Drug description file¶

This chapter presents the way medical drugs have to be described. Tucuxi is flexible in terms of handling various medical drugs (or substances). The drugs are defined within XML files, and can be edited thanks to the special Tucuxi drug editor.

The suffix of a Tucuxi Drug Description file is .tdd, and by convention, the name of the file shall be similar to the drug model Id.

General elements ¶

This section lists some useful elements used everywhere in the drug model file.

Units¶

The units in Tucuxi consist of a base and a multiplier. For example in ug/l, g/l is the base, and u the multiplier, which express a unit of micrograms per liter. The convertion factors are used to convert the data produced by the models of Tucuxi. The molar mass is used to give the user the choice to use moles instead of the default units.

stdAprioriValue¶

In various parts of the drug model, elements can have a default value, and an apriori value calculated with the help of the patient covariates. Every time such a pattern is used, the element is of type stdAprioriValue.

stdAprioriValue content¶
Tag name	Format	Occ.	Description
<standardValue>	double	1:1	The default value
<aprioriComputation>	Operation	0:1	The operations to calculate the a priori value

When such an element is used, the software will use default values for calculation involving the typical patient. In case of a priori and a posteriori calculations, it will try to apply the operations on each of such value, in order to better fit the patient reality.

Operation¶

The operations are used to modify the values of the parameters in accordance with the patient’s covariates, as well as other values (targets, domain validity, …). They are used to compute the a priori parameters and can be of three types:

softFormula
hardFormula
multiFormula

These three options are mutually exclusive, so the possible styles of operations are:

SoftFormula

<operation>
  <softFormula>
    <inputs>
      <input>
        <id>bodyweight</id>
        <type>double</type>
      </input>
    </inputs>
    <code><![CDATA[
        return bodyweight < 100.0 and bodyweight > 0.0;
            ]]>
    </code>
  </softFormula>
  <comments/>
</operation>

hardFormula

<operation>
  <hardFormula>formulaId</hardFormula>
  <comments/>
</operation>

Where the formulaId can be:

IdealBodyWeight
BodySurfaceArea
eGFR_CockcroftGaultGeneral
OperationEGFRCockcroftGaultGeneral
eGFR_CockcroftGaultIBW
eGFR_CockcroftGaultAdjIBW
GFR_MDRD
GFR_CKD_EPI
eGFR_Schwartz
GFR_Jelliffe
eGFR_SalazarCorcoran
direct
sum2
multiFormula (not yet supported)

<operation>
  <multiFormula>
    <softFormula>
      <inputs>
        <input>
          <id>bodyweight</id>
          <type>double</type>
        </input>
      </inputs>
      <code><![CDATA[
          return bodyweight < 100.0 and bodyweight > 0.0;
              ]]>
      </code>
    </softFormula>
    <hardFormula>formulaId</hardFormula>
  </multiFormula>
  <comments/>
</operation>

Tag name	Format	Occ.	Description
<operation>			Description of an operation
____<softFormula>		0:1	An Javascript operation
________<inputs>		1:1	The list of required inputs
____________<input>		1:∞	An input for the formula
________________<id>		1:1	The Id of the required input for the formula
________________<type>		1:1	The type of data : double, int or bool
________<code>	Code	1:∞	The operation formula
____<hardFormula>	string	0:1	A hardcoded operation
____<multiFormula>		0:1	A multi-operation formula
________<…>		1:∞	Any of softFormula and hardFormula
____<comments>		1:1	Comments about the operation

An operation can be used in many elements. For instance it is used in parameters, targets, covariates, in order to calculate a priori values.

A formula can use the value of any global or drug-specific covariate. To do so, you must use the covariate’s ID , as shown above with bodyweight. You can also use any of the drug’s parameters, using its ID followed by the _population keyword, as in V_population. The formula should simply return a value, nothing else is mandatory.

The formula must always be surrounded by the <![CDATA[ and ]] markers. The language used to express the formula is based on Javascript and supports a subset of it. It is to be noted that the online editor takes care of the addition of the markers.

A formula must always return a value.

When an operation can contain a list of formula the computing engine shall try to apply the first formula. If there are missing covariates for such formula, then the second formula is tried, and so on, until a valid formula is found.

The list of inputs is important and should contain all the inputs required by the formula (for a soft formula). The type should obviously be correct. In case the input is a covariate, the type should be the same type as the covariate, and in case of a parameter the type is double. The editor does not check if the input list is correct or not, so be careful.

Code¶

A formula is an operation returning a value, based on some inputs. The content of the element is then a source code in the correct format.

The source code must always be surrounded by the <![CDATA[ and ]] markers. The language used to express the formula is based on Javascript and supports a subset of it. A formula must always return a value.

The following mathematical functions are available within scripts:

Math.E()
Math.log(a)
Math.log10(a)
Math.exp(a)
Math.pow(a,b)

Math.sqr(a)
Math.sqrt(a)

Math.abs(a)
Math.round(a)
Math.min(a,b)
Math.max(a,b)
Math.range(x,a,b)
Math.sign(a)

Math.PI()
Math.toDegrees(a)
Math.toRadians(a)
Math.sin(a)
Math.asin(a)
Math.cos(a)
Math.acos(a)
Math.tan(a)
Math.atan(a)
Math.sinh(a)
Math.asinh(a)
Math.cosh(a)
Math.acosh(a)
Math.tanh(a)
Math.atanh(a)

Such function must always be written with the Math. as prefix. Example:

newValue = Math.pow(aValue, anotherValue) + Math.exp(yetAnotherValue);

Structures such as if/then/else are supported, as in the following example:

if (sex > 0.5) {
  aValue = 12;
}
else {
  aValue = 10;
}

Examples¶

Computation of the PK parameter V based on sex, for Imatinib:

theta2 = V_population;
theta8 = 46.2;
tvv = theta2 + theta8 * sex - theta8 * (1 - sex);
return tvv;

Computation of the PK parameter CL based on sex, bodyweight, age and Gist, for Imatinib:

theta1 = CL_population;
theta4 = 5.42;
theta5 = 1.49;
theta6 = -5.81;
theta7 = -0.806;

MEANBW = 70;
FBW = (bodyweight - MEANBW) / MEANBW;

MEANAG = 50;
FAGE = (age - MEANAG) / MEANAG;

if (gist)
  PATH = 1;
else
  PATH = 0;

MALE = sex;

TVCL = theta1 + theta4 * FBW + theta5 * MALE-theta5 * (1-MALE) + theta6 * FAGE + theta7 * PATH - theta7 * (1 - PATH);

return TVCL;

And the list of inputs for that specific computation:

