Pharmaceutical manufacturers are increasingly interested in the tenets of Industry 4.0, including the use of digital twins to simulate, test and optimize manufacturing processes on a computer before using them in production, according to technology advisory firm ABI Research. It projects spending by pharmaceutical manufacturers on data analytics tools--including the digital twin -- to grow by 27% over the next seven years, to reach $1.2 billion in 2030. As with other manufacturers, pharmaceutical makers plan to use the digital tools to boost productivity and to track their operations. Toronto-based Basetwo recently moved into this market with its software-as-a-service (SaaS) artificial intelligence (AI) platform. Today, the year-old company announced an upcoming $3.8 million seed financing round led by Glasswing Ventures and Argon Ventures.

Advances in the application of artificial intelligence (AI) are starting to have a significant impact on automation technologies used across industry--most notably with machine vision and analytics. And some of the more impactful applications of AI are happening in the pharmaceutical industries. It shouldn't be too surprising that the pharmaceutical industries are looking to optimize production with AI, considering that single batch values for some drugs can exceed $3 million. Yet, research indicates that this industry lags many others when it comes to using analytics to improve production. David Leitham, senior vice president and general manager, pharmaceuticals, at AspenTech.According to David Leitham, senior vice president and general manager, pharmaceuticals, at AspenTech (a supplier of AI software for industrial manufacturers), while other industries have been applying analytics and predictive capabilities to optimize performance and react rapidly to changes in demand, 87% of pharmaceutical industry executives admit their organizations have a poor digital culture.

Any unnecessary downtime can be expensive for pharmaceutical manufacturing operations. What's more, unplanned stoppages can delay the delivery of much-needed product, potentially causing damage to a company's reputation. David Leitham, senior vice president and general manager at industrial artificial intelligence (AI) technology firm AspenTech, recently spoke with Outsourcing-Pharma (OSP) about how AI can be put to use to help predict when maintenance is needed, and avoid unplanned or over-maintenance. OSP: Could you please share an'elevator presentation' description of AspenTech? DL: AspenTech develops software to help customers in capital-intensive industries (such as energy, chemicals, and pharmaceuticals), address their biggest challenges: delivering increased value to stakeholders, responding to an evolving global population, and reducing environmental impact and waste.

Former FDA Commissioner Dr. Scott Gottlieb stressed the need for modernizing the clinical trials process in a speech to the Bipartisan Policy Center in January of this year.1 He is quoted as saying, "digital technologies are one of our most promising tools for making healthcare more efficient." Improving efficiency in clinical trial development is only one potential enhancement that can result from the use of machine learning. Machine learning and artificial intelligence (AI) are often used interchangeably, but that assumption is incorrect. Machine learning is the subset of AI that is related to the development of algorithms that can make accurate predictions of future outcomes via pattern recognition and rules-based logic. Such use of logic and algorithms can improve patient selection, provide predictive long-term outcomes, and reduce the time and cost in the execution of clinical trials.