Amazon Web Services, Inc. (AWS) announced that it is working with Pfizer to create innovative, cloud-based solutions with the potential to improve how new medicines are developed, manufactured, and distributed for testing in clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. For instance, AWS is helping Pfizer enhance its continuous clinical manufacturing processes by incorporating predictive maintenance capabilities built with AWS machine learning services like Amazon Lookout for Equipment (AWS’s service for detecting abnormal equipment behavior by analyzing sensor data). As a result, Pfizer can maximize uptime for equipment such as centrifuges, agitators, pulverizers, coaters, and air handlers used in clinical drug manufacturing. The overall focus of this collaboration is to support Pfizer in more rapidly and reliably producing new drugs and evaluating their potential health benefit for patients.
“Our life sciences customers are increasingly looking for opportunities to scale expertise, insight, and secure access to the right information, at the right time, with the aim of reducing the time and cost for drug development and clinical trials,” said Kathrin Renz, Vice President of Business Development and Industries at Amazon Web Services, Inc. “AWS’s breadth and depth of cloud capabilities help support Pfizer’s teams through secure, novel research methods as they work to optimize drug development and clinical manufacturing processes. The past two years have reinforced for the world just how much speed and agility matter at every step of the research, development, and clinical manufacturing cycle when lives are on the line. We’re proud to work with Pfizer and lend our deep domain expertise to assist in developing solutions that could significantly improve the lives of patients globally.”
“Pfizer’s goal with AWS is to expedite the processes for drug discovery and development in ways that can ultimately enhance patient experiences and deliver new therapies to market. Working closely with AWS experts in machine learning and analytics, we aim to provide our scientists and researchers with the insights they need to help deliver medical breakthroughs that change patients’ lives,” said Andrew McKillop, Vice President of Pharmaceutical Sciences, Worldwide Research, Development, and Medical at Pfizer.
AWS is working with Pfizer to develop a prototype solution for detecting abnormal data points in its drug product continuous clinical manufacturing platform for solid, oral-dose medicines. The prototype solution uses Amazon SageMaker (AWS’s service for building, training, and deploying machine learning models quickly in the cloud and at the edge), Amazon Lookout for Equipment, Amazon Lookout for Metrics (AWS’s service for automatically detecting anomalies in metrics and identifying their root cause), and Amazon QuickSight (AWS’s scalable machine learning-powered business intelligence service for the cloud). The machine learning models used in the prototype were able to provide early warnings for alarms with minimal false positives and direct users to the relevant signals. As a result, Pfizer can process data from the equipment and sensors involved in Portable Continuous Miniature and Modular (PCMM) manufacturing to detect anomalies as they occur, predict maintenance needs, and reduce potential equipment downtime.
Pfizer scientists will also collaborate with AWS healthcare and life sciences professionals to explore how researchers in Pfizer’s Pharmaceutical Sciences Small Molecules teams can extract and mine information from legacy documents by leveraging AWS analytics and machine learning services. Pfizer has an extensive collection of documents that contain valuable data from a variety of drug development processes. The documents include data related to synthetic chemistry routes, recipes, analytical tests, method development, formulation composition, clinical manufacturing campaigns, batch records, technology transfer, and many other types of work. Housed within these documents are potentially powerful insights that could point Pfizer researchers in the right direction for developing new drugs or repurposing existing ones—if the researchers can identify and link the right information efficiently. To gain quick, secure access to the right information at the right time, Pfizer’s Pharmaceutical Sciences Small Molecules teams are working with AWS to develop a prototype system that can automatically extract, ingest, and process data from this documentation to help in the design of lab experiments. The prototype system is powered by Amazon Comprehend Medical (AWS’s HIPAA-eligible natural language processing (NLP) service to extract information from unstructured medical text accurately and quickly) and Amazon SageMaker and uses Amazon Cognito to deliver secure user access control.AWS Helps Pfizer Accelerate Drug Development and Clinical Manufacturing
AWS works with Pfizer to support more rapid innovation and improved clinical manufacturing operations to help develop tomorrow’s therapies
Bangalore—Dec. 3, 2021—Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced that it is working with Pfizer to create innovative, cloud-based solutions with the potential to improve how new medicines are developed, manufactured, and distributed for testing in clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. For instance, AWS is helping Pfizer enhance its continuous clinical manufacturing processes by incorporating predictive maintenance capabilities built with AWS machine learning services like Amazon Lookout for Equipment (AWS’s service for detecting abnormal equipment behavior by analyzing sensor data). As a result, Pfizer can maximize uptime for equipment such as centrifuges, agitators, pulverizers, coaters, and air handlers used in clinical drug manufacturing. The overall focus of this collaboration is to support Pfizer in more rapidly and reliably producing new drugs and evaluating their potential health benefit for patients.
