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D to K

D

Data

Set of values of a qualitative or quantitative variables observed on a defined set of subject (e.g. patients). It refers to the information obtained from a single statistical units in an observational study (e.g. cohort survey, omics study) or an experiment (e.g. animal experiment, in vitro experiment, clinical trial).

Descriptors

Descriptors are all the scalar values, varying from one patient to another, that one could wish to extract from the model, either from its dynamics (observed at a given time) or its inputs or its formulas. In “real-life” those would correspond to all the data points a clinician would like to observe and measure on a patient. In practice some descriptors are indeed observable in “real-life” depending on the capabilities but several of them are not in practice. An observable descriptor may be derived from epidemiological or clinical data.

We distinguish three kind of descriptors that we have at the model level:

  • Input descriptors: those do correspond to the scalar inputs of the model.
  • Baseline descriptors: they are all the descriptors known at the start of or a clinical trial. All Input descriptors are Baseline descriptors but the reverse is not true because some of them are formulaic. For instance allometrized parameters do depend on a reference parameter and on the patient weight, so they are not Input descriptors but can be valued at the trial start and are therefore part of the Baseline descriptors.
  • output descriptors: those are all the observable values that one can compute from the simulation outputs.

See also Virtual Population

Disease biology submodel

Component of a pathophysiological model which represents a particular phenomenon or an aggregation of phenomena. It is defined by its granularity level (genes, cells, tissues, organs) and by the Context Of Use.

Disease biology submodels exist in different forms:

  • Knowledge (i.e., discursive & graphical),
  • Computational (i.e. a series of code lines implementing the equations.) See Computational Model

Dose effect relationship

Link between the quantity of a substance administered (the dose) and its overall effect (the response) on an organism. This may apply to individuals (e.g.: a small amount has no significant effect, a large amount is fatal), or to populations (e.g.: how many people or organisms are affected at different levels of exposure).

Studying dose response, and developing dose--response models, is central to determining "safe" and "hazardous" levels and dosages for drugs, potential pollutants, and other substances to which humans or other organisms are exposed. These conclusions are often the basis for public policy. The U.S. Environmental Protection Agency has developed extensive guidance and reports on dose-response modeling and assessment, as well as software.

Warning: Concentration effect relationship is different.

Drug

A drug is a compound with more or less complex therapeutic system, recognized in an official pharmacopoeia or formulary, incorporating a substance (chemical or natural) that has known biological effects on humans or other animals physiology, intended for use in the diagnosis, cure, mitigation, treatment, or prevention of a disease or used to otherwise enhance physical or mental well-being. Because all drugs have the potential for adverse drug reactions, risk-benefit analysis (analyzing the likelihood of benefit vs risk of adverse drug reactions) is necessary whenever a drug is prescribed.

Drug discovery

The scientific and technological process through which new therapeutic targets, new pharmacologically active molecules, new modes of action, new mechanisms of action are investigated as an early-stage effort to discover new medicines.

Drug regimen

The way a drug is given: daily dose (or dose per intake), number of administrations per day (intakes), way of administration (including inhalation, injection, smoking, ingestion, absorption via a patch on the skin or dissolution under the tongue) and duration.

Drug submodel

The drug submodel describes the disposition of a pharmaceutical compound within the organism. At Nova, we aim to use for that purpose PBPK (physiologically based pharmacokinetic) models which predict the concentration versus time profile of a drug by describing tissue physiology, anatomy and biochemistry. Anatomical data as well as the physicochemical data available for the drug is used to predict the drug's interactions with the organism and its absorption, distribution, metabolism and excretion (ADME).

Used to simulate the action of a (potential) drug on its target(s) in order either to figure out the optimal ligand profile or to introduce the known drug mode of action in the global therapeutic model.

The drug model should represent the type of action (activation/inhibition), the type of chemical interaction(s) as well as the characterization of this action (full, partial, competitive, non competitive, duration).

Drug repositioning

The drug repositioning is the redeployment of existing (including approved) drugs for indications other than the one they were developed for.

These drugs may have

  • Failed to show efficacy in late stage clinical trials, without safety issues
  • Stalled in development for commercial reasons
  • Passed the point of patent expiry
  • Are being explored in new geographical markets

Drug target

The drug target is defined as the biological component (e.g. an enzyme, a ionic channel, a receptor) the drug binds to in order to induce a stimulus (activity). The drug target is located at the site of action.

By extension, the target is the site of action.

See also ligand.

Drug toxicity

The drug toxicity refers to the level of damage that a drug can cause to an organism. The toxic effects of a drug are supposed to be drug-dependent and can affect the entire system.

It may occur when the dose given is too high or the liver or kidneys are unable to remove the drug from the bloodstream, allowing it to accumulate in the body. Drug toxicity usually occurs at doses that exceed the therapeutic efficacy, however toxic and therapeutic effects can occurs simultaneously.