<inputs>
    <input>
        <id>CL_population</id>
        <type>double</type>
    </input>
    <input>
        <id>bodyweight</id>
        <type>double</type>
    </input>
    <input>
        <id>age</id>
        <type>double</type>
    </input>
    <input>
        <id>gist</id>
        <type>bool</type>
    </input>
    <input>
        <id>sex</id>
        <type>double</type>
    </input>
</inputs>

Comments¶

Before explaining all the specific fields, a word on comments is required, as the <comments> tag can be found at different places of the file. A comment has the following structure:

<comments>
  <comment lang='en'>This work is based on the paper nice_paper, that can be found here.</comment>
  <comment lang='fr'>La description de ce médicament est basée sur ce nice_paper, qui peut être trouvé ici.</comment>
</comments>

Tag name	Format	Occ.	Description
<comments>		1:1	List of translated comments
___<comment lang=’xx’>	string	0:∞	Comment for a specific language

It contains as many <comment> tags as required. Each <comment> tag has an attribute lang defining the language of the comment, enabling multi-language support for the description of the medical drugs.

A validation element allows to specify a validity function to check another element value. It is used in covariates and parameters.

It is based on an operation, and embeds an error message that can serve to display relevant information to the user.

<validation> <!-- pourrait être une contrainte -->
  <errorMessage><text lang="fr"></text></errorMessage>
  <operation>
    <softFormula>
      <inputs>
        <input>
          <id>bodyweight</id>
          <type>double</type>
        </input>
      </inputs>
      <formula><![CDATA[
          return bodyweight < 300 and bodyweight > 0;
          ]]>
      </formula>
    </softFormula>
    <comments/>
  </operation>
  <comments/>
</validation>

head content¶
Tag name	Format	Occ.	Description
<validation>		1:1	Description of a validation
___<errorMessage>		1:1	List of error messages
______<text lang=’xx’>	string	0:∞	Message for a specific language
___<operation>	Operation	1:1	The checking operation
___<comments>	Comments	1:1	Comments about validation

Model ¶

The global structure of the XML file is the following:

<?xml version="1.0" encoding="UTF-8" standalone="no" ?>
<?xml-stylesheet href="drugsmodel2.xsl" type="text/xsl" ?>
<model version='1.0'
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="drug2.xsd">
  <history></history>
  <head></head>
  <drugModel></drugModel>
</model>

Tag name	Format	Occ.	Description
<history>	History	1:1	History of the file
<head>	Head	1:1	General information
<drugModel>	DrugModel	1:1	Everything needed for computation

The XML format is described in the file drug2.xsd which is used by Tucuxi in order to check the structure correctness before loading the drug description file.

History ¶

Used by Model.

The history of the file is described inside the <history> tag. A standard history looks like this:

<history>
  <revisions>
    <revision>
      <revisionAction>creation</revisionAction>
      <revisionAuthorName>John Doe</revisionAuthorName>
      <institution>Name of his institution</institution>
      <email>john@doe.com</email>
      <date>2014-07-17</date>
      <comments/>
    </revision>
    <revision>
      <revisionAction>modification</revisionAction>
      <revisionAuthorName>Jane Doe</revisionAuthorName>
      <institution>Name of her institution</institution>
      <email>jane@doe.com</email>
      <date>2014-07-30</date>
      <comments/>
    </revision>
  </revisions>
</history>

Tag name	Format	Occ.	Description
<revisions>		1:1	List of revisions
___<revision>		1:∞	Revision
________<revisionAction>	string	1:1	Type of revision
________<revisionAuthorName>	string	1:1	Name of the person who wrote the revision
________<institution>	string	1:1	Name of institution from which the revision was written
________<email>	string	1:1	Email of the person who wrote the revision
________<date>	date	1:1	Date of the revision
________<comments>	Comments	1:1	Comments about the modifications done in this revision

The type of revision is one of the following possibilities:

Value	Description
creation	Creation of the file. Only one revision of this type is allowed.
modification	Modification of the file.
review	Review by a person. Comments in the revision tag allows to comment the review.
validation	Validated by a person. No modification done on the document.

The history section keeps track of the creator of the drug XML file as well as the people that modified it. The <revisions> contains as many <revision> tag as necessary. Each <revision> tag contains the same structure, as shown in the figure above, and the correct revisionAction should be used according to the action done on the file.

Head ¶

Used by Model.

The head of the file contains general information about the drug, such as its ID, the model it uses, its name, description and so on. It does not embed information required by the computing engines. The head structure is the following:

<head>
  <drug>
    <atcs>
      <atc>J01XA01</atc>
    </atcs>
    <activeSubstances>
      <activeSubstance>vancomycin</activeSubstance>
    </activeSubstances>
    <drugName>
      <name lang="en">Vancomycin</name>
      <name lang="fr">Vancomycine</name>
    </drugName>
    <drugDescription>
      <desc lang="fr">Vanc est un un antibio super sympa</desc>
    </drugDescription>
    <tdmStrategy>
      <text lang="fr">Pourquoi et comment le TDm devrait être appliqué</text>
    </tdmStrategy>
  </drug>
  <study>
    <studyName>
      <name lang="en">Population pharmacokinetic parameters of vancomycin in critically ill patients</name>
      <name lang="fr">Paramètres pharmacocinétiques de la vancomycine de patients en soins intensifs</name>
    </studyName>
    <studyAuthors>Llopis-Salvia, P. and Jiménez-Torres, N. V.</studyAuthors>
    <description>
      <desc lang="en">Study description on the parameters concerning the administration of vancomycin</desc>
      <desc lang="fr">Description de l'étude sur les paramètres ayant attrait à l'administration de la vancomycine</desc>
    </description>
    <references>
      <reference type="bibtex">@article{llopis-salvia_population_2006,
        title = {Population pharmacokinetic parameters of vancomycin in critically ill patients},
        volume = {31},
        url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2710.2006.00762.x/full},
        number = {5},
        urldate = {2014-10-23},
        journal = {Journal of clinical pharmacy and therapeutics},
        author = {Llopis-Salvia, P. and Jiménez-Torres, N. V.},
        year = {2006},
        pages = {447--454},
        file = {Llopis-Salvia_Vancomycin.pdf:/home/rob/.zotero/zotero/iv5zqg2p.default/zotero/storage/B5WXR3BE/Llopis-Salvia_Vancomycin.pdf:application/pdf}
        }
      </reference>
    </references>
  </study>
  <comments/>
</head>

head content¶
Tag name	Format	Occ.	Description
<drug>		1:1	Description of the drug itself
___<atcs>		1:1	List of ATCs
______<atc>	string	1:∞	The drug code, or Anatomical Therapeutic Chemical (ATC)
___<activeSubstances>		1:1	List of active substances
______<activeSubstance>	string	1:∞	An active substance present in the drug
___<drugName>		1:1	The translated drug’s names
______<name>	string	1:∞	Name of the drug for the specified language
___<drugDescriptions>		1:1	The translated drug’s descriptions
______<desc>	string	1:∞	Description of the drug for the specified language
___<tdmStrategy>		1:1	The translated drug’s TDM strategy
______<text>	string	1:∞	The description of the TDM strategy
<study>		1:1	Description of the study used to fill this file
___<studyName>		1:1	The translated drug’s study names
______<name>	string	1:∞	Study name of the drug for the specified language
___<studyAuthors>	string	1:1	The name of the study authors
___<description>		1:1	The translated descriptions of the study
______<desc>	string	1:∞	Description of the study for the specified language
___<references>		1:1	References to related publications
______<reference>	string	0:∞	Reference to a specific article or publication
<comments>	Comments	1:1	Comments about the drug’s header

