L

Ligand

Substance that binds specifically and reversibly to a biomolecule to form a complex (e.g. an antigen binding to an antibody, a hormone or neurotransmitter binding to a receptor, or a substrate or allosteric effector binding to an enzyme). It generally plays a functional role such as structural stabilization, catalysis, modulation of enzymatic activity or transmission of a signal.

See also agonist, antagonist.

Ligand-receptor interaction

Ligand binding to a receptor generally through intermolecular forces, such as ionic bonds, hydrogen bonds and van der Waals forces.

See associated note Modeling Biochemical reactions

Literature review

Searching the scientific literature (i.e. the collection of scientific articles that have been published on a particular field) in order to assess the current level of understanding on a particular disease, or on a particular mechanism of interest.

Logical model

A model made of logical (e.g. Boolean) equations linking nodes (model entities).

M

Maximum effect model (Emax) - Hill equation

Simple relationship between the concentration and effect of a drug acting at a receptor? It assumes that the drug interacts reversibly with its receptor and produces an effect proportional to the number of receptors occupied, up to a maximal effect when all receptors are occupied.

  

where E is the effect observed at concentration C, E_max is the maximal response that can be produced by the drug, and EC₅₀ is the concentration of drug that produces 50% of maximal effect.

See associated note Modeling Biochemical reactions.

Measurable variable

A variable that can be measured routinely by laboratory tests and measurement techniques (blood concentration, radiologic tumor size, etc.).

Mechanism of action

Once the drug (active agent) has modified the functioning of its target, the resulting changes in the biological system which are expected to alter the course of the disease.

By extension, the biological system components (entities, relations, signals) involved.

See also mode of action.

Mechanistic model

Model aimed at catching causal relationships (i.e. knowledge) as opposed to a a correlation model based on co-occurrence (i.e. data only).

Michaelis-Menten kinetics

A model for enzyme kinetics, that involves an enzyme E binding to a substrate S to form a complex ES, which in turn is converted into a product P and the enzyme E. Under certain assumptions, this leads to a dynamics of the type :  
This rate can also be used to include saturation in a bioreaction.

See Kinetic laws for more detail.

Mode of action

The process by which the active agent modifies the functioning of its target.

According to the current theory, the input is the agent concentration at the site of action, assumed to be, in a PKPD model, the predicted concentration in the effect compartment.

The output is called the 'stimulus' in the classical pharmacological theory (see efficacy, affinity).

In more modern views, it initiates the mechanism of action.

See also mechanism of action.

Model

• Detailed: a model the components of which are molecules (high granularity)
• Knowledge (KM): It is the founding material of the formal model. It is made of: 1) a discursive model: textual arrangement of assertions, the KM assertions; 2) series of lists (links to the list section); 3) a graph model.
• Formal: a series of mathematical or logical equations that represent all knowledge identified as relevant to the question of interest
• Computational: a translation in computing code of the formal model
• (Global) Therapeutic: merging of a disease model and a treatment model
• Phenomenological: a model where the components that are linked through relations (functions) are summary of biological systems or phenomenons (low granularity)
• Mechanistic: a model based on causal relationship between entities (see mechanistic)
• Macro KM model: a cutting up at a macroscopic level of the disease mechanism in more or less independent units. A first and comprehensive view of what the disease is made of. Macro-KM units prefigure the sub-models.
• Granularity: refers to the degree of details that are represented in the model

Model components

There are 6 major categories of model components:

• Entities refer to any biological construct that plays a role in the pathophysiology. It can be a molecule (e.g. protein, ion, length of DNA, mRNA), a biological compartment (e.g. a neutrophil, a mitochondria, milieu interieur), etc.
• Variables refer to a signal of whatever nature (e.g. electrical, molecule or ion concentration) which is an output from X and an input to Y. Expresses a relation and linked to one or more parameters.
• Parameters refer to the adjusting component of a relation or a signal. It appears in the Formal Model. See parameter
• Connectors refer to the model translation of a relation between two entities (which are nodes in the model). More specifically, links between submodels.
• Relation refers to a quantitative connection between two entities. Translation (and by extension, synonym) of a process.
• Signal - see variable

Model form

Conceptual and mathematical formulation of the computational model VVUQ 40

Model input

Values for parameters used in the model components VVUQ40. It is any data necessary for model execution. The model inputs for the computational model are written in parameter, variables, compartment, event and configuration file.

Model objective

Purpose of the model in the context of the project in order to address the question of interest. The objective of any model is to allow us to answer the client's questions, and to do so it will need to be calibrated then validated.

Model outcome or quantity of interest

The calculated or measured result from a Computational Model to be used to answer the question of interest (e.g. a clinical outcome). It is made of one or several model outputs.

Model output

Result of a simulation of the model given a set of parameter and variable initial condition values. It describes time series of each model variables, including variables of interest for the research questions.

See also model outcome

Model scope

Requirements and model's specifications implied by the question of interest. It can include a list of submodels (ex: immune system) with key mechanisms to be implemented, their width (ex: including myeloblastic cells or not) and their depth (ex: detailing signaling pathway level).

