PDA Technical Glossary

AI driven identification of potential drug side effects and adverse events, supporting risk mitigation.

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.

Systematic and consistent errors in AI algorithm outcomes, caused by biases in training data, potentially leading to unfair or discriminatory results.

A mathematical or computational framework used to represent and analyze relationships between variables in drug development and manufacturing. Unlike empirical models, which rely solely on observed data, analytical models are based on theoretical principles, scientific knowledge, and mathematical equations. These models are often used to simulate and predict complex systems such as drug-receptor interactions, pharmacokinetic processes, or biopharmaceutical properties. An application for the pharmaceutical industry is describing a unit operation, such as pumping a buffer into a reaction vessel, using precise mathematical formulations.

The process of identifying unusual patterns or observations in data, often signaling errors, fraud, or other irregularities. Pharmaceutical industry applications include root cause analysis and transport surveillance, where comparing images of impacted versus non-impacted samples help detect anomalies.

A metric that quantifies the overall performance of a classification model by measuring the area under the ROC curve. A higher AUC value indicates better model accuracy in distinguishing between different classes. [see Receiver Operating Characteristic Curve (ROC Curve)]

A highly advanced form of artificial intelligence with the capability to understand, learn, and apply knowledge across a broad range of tasks at a level comparable to human intelligence. In pharmaceutical applications, AGI could revolutionize drug discovery, development, and manufacturing by autonomously designing experiments, optimizing complex processes, interpreting vast amounts of scientific data, and even generating novel hypotheses.

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.

The automated application of machine learning techniques to design, build, and deploy AI models with minimal human intervention. AutoML platforms streamline model selection, feature engineering, hyperparameter tuning, and evaluation, making machine learning more accessible. In pharmaceuticals, AutoML accelerates drug discovery, predictive modeling of patient outcomes, and manufacturing process optimization.

A systematic analysis and interpretation of historical data using statistical and computational techniques to elucidate past patterns, trends, and insights. This type of analysis is applied in drug discovery, development, and manufacturing processes to gain a retrospective understanding of key performance indicators, scientific observations, and operational metrics.

The use of artificial intelligence, business process management, and robotic automation to streamline and scale decision-making across organizations. Intelligent automation solutions must include human oversight to ensure data security and privacy.

Artificial intelligence applications and techniques designed for specific tasks, such as speech recognition, image classification, fraud detection, and predictive maintenance. Unlike artificial general intelligence (AGI), narrow AI (also known as weak AI) a specific task or a limited range of tasks with high efficiency and accuracy, but without human-like general reasoning. It relies on well-defined datasets, structured objectives, and clear operational contexts, which is the perfect combination for GMP environments where predictability, reliability, and explainability are mandatory. Some examples of pharmaceutical applications are anomaly detection in fill-finish operations, predictive maintenance for sterile manufacturing equipment, environmental monitoring data analysis, real-time process parameter optimization, automated visual inspection of parenteral products, label verification in secondary packaging, AI-driven root cause analysis (RCA) in deviation management, and process analytical technology (PAT) models for in-line monitoring.

The application of statistical and AI techniques to analyze historical data and identify patterns, trends, and relationships that can be used to forecast trends, identify risks, and make and make data driven predictions in drug discovery, development, and manufacturing.

The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data, and recommend optimal actions based on these predictions, improving pharmaceutical processes such as manufacturing efficiency and regulatory compliance.

The use of software robots or "bots" to automate routine and repetitive tasks, often used in administrative processes (e.g., small chat bubbles that appear when you enter a website and offer support or guidance). In pharmaceutical applications, RPA may be used to automate data entry and documentation processes.