The active substances are identifiers, and shall come from a dictionary.

The corresponding ATC can be found online using the Anatomical Therapeutic Chemical Classification System. The drug ID must be unique amongst all the drugs. By convention, it starts with ch.heig-vd.ezechiel., followed by the drug name and eventually the domain and/or study names, for all the drugs that are shipped with the default package of Tucuxi.

The drug name is the general name of the drug, eg. Gentamicin. The study name finally specifies the study on which this drug is based. Any of those names can be translated by using the lang attribute to specify the target language. It is to be noted that the lang attribute is mandatory for such translatable strings.

DrugModel ¶

Used by Model.

This third part of a model file embed every information required for any computation.

<drugModel>
  <drugId>vancomycin</drugId>
  <drugModelId>ch.heig-vd.vancomycin</drugModelId>
  <domain></domain>
  <covariates></covariates>
  <activeMoieties></activeMoieties>
  <analyteGroups></analyteGroups>
  <formulationAndRoutes></formulationAndRoutes>
  <timeConsiderations></timeConsiderations>
  <comments />
</drugModel>

drugModel content¶
Tag name	Format	Occ.	Description
<drugModel>			Everything needed for any calculation
____<drugId>	string	1:1	Unique identifier of the drug
____<drugModelId>	string	1:1	Identifier of the model described in the file
____<domain>	Domain	1:1	Usage domain for this model. TB
____<covariates>	Covariates	1:1	List of covariates used by the model
____<activeMoieties>	ActiveMoieties	1:1	List of active moieties. TB
____<analyteGroups>	Analyte groups	1:1	List of groups of analytes
____<formulationAndRoutes>	Formulation and route	1:1	List of formulation and routes of administration
____<timeConsiderations>	Time considerations	1:1	Some information about time, such as halflife
____<comments>	Comments	1:1	General comments on the model

The drugId shall uniquely identify the drug. It shall come out of a dictionary.

The drugModelId is the identifier of the model proposed in the file. It shall be unique. Some conventions will help maintain such models. It shall consist of alphanumeric characters separated by dots, such as ch.heig-vd.vancomycin. The first part is the country identifier, the second the instution identifier, and the third the drug identifier. A fourth one can be added in case various models for the same drug are proposed by the same institution: ch.heig-vd.vancomycin.model2.

Except the drugId, the drugModelId, and the comments fields, the other ones are described in specific sections.

The Domain represents the validity of the drug model for a specific patient. So, depending on his covariates, the model should not be used.

The Covariates list all the covariates used in various parts of the drugModel.

The ActiveMoieties list the active moieties of the drug. For a majority of drugs there should be only a single active moiety, and so filling this part should be straightforward.

The Analyte groups list the groups of analytes. This concept of groups allow to have independent or dependent analytes in terms of Pk models.

The Formulation and route list the formulations and routes of administrations. As such it mainly embeds absorption parameters descriptions.

The Time considerations allows to indicate the half life of the drug and the validity time of measures.

Domain ¶

The domain of application gives information about the population on which the model can be applied.

<domain>
    <description>
        <desc lang="en">This is the domain, bla bla bla</desc>
    </description>
    <constraints>
        <constraint>
            <constraintType>hard</constraintType>
            <errorMessage>
                <text lang="en">The age shall be positive</text>
            </errorMessage>
            <requiredCovariates>
                <covariateId>age</covariateId>
            </requiredCovariates>
            <checkOperation>
                <softFormula>
                    <inputs>
                        <input>
                            <id>age</id>
                            <type>int</type>
                        </input>
                    </inputs>
                    <formula><![CDATA[return (age > 0);
                        ]]>
                    </formula>
                </softFormula>
                <comments/>
            </checkOperation>
            <comments/>
        </constraint>
        <constraint>
            <constraintType>soft</constraintType>
            <errorMessage>
                <text lang="en">The weight should not be too much</text>
            </errorMessage>
            <requiredCovariates>
                <covariateId>bodyweight</covariateId>
            </requiredCovariates>
            <checkOperation>
                <softFormula>
                    <inputs>
                        <input>
                            <id>bodyweight</id>
                            <type>double</type>
                        </input>
                    </inputs>
                    <formula><![CDATA[return (return (bodyweight < 100);
                        ]]>
                    </formula>
                </softFormula>
                <comments/>
            </checkOperation>
            <comments/>
        </constraint>
    </constraints>
</domain>

domain content¶
Tag name	Format	Occ.	Description
<description>		1:1	The translated descriptions of the domain
___<desc>	string	1:∞	Description of the domain for the specified language
<constraints>	string	1:1	List of constraints allowing the model to be used
___<constraint>	Constraint	0:∞	A constraint for using the model
______<constraintType>	ConstraintType	1:1	Importance of the constraint: soft or hard
______<errorMessage>		1:1	The translated error message for the constraint
_________<text>	string	1:∞	Error message in the specified language
______<requiredCovariates>		1:1	List of required covariates for checking this constraint
_________<covariateId>	string	1:∞	Id of a covariate required by this constraint
______<checkOperation>	Operation	1:1	formula used to check the validity of covariates
______<comments>	Comments	1:1	Comments conserning the constraint

The description shall allow to understand the domain of application of the model. It shall be as complete as possible.

The constraints then allow the software to check wether the model can be used for a specific patient or not.

For instance, if the model is valid for adults from 20 to 60 years old, the covariate ageInYears of the patient shall be available and checked in order to avoir the use of the model for children.

In case a specific constraint is not met, then the errorMessage is used to notify the user.

