PFI M Po pED Des ign Tools - Society of Toxicology · PDF fileintended as the gold standard...

On behalf of the DDMoRe consortium

PharmML—exchange format for models used in QSP and PMx

Maciej J Swat

EMBL-EBI

PharmML

PFIM PopEDOptimal Design Tools

MatlabR Simcyp SimulatorSimulx

Simulation (CTS) Tools

NONMEMEstimation Tools

winBUGSMonolix MatlabR

SO

Programming Languages FORTRAN

IMI/DDMoRe – brief introduction

Exchange standards & databases

•PharmML

•SO

•ProbOnto

Model repository

Conclusions

Outline

Introduction to DDMoRe

• The Drug Disease Model Resources (DDMoRe) consortium builds and maintains a universally applicable, open source, model based framework, intended as the gold standard for future collaborative drug and disease Modelling & Simulation.

• Before the DDMoRe consortium, a lack of common tools, languages and ontologies for Modelling and Simulation limited the access to stored information which created significant gaps in the way knowledge could be exploited within drug development.

• The DDMoRe consortium is generating a public drug and disease model repository as well as an open source interoperability framework containing standards and tools to cover the identified gaps in the Modelling and Simulation software ecosystem.

Introduction

DDMoRe – Time and Money

Timing:

• Starting Date: 01-03-2011

• Duration: 5 years (+6 months)

Financing:

• IMI funding: € 9 615 058

• Other contributions: € 1 729 833

• EFPIA in kind contribution: € 9 820 120

• ENSO funding € 1 580 737

• Total Project Cost: € 22 745 798

Key Objective – Interoperability

Introduction Source: NLME consortium, active until 2008

Key Objective – Interoperability – PharmML centered view

Introduction

PharmML

generator, e.g.

MDL-IDE or

infix2pharmml

PharmML

Monolix NONMEM

winBUGS, openBUGS

Matlab

R

Simulx

PFIM

PopED Simcyp Simulator

A long-standing problem in Pharmacometrics is the lack of a common standard allowing for exchangeability of models between existing software tools, such as Bugs, Monolix, NONMEM and others.

PharmML, Pharmacometrics Markup Language, as part of the DDMoRe interoperability platform, tries to fill this gap.

DDMoRe – Key products

Interoperability framework

Pharmacometric workflows

Public model repository (EBI)

Exchange Formats, e.g. PharmML, SO (EBI)

Modeling Language, MDL

API’s – e.g. libPharmML, libSO (EBI)

Annotation framework/Pharmacometrics related ontologies (EBI)

Tool connectors/Translators

• Estimation: Monolix, NONMEM, BUGS

• Simulation: simulx, Clinical Trial Simulator

• Optimal design: PFIM, PopED

• QSP, PBPK: Simcyp Simulator

• Multipurpose tools: MATLAB and toolboxes (Statistical TB, SBPOP, SimBiology), R and packages (nlme, nlmeODE, saemix) 7

Timeline

8

- Discrete data models (Count, Categorical, Time-To-Event) - DDEs - Matrices/Vectors

0.2.1 - 1st public

release

0.6 - 2nd public

release

M0

0.2

0.3.1

0.4

0.5

0.5.1

PK Macros

- External datasets - NONMEM file driven design - Lookup-tables & Interpolation

- Revised TrialDesign (CDISC) - UncertML - Extended residual error model

- Updated mapping rules - Dosing scaling

- Continuous data models - Variability, Covariate, Parameter, Structural, Observation Models - TrialDesign - Estimation/Simulation tasks

M12 M24 M36 M48 M60

March 2011

0.4.1

0.3

0.1

March 2012

March 2013

March 2014

March 2016

Minor changes

Minor changes

Minor changes

- Bayesian inference - Redesigned design (Optimal design)

March 2015

0.7-0.7.2

- Conditional statements - Nonparam distributions - Stats/Nary operators - Transition matrix

0.8

105 distr. 160 rels.

