Q

Question of interest

Specific question, decision, or concern that is being addressed with modeling and simulation (e.g. identification of best responders or surrogate markers).

See also: context of use.

R

Receptor

Proteic macromolecule (or a complex of them) that receives and transduces biochemical signals by selective binding of a ligand to its active site. Ligand typically triggers activation or inhibition of the receptor's associated biochemical pathway.

It can be located within a cell (intracellular receptor) or on its surface (cell-surface receptor).

Reference patient

Virtual patient associated with reference scenario(s), expected behavior(s) and current behavior(s). Throughout the calibration step, the Computational Model aims to reproduce the expected behaviors of each reference patient (RP).
Each RP is intended to represent a class of patients within the population of interest with a similar qualitative behavior.
Each RP is defined by Patient Descriptors and each RP reference scenario is defined by ProtocolSpecific parameters (inputs). Each RP expected behaviors is defined by scorings (outputs).
RPs are by definition dynamic within a project in which their set of inputs will be updated in order to improve as best as possible their current behaviors to match the expected behaviors.

See also: Reference patient methodology note

Responders

Subjects from a treated arm of a trial for whom the change in clinical endpoint value is above a predefined threshold, called response threshold.

According to a draft guidance from the FDA on patient-reported outcomes (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2164942/), categorizing a patient as a responder can be based upon a prespecified change from baseline on one or more scales; a change in score of a certain size or greater (e.g., a 2-point change on an 8-point scale); or a percent change from baseline.

Response threshold

Pre-defined value relative to the change in clinical endpoint from its baseline measured value, used to discriminate between "responders" and "non-responders" in a treated population.

Review article

Scientific article that summarizes findings and understandings on a topic by surveying previously published articles, as opposed to an original article.

Risk factor

Behavior, condition, genotypic or phenotypic characteristics that increase the likelihood of occurrence of a morbid event.

It is defined by a statistical correlation between the factor measured at time t0 on a population (cohort) and the occurrence of a morbid event between t0 and t0 + t. Note that, since the correlation does not imply causality, it cannot be considered that "normalization" of the risk factor eliminates the risk.

ROC curve

Refers to Receiving Operating Characteric curve, a plot of the true positive rate (y-axis) against the false positive rate (x-axis) when the threshold of a binary classifier is varied over the range of possible values. The Area Under the Curve (AUC) is a metric of the binary classifier's performances.

S

Scorings

A scoring is a numerical expression based on a level analysis of a virtual patient's biological behavior. Each scoring is between 0 and 1 and informs on the adequacy between the model and the reality (data from the client or from the literature). In other words, scorings are quantitative evaluations of how close to an expected behavior our model is, for a given set of input parameters. An example of scoring can be : The viral load increases between the beginning o the infection and the viral peak. Score evaluations are the results of a scoring on a patient. Indeed, we solve the model for a set of patient and in a second time we evaluate the scores on each of these patients.

Selectivity

Ligands tendency to bind to a very limited set of receptors. Selective ligands have a tendency to bind to a very limited set of receptors, whereas non-selective ligands bind to a diverse array of receptors. This plays an important role in pharmacology, where drugs that are non-selective tend to have more adverse effects, because they bind to several other receptors in addition to the one generating the desired effect.

Sensitivity analysis

Systemic investigation that leads to an understanding of how quantitative changes in model inputs influence the model outputs and thus characterizes the level of confidence in the output.

Side effect

In medicine, a side effect is an effect, whether therapeutic or adverse, that is secondary to the one intended; although the term is predominantly employed to describe adverse effects, it can also apply to beneficial, but unintended, consequences of the use of a drug.

Source of knowledge

There are two categories of sources. Primary sources are original reports, either published (original articles) or unpublished (internal reports, theses). Secondary sources are reviews or meta-analyses which compile knowledge from primary sources.

See also: piece of knowledge.

Spare receptors

In some cases, agonists are able to elicit maximal response at very low levels of receptor occupancy (<1%). In these cases, the tissue is said to possess spare receptors. From a functional perspective, spare receptors are significant because they increase both the sensitivity and speed of a tissue's responsiveness to a ligand.

Strength of evidence

The higher or lower extent to which a piece of knowledge may be regarded as reliable. In its simplest form, it takes one of 3 values: Poor, Average or Good.

See also: uncertainty and variability.