Constraint¶

A constraint allows to check for the possibility to use the model with a specific patient, depending on his covariates validity.

constraint content¶
Tag name	Format	Occ.	Description
<constraint>			A constraint for using the model
____<constraintType>	ConstraintType	1:1	Importance of the constraint: soft or hard
____<errorMessage>		1:1	The translated error message for the constraint
________<text>	string	1:∞	Error message in the specified language
____<requiredCovariates>		1:1	List of required covariates for checking this constraint
________<covariateId>	string	1:∞	Id of a covariate required by this constraint
____<checkOperation>	Operation	1:1	formula used to check the validity of covariates

A constraint can be soft or hard. In case of a soft one, only a warning is issued, while a hard constraint shall impose a rejection of a priori and a posteriori calculations.

The list of required covariates allows to check wether all covariates are available, and the formula is used to check the constraint validity. This formula shall return a boolean returning true in case the constraint is met, and false otherwise.

ConstraintType¶

Used by Constraint.

Type of constraint

constraintType¶
Value	Description
soft	If the patient covariate can not be retrieved, a warning is issued, but the model can be used
hard	If the patient covariate can not be retrieved, a warning is issued, and the model can not be used

Covariates ¶

A covariate is a medical information about the patient. A drug can contain from 0 to an unlimited number of covariates that can then be used by Tucuxi to compute and adapt the parameters for the given patient. They are defined in this manner:

<covariate>
    <covariateId>bodyweight</covariateId>
    <covariateName>
        <name lang='en'>Total Body Weight</name>
        <name lang='fr'>Poids total</name>
    </covariateName>
    <description>
        <desc lang='en'>Total body weight of patient, in kilogramms</desc>
        <desc lang='fr'>Poids total du patient, en kilogramme</desc>
    </description>
    <unit>kg</unit>
    <covariateType>standard</covariateType>
    <dataType>double</dataType>
    <interpolationType>linear</interpolationType>
    <refreshPeriod>
      <unit>d</unit>
      <value>30</value>
    </refreshPeriod>
    <covariateValue>
        <standardValue>70</standardValue>
    </covariateValue>
    <validation>
        <errorMessage>
            <text lang='fr'>The body weight shall be in the interval [44,100].</text>
        </errorMessage>
        <operation>
            <softFormula>
                <inputs>
                    <input>
                        <id>bodyweight</id>
                        <type>double</type>
                    </input>
                </inputs>
                <code>
                    <![CDATA[return ((bodyweight >= 44) && (bodyweight <= 110));
                    ]]>
                </code>
            </softFormula>
            <comments/>
        </operation>
        <comments/>
    </validation>
    <comments/>
</covariate>

covariates content¶
Tag name	Format	Occ.	Description
<covariate>		0:∞	Description of a covariate
____<covariateId>	string	1:1	The covariate’s unique identifier
____<covariateName>		1:1	The translated covariate’s names
_______<name>	string	1:∞	Name of the covariate for the specified language
____<description>		1:1	The translated covariate’s descriptions
_______<desc>	string	1:∞	Description of the covariate for the specified language
____<unit>	string	1:1	The covariate’s unit
____<covariateType>	covariateType	1:1	The covariate’s type
____<dataType>	covariateDataType	1:1	The covariate’s type
____<interpolationType>	interpolationType	1:1	The covariate’s type
____<refreshPeriod>		0:1	The refresh period for the covariate value
________<unit>		1:1	The unit of the refresh period
________<value>		1:1	The duration of the refresh period
____<covariateValue>	stdAprioriValue	1:1	The covariate’s value, that can be modified by other covariates
____<validation>	ref:validation	1:1	Potential validation function for the covariate value
____<comments>	Comments	1:1	Comments about the covariate

The interpolation type allow to decide how the calculation interprets covariates changing over time.

The covariate type allows to indicate if the covariate is standard, related to birth, or related to sex.

The covariate data type allows to indicate the type in terms of int, double, bool or date.

The refresh period is used when the interpolation type is not direct. The period defines the time between two modifications of the real covariate value to be used for adjusting other parameters.

A covariate ID must identify the covariate. Some conventions allow to handle generic covariates and are described below. The name and desciption of the covariate can be translated using the lang attribute of the <name> and <desc> tags.

The <unit> of the covariate is the unit presented to the user - for example kg for the covariate weight. The <type>, on the other hand, is the internal type used to store the covariate’s value. It can be either an int, a double or a boolean. In case of boolean, then the term is bool, and the value shall be 1 for true and 0 for false.

For genetic factors, it is suggested to use gene_XXX for the covariate ID, where XXX is the gene identification.

Covariate ID	Description
birthbodyweight	weight of the patient at birth
bodyweight	current body weight of the patient
age	current age of the patient, in years
pna	for neonates, post natal age, in days
ga	gestational age, in weeks
clcr	clearance of creatinine
gist	boolean indicating the presence of a gastrointestinal stromal tumor
sex	sex of the patient (0: female, 1: male)

When it is not possible to know the value of a covariate for a specific patient, the default value is used instead. It corresponds to the value of the average individual, also called the typical patient.

covariateType¶

Used by Covariates.

Type of covariate

covariateType¶
Value	Description
standard	A normal covariate
sex	The covariate represents the sex of the patient. Can be automatically retrieved from administrative data, specifically in a patient covariate called sex
ageInYears	The age of the patient, in years. Can be automatically retrieved from administrative data, specifically in a patient covariate called birthdate
ageInMonths	The age of the patient, in months. Can be automatically retrieved from administrative data, specifically in a patient covariate called birthdate
ageInWeeks	The age of the patient, in weeks. Can be automatically retrieved from administrative data, specifically in a patient covariate called birthdate
ageInDays	The age of the patient, in days. Can be automatically retrieved from administrative data, specifically in a patient covariate called birthdate
dose	The current dose is automatically from the intake list

covariateDataType¶

Used by Covariates.

covariateDataType¶
Value	Description
int	An integer
double	A 64-bit double
bool	A boolean value
date	A date in format YYYY-MM-DDTHH:MM:SS

interpolationType¶

Used by Covariates.

interpolationType¶
Value	Description
direct	As soon as a new covariate value exists, its value is applied
linear	If the covariate has two values at two different times, a linear interpolation is applied between both time points

ActiveMoieties ¶

<activeMoieties>
  <activeMoiety>
    <activeMoietyId>vancomycin</activeMoietyId>
    <activeMoietyName>
      <name lang="fr">        </name>
    </activeMoietyName>
    <unit>mg/l</unit>
    <analyteIdList>
      <analyteId>vancomycin</analyteId>
    </analyteIdList>
    <formula>...</formula>
    <!-- Drug targets -->
    <targets>
    </targets>


  </activeMoiety> <!--fraction active -->
</activeMoieties>

activeMoiety content¶
Tag name	Format	Occ.	Description
<activeMoiety>			An active moiety
____<activeMoietyid>	string	1:1	active moiety unique identifier
____<activeMoietyName>		1:1	The translated active moiety’s names
_______<name>	string	1:∞	Name of the active moiety for the specified language
____<unit>	string	1:1	The active moiety’s unit
____<analyteIdList>		1:1	The list of analytes influencing this active moiety
_______<analyteId>	string	1:∞	The Id of an analyte required to compute this active moiety
____<analytesToMoietyFormula>	Operation	1:1	The formula for calculating the active moiety concentration based on the analytes
____<targets>	Targets	1:1	A list of targets
________<target>	Targets	0:∞	A target to be reached

An active moiety corresponds to an active substance of the medical drug. A drug model can be composed of more than one active moiety, but this case is not very common. The activeMoietyId uniquely identifies the active moiety. In case of a single analyte drug model it is usually convenient to share the same Id as the analyte.

