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.

Figure 1: Glass ampoules for pharmaceutical products.
Technical
AI Retrofit: Tackling Challenges with Cutting-Edge Technologies
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...
Figure 3: Overview of AI application fields in the pharmaceutical production process and value chain.
Technical
AI Governance and QA Framework: AI Governance Process Design
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.Trinity Life Sciences. “Ninety Percent of Large Pharma Companies Initiated Artificial Intelligence/Machine Learning Projects In...
Navigating Data Integrity and 21CFR.11
Sponsored Content
Navigating Data Integrity and 21CFR.11
As widely accepted, a complete record-keeping system installed within Good Manufacturing Practice (GMP) environments may be validated to meet the requirements of 21CFR.11 and Annex 11-compliant operations. The 7000RMS Microbial Detection Analyzer is designed to meet the needs of such validation of the entire system to help achieve overall compliance. This white paper considers the basics of...
Reimagining CPV for a Pharma 4.0™ World
Features
Reimagining CPV for a Pharma 4.0™ World
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).US Food and Drug Administration Center for Drug Evaluation...
Figure 1: Glass ampoules for pharmaceutical products.
Technical
AI Retrofit: Tackling Challenges with Cutting-Edge Technologies
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...
Figure 3: Overview of AI application fields in the pharmaceutical production process and value chain.
Technical
AI Governance and QA Framework: AI Governance Process Design
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.Trinity Life Sciences. “Ninety Percent of Large Pharma Companies Initiated Artificial Intelligence/Machine Learning Projects In...
Navigating Data Integrity and 21CFR.11
Sponsored Content
Navigating Data Integrity and 21CFR.11
As widely accepted, a complete record-keeping system installed within Good Manufacturing Practice (GMP) environments may be validated to meet the requirements of 21CFR.11 and Annex 11-compliant operations. The 7000RMS Microbial Detection Analyzer is designed to meet the needs of such validation of the entire system to help achieve overall compliance. This white paper considers the basics of...
Reimagining CPV for a Pharma 4.0™ World
Features
Reimagining CPV for a Pharma 4.0™ World
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).US Food and Drug Administration Center for Drug Evaluation...
Figure 1: Key characteristics of the ISO/IEC 25010:2011 product quality model
Features
Quality Agreements for SaaS Solutions Intended for GxP Use
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...
Maturity model
Features
AI Maturity Model for GxP Application: A Foundation for AI Validation
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
GAMP® Considerations When Relying on Open-Source Software
Features
GAMP® Considerations When Relying on Open-Source Software
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...
Figure 1: The 4 Ps of the pharmaceutical value chain are interconnected. Each element finds its virtual representation in a digital twin (figure © Zeta).
Features
Driving Biopharma Solutions with Digital Technologies
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...
AI’s Promise for ATMPS - C0ver Image
Features
AI’s Promise for ATMPs
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...
Effect of Industry 4.0 enablers on the product life cycle.
Features
Data Science for Pharma 4.0™: Drug Development, & Production—Part 2
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.Herwig, C., et al. “Data Science for Pharma 4.0™, Drug Development, and Production—Part...
Functional interface overview.
Features
Cloud Computing Implications for Manufacturing Execution 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...
Best Practices for Deploying Real-World Evidence Solutions
Features
Best Practices for Deploying Real-World Evidence Solutions
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.US Food and Drug Administration. “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices: Guidance for Industry and Food...
IT Services: Applying Good IT Practice & Automation
Features
IT Services: Applying Good IT Practice & Automation
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 initiativeUS Food and Drug Administration Center for Devices and Radiological Health. “Case for Quality.” 29 July 2020.
Industry 4.0 & the Future of the Pharmaceutical Industry
Online Exclusives
Industry 4.0 & the Future of the Pharmaceutical Industry
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.Zawadzki, P., and K. Żywicki. “Smart Product Design and Production Control for Effective Mass Customization in the Industry 4.0 Concept.” Management and...
ISPE Pharma 4.0™ SIG & Its Working Groups
Features
ISPE Pharma 4.0™ SIG & Its Working Groups
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 holistic supply chain
Features
Holistic Control Strategy: From ICH Quality Guidelines to Pharma 4.0™
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...
Emerging Leaders Editorial John Clarke Banner
Insights
Emerging Leaders Editorial: Pharma 4.0™ - The Pillars for Success
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”...
Figure 1: CNN model training process and validation results for a lyophilized vial AVI system.
Features
Vision Inspection Using Machine Learning/Artificial Intelligence
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...
Technology Trends: The Transition to Digitalization
Features
Technology Trends: The Transition to Digitalization
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,...
The Untapped Potential of AI &  Automation in Pharmacovigilance
The Untapped Potential of AI &  Automation in Pharmacovigilance
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).US Food and Drug Administration. “FDA Adverse Events Reporting System (FAERS) Public Dashboard: Data as of December 31, 2019.”...
Generic information fl ow for digital design of manufacturing process
Technical
A Systems-Based Approach to Digital Design & Operation
The Advanced Digital Design of Pharmaceutical Therapeutics (ADDoPT) project“ADDoPT—Advanced Digital Design Transforming Pharmaceutical Development and Manufacture.” Accessed 1 September 2019. https://www.addopt.org is a recently completed UK-based design manufacture and supply chain research collaboration. This collaboration catalyzed work to define a system for top-down,...
Application of SOC 2+ Process to Assessment of GxP Suppliers of Services
Features
Application of SOC 2+ Process to Assessment of GxP Suppliers of Services
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.
Article
Technical
Blockchain for Pharmaceutical Engineers
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 Integrity: A Vertical Journey Banner
Features
Data Integrity: A Vertical Journey
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...
Data
Technical
Getting Ready for Pharma 4.0™
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....
ISPE Pharmaceutical Engineering
Special Reports
Lessons Learned from the Sarbanes-Oxley Act
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.