Topics should cover the financial impact of data management systems on drug development, manufacturing, and distribution; the basic computer system life cycle model as well as the activities and software quality assurance practices in each phase; and the controls and methods necessary to maintain data integrity and security.
Funded by the European Commission from 2019, the Smart Pharmaceutical Manufacturing Project (SPuMoNI)1 harnesses the potential of state-of-the-art technologies for the pharmaceutical industry. This article discusses the main SPuMoNI accomplishments.
In the context of data integrity, data flows are essential. The FDA, PIC/S, and WHO have all emphasized the importance and benefits of data flows in their guidance on data integrity. The key to data integrity compliance is a well-functioning data governance system1
We will show how continuous, real-time capturing of data with immediate data analysis by an ML algorithm can improve control over a critical quality attribute. The ML-analyzed data provides the evidence for validation of the change by demonstrating more control over the process along with a decrease in process risks.
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning (ML) algorithms demonstrating advances in performance in a wide range of tasks. However, this comes with an ever-increasing complexity of the algorithms used, rendering such systems more difficult to explain.1
Funded by the European Commission from 2019, the Smart Pharmaceutical Manufacturing Project (SPuMoNI)1 harnesses the potential of state-of-the-art technologies for the pharmaceutical industry. This article discusses the main SPuMoNI accomplishments.
In the context of data integrity, data flows are essential. The FDA, PIC/S, and WHO have all emphasized the importance and benefits of data flows in their guidance on data integrity. The key to data integrity compliance is a well-functioning data governance system1
We will show how continuous, real-time capturing of data with immediate data analysis by an ML algorithm can improve control over a critical quality attribute. The ML-analyzed data provides the evidence for validation of the change by demonstrating more control over the process along with a decrease in process risks.
Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning (ML) algorithms demonstrating advances in performance in a wide range of tasks. However, this comes with an ever-increasing complexity of the algorithms used, rendering such systems more difficult to explain.1
ISPE’s GAMP® 5: A Risk-Based Approach to Compliant GxP Computerized Systems (Second Edition) (GAMP® 5 Guide, 2nd Edition) maintains the principles and framework of the first edition and updates their application in the modern world, including the increased importance of service providers, evolving approaches to software development, and expanded use of software tools and...
In this article, potential Pharma 4.0™ technological solutions that can enhance continuous process verification (CPV) 4.0 are discussed. The necessary paradigm shift will allow companies to predict deviations more accurately, perform root cause analysis (RCA), ensure data integrity and GxP compliance, and ultimately be more competitive in a highly regulated industry.
This article explores life-cycle activities for machine learning (ML) within regulated life sciences. It positions and contextualizes the life cycle and management of the machine learning subsystem or components within a wider system life cycle. It also gives general descriptions and guidance illustrated by a case study demonstrating a machine learning application to medical image recognition,...
This artificial intelligence (AI) retrofit project was a unique approach to implementing AI technology in a pharmaceutical environment within three months. This project tackles a commonly known industry challenge by integrating AI into an existing automatic visual inspection (AVI) machine. The proof of value allowed us to benchmark added value through AI compared with state-of-the-art...
Artificial intelligence (AI) has the potential to benefit the pharmaceutical industry and its GxP-regulated areas. Several pharmaceutical companies are currently running digital pilots; 90% of large pharmaceutical companies have initiated AI projects.1
The life cycle approach to process validation stresses the need for continued monitoring of process performance to ensure that the manufacturing process remains stable and predictable, i.e., in a state of control. This life cycle stage is known as continued process verification (CPV) or ongoing process verification (OPV).1
As adoption of cloud technology continues to increase across the life sciences industry, so too does the need to establish a standardized and pragmatic approach for ensuring the quality of software applications used in support of GxP data and associated processes. This article focuses on the application level and the growing use of software as a service (SaaS) within the life sciences...
Artificial intelligence (AI) has become one of the supporting pillars for digitalization in many areas of the business world. The pharmaceutical industry and its GxP-regulated areas also want to use AI in a beneficial way. Several pharmaceutical companies are currently running digital pilots, but only a small fraction follows a systematic approach for the digitalization of their operations
This article aims to refresh information on open-source software (OSS) within regulated computerized systems that was first discussed in an article in May-June 2010 Pharmaceutical Engineering®. The adoption of OSS advanced since then, and the article explores the importance of recognizing when an organization is relying on OSS and the benefits and risks this brings from a GAMP® 5...
Developing comprehensive digital solutions is crucial for the entire value creation process for pharmaceuticals. A holistic view of the interrelations of product, production process, and plant is becoming increasingly significant. In this context, the application of model-based technologies provides support in drug development, process scale-up, and manufacturing. Furthermore, it accelerates...
Industry 4.0 applications in biopharma involve the complete spectrum of data science throughout the entire product life cycle of many disparate entity types. Tools such as digitalization, modern data science, and the industrial internet of things (IIoT) exist now, and examples from other industries such as Siri and Alexa, face identification, and self-driving cars can guide their...
Use of software as a service (SaaS) applications within the life sciences industry is growing. This article reviews the issues and challenges faced by SaaS providers and identifies the qualities of successful providers for this highly regulated industry.
This second of a two-part series explores digital transformation and digitalization in the biopharmaceutical industry with information about how data science enables digitalization along the product life cycle. (Part 1 was published in the March-April 2021 issue of Pharmaceutical Engineering.1
This article provides a beginner’s overview of how organizations can achieve a state of preparedness (readiness) for inspections, with a specific focus on IT systems.
