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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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Displaying results 1 - 50 of 109

D-Value

The time in minutes required for a one-logarithm, or 90%, reduction of the population of microorganisms used as a biological indicator under specified lethal conditions. For dry-heat sterilization, the D-value should always be specified with a reference temperature, DT. For example, a biological indicator (BI) challenge system with a D 160°C=1.9 minutes, requires 1.9 minutes at 160°C to reduce the population by one logarithm. (TR3) The time in minutes at a specific temperature required to reduce the population of a specific microorganism by 90% [or one (1) log] in defined conditions [e.g., method of sterilization (dry heat versus steam), solute, or carrier]. (TR13)

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D-value (D10 -Value)

The time in minutes required for a one-logarithm, or 90%, reduction of the population of microorganisms used as a biological indicator under specified lethal conditions. (TR51)

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DT Value

The time in minutes required for a onelogarithm, or 90%, reduction of the population of microorganisms used as a biological indicator under specified lethal conditions. For steam sterilization, the D-value should always be specified with a reference temperature, DT . For example, a BI system with a D121°C = 1.4 minutes requires 1.4 minutes at 121°C to reduce the population by one logarithm.(TR1) (TR61)

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Darcy Permeability

The constant of proportionality of the material as defined by Darcy’s Law. (TR45)

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Darcy’s Law

Darcy’s Law states that the volumetric flow rate Q of liquid through a specimen of porous material is proportional to the hydrostatic pressure difference ∆p across the specimen, inversely proportional to the length L of the specimen and proportional to the cross-sectional area A. Darcy’s Law is expressed as Q = kA ∆p/L. (TR45)

Data

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

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Data (MHRA)

Facts, figures and statistics collected together for reference or analysis. All original records and true copies of original records, including source data and metadata and all subsequent transformations and reports of these data, that are generated or recorded at the time of the GXP activity and allow full and complete reconstruction and evaluation of the GXP activity.(TR80)

Data (WHO)

Data means all original records and true copies of original records, including source data and metadata and all subsequent transformations and reports of this data, which are generated or recorded at the time of the GXP activity and allow full and complete reconstruction and evaluation of the GXP activity. Data should be accurately recorded by permanent means at the time of the activity. Data may be contained in paper records (such as worksheets and logbooks), electronic records and audit trails, photographs, microfilm or microfiche, audio- or video-files or any other media whereby information related to GXP activities is recorded.(TR80)

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Data Catalog

An organized inventory of data assets within an organization which provides a comprehensive overview of available data sources, including definitions, origins, formats, and relationships. A data catalog serves as a centralized repository for data discovery, management, and governance by incorporating metadata, classification, and descriptive details about datasets. In pharmaceuticals, it helps enhance data accessibility, ensure compliance with governance policies, foster collaboration among researchers, and support effective AI and analytics in drug development and manufacturing. Additional applications would include helping batch reviewers identify trends and assisting developers in comparing the characteristics of new compounds versus previous ones.

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Data Cleaning

The process of identifying and correcting errors, and inconsistencies in datasets to reduce noise and improve data quality and reliability for analysis (e.g., implementing audit trails).

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Data Concept

An abstract notion representing a fundamental unit of information within a given domain, providing a structured framework for organizing, categorizing, and understanding data. Data concepts standardize definitions, ensure systematic consistency, and facilitate integration and communication across various systems and processes. In pharmaceuticals, data concepts might include patient demographics, drug compounds, clinical trial results, and manufacturing parameters, supporting accurate and interoperable AI models, data-driven decision-making, and regulatory compliance.

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Data Engineering

The systematic design, construction, and management of data infrastructure and processes to enable the reliable and efficient handling of large and heterogeneous datasets. This includes building robust data pipelines, storage systems, and integration mechanisms to support data acquisition, storage, transformation, and analysis across all stages of drug development and manufacturing.

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Data Governance

The set of policies and procedures for managing data collection, storage, usage, and security. Strong data governance ensures data integrity, security, and regulatory compliance, and supports informed decision-making throughout the drug development and manufacturing processes.

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Data Integrity

The assurance that the data remains complete, consistent, accurate, trustworthy, and reliable throughout its life cycle. Maintaining data integrity is crucial for ensuring the validity of AI-driven insights, supporting regulatory compliance, and enabling reliable decision-making in pharmaceutical manufacturing.

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Data Integrity (FDA)

Refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate (ALCOA).(TR80) (TR84)

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Data Integrity (MHRA)

The degree to which data are complete, consistent, accurate, trustworthy, reliable and that these characteristics of the data are maintained throughout the data life cycle. The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy) and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.(TR80) (TR84)

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Data Integrity (WHO)

The degree to which data are complete, consistent, accurate, trustworthy and reliable and that these characteristics of the data are maintained throughout the data life cycle. The data should be collected and maintained in a secure manner, such that they are attributable, legible, contemporaneously recorded, original or a true copy and accurate. Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.(TR80) (TR84)

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Data Integrity Controls

Controls put in place to either minimize the potential for a data integrity issue to occur or, if an issue does occur, the controls applied to increase the probability of detection.(TR84)

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Data Labeling

The process of annotating datasets with meaningful tags or labels to describe their content, characteristics, or relevance, enabling supervised machine learning to identify patterns and improve predictive accuracy. In pharmaceutical applications, data labeling includes tagging images of cells with their health statuses, categorizing patient records based on treatment outcomes, and annotating chemical compounds with their properties and effects.

