Artificial intelligence solutions are now employed at every stage of the pharmaceutical company value chain, from molecule discovery to preclinical studies, clinical trials to manufacturing, and supply chain management, treatment availability, and pharmacovigilance.
Another use involves adopting AI to reduce the environmental impact of companies by optimizing production processes, reducing energy consumption and fostering more sustainable practices.
Improving efficiency
By analyzing real-time data, predictive models make it possible to optimize energy consumption in production processes, reducing waste and improving overall supply chain efficiency.
According to an Ammagamma analysis, adopting AI systems enables companies to improve predictive capabilities by 60 percent, leading to up to 10 percent reduction in production costs.
Angelini Pharma, for example, has launched a strategic project that aims to digitize and make production processes more sustainable through the integration of artificial intelligence, augmented reality, and other advanced technologies.
The program involves the implementation of intelligent automation systems to optimize operational efficiency, especially technologies dedicated to improving the materials process for sachet products with a view to sustainability and circular economy.
New technology, in fact, will optimize production efficiency by reducing intermediate steps and enabling the use of more sustainable packaging. The project also includes the adoption of predictive algorithms to monitor and control plant operations in real time while minimizing wasted resources and energy.
Green Chemistry
Process optimization enables more sustainable production lines for chemical synthesis, another step toward “green chemistry.”
AstraZeneca, for example, has integrated AI, robotics, and automation solutions into R&S processes in its green chemistry journey. These technologies enable researchers to make better decisions faster. The iLab in Gothenburg, Sweden, is a fully automated medicinal chemistry laboratory prototype that the British big-pharma says is taking the drug discovery design, creation, testing, and analysis cycle to new levels of efficiency through full integration with the molecular artificial intelligence group.
The potential of AI to compare huge quantities of molecules with each other very quickly can also be used to identify more sustainable molecules, as in the case of the collaboration between IBM and L’Oréal, which aims to develop a generative AI model to select ingredients with a lower environmental impact for the production of the French house’s cosmetics. This project aims to reduce waste of energy and materials in the formulation process, contributing to the sustainability of the entire production cycle.
Environmental monitoring
AI is also revolutionizing environmental monitoring. Machine learning systems can analyze huge amounts of data from environmental sensors, detecting contamination and leakage of harmful substances in real time.
In the pharmaceutical sector, the use of these tools is particularly important for monitoring and reducing pollutant emissions from manufacturing. Companies in the sector can use AI for control of emissions of organic solvents and other volatile substances, optimizing purification processes and reducing the overall environmental impact.
Challenges
Despite the undoubted potential of AI in promoting sustainability, some challenges remain. Integrating these technologies into production processes requires significant investment and a sound data management strategy.
In addition, environmental regulation and the introduction of international standards for sustainability in life sciences will be crucial in fostering widespread adoption of these solutions
Another relevant challenge is data quality and availability. AI requires complete and accurate datasets to function effectively, but in the pharmaceutical sector, data fragmentation and barriers to sharing are obstacles.
In addition, the complexity of pharmaceutical manufacturing means that the adoption of AI must be adapted to strict safety and regulatory compliance standards, slowing its large-scale implementation.
As always, there is also the fact that the adoption of these somewhat disruptive technologies requires careful change management. Indeed, resistance to change on the part of industry players can be a fatal obstacle in an already uneasy transition. The shift toward automated, AI-based systems requires specialized training and a change in corporate mindset, aspects that can slow-if not prevent-the adoption of new technologies.