The activeMoietyName is the name that will be used when information display is required.

The unit is the unit in which the computation results shall be displayed.

In case of a single analyte drug model, the active moiety concentration is the concentration of the analyte. However, in case of multi-analytes drug models, the computation of an active moiety concentration requires a formula exploiting the concentrations of the analytes. Therefore, a list of required analyte Ids, and a formula, are required to convert correctly the analyte. It is to be noted that these are mandatory in any case, but the formula can be an hardcoded direct in case of a single analyte. The software will then be able to avoid computation.

Finally, therapeutic targets are related to an active moiety. The list is mandatory, but it could be empty. It is important to notice that if no target is given, the dosage adaptation won’t be possible, except if individual targets are supplied for computation.

Targets¶

The targets of an active moiety are described in the <target> tag. It contains all the data about the targeted residual concentration, peak concentration and mean concentration. The structure is as follows:

<target>
  <targetType>residual</targetType>
  <targetValues>
    <unit>mg/l</unit>
    <min>
      <standardValue>10</standardValue>
    </min>
    <max>
      <standardValue>20</standardValue>
      <aprioriComputation>
        <formula type="softcoded"><![CDATA[
          if gist then
          return 20;
          else
          return 30;
          ]]>
        </formula>
        <comments/>
      </aprioriComputation>
    </max>
    <best>
      <standardValue>15</standardValue>
    </best>
    <toxicityAlarm><standardValue>15</standardValue></toxicityAlarm>
    <inefficacyAlarm><standardValue>3</standardValue></inefficacyAlarm>
  </targetValues>
  <comments>
    <comment lang="en">As seen with Aline for the residual concentration</comment>
    <comment lang="fr">Vu avec Aline pour la concentration résiduelle</comment>
  </comments>
</target>

target content¶
Tag name	Format	Occ.	Description
<target>			A target
____<targetType>	TargetType	1:1	Type of target, from an enumeration
____<targetValues>		1:1	The target values
________<unit>	string	1:1	The target unit
________<min>	stdAprioriValue	1:1	Minimum targeted value
________<max>	stdAprioriValue	1:1	Maximum targeted value
________<best>	stdAprioriValue	1:1	Best targeted value
________<toxicityAlarm>	stdAprioriValue	1:1	Threshold over which an alarm shall be triggered
________<inefficacyAlarm>	stdAprioriValue	1:1	Threshold under which an alarm shall be triggered
________<mic>		0:1	The MIC value, optional
____________<unit>	string	1:1	The MIC unit
____________<micValue>	stdAprioriValue	1:1	The MIC value
____<times>		0:1	Time targets when required by the target type
________<unit>	string	1:1	The time unit
________<min>	stdAprioriValue	1:1	Minimum targeted time
________<max>	stdAprioriValue	1:1	Maximum targeted time
________<best>	stdAprioriValue	1:1	Best targeted time
____<comments>	Comments	1:1	Comments about the target

TargetType¶

A target can be of any of these types:

targetType¶
Value	Description
peak	The target is the peak concentration. Times are to be added in the target to define when the peak should be found
residual	The target is the residual concentration
mean	The target is the mean concentration
auc	The target is the area under curve, for a single intake cycle
auc24	The target is the area under curve on 24h
cumulativeAuc	The target is the cumulative area under curve since the beginning of the treatment
aucOverMic	The target is the area under the concentration curve, but only the portion over the MIC, for a single intake cycle
auc24OverMic	The target is the area under the concentration curve, but only the portion over the MIC, for 24h
timeOverMic	The target is the time spent over the MIC, for a single intake cycle
aucDividedByMic	The target is the area under the concentration curve divided by the MIC, for a single intake cycle
auc24DividedByMic	The target is the area under the concentration curve for 24h divided by the MIC
peakDividedByMic	The target is the value of the peak concentration divided by the MIC
residualDividedByMic	The target is the value of the residual concentration divided by the MIC
fractionTimeOverMic	The target is the fraction of time spent over the MIC, in [0,1]

Analyte groups ¶

<analyteGroups>
  <analyteGroup>
    <groupId>vanco1</groupId>
    <pkModelId>std.linear.2comp</pkModelId>
    <analytes>
      <analyte>
        <analyteId>vanco</analyteId>
        <unit>mg/l</unit>
        <molarMass>
          <value>0</value>
          <unit>g/mol</unit>
        </molarMass>
        <description>
          <desc lang="fr"></desc>
        </description> <!-- peut être vide -->

        <errorModel> <!-- optional -->
          <errorModelType>formula</errorModelType>
          <formula>
            <formula type="softcoded"><![CDATA[
              return eps * sigma0 + eps * exp(sigma1);
              ]]>
            </formula> <!-- probablement une autre formule pour Bayes -->
            <comments/>
          </formula>
          <sigmas>
            <sigma>
              <standardValue>0.239</standardValue>
            </sigma>
            <sigma>
              <standardValue>0.185</standardValue>
            </sigma>
          </sigmas>
          <comments>
            <comment lang="fr">Une fonction d'erreur model définie par la fiche médicament</comment>
          </comments>
        </errorModel>
        <comments/>
      </analyte>
    </analytes>

    <!-- Drug parameters -->
    <dispositionParameters>
      <parameters>
      </parameters>

      <!-- elimination parameters correlations -->
      <correlations />
    </dispositionParameters>



  </analyteGroup>
</analyteGroups>

analyteGroups content¶
Tag name	Format	Occ.	Description
<analyteGroups>		0:∞	List of groups of analytes
____<analyteGroup>	string	1:∞	A group a analytes
________<groupId>	string	1:1	A unique Id for the group of analytes
________<pkModelId>	string	1:1	The Id of the Pk Model to be used for computation related to this group
________<analytes>		1:1	The list of analytes of the group
____________<analyte>	Analyte	1:∞	An analyte
________<dispositionParameters>	ParameterSet	1:1	A set of disposition parameters
____________<parameters>		1:1	List of parameters
________________<parameter>	Parameter	1:∞	A disposition parameter
____________<correlations>	Correlations	1:1	correlation between disposition parameters

The groupId uniquely identifies the group within the drugModel. It is required by the Formulation and route in order to indicate on which group the various parameters are connected to.

