issues, and increase production overall.
4. High-Grade Clinical Trials
Clinical trials can be considered as the most crucial portion of the drug development process. Clinical trials provide the data needed to assess the safety and efficacy of new drugs. Artificial Intelligence and machine learning are able to streamline the clinical trial process by accurately identifying potential patients who may be suitable for trials, in turn improving recruitment and retention rates. AI/ML can also analyze data from clinical trials, identifying trends and patterns that may not be immediately apparent to researchers. This can help to improve the accuracy and efficiency of clinical trials, leading to better quality data and more effective drugs.
5. Customized/Personalized Medicine
Personalized medicine is a new field in the pharmaceutical industry, that is centered around developing drugs that are tailored to individual patients. AI is able to make significant strides in personalized medicine by utilizing pre-existing patient data, such as genetics, lifestyle, and medical history, to identify the most effective treatments for individual patients. Combining this information with the vast amount of medical data out there can and will improve the quality of care and outcomes for many. Tailored treatment plans are accompanied with the added benefit of minimizing risk and adverse effects.
6. Streamlined Supply Chain
Supply chain in Big Pharma is complex and demanding. The challenge of involving multiple stakeholders and processes to achieve a common goal is an everyday endeavor. Recently, artificial intelligence has improved the efficiency of supply chain processes via machine learning. As data is analyzed, patterns and insights such as identifying potential bottlenecks allow room for innovation and risk mitigation. On a more basic level, AI is able to monitor inventory levels, predicting when stocks may run low and alerting stakeholders to potential issues. This prevents any delays and ensures drugs are delivered on time, reducing the risk of stock-outs and improving patient access to medications.
Conclusion
The landscape of Healthcare and Pharmaceutical industry is constantly transforming. New ways of innovation are being developed to improve the quality of drugs, services, and quality of life for patients. In the end, AI/ML is a tool we can leverage to speed up the drug discovery and development process, enhance clinical trials, personalize medicine, and transform the everyday quality of life for patients throughout the world. As AI continues to evolve, we can expect to see even more significant advancements in the quality of pharmaceuticals, leading to better outcomes for all.