Cloud computing can be described as networked access and utilization of configurable computing resources such as data and information storage, processing capabilities, applications, and other services on computerized systems provided and/or maintained by a remote organization. As life sciences companies consider the advantages and costs of utilizing cloud services, they first need to invest...
Real-world evidence (RWE) is clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of real-world data (RWD) relating to patient health status and the healthcare delivery.1
This article focuses on pragmatic quality- and risk-based approaches to IT infrastructure. It covers recommendations made by a US FDA/industry team linked to the US FDA Center for Devices and Radiological Health (CDRH) Case for Quality initiative1
Industry 4.0 is the recent movement toward intelligent automation technology. In this new era, the integration of modern manufacturing skills and novel information technologies plays an important role on economic competitiveness.1
The ISPE Pharma 4.0™ Special Interest Group (SIG) launched in 2015 to provide a road map for new challenges of digitalization, Industry 4.0, and the smart factory. The Special Interest Group addresses how pharmaceutical industry stakeholders, including regulatory authorities, can achieve benefits from
The fourth Industrial Revolution (also known as Industry 4.0) is the era of smart machines, storage systems, and production plants that can autonomously exchange information, trigger actions, and control operations free of any human intervention. To ensure future success in the delivery of therapeutic medicines to patients, it is imperative that the pharmaceutical industry move deeper into the...
During the ISPE Pharma 4.0™ Virtual Conference, the Management Communication working group of the ISPE Pharma 4.0™ Special Interest Group (SIG) held a workshop to support ISPE members in pitching,...
Through this difficult time of the COVID-19 pandemic, ISPE has remained active. At the 2020 ISPE Pharma 4.0™ Virtual Conference, 17–18 November, 174 attendees gathered online to discuss and learn about...
Understanding and utilization of Pharma 4.0™ technologies will be critical for students and Emerging Leaders (ELs) as they develop in their careers in the pharmaceutical and life sciences industries. To learn more, I spoke with Edoardo Schiraldi, an Emerging Leader based in Florence, Italy, who works as a Corporate R&D Business Solutions Specialist with Menarini Group, about the “pillars”...
This article describes the Pharmaceutical Equipment Exposure Measurement Database (PEEM-DB), which was launched in July 2019 by the ISPE Japan Affiliate for its members. PEEM-DB is offered as a tool for rationally advancing optimal containment equipment settings by collecting exposure measurement results for...
Pharmaceutical companies rely on automated vision inspection (AVI) systems to help ensure product safety. Although these systems overcome challenges associated with manual inspection, they can be hindered by limitations in their programming—if the system is programmed to consider every variation in inspection conditions, it is likely to falsely identify defects in safe products. This article...
As Pharma 4.0™ increasingly becomes reality, our validation practices must change. We can no longer apply 20th-century thinking to 21st-century technology and resources. Validation must adapt to industry shifts from iterative to...
In the pharmaceutical industry, digitalization involves developing and implementing digital technologies at all levels of pharmaceutical operations. The aim is to transform the industry by capturing, analyzing, and using vast amounts of data collected from a wide range of sources to support research and development (R&D), clinical development, drug manufacturing, supply chain management,...
Between 2009 and 2019, the number of adverse events (AEs) for drugs and therapeutic biologic products recorded by the US FDA Adverse Event Reporting System (FAERS) increased more than 300%, from 490,032 to 2.19 million cases (as of 31 December 2019).1
The Advanced Digital Design of Pharmaceutical Therapeutics (ADDoPT) project1 is a recently completed UK-based design manufacture and supply chain research collaboration. This collaboration catalyzed work to define a system for top-down,...
To facilitate the assessment and mitigation of compliance risks associated with a third-party service organization, its services, and the systems used to provide the services, this article proposes adopting an approach from the financial sector that, with a little modification, could be used to assess suppliers of GxP-regulated IT services.
This article presents an adaptation of clinical trial management to ICH E6 (R2). A case study assesses quality risks in a clinical data management system.
This article discusses how blockchain technology may disrupt the way we collect and manage data within regulated processes. The first section is a nontechnical summary of blockchain’s features, including a description of what it is (and what it is not). This sets the context for the next section, in which we discuss several blockchain use cases currently being piloted by life sciences...
Data is an important factor that is reshaping the pharmaceutical industry and triggering significant innovation. Vertical integration of equipment can represent an optimal solution to manage the increasing flow of data efficiently, innovate the manufacturing environment, and fulfill data integrity requirements. Regulators and health agencies are strongly enforcing data integrity related...
The 2018 ISPE Quality Manufacturing Conference, held 4–6 June 2018 in Arlington, Virginia, included a well-attended session entitled “Data Integrity—Beyond the Lab,” which reaffirmed continued focus...
Pharmaceutical Engineering magazine’s March–April 2016 Special Report1 highlighted the increasing...
The amount of data collected in a typical pharmaceutical manufacturing operation is staggering, yet research shows that much of this information is rarely used for anything more than compliance. New technologies such as big data, artificial intelligence, machine learning, and deep learning permit unprecedented analysis of realtime data and can even predict trends in processes and operations....
Fifteen years ago, corporations embarked on a journey toward SOX compliance; along the way they have learned a tremendous amount about data integrity as it relates to financial systems. Those lessons learned are directly applicable to many of the data-integrity challenges facing the pharmaceutical industry today.
Individuals with privileged access have the technical means to bypass the user interface to access and modify data, often times without traceability.
By the GAMP Cloud Computing Special Interest Group (SIG)
This article presents the key items that must be addressed to adopt an IaaS model within a regulated firm.
Written by the GAMP Cloud Computing Special Interest Group (SIG)
This article presents a strategy for providing guidance on the use of the cloud within the pharmaceutical industry.