Machine Learning Risk and Control Framework
Cover: Stakeholders across industries are becoming accustomed to using information technology (IT) systems, applications, and business solutions that feature artificial intelligence (AI) and machine learning (ML). Even though some of these uses show phenomenal performance, thorough risk management is required to ensure quality and regulatory compliance are met within the life sciences industry. By leveraging specialized frameworks and methods, we compiled a holistic framework to dynamically identify, assess, and mitigate risks when AI and ML features are in use.
Quality Considerations in Disaster Recovery: A Case Study
Feature: Due to the growing digitalization of the industry, we are highly dependent on information technology (IT) systems and data. The basic ability to execute our pharmaceutical business and decision-making processes relies on the permanent availability of these IT systems and data to ensure compliance and efficiency of our business operations. But numerous factors—including criminal activities, political unrest, and environmental hazards— have made disaster recovery (DR) and business continuity planning essential.
The Use of Infrastructure as Code in Regulated Companies
Feature: IT infrastructure has traditionally been provisioned using a combination of scripts and manual processes. This manual approach was slow and introduced the risk of human error, resulting in inconsistency between environments or even leaving the infrastructure in an unqualified state. In this article, we investigate some fundamental advantages of using Infrastructure as Code (IaC) for provisioning IT infrastructure.
Calibration Performance Improvement Case Study
Technical: Calibration plays a critical role in ensuring a measurement instrument’s accuracy—especially if the instrument has a direct impact on product quality and patient safety. However, the calibration process is a complex system, and the traditional analytical approach for planning this process is often not sufficient to improve service performance. Using a digital simulation model as a representation of the actual situation allows creation of optimization scenarios for improvement purposes before they are implemented.
Cold Systems as a Solution to Decarbonize Water Purification
Technical: The biotechnology and pharmaceutical sectors have pledged to reduce greenhouse gas (GHG) emissions as the climate concerns of consumers, investors, and regulators continue to grow. In seeking to benefit from this demand for sustainability and the potential for cost saving opportunities, life science product manufacturers have started to evaluate the climate impact of their own labs and production facilities. This in-depth examination of the sectors’ direct manufacturing processes uncovered one of the largest carbon emitters: water for injection (WFI).