NIIMBL Monitoring And Control Ontology
type: Ontology
label: Biopharma Monitoring And Control Ontology
abstract: This module contains terms and relations necessary for representing the planning and execution of monitoring and/or controlling for both laboratory and manufacturing scale.
contributor: Adlane Rebai, Millipore Sigma
contributor: Ana Nikolov, OAGi
contributor: Boonserm Kulvatunyou, NIST
contributor: Cameron Gibbs, CrownPoint Technologies, LLC
contributor: Gabriela Henning, NIST
contributor: Jan Kemper, AstraZeneca
contributor: Marie-Aude Coutouly, Millipore Sigma
contributor: Melissa Weller, CrownPoint Technologies, LLC
contributor: Milos Drobnjakovic, OAGi
contributor: Stephen J. Granite, CrownPoint Technologies, LLC
contributor: Stephen Kahmann, CrownPoint Technologies, LLC
creator: NIIMBL BD-1 Ontology Group
license: http://opensource.org/licenses/MIT
imports: BiopharmaParameter
imports: ReferenceOntologyCandidates
version IRI: BiopharmaMonitoringAndControl
copyright: Copyright (c) 2022, 2023, 2024, 2025, 2026 Open Applications Group
maturity: Released

control strategy
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: BFO_0000110
  some values from: Class
    union of: PerformanceIndicatorSpecification
    union of: ProcessParameterSpecification
    union of: QualityAttributeSpecification
alternative label: control plan specification
alternative label: integrated control strategy
example: A control strategy for sterile filling includes gravimetric sensors to monitor and regulate fill volume (process parameter), cleanroom environmental monitoring to monitor particulate levels and temperature (quality attributes), and automated rejection of out-of-spec units. The process performance indicator is fill volume deviation rate, calculated as the percentage of vials outside volume targets; A control strategy for fed-batch cell culture includes specifying a gas controller to regulate dissolved oxygen (process parameter), a pH control loop to maintain pH within range (process parameter), and monitoring of cell viability over time as a process performance indicator. It also specifies that the glycosylation profile and aggregate levels as critical quality attributes and their periodic monitoring throughout the culture using off-line analysis
adapted from: https://database.ich.org/sites/default/files/Q10%20Guideline.pdf and ISA88
explanatory note: Per ICH: A planned set of controls, derived from current product and process understanding, that assures process performance and product quality. Control strategy can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control. Per ICH: Quality risk management is used to establish the control strategy. This can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in process controls, finished product specifications, and the associated methods and frequency of monitoring and control. The control strategy should facilitate timely feedback / feedforward and appropriate corrective action and preventive action.
first order logic axiom: ControlStrategy(x) → PlanSpecification(x) ∧ ∃y((ProcessParameterSpecification(y) ∨ QualityAttributeSpecification(y) ∨PerformanceIndicatorSpecification(y)) ∧ hasContinuantPartAtAllTimes(x, y))
is primitive: true
natural language definition: plan specification consisting of one or more specifications of methods and technologies that prescribe the monitoring or regulation of process parameters, quality attributes, and process performance indicators associated with a particular process and its resulting material product
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a control strategy.
semi formal natural language axiom: if x is a 'control strategy', then x is a 'plan specification' that 'has continuant part at all times' some y, where y is either a 'process parameter specification', a 'quality attribute specification', or a 'performance indicator specification'

material quality plan
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: BFO_0000110
  some values from: QualityAttributeSpecification
subclass of: Restriction
  on property: BFO_0000110
  some values from: Class
    intersection of: PlanSpecification
    intersection of: Restriction
      on property: prescribes
      some values from: MeasurementProcess
subclass of: Restriction
  on property: prescribes
  some values from: Class
    intersection of: PlannedProcess
    intersection of: Class
      union of: Restriction
        on property: BFO_0000062
        some values from: ProductProductionProcess
      union of: Restriction
        on property: BFO_0000132
        some values from: ProductProductionProcess
      union of: Restriction
        on property: isTemporallyOverlappedBy
        some values from: ProductProductionProcess
example: A material quality plan for particular chromatography resin prescribes synthesis process controls, intermediate and final QC testing, and release criteria to ensure the resin meets defined binding capacity, particle size distribution, and metal impurity limits. It includes in-process monitoring of pH and temperature during functionalization, validated leachables testing, and visual inspection.
explanatory note: 1) At least one process prescribed by some part of the quality plan has to be a measurement process 2) The processes embedded in the quality plan typically include testing, inspection, characterization
first order logic axiom: MaterialQualityPlan(x) → PlanSpecification(x) ∧ ∃a(PlanSpecification(a) ∧ ∃m(MeasurementProcess(m) ∧ prescribes(a, m)) ∧ hasContinuantPartAtAllTimes(x, a)) ∧ ∃q(QualityAttributeSpecification(q) ∧ hasContinuantPartAtAllTimes(x, q)) ∧ ∃p(PlannedProcess(p) ∧ (∃pp(ProductProductionProcess(pp) ∧ (precededBy(p, pp) ∨ occurrentPartOf(p, pp) ∨ isTemporallyOverlappedBy(p, pp))) ∧ prescribes(x, p)))
is primitive: true
natural language definition: planned specification that prescribes the processes needed to ensure that a material product meets the required quality objectives
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a material quality plan
semi formal natural language axiom: if x is a 'material quality plan', then x is a 'plan specification' that 'has continuant part at all times' some 'plan specification' a that 'prescribes' some 'measurement process', and 'has continuant part at all times' some 'quality attribute specification', and 'prescribes' some 'planned process' p that either 'precedes', is an 'occurrent part of', or 'is temporally overlapped by' some 'product production process'