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Data Lake

A storage repository that holds, in a structured way, a vast amount of raw data, including metadata, in its native format until it is needed. (TR84)

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Data Lifecycle (MHRA)

All phases in the life of the data (including raw data) from initial generation and recording through processing (including transformation or migration), use, data retention, archive/retrieval and destruction.(TR80) (TR84)

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Data Lifecycle (WHO)

All phases of the process by which data is created, processed, reviewed, analyzed and reported, transferred, stored and retrieved and monitored until retirement or disposal. There should be a planned approach to assessing, monitoring and managing the data and the risks to those data in a manner commensurate with potential impact on patient safety, product quality and/or the reliability of the decisions made throughout all phases of the data life cycle. (TR80)(TR84)

Data Lineage

The comprehensive tracking of data origins, movements, transformations, and dependencies throughout its lifecycle. This involves documenting data flow from its source, through various processing stages, to its final form in reports or AI models while preserving how the data is manipulated and utilized. Strong data lineage ensures data integrity, traceability for regulatory compliance, the identification of root causes of data quality issues, and optimized data management practices.

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Data Management

The overall process of acquiring, storing, organizing, and maintaining data, including activities such as data cleaning, integration, and governance.

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Data Observability

The ability to monitor, track, and analyze the state of data as it flows through various systems and processes, enabling real-time detection of issues such as data anomalies, quality degradation, and processing errors. Strengthening data observability ensures that data-driven insights remain accurate and trustworthy, supporting regulatory compliance, optimized manufacturing, and the overall quality and efficacy of pharmaceutical products.

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Data Privacy and Security

The protection of patient and healthcare data from unauthorized access, use, disclosure, disruption, modification, or destruction.

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Data Process Flow Map

A flow map that uses a baseline process flow map and overlays the data flow. (TR84)

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Data Quality

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.

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Data Quality Rules

A set of predefined criteria and guidelines that maintain data accuracy, completeness, consistency, and reliability throughout the data collection, processing, and analysis. Adherence to data quality rules is critical to ensure that AI models and analytical tools produce reliable and valid insights to support effective decision-making, regulatory compliance, and optimization of drug development and manufacturing processes. Examples of data quality rules include range checks, format validation, consistency checks, and completeness verification.

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Data Science

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.

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Data Structure

The organization and formatting of data (elements), crucial for efficient data access, analysis, and integration across systems. Data structures play a critical role in the pharmaceutical industry in managing and analyzing diverse types of data including chemical structures, biological sequences, and clinical trial data. Applications include relational databases used to store structured data in tables with predefined schemas, enabling efficient querying and analysis of large datasets and specialized data structures such as molecular fingerprints and protein structures used to represent complex biological information in a computationally tractable format. Appropriately designed data structures allow pharmaceutical companies to optimize data management, facilitate data integration, and enhance data analysis capabilities to support drug discovery, development, and manufacturing processes.

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Data Vulnerability

An indicator of data’s level of exposure to data integrity failures due to intrinsic weaknesses in manufacturing processes, data-capture technology, and human factors or a combination thereof.(TR84)

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Date of Manufacture

For small molecules, the date of manufacture of a drug product is considered to be the initial date that an active ingredient has been added during manufacturing. For biologics the date of manufacture can be determined in multiple ways and should be consistent with internal quality systems and the product license application. (TR53)

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De Novo Sequence Assembly

Assembly of short reads of a sequence to generate a contiguous sequence (contig). (TR71)

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Dead Leg

Area of entrapment in a vessel or piping run that could lead to contamination of the product. (TR69)

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Deadlegs

An area of entrapment in the vessel or piping run that could lead to contamination of the product due to insufficient exposure to moist heat. (TR61)

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Decision Maker(s)

Person(s) with the competence and authority to make appropriate and timely quality risk management decisions.(TR54) (TR54-2)

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Decommissioning

A planned and orderly removal of a facility, operation or system from use. (TR48) The process of retiring equipment/systems/facilities from production use. (TR54-5)

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Decontamination

A process that is designed to remove soil (including microorganisms) and may consist of cleaning and/or disinfection. (TR51)

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Dedicated Equipment

Equipment used exclusively for the manufacture of only one drug product, bulk drug substance, or intermediate. (TR29)

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Deep Learning

Defect

(1) A departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to satisfy its intended normal or foreseeable usage requirements. (TR51) (2) The nonfulfillment of intended usage requirements. The departure or absence of one or more quality characteristics from intended usage requirements. (TR43)

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Defect (ANSI def.)

A departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to satisfy its intended normal or foreseeable usage requirements. (TR76)

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Defect (ISO def.)

The nonfulfillment of intended usage requirements. The departure or absence of one or more quality characteristics from intended usage requirements. (TR76)

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Degradation

The breakdown (usually chemical) of material during manufacture, including during and after the cleaning process. (TR49) (TR70)

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Degradation Product

Molecular variants resulting from changes in the desired product or product-related substance brought about over time and/or by the action of light, temperature, pH, water, etc., or by reaction with an excipient and/or the immediate container/ closure system. Such changes may occur because of manufacture and/or storage (e.g., deamidation, oxidation, aggregation, proteolysis). Degradation products may be either product-related substance or product-related impurities. (TR57)

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Deionized Water

Water treated by passing through both cation- and anion-exchange resin beds, or a mixed-resin bed to remove both positive and negative ions. (TR45)

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Deployment

Activities involving the hands-on steps required to successfully assemble and make a system ready for use for a specific SUS application. (TR66)

Depth Filter

A matrix of randomly distributed fibers creating a tortuous path with pores of undefined size and shape. A filter that removes particles by a combination of adsorption and size exclusion within its porous matrix rather than on its frontal surface. (TR45)

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Depyrogenation

The destruction and/or removal of bacterial endotoxins. A depyrogenation process should demonstrate at least 99.9% or a 3-log endotoxin reduction. (TR3) Removal or destruction of pyrogens. (TR70)

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Displaying results 1 - 50 of 109