DTA

DTA refers to Data Transfer Agreement. It is contracted between the client and Nova and specifies which data they must provide us and when (e.g. they must send the validation data only once the calibration is over, at the validation step, in order to be sure to remain blind).

EC50

The EC50 is a measure of the potency of a drug and is related to both efficacy and affinity.

The EC50 of an agonist is defined as the molar concentration of the agonist that produces 50% of the maximum possible response for that agonist.

The EC50 value is useful for comparing the potency of drugs producing physiologically similar effects. The smaller the EC50 value, the greater the potency of the agonist, the lower the concentration of drug that is required to elicit the maximum biological response. The EC50 of an antagonist is defined as the concentration of antagonist needed to elicit half inhibition of the maximum biological response of an agonist. The lower the EC50 the greater the potency of the antagonist, and the lower the concentration of drug that is required to inhibit the maximum biological response.

Effect model law

Law which states that a natural relationship exists for each individual between the frequency (observation) or the probability (prediction) of a morbid event without any treatment Rc and the frequency or probability of the same event with a treatment Rt.

More information can be found on wikipedia.

Efficacy

The efficacy of a ligand is its ability to modulate the activity of a receptor upon binding and produce a functional response and/or the quantitative magnitude of this response. The final biological response is only achieved after a significant number of receptors are activated.

By extension, efficacy of a drug is the quantity of its beneficial effect on clinical outcome(s).

EMA

The European Medicines Agency (EMA) is the agency of the European Union (EU) in charge of the evaluation and supervision of medicinal products. It is the equivalent of the FDA in the US.

Epidemiology

Study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems.

Definition and further information on Introduction to Epidemiology.

F

FDA

The Food and Drug Administration is the federal agency of the United States Department of Health and Human Services responsible for protecting and promoting public health through the control and supervision of food safety, prescription and pharmaceutical drugs, medical devices...

Formal model

Mathematical, symbolic or algorithmic representation of the mechanisms represented in a given submodel, derived from knowledge by translating the assertions Knowledge Model with the selected formalism.

Free fraction

Fraction of a ligand (typically a drug) in a medium (often plasma or tissue) that is not bound to proteins (such as albumin in blood).
It is an important parameter in pharmacokinetics as, in most cases, only the drug free fraction is able to bind its target receptor and exhibit pharmacological effects. Common blood proteins that bind drugs are human serum albumin, lipoprotein, glycoprotein, and α, β‚ and ϵ globulins.

Full agonist

Agonist able to activate the receptors and result in a maximal biological response. The natural endogenous Ligand with the greatest efficacy for a given receptor is by definition a full agonist (100% efficacy).

H

Hazard rate

Instantaneous rate that an event (typically death) occurs in the conditional population that is event-free. In other words, it is the probability that the event will happen at the time t knowing that it did not until that time. Mathematically, the hazard rate is defined as:

  

where N(t) is the size of the event-free population at time t and 𝚫N([t,t+𝚫T]) is the number of event in the interval [t,t+𝚫T].

Hazard ratio

Instantaneous ratio between hazard rates of an event in two different populations described by one or several explanatory variables, traducing the relative event occurrence probability.

See the application of hazard ratio in the Effect Model methodology with Rc and Rt.

See also: Hazard rate, Treated rate, Control rate.

I

Iatrogenic effects

Outcomes inadvertently induced by a physician or surgeon or by medical treatment or diagnostic procedures.

Definition from Gebhart G.F., Schmidt R.F. (eds) Encyclopedia of Pain.

See also: Side effects.

In silico clinical trials

The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention[3].

Definition from Viceconti, Marco, et al. "In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies.".

Inter-individual variability or inter-patient variability

Variability between individual across a population (e.g. physiological difference in average heart rate between individuals).

Intra-individual variability or intra-patient variability

Variability within a given individual over different periods of time (e.g. physiological changes in heart rate in a given individual that may fluctuate across time).

Inverse agonist

Agonist that reduces the activity of receptors by inhibiting their constitutive activity (negative efficacy).

Investigation

Initial and general literature review on the specific subject for the Context of Use in order to understand its substance, its parts and its stakes.

Through this step, the biomodelers becomes an expert on his/her submodel given the Context of Use of the project, by building a library of assertions in jinkō platform and identifying the knowledge gaps.

K

Knowledge

Information about a phenomenon that is obtained by observations and/or experiments made by researchers.

See also Strength of Evidence.

Knowledge model

Deliverable created from a frozen version of the model documentation (template available here). It is the founding material of the Formal Model describing the knowledge which will be implemented with assertions, the associated knowledge gaps, a graphical representation, and a list of variables.

Knowledge gap

A biological phenomenon whose mechanism can not be fully described by any scientific consensus or proof-supported hypothesis.

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