PDA Technical Glossary

A sophisticated digital replica of a physical manufacturing process, system, or equipment, encompassing its structure, behavior, performance, and functionality. Digital twins integrate sensor and equipment data to create real-time simulations that mirror actual system behavior, enabling companies to monitor, analyze, and optimize production processes, equipment performance, and product quality. Additional benefits include predictive maintenance, process optimization, and scenario testing without disrupting real operations.

The process of optimizing the configuration settings (hyperparameters) of a machine learning model to maximize performance. Proper tuning prevents underfitting and overfitting, improving overall accuracy.

The merging of Information Technology (IT), which manages computing, networking, and data infrastructure, with Operational Technology (OT), which focuses on monitoring and automating physical processes. IT/OT integration enables seamless data exchange, process optimization, and enhanced system control for greater efficiency and regulatory compliance.

The phase in machine learning where a model learns patterns and relationships from training data, enabling it to make predictions for unseen data. Proper model training ensures generalization and reliability in real world applications.

The structured classification and organization of data elements into hierarchical categories and subcategories based on their relationships and attributes. Standardized data definitions and terminology streamline data management, improve retrieval, and enhance analysis across various stages of drug development and manufacturing. Taxonomies also boost interoperability, consistency, and clarity, fostering effective communication and collaboration among stakeholders.

The basic building blocks of natural language processing (NLP), created by splitting text into smaller components such as words, sub-words, or symbols. Tokens represent individual linguistic units, including words, punctuation, and numbers, enabling AI models to process and understand human language efficiently in tasks such as sentiment analysis, machine translation, and speech recognition.

Labeled data used to train machine learning models and algorithms to recognize patterns, make predictions, or perform other tasks. Pharmaceutical industry training data may include diverse types of data such as chemical structures, biological assays, and clinical outcomes, depending on the specific application. Applications include using training data to develop predictive models for drug toxicity, efficacy, or pharmacokinetics, based on historical data from preclinical and clinical studies. Selecting representative and high-quality training data can provide accurate and robust machine learning models that can accommodate new data and support decision-making in drug discovery and development. As best practice, training data should be documented and traceable.

A machine learning technique where a pretrained model is adapted for a different but related task, often applied in cases with limited labeled data. This method accelerates training by reusing knowledge from previous learning, improving performance in pharmaceutical applications such as drug discovery, patient diagnosis, and molecular analysis.

A measure of an AI system’s ability to exhibit intelligent behavior indistinguishable from that of a human. In pharmaceutical manufacturing, passing the Turing Test would imply that an AI system can engage in complex reasoning and human-like communication, potentially transforming automated research, patient interactions, and regulatory affairs by providing expert level responses and insights.