material sample
type: Class
subclass of: BFO_0000040
equivalent class: Class
  intersection of: BFO_0000040
  intersection of: Restriction
    on property: isSpecifiedOutputOf
    some values from: sampling process
example: A 10 mL aliquot of bioreactor culture collected during mid-log phase for cell viability analysis; a pre-filled vial of harvested supernatant taken from a chromatography pool for impurity testing; a powder sample taken from the middle of a drum of API to test for uniformity of composition; a tablet randomly selected from a production batch during compression for hardness testing
first order logic definition: MaterialSample(x) ↔ MaterialEntity(x) ∧ ∃s(SamplingProcess(s) ∧ isSpecifiedOutputOf(x, s))
natural language definition: material entity that is the specified output of a sampling proces
semi formal natural language definition: every instance of 'material sample' is defined as exactly an instance of 'material entity' that 'is specified output of' some 'sampling process'

monitoring plan
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: prescribes
  some values from: monitoring process
example: A monitoring plan for the tablet compression process prescribes in-process testing of tablet samples every 30 minutes or every 50,000 tablets. It targets weight (540–560 mg), hardness (8–12 kp), thickness (4.8–5.2 mm), and disintegration time (<15 minutes). The responsible agents are the production operator and QC technician;An environmental monitoring plan for the production area prescribes continuous monitoring of temperature (18–25 °C) and humidity (45–65%) using data loggers. The responsible agent is the facilities technician; A bioreactor monitoring plan prescribes continuous monitoring of pH, dissolved oxygen, temperature, and agitation speed during a cell culture process.
explanatory note: A monitoring plan typically specifies the measurement frequency, target metrics, and the responsible agent.
first order logic axiom: MonitoringPlan(x) → PlanSpecification(x) ∧ ∃y(MonitoringProcess(y) ∧ prescribes(x, y))
is primitive: true
natural language definition: plan specification that prescribes some monitoring process
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a monitoring plan.
semi formal natural language axiom: if x is a 'monitoring plan', then x is a 'plan specification' that 'prescribes' some 'monitoring process'

monitoring process
type: Class
subclass of: PlannedProcess
equivalent class: Class
  intersection of: PlannedProcess
  intersection of: Restriction
    on property: BFO_0000117
    some values from: MeasurementProcess
  intersection of: Restriction
    on property: BFO_0000132
    some values from: PlannedProcess
  intersection of: Restriction
    on property: prescribedBy
    some values from: monitoring plan
alternative label: process monitoring
example: UV absorbance and conductivity monitoring during column loading; continous monitoring of temperature and humidity in a clean room; Tablet weight is measured in-line every 30 minutes during compression using an integrated balance; During a fed-batch cell culture process, at-line nutrient monitoring is performed using a Nova BioProfile® FLEX analyzer to measure glucose, lactate, glutamine, and ammonia levels; Flow rate is continuously monitored during tangential flow filtration to ensure consistent membrane performance
adapted from: https://www.biopharminternational.com/view/monitoring-biopharmaceutical-processes-present-and-future-approaches
explanatory note: 1)Each monitoring process is an execution of a monitoring plan that prescribes it 2) In case a monitoring process is responsible for monitoring the attribute/parameter/indicator in multiple processes (e.g., multiple unit operations) it is a part of the 'larger process' which has the 'occurrent part' the unit operations and it does not imply that it is an occurrent part of the unit operations themselves".
first order logic definition: MonitoringProcess(x) ↔ PlannedProcess(x) ∧ ∃m(MeasurementProcess(m) ∧ hasOccurrentPart(x, m)) ∧ ∃p(PlannedProcess(p) ∧ occurrentPartOf(x, p)) ∧ ∃mp(MonitoringPlan(mp) ∧ prescribedBy(x, mp))
natural language definition: planned process that is a part of another planned process and that consists of one or more observation or measurement processes in which a particular attribute (specifically dependent continuant or temporal region or process characteristic or process profile) of some entity of interest is measured or observed
semi formal natural language definition: every instance of 'monitoring process' is defined as exactly an instance of 'planned process' that 'has occurrent part' some 'measurement process', is 'occurrent part of' some 'planned process', and is 'prescribed by' some 'monitoring plan'