“Our life sciences customers are increasingly looking for opportunities to scale expertise, insight, and secure access to the right information, at the right time, with the aim of reducing the time and cost for drug development and clinical trials,” said Kathrin Renz, Vice President of Business Development and Industries at Amazon Web Services, Inc. “AWS’s breadth and depth of cloud capabilities help support Pfizer’s teams through secure, novel research methods as they work to optimize drug development and clinical manufacturing processes. The past two years have reinforced for the world just how much speed and agility matter at every step of the research, development, and clinical manufacturing cycle when lives are on the line. We’re proud to work with Pfizer and lend our deep domain expertise to assist in developing solutions that could significantly improve the lives of patients globally.”
“Pfizer’s goal with AWS is to expedite the processes for drug discovery and development in ways that can ultimately enhance patient experiences and deliver new therapies to market. Working closely with AWS experts in machine learning and analytics, we aim to provide our scientists and researchers with the insights they need to help deliver medical breakthroughs that change patients’ lives,” said Andrew McKillop, Vice President of Pharmaceutical Sciences, Worldwide Research, Development, and Medical at Pfizer.
AWS is working with Pfizer to develop a prototype solution for detecting abnormal data points in its drug product continuous clinical manufacturing platform for solid, oral-dose medicines. The prototype solution uses Amazon SageMaker (AWS’s service for building, training, and deploying machine learning models quickly in the cloud and at the edge), Amazon Lookout for Equipment, Amazon Lookout for Metrics (AWS’s service for automatically detecting anomalies in metrics and identifying their root cause), and Amazon QuickSight (AWS’s scalable machine learning-powered business intelligence service for the cloud). The machine learning models used in the prototype were able to provide early warnings for alarms with minimal false positives and direct users to the relevant signals. As a result, Pfizer can process data from the equipment and sensors involved in Portable Continuous Miniature and Modular (PCMM) manufacturing to detect anomalies as they occur, predict maintenance needs, and reduce potential equipment downtime.
Pfizer scientists will also collaborate with AWS healthcare and life sciences professionals to explore how researchers in Pfizer’s Pharmaceutical Sciences Small Molecules teams can extract and mine information from legacy documents by leveraging AWS analytics and machine learning services. Pfizer has an extensive collection of documents that contain valuable data from a variety of drug development processes. The documents include data related to synthetic chemistry routes, recipes, analytical tests, method development, formulation composition, clinical manufacturing campaigns, batch records, technology transfer, and many other types of work. Housed within these documents are potentially powerful insights that could point Pfizer researchers in the right direction for developing new drugs or repurposing existing ones—if the researchers can identify and link the right information efficiently. To gain quick, secure access to the right information at the right time, Pfizer’s Pharmaceutical Sciences Small Molecules teams are working with AWS to develop a prototype system that can automatically extract, ingest, and process data from this documentation to help in the design of lab experiments. The prototype system is powered by Amazon Comprehend Medical (AWS’s HIPAA-eligible natural language processing (NLP) service to extract information from unstructured medical text accurately and quickly) and Amazon SageMaker and uses Amazon Cognito to deliver secure user access control.
