PDA Technical Glossary
PDA Technical Reports are highly valued membership benefits because they offer expert guidance and opinions on important scientific and regulatory topics and are used as essential references by industry and regulatory authorities around the world. These reports include terms which explain the material and enhance the reader’s understanding.
The database presented here includes the glossary terms from all current technical reports. The database is searchable by keyword, topic, or by technical report. Each definition provided includes a link to the source technical report within the PDA Technical Report Portal.
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Analytical Model
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.
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Auto Machine Learning (AutoML)
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.
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Confusion Matrix
A structured table used to evaluate the accuracy of a classification model, displaying true positive, true negative, false positive, and false negative counts. It helps identify model strengths and areas for improvement.
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Hybrid Model
A computational model that combines elements from multiple modeling approaches, such as empirical, mechanistic, and machine learning techniques, to solve complex problems. By leveraging the strengths of each approach, hybrid models enhance predictive accuracy, robustness, and interpretability. Applications include integrating mechanistic drug metabolism models with machine learning algorithms trained on experimental data to predict the pharmacokinetic behavior of new drug candidates. Similarly, hybrid models can merge empirical data with physiological knowledge to simulate drug-disease interactions or optimize formulation designs.
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Model Scoring
The process of evaluating a machine learning model's performance using metrics such as accuracy, precision, recall, and F1 score. Effective model scoring ensures that predictions are valid and trustworthy.
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Model Training
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.