PDA Technical Glossary

The principles and guidelines that govern the responsible use of AI ensuring fairness, transparency, and data protection.

A structured sequence of steps used in software systems to solve specific problems or perform tasks efficiently. Algorithms analyze complex datasets, detect patterns, forecast outcomes, and optimize processes such as drug discovery, development, and manufacturing in the pharmaceutical industry. Custom methodologies include machine learning, deep learning, natural language processing, and optimization algorithms.

Artificial intelligence is a scientific discipline established at the intersection of computer science, applied mathematics, and engineering, dedicated to the development of systems capable of performing tasks traditionally requiring human intelligence. From a functional perspective, AI can be defined as the simulation or approximation of human intelligence in machines. AI encompasses machine-driven learning, reasoning, and perception, and is widely used across various industries including finance and life sciences.

Extremely large and complex datasets that require advanced processing methods. In pharmaceuticals, big data applications include identifying patient subpopulations for particular drug therapies, optimizing manufacturing processes, and predicting safety or regulatory risks.

Raw, unprocessed facts and figures collected from various sources. In pharmaceuticals, data sources include laboratory instruments, manufacturing systems, clinical trials, and patient health records.

The concepts and practices which adhere to the ALCOA principles (Attributable, Legible, Contemporaneous, Original, Accurate), which expanded to ALCOA + (adding Complete, Consistent, Enduring, Available) and ALCOA++ (which added Traceable). A similar concept is the FAIR principles (Findable, Accessible, Interoperable, and Reusable), which refers to the reliability, accuracy, completeness, and consistency of data collected and used throughout drug development and manufacturing. Aligning with these principles is paramount for ensuring accurate analysis, informed decision-making, and regulatory compliance.

An interdisciplinary field that combines statistics, AI, computer science, and domain expertise (e.g., chemometrics) to extract actionable insights and knowledge from large and complex datasets. Applications in the pharmaceutical industry include using spectral data to identify key chemical components in drug formulations, monitoring process parameters during manufacturing, and ensuring product quality and consistency.

A subset of artificial intelligence that creates new content, such as images, text, or music based on patterns and examples learned from existing data. Applications within the pharmaceutical industry include 1) algorithms which can be trained on large databases of known drug compounds and their biological activities to predict new molecules that are likely to exhibit similar pharmacological effects, and 2) the design of optimized formulations and delivery systems for drugs which account for factors such as solubility, stability, and bioavailability.

The ongoing automation and digitization of traditional manufacturing and industrial practices to create "smart factories" that are more efficient, scalable, and interconnected. This includes integrating advanced technologies such as Internet of Things (IoT) sensors, artificial intelligence (AI) driven analytics, cloud computing, and cyber-physical systems. (Also known as the “fourth industrial revolution”.) In the pharmaceutical sector, Industry 4.0 enhances production efficiency, regulatory compliance, and product quality.

Processed and organized data used for meaningful decision-making. High-quality information enables pharmaceutical companies to optimize workflows, improve product quality, ensure regulatory compliance, and drive innovation through AI-driven insights.

Actionable understanding derived from processed data into meaningful insights. In pharmaceuticals, the knowledge generation process begins with data collection from various sources such as sensors, laboratory instruments, and clinical trials. Data is then processed and analyzed to produce information, which involves identifying patterns, trends, and correlations. Information becomes knowledge when it is contextualized, interpreted, and applied to make informed decisions, optimize manufacturing processes, enhance drug formulations, and improve overall production efficiency. Knowledge in this framework enables pharmaceutical companies to leverage AI-driven insights to drive innovation, ensure quality control, and maintain regulatory compliance throughout the drug manufacturing lifecycle.

A systematic approach to acquiring, analyzing, storing, and disseminating information (related to products, manufacturing processes and components), and dictating how product and process knowledge should be managed from development to commercialization, through the product end of life, including product discontinuation. (ICH Q10). (TR54) (TR68) (TR54-5)

Computer programs that understand and generate human-like text. In the pharmaceutical industry, LLMs act as intelligent assistants that process large amounts of written information, enabling researchers and professionals to make better-informed decisions.

A specialized branch of artificial intelligence (AI) that utilizes algorithms and statistical models to process data, generate insights, make predictions, and provide recommendations within complex datasets. In the Pharmaceutical industry, ML identifies patterns, trends, and anomalies within complex datasets derived from drug discovery, clinical trials, manufacturing, regulatory compliance, pharmacovigilance, and patient outcomes. Companies leverage ML to streamline drug discovery, improve manufacturing efficiency, enhance quality control, personalize patient treatments, and expedite regulatory approvals.

The ability of a computer system to understand, interpret, and generate human-like text. The application of NLP in healthcare could facilitate patient interactions with connected devices and support efficient clinical documentation.

The ethical and accountable development, deployment, and use of artificial intelligence (AI) technologies which encompasses principles, practices, and policies aimed at ensuring that AI systems are designed, implemented, and governed in a manner that prioritizes patient safety, privacy, transparency, fairness, social benefit, and regulatory compliance. In the pharmaceutical industry, key considerations include data privacy and security, algorithmic bias and fairness, AI model explainability and interpretability, regulatory compliance, and ethical decision making in clinical applications. By adhering to principles of responsible AI, pharmaceutical companies can build trust with stakeholders, mitigate risks associated with AI deployment, and maximize the societal value of AI-driven innovations in healthcare.