Model specifications

Requirements the model must meet, either qualitative or quantitative. They can be translated into Scoring for calibration and validation.

See also: Scoring

N

Number of induced events

Refers to the number of iatrogenic events induced by a drug.

For some patients, a treatment may generate a negative outcome - there is a balance (i.e. benefit/risk ratio) between:

• The Absolute Benefit on the clinical event of interest and
• The occurrence of iatrogenic clinical events (most of the time of a different nature) produced by the treatment. When the iatrogenic effect outcome is the same as the expected beneficial effect (eg death), the Absolute Benefit is negative and the Number of Prevented Events becomes the Number of Induced Events.

Number of prevented events

The word "event" refers to a clinical negative outcome caused by a disease (e.g. the patient's death in the case of a cancer, a heart attack in the case of a coronary disease, liver failure in the case of a chronic hepatitis C infection, etc.)

Thus, the NPE refers to the number of patients, in a given population, over a given duration, for which such a negative event would be prevented through a specific treatment, in comparison to an equivalent population for which the treatment under study would not be administered

The NPE is expressed as follows: N P E = ∑ AB_i

Refers to the Effect Model law.

O

Original article

It is a published scientific article in which the authors present for the very first time the results of an experimental study, as opposed to a review article.

P

Parameter

It is a quantitative entity within a Formal Model whose value is set to remain constant in a particular context. Its value is a phenotype. It is either documented through the piece of knowledge (assertion) annotation process or adjusted during the Formal Model calibration phase. It could vary with gene (or protein) variants. See also patient descriptor.

For example, in physics, the acceleration of gravity is supposed to remain invariable (g = 9.8 m/s2) in most models even though it is well known that slight variations may occur.

See also variable.

Partial agonist

Agonist that does not activate receptors with maximal efficacy, even with maximal binding, causing responses which are partial compared to those of full agonists (efficacy between 0 and 100%).

Pathophysiology

It refers to the study of the changes of normal functions such as mechanical, physical, and biochemical, either caused by a disease or resulting from an abnormal syndrome.

Pathway

At the cellular level, a pathway is a series of connected actions among molecules in a cell that, once activated, leads to a certain product or a change in a cell (such as the assembly of new molecules or the switch on/off of the transcription of genes).

Pharmacodynamics

Pharmacodynamics is the study of the biochemical and physiologic effects of drug and their mechanism of action in an organism.

Pharmacokinetics

Pharmacokinetics describes how the body affects a specific drug after administration. It is generally divided into 4 areas including Absorption, Distribution, Metabolism and Excretion.

Pharmacokinetic model

It is a rather simple and usually phenomenological mathematical model used for predicting the active concentration of a bioactive molecule at the site of action.

Pharmacokinetic parameters

Pharmacokinetic parameters are used to describe and quantify interactions between xenobiotics and patients (human and non-human). It is normally applied to quantify drug, disease and trial information to aid efficient drug development, regulatory decisions and rational drug treatment in patients. A single kinetic profile may be well summarized by C_max, T_max, t_1/2 and AUC and, having more than one profile, 8 parameters at least, the mean and standard deviation of these parameters, may well summarize the drug kinetics in the whole population. More information on PK parameters can be found in this document.

Phenomenological

It refers to a level of granularity of a given submodel, where the components linked through relations (functions) are a summary of biological systems or phenomenons (low granularity).

Phenomenon

Biological process defined by its given inputs, outputs, and granularity level (e.g. a metabolic pathway or circulation of one entity). It is the elementary brick of a submodel as the smallest set of bioreactions that is standalone. Depending on model scope, a process may be modeled by a phenomenon of by a set of phenomena: For example, cellular death may be a phenomenon at the cellular granularity level, or made of several other phenomena detailing the process at the molecular granularity level.

Phenotype

It refers to a list of traits or observable characteristics of a biological entity, variable, process, system that can be observed and measured in an organism.

Physiology

Study of biological functions and mechanisms of living organisms, their tissues and cells under normal (non pathological) conditions.

Piece of knowledge

In biomedical science, elementary unit of knowledge extracted from scientific literature.

It can take the form of a citation or and assertion, and can refer to an entity (e.g. a ionic channel, a gene), a relationship (e.g. a ionic current which brings a signal from entity X to entity Y), a phenotype (e.g. the observed value of the signal), a genotype (e.g. the gene variant associated with the value of the signal), etc.

Potency

Measure of necessary amount of a ligand to produce an effect of a given magnitude by binding to its target.

It is the result of a complex interplay of both the binding affinity and the ligand efficacy. The potency of an agonist is inversely related to its EC₅₀ value.

Principal component analysis (PCA)

A unsupervised approach (no target variable or no response value). PCA simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions (called principal components; uncorrelated from each other). The PCAs are defined as a linear combination of the data's original variables. For more details (wikipedia).

Prognostic model

Statistical or non-statistical mathematical model that predicts the probability of occurrence of a clinical event over time (e.g. the probability of having a heart attack between t = 0 and t = 24 months) at the patient level as a function of the clinical and biological state of the patient at t = 0.