The drug’s Pk model ID must reference an existing pharmacokinetic model. The list of models and their corresponding parameters are presented below.

Todo

Update this list

Pk Model ID	Description
std.linear.1comp.macro	linear elimination, 1 compartment
std.linear.1comp.micro	linear elimination, 1 compartment
std.linear.2comp.macro	linear elimination, 2 compartment
std.linear.2comp.micro	linear elimination, 2 compartment
std.linear.3comp.macro	linear elimination, 3 compartment
std.linear.3comp.micro	linear elimination, 3 compartment

The difference between macro and micro model consists in the parameters supplied. In case of macro the parameters are typically clearance and volume, while in case of micro the parameters are the micro constants k.

Analyte¶

<analyte>
  <analyteId>vanco</analyteId>
  <unit>mg/l</unit>
  <molarMass>
    <value>0</value>
    <unit>g/mol</unit>
  </molarMass>
  <description>
    <desc lang="fr"></desc>
  </description> <!-- peut être vide -->

  <errorModel> <!-- optional -->
    <errorModelType>formula</errorModelType>
    <formula>
      <formula type="softcoded"><![CDATA[
        return eps * sigma0 + eps * exp(sigma1);
        ]]>
      </formula> <!-- probablement une autre formule pour Bayes -->
      <comments/>
    </formula>
    <sigmas>
      <sigma>
        <standardValue>0.239</standardValue>
      </sigma>
      <sigma>
        <standardValue>0.185</standardValue>
      </sigma>
    </sigmas>
    <comments>
      <comment lang="fr">Une fonction d'erreur model définie par la fiche médicament</comment>
    </comments>
  </errorModel>
  <comments/>
</analyte>

analyte content¶
Tag name	Format	Occ.	Description
<analyte>			An analyte
____<analyteId>	string	1:1	The Id of the analyte
____<unit>	string	1:1	The unit used to do calculation with the analyte
____<molarMass>	string	1:1	The molar mass of the analyte
________<value>		1:1	Value of the molar mass
________<unit>	Analyte	1:∞	Unit of the molar mass
____<description>		1:1	The translated descriptions of the analyte
________<desc>	string	1:∞	Description of the analyte
____<errorModel>	Error model	1:1	The error model corresponding to this analyte
____<comments>	Comments	1:1	Comments about this analyte

Error model¶

The error model is the representation of the intra-individual error. It is used by the post engines and the percentiles engines to regulate the patient’s measures and intra-individual variability.

The structure is the following:

<errorModel>
  <errorModelType>formula</errorModelType>
  <formula>
    <formula type="softcoded"><![CDATA[
      return eps * sigma0 + eps * exp(sigma1);
      ]]>
    </formula> <!-- probablement une autre formule pour Bayes -->
    <comments/>
  </formula>
  <sigmas>
    <sigma>
      <standardValue>0.239</standardValue>
    </sigma>
    <sigma>
      <standardValue>0.185</standardValue>
    </sigma>
  </sigmas>
  <comments>
    <comment lang="fr">Une fonction d'erreur model définie par la fiche médicament</comment>
  </comments>
</errorModel>

errorModel content¶
Tag name	Format	Occ.	Description
<errorModel>			Some time considerations
____<errorModelType>	errorModelType	1:1	Type of error model, an enum
____<errorModelFormula>	Operation	0:1	A formula if required by the errorModelType
____<sigmas>		1:1	A list of sigmas
________<sigma>	stdAprioriValue	1:∞	A sigma used by the error model
____<comments>	Comments	1:1	Comments about the error model

errorModelType¶

Used by Error model.

errorModelType¶
Value	Description
additive	An additive error model. Requires a single sigma
proportional	A proportional error model. Requires a single sigma
exponential	An exponential error model. Requires a single sigma
mixed	A mixed error model. Requires two sigmas
propexp	A proportional error model for a posteriori computation, but exponential for percentiles. Requires a single sigma
softcoded	The error model is defined by the formula following the declaration of the errorModelType

For additive, proportional, exponential or propexp, a single sigma is required. For mixed, two sigmas are mandatory: The first corresponds to the additive error, and the second to the proportional error.

The propexp model is provided because some papers are exploiting an exponential which is in fact interpreted by NONMEM as a proportional variability model. Therefore, the NONMEM cross validation does not work as expected. If the user really wants a pure exponential model, then they should use exponential. However if the paper has been published based on NONMEM computations, it is safer to choose propexp.

For each error model except the softcoded one, the model is implemented in the software. For a softcoded, the formula supplied in the file is used instead.

Formulation and route ¶

<formulationAndRoutes default="id0">
  <formulationAndRoute>
    <formulationAndRouteId>id0</formulationAndRouteId>
    <formulation>parenteralSolution</formulation>
    <administrationName>champ libre</administrationName>
    <administrationRoute>i.v.</administrationRoute>
    <routeModelId>bolus</routeModelId>

    <dosages>
    </dosages>

    <absorptionParameters>
      <analyteGroupId>vanco1</analyteGroupId>
      <parameters>
      </parameters>
      <correlations />
    </absorptionParameters>
  </formulationAndRoute>
</formulationAndRoutes>

formulationAndRoute content¶
Tag name	Format	Occ.	Description
<formulationAndRoute>			Formulation and route
____<formulationAndRouteId>	string	1:1	Id of the formulation and route
____<formulation>	string	1:1	the formulation. Taken from a dictionary
____<administrationName>	string	1:1	A free field to discriminate vendors
____<administrationRoute>	string	1:1	The route of administration, taken from a dictionary
____<absorptionModel>	absorptionModel	1:1	Id of the absorption model
____<dosages>	Dosages	1:1	Possible dosages
____<absorptionParameters>		1:1	Sets of absorption parameters
________<parameterSetAnalyteGroup>		0:∞	A set of absorption parameters for an analyte group
____________<analyteGroupId>	string	1:1	Id of the analyte group
____________<absorptionModel>	string	1:1	Id of the absorption model
____________<parameterSet>		1:1	The absorption parameters
________________<parameters>	Parameter	1:1	List of parameters
____________________<parameter>	Parameter	0:∞	A parameter
________________<correlations>	Correlations	1:1	Correlations between absorption parameters or between absorption and disposition parameters
____________________<correlation>	Correlations	0:∞	Correlations between absorption parameters or between absorption and disposition parameters

The formulationAndRouteId is an Id identifying this formulation and route.