M66

August 2016

0.8.1

0.0.1

0.10.2

0.3

- Structure changes

- Optimal Design

- ML & MKS Proposals

- Six basic sections

0.3.1

- Mixture models - DE redesign - User-def trafos/distr - Autocorr residual errors

Minor changes

0.9

109 distr. 180 rels. New HFs/SFs

PharmML

SO

ProbOnto

Release legend

57 distr.

81 distr. >120 rels.

0.20.3 1.1 1.2

- Higher var levels

lines of documentation? change prononto 1.2 to 2.0

PharmML & SO – Big Picture

9

PharmML

Tools generating PharmML

(MDL-IDE or infix2pharmml)

SBML






SO

Encoding of model, trial design and modelling tasks

Recording of results from various tasks


10

Model Definition

Variability ModelParameter Variability

Residual VariabilityLevel

Parent Level

Parameter ModelStructured w.

Fixed/Random Effcts

Pair-wiseCorrelation Structure

General

Matrix

Individual

Population

Distribution

General

Distribution

Var

iability

Ref

eren

ce

Covariate Model

Observation Model

Continuous

Interpolation (cont)

TransformationCategorical/Continuous Distribution

Structural Model

Algebraic Eqs

ODE

DDE

Initial Condition

History

PK Macros

Discrete

Count

Categorical

Time-To-Event

PMF

Hazard/Survival Fct

Censoring

Assignment

Distribution

General

Standard Var

.Ref

.

PMF/Transition Matrix

Parameter

Trial Design

NONMEM/MONOLIX Dataset

Mapping/Transformation

Column/Target/Category

AdministrationsBolus

Infusion

Washout

InterventionsVariable Reset

Actions

Interventions-Combinations

Individual Administrations

Dosing Time

Dose Amount

Lookup Table

Continuous Discrete

Observations

Observations Combinations

Observation

Indiv. Observations

CovariatesCovariate Model

Indiv. Covariates

Occasions Start/End Points

Arms

Design Spaces

ArmSize, DoseAmount/Times, Duration, Stage,

NumberArms/Samples/Times, ObsTimes, RefsReference to any design element with space

definition

Lookup Table

EXPL

ICIT

TRIA

L DESI

GN

Modelling Steps

Software Settings

Estimation StepParameter Estimation

Algorithm

PropertyOperation

Intitial Estimate

Lower/Upper Bound

Design Evaluation

FIM

Optimize On

Method

CostDesign Optimisation Prior Information

Compute

Simulation StepObservations

Dataset/Observation Reference

Model

Task

Design

PharmML is about …

11

Scope of PharmML

A structural model defined as a system of ordinary differential equation (ODE) and/or algebraic equations, delay differential equations (DDE).

PK macros allowing for encoding of compartmental models without equations.

A flexible parameter model allowing for implementation of virtually any parameter type used in the majority of models.

Discrete or continuous covariates (interpolation, distribution, transformation).

A nested hierarchical variability model capable of expressing very complex random error structures.

An observation model with flexible residual error model supporting untransformed or transformed data.

Discrete data models (count, categorical, time-to-event data models).

Trial design model allowing for definition of many common design and drug administration types and encoding of experimental data needed for typical simulation or estimation tasks, such as dosing, observations and covariates

Optimal Experimental Design extends trial design with design spaces on any model element

Typical modeling steps such as estimation, simulation, design optimization/evaluation.

Standard Output – SO (now developed as separate format)

Hierarchical Models/Bayesian Inference via assignment of distributions to any model parameter

PharmML can handle single subject and population data

Individual data Multiple subject data

12

PharmML is based on well-defined mathematical formalism

13

Working with PharmML – step 1: dependent on data, implement model

14

DATA TYPE

Structural Model

Observation Model

Count DataObservation

Model

Parameter Model

Variability Model

Covariate Model

Residual ErrorCategorical

Data

TTE Data

DISCRETECONTINUOUS

Variability Model

Covariate Model

Parameter