PDA Technical Glossary

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 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 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.

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.

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.

Descriptive information that provides context and additional details about a dataset, such as its origin, structure, and meaning. Metadata plays a crucial role in the pharmaceutical industry in ensuring data quality, integrity, and interoperability across different systems and applications. This may include information about the source of a dataset, the methodology used to collect and process the data, and the relevant standards and conventions followed. In drug development, metadata can help researchers and regulators understand the provenance and reliability of experimental data, facilitate data sharing and collaboration, and support regulatory compliance and auditability.

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.

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.

An approach to medical treatment and healthcare that customizes interventions based on the individual patient’s characteristics, often leveraging AI for personalized diagnostics and tailored treatment plans.