The formulation is taken from a dictionary that still has to be defined. Example: parenteralSolution.

For now it supports the following values:

“undefined”
“parenteralSolution”
“oralSolution”

The administrationName is a free string, and can allow to differentiate between identical formulations offered by different vendors.

The administrationRoute is taken from a dictionary that still has to be defined. Example : i.v..

For now it supports the following values:

“undefined”
“intramuscular”
“intravenousBolus”
“intravenousDrip”
“nasal”
“oral”
“rectal”
“subcutaneous”
“sublingual”
“transdermal”
“vaginal”

The absorptionModel can be either extra, infu or bolus, respectiveley for extravascular, infusion or bolus.

absorptionModel¶

absorptionModel¶
Value	Description
extra	Extravascular route. Can be used for various administration routes, like in a muscle, oral, anal, …
infusion	Infusion in the central compartment
bolus	Immediate availability of the drug in the blood, like an intravenous bolus.

The dosages are the possible dosages, used for proposing dosage adaptation.

Finally, the absorptionParameters are the absorption parameters corresponding to the absorptionModel selected.

For these absorption parameters, the analyteGroupId allows to identify the analytes group related to the parameter set.

Dosages¶

This section contains all the information about the dosages, such as the default units and values of the doses, intervals and infusions. It also contains the lists of doses, intervals and infusions used by the dosage adaptation module [1]. The dosages structure looks like this:

<dosages>
  <analyteConversions>
      <analyteConversion>
          <analyteId>imatinib</analyteId>
          <factor>1</factor>
      </analyteConversion>
  </analyteConversions>
  <availableDoses>
      <unit>mg</unit>
      <default>
          <standardValue>400</standardValue>
      </default>
      <rangeValues>
          <from>
              <standardValue>100</standardValue>
          </from>
          <to>
              <standardValue>400</standardValue>
          </to>
          <step>
              <standardValue>100</standardValue>
          </step>
      </rangeValues>
      <fixedValues>
          <value>600</value>
          <value>800</value>
      </fixedValues>
  </availableDoses>
  <intervals>
      <unit>h</unit>
      <default>
          <standardValue>24</standardValue>
      </default>
      <fixedValues>
          <value>12</value>
          <value>24</value>
      </fixedValues>
  </intervals>
  <comments/>
</dosages>

dosages content¶
Tag name	Format	Occ.	Description
<dosages>			Available dosages associated with a formulation and route
____<standardTreatment>		0:1	A potential standard treatment
________<isFixedDuration>	bool	1:1	Has the treatment a fixed duration?
________<timeValue>		0:1	The duration of the fixed duration
____________<unit>		1:1	The unit of the duration
____________<value>		1:1	The duration value
____<analyteConversions>		1:1	List of analyte conversions
________<analyteConversion>		1:∞	Conversion from the quantity of drug to the quantity of analyte
____________<analyteId>	string	1:1	The Id of the analyte
____________<factor>	double	1:1	The factor to be multiplied to the drug quantity to obtain the analyte quantity
____<isLoadingDoseRecommended>	boolean	0:1	Is a loading dose recommended if the current dosage is too low?
____<isRestPeriodRecommended>	boolean	0:1	Is a rest period recommended if the current dosage is too high?
____<availableDoses>	AvailableDoses	1:1	Available doses
____<availableIntervals>	AvailableIntervals	1:1	Available intervals
____<availableInfusions>	AvailableInfusions	0:1	Available infusion times
____<comments>	Comments	1:1	Comments about the dosages

The analyte conversion is important if not all the drug is part of a single analyte. In that case the factor allows to link the quantity of analyte corresponding to a certain quantity of drug.

The available doses, intervals and infusions are used by the dosage adaptation engine to propose a suitable dosage.

AvailableDoses¶

dosages content¶
Tag name	Format	Occ.	Description
<availableDoses>			Available doses
____<unit>		1:1	Unit of the doses
____<default>	stdAprioriValue	1:1	Default dose
____<rangeValues>		0:∞	Available doses represented as a range
________<from>	stdAprioriValue	1:1	Starting value of the range
________<to>	stdAprioriValue	1:1	Ending value of the range
________<step>	stdAprioriValue	1:1	Step to be applied between from and to
____<fixedValues>		0:1	A list of fixed doses
________<value>	double	1:1	A dose value

The idea here is to use rangeValues OR fixedValues, but not both at the same time. However the software support both at the same time. Using stdAprioriValue fields allow the dose range to be very flexible, depending on the patient covariates.

If rangeValues is used, then the dosage adaptation engine will try every dose between the boundaries from and to, using step. For instance, if from=5, to=25, and step=10, then the values will be 5, 15, and 25.

AvailableIntervals¶

dosages content¶
Tag name	Format	Occ.	Description
<availableIntervals>			Available intervals
____<unit>		1:1	Unit of the intervals
____<default>	stdAprioriValue	1:1	Default interval
____<rangeValues>		0:∞	Available intervals represented as a range
________<from>	stdAprioriValue	1:1	Starting value of the range
________<to>	stdAprioriValue	1:1	Ending value of the range
________<step>	stdAprioriValue	1:1	Step to be applied between from and to
____<fixedValues>		0:1	A list of fixed intervals
________<value>	double	1:1	An interval value

The idea here is to use rangeValues OR fixedValues, or both at the same time. Using stdAprioriValue fields allow the dose range to be very flexible, depending on the patient covariates.

If rangeValues is used, then the dosage adaptation engine will try every interval between the boundaries from and to, using step. For instance, if from=5, to=25, and step=10, then the values will be 5, 15, and 25.

AvailableInfusions¶

dosages content¶
Tag name	Format	Occ.	Description
<availableInfusions>			Available infusion times
____<unit>		1:1	Unit of the infusion times
____<default>	stdAprioriValue	1:1	Default infusion time
____<rangeValues>		0:∞	Available infusion times represented as a range
________<from>	stdAprioriValue	1:1	Starting value of the range
________<to>	stdAprioriValue	1:1	Ending value of the range
________<step>	stdAprioriValue	1:1	Step to be applied between from and to
____<fixedValues>		0:1	A list of fixed infusion times
________<value>	double	1:1	An infusion time value

The idea here is to use rangeValues OR fixedValues, or both at the same time. Using stdAprioriValue fields allow the infusion time range to be very flexible, depending on the patient covariates.