Submodel

Generally, a model (disease and treatment) can be divided into submodels.

There are three main types of submodels:

• Generic: model of the physiology and/or pathophysiology of a tissue, organ or important group of biological processes. It is connected to other submodels by its inputs and outputs.
• Clinical: submodel that links the disease process to clinical outcomes.
• Treatment: typically a PK-PD model predicting amount of drug at the site of action as well as its effect. Several treatments model can be incorporated if it is necessary to model several drugs.

T

Theories of drug receptor interaction

The main theories of drug receptor interaction are:

• Occupation theory: the central dogma of receptor pharmacology is that the drug effect is directly proportional to the number of receptors occupied by the drug. Furthermore, the drug effect stops as drug-receptor complexes dissociate.
• Rate theory: in contrast to the more generally accepted occupation theory, rate theory proposes that the activation of the receptors is directly proportional to the total number of encounters of drug molecules with receptors per unit of time. Pharmacological activity is then directly proportional to the rates of dissociation and association, rather than to the number of receptors occupied.
• Induced fit theory: as the drug approaches the receptor, the receptor alters the conformation of its binding site to produce drug-receptor complex. Several other theories exist such as the inactivation theory.

Therapeutic objective

What the patient wants to achieve with a given treatment; generally get the best effect of a treatment with lower iatrogenic effects. Therapeutic objectives include survival, avoidance of nonlethal morbid events, riddance of annoying symptoms and improvement of the quality of life.

For example, for a hypertensive patient, the therapeutic objective should not be to lower blood pressure but rather to prevent cardiovascular events subsequent to high blood pressure. The suitable therapy must be chosen from the assessment of the global risks of cardiovascular events for the patient, the evaluation of the part of these risks "attributable" to the blood pressure, from the therapeutic objective set in function of these risks and, overall, the predicted absolute benefit with the therapy.

Transposability

Possibility to extrapolate a treatment efficacy observed (in clinical trials and meta-analysis) or predicted (in silico) from the population in which it has been established (a clinical trial population in the former case and a Virtual Population in the later) to a different population.

Treatment rate

Cumulative hazard rate of an individual without treatment. It appears in the Effect Model law under its instantaneous index called R_c.

Treatment risk

Risk of event (outcome) in treated individual(s) in the context of a prediction. It is cumulative over time. The corresponding instantaneous index is the treated hazard Rt (t)

U

Uncertainty

Potential deficiency in any phase or activity of the modeling, computation, or experimentation process that is due to inherent variability, lack of knowledge or not reliable knowledge (with a low Strength of Evidence). Prediction uncertainty resulting from biological variability can be tackled with through exploration of the predicted output through the Virtual Population.

See also variability, Strength of Evidence and Virtual Population.

V

Validation

Process of determining the degree to which a model or simulations accurately represent the real world from the perspective of the intended application of the model under similar conditions, i.e. the Context of Use. During this operation the model is challenged to reproduce a known behavior or dataset that has not been used in the model design or calibration steps.

See also ASME V&V 40 guidelines

Variable

Quantitative entity within a model, which value depends on parameters and/or other variables and is expected to vary.

For instance, in physics, one may consider the speed of a body to be a variable, with value depending on other variables (such as the forces that are applied to it) as well as on some parameters (such as the coefficient of friction between the body and the surface on which it moves).

See also parameter.

Variability

Interindividual—differences between patients due to differences in diet, genetics or immune status or intra-individual—differences in the same subject over time due to diurnal cycles and other rhythms, biological repair mechanisms, dietary variables, ageing, etc.

See also Uncertainty and Strength of Evidence.

Virtual patient

Vector of patient descriptors that can be obtained through a random sampling from the descriptors joint distributions. If the vector is non-valued, the patient is called theoretical virtual patient.

See also patient descriptors and Virtual Population.

Virtual population

Collection of virtual patients generated in silico. It can contain only unmeasurable patient descriptors (i.e. parameters and variables of the model) or a mix of unmeasurable and realistic descriptors derived from a real population study.

The generation of a Virtual Population requires a list containing the disease model variables and parameters that vary within a population, as well as a thorough characterization of these variables and parameters (mean values, type of distribution, parameters of distribution, multivariate distribution also called joint distribution).

A model development process is often associated with several successive Virtual Populations that are deemed to be more and realistic.

See also patient descriptors and virtual patient.