sample plan
type: Class
subclass of: PlanSpecification
subclass of: Restriction
  on property: prescribes
  some values from: sampling process
contributor: Merck KGaA
alternative label: sampling plan
alternative label: sampling strategy
example: Sampling plan that prescribes in-process sampling on Days 5, 7, and 9 for glycan profiling of the expressed monoclonal antibody; Sampling plan that prescribes post-harvest sampling to assess product titer and host cell protein levels before downstream purification; Sampling plan that prescribes manual collection of 20 tablets per batch for disintegration and hardness testing in the QC lab; Sampling plan that prescribes taking an aliquot from every third eluate fraction during protein A chromatography for titer and impurity analysis (HCP and DNA)
adapted from: https://itl.nist.gov/div898/handbook/ppc/section3/ppc33.htm
explanatory note: 1) A sampling plan in the context of production and quality management refers to a documented strategy that defines how, when, and how often samples are to be taken from materials, products, or processes for testing or inspection. 2) Key aspects of a sampling plan include: Frequency: Specifies how often samples should be taken (e.g., every batch, every tenth delivery, or at regular time intervals). Sample Size: Defines the number or quantity of items to be sampled at each interval.
first order logic axiom: SamplePlan(x) → PlanSpecification(x) ∧ ∃y(SamplingProcess(y) ∧ prescribes(x, y))
is primitive: true
natural language definition: plan specification that prescribes how, when and how often samples are to be taken during a particular process
primitive rationale: There are insufficient constructs in this release to fully capture the structure of a sample plan
semi formal natural language axiom: if x is a 'sample plan', then x is a 'plan specification' that 'prescribes' some 'sampling process'

sampling process
type: Class
subclass of: PlannedProcess
subclass of: Restriction
  on property: hasInput
  some values from: BFO_0000040
subclass of: Restriction
  on property: hasSpecifiedOutput
  some values from: Class
    union of: Restriction
      on property: BFO_0000129
      some values from: BFO_0000040
    union of: Restriction
      on property: BFO_0000175
      some values from: BFO_0000040
subclass of: Restriction
  on property: prescribedBy
  some values from: sample plan
contributor: Merck KGaA
alternative label: material sampling process
example: Sampling process in which a technician disinfects the sampling port of a 2000 L CHO cell culture bioreactor with 70% ethanol, withdraws 15 mL of culture using a sterile syringe, discards the first 2 mL, collects the remaining 10 mL into a labeled sterile vial, and seals it for downstream analysis; Sampling process in which an operator manually collects 5 tablets from the compression chute using a stainless-steel scoop, places them into a labeled clean glass container, records the batch number and time, and seals the container for later analysis
adapted from: https://www.sciencedirect.com/topics/engineering/sampling-process
counter example: A technician places a labeled sample vial in a cold box and transports it to the analytical lab (this is sample transport and not sampling itself); An operator fills final product into vials for distribution; A lab analyst measures glucose concentration in a sample using a Nova BioProfile® FLEX analyzer (this is an analysis conducted after the sampling and not the sampling itself)
explanatory note: sampling might involve conditioning (e.g., cooling, filtering, putting in a specific container), preparing, and documenting the sample The sampling process is designed to ensure that the test results subsequently conducted on the sample are reliable and representative of the overall material or product, supporting effective quality control and decision-making.
first order logic axiom: SamplingProcess(x) → PlannedProcess(x) ∧ ∃m(MaterialEntity(m) ∧ hasInput(x, m)) ∧ ∃s((MaterialEntity(s) ∧ (memberPartOfAtSomeTime(s, m) ∨ properContinuantPartOfAtSomeTime(s, m))) ∧ hasSpecifiedOutput(x, s)) ∧ ∃p(SamplePlan(p) ∧ prescribedBy(x,p))
is primitive: true
natural language definition: planned process of selecting and collecting a limited amount or number of some material entity in a manner which ensures it is representative of the input material entity
primitive rationale: There are insufficient constructs to create a set of necessary and sufficient conditions. In particular, pattern and all the constructs for representing that the sample is taken in a manner which ensures it is representative of the input are missing
semi formal natural language axiom: if x is a 'sampling process', then x is a 'planned process' that 'has input' some 'material entity' m and that is 'prescribed by' some 'sampling plan' p and 'has specified output' some s, where s is either a 'member part of at some time' or a 'proper continuant part of at some time' of m