If rangeValues is used, then the dosage adaptation engine will try every infusion time between the boundaries from and to, using step. For instance, if from=5, to=25, and step=10, then the values will be 5, 15, and 25.

Notes

Time considerations ¶

Used by DrugModel.

Time considerations can help the software to optimize some computations, and also to get information about the relevance of a measure.

Here is an example of time consideration:

<timeConsiderations>
  <!-- Drug half-life -->
  <halfLife>
    <unit>h</unit>
    <value>
      <standardValue>12</standardValue>
    </value>
    <multiplier>10</multiplier>
    <comments/>
  </halfLife>


  <outdatedMeasure>
    <unit>d</unit>
    <value>
      <standardValue>100</standardValue>
    </value>
    <comments/>
  </outdatedMeasure>
</timeConsiderations>

The second part of the time considerations consists in the time after which a measure is considered irrelevant, and shall not be used for a posteriori computations.

timeConsiderations content¶
Tag name	Format	Occ.	Description
<timeConsideration>			Some time considerations
____<halfLife>	halfLife	1:1	Half life of the drug
_______<unit>		1:1	Time unit of the half life
_______<duration>	stdAprioriValue	1:1	value of the half life
_______<multiplier>		1:1	Number of half lifes to reach steady state
_______<comments>	Comments	1:1	Comments about the half life
____<outdatedMeasure>	outdatedMeasure	1:1	Indication about the relevance of a measure
_______<unit>	string	1:1	Time unit
_______<duration>	stdAprioriValue	1:1	Time after which a measure shall be considered as irrelevant
_______<comments>	Comments	1:1	Comments about the outdate measure fields

Half life¶

The half-life describes the time it takes for the plasma concentration, or the amount of drug in the body, to be reduced by 50%. Therefore, in each succeeding half-life, less drug is eliminated. After one half-life the amount of drug remaining in the body is 50%, after two half-lives 25%, etc. After 4 half-lives the amount of drug (6.25%) is considered to be negligible regarding its therapeutic effects.

The half-life is used to determine the residual concentration of a drug at steady-state. The half-life duration given above is multiplied by the cycle multiplier in order to find out how many cycles need to be completed before reaching the steady-state. It is then possible to compute the residual concentration of the drug at steady-state. In most cases, a multiplier of 10 is sufficient, but it is suggested to have a a bigger multiplier. At the end, the automated tests allow to detect if a multiplier was suffenciently big.

ParameterSet ¶

A parameter set is composed of a list of parameters and a list of correlations. It is used by disposition parameters, and is directly mapped by the absorption parameters. In other words, an absorptionParameters is a parameterSet, but a dispositionParameters embeds a parameterSet.

parameterSet content¶
Tag name	Format	Occ.	Description
<parameterSet>		1:1	The absorption parameters
____<parameters>	Parameter	1:1	List of parameters
________<parameter>	Parameter	0:∞	A parameter
____<correlations>	Correlations	1:1	Correlations between absorption parameters or between absorption and disposition parameters
________<correlation>	Correlations	0:∞	Correlations between absorption parameters or between absorption and disposition parameters

Parameter ¶

The parameters are used by the models to compute the curves and their value depend on the type of the prediction. If the prediction is made for the typical patient, the model will use the population parameters. If the prediction is made a priori for a specific patient, the population parameters will be adapted using patient’s covariates. Finally, if the prediction is made a posteriori, the a priori parameters will be adapted using the patient’s measures and a post engine. The parametrs are declared this way:

<parameter>
  <id>CL</id>
  <unit>l/h</unit>
  <value>
    <standardValue>3.505</standardValue>
    <aprioriComputation>
      <formula type="softcoded"><![CDATA[
        CCR = covariate_CLcr
        BW = covariate_bodyweight
        theta_1 = 0.034;
        theta_2 = 0.015;

        TVCL = theta_1*CCR+theta_2*BW;
        return TVCL;
        ]]>
      </formula>
      <comments/>
    </aprioriComputation>
  </value>
  <bsv>
    <bsvType>normal</bsvType> <!-- même chose que le modèle d'erreur -->
    <stdDevs>
      <stdDev>0.292</stdDev>
    </stdDevs>
  </bsv>
  <validation>
    <errorMessage><text lang="fr"></text></errorMessage>
    <formula type="softcoded"><![CDATA[
      return bodyweight < 300 and bodyweight > 20;
      ]]>
    </formula>
    <comments/>
  </validation>
  <comments>
    <comment lang="en">Typical clearance calculated for a patients with weight = 75 kg et CCR = 70 ml/min</comment>
  </comments>
</parameter>

parameter content¶
Tag name	Format	Occ.	Description
<parameter>			A Pk parameter
____<parameterId>	string	1:1	Id of the parameter
____<unit>	string	1:1	the unit of the parameter value
____<parameterValue>	stdAprioriValue	1:1	The parameter value and its optional apriori computation
____<bsv>	string	1:1	Between Subject Variability (BSV)
________<bsvType>	BsvType	1:1	Type of BSV
________<stdDevs>		1:1	A list of standard deviations
____________<stdDev>	double	0:∞	A standard deviation
____<validation>		1:1	A potential validation of the parameter value
________<errorMessage>		1:1	A translated list of error messages
____________<text>	string	1:∞	An error message, translated in a specific language
________<formula>	Operation	1:1	A formula to check the validity of the parameter
________<comments>	Comments	1:1	Comments about the validation
____<comments>	Comments	1:1	Comments about the parameter

The default value of the parameter corresponds to the typical patient’s value, and is used by the model as the population parameter. The parameters identifier must match one of the parameter IDs used by the model.

Warning

Please check carefully the ID of the parameter with respect to the selected model.

BsvType¶

bsvType¶
Value	Description
none	The parameter does not have variability. It is fixed
normal	The parameter variability follows a Normal distribution
lognormal	The parameter variability follows a LogNormal distribution
logit	The parameter variability follows a logit distribution

For bsv type, the model is implemented in the software.

Correlations ¶

With the help of the BSV (Between Subject Variability) of each parameter, the correlations are used to build the correlation and covariance matrices. It is possible to express correlations between two parameters in the following structure:

<correlations>
  <correlation>
    <param1>CL</param1>
    <param2>V</param2>
    <value>0.798</value>
    <comments></comments>
  </correlation>
</correlations>

Tag name	Format	Occ.	Description
<correlation>		0:∞	Description of the correlation
____<param1>	string	1:1	The first parameter’s ID
____<param2>	string	1:1	The second parameter’s ID
____<value>	double	1:1	The correlation’s value
____<comments>		1:1	Comments about the correlation

The first and second parameters IDs must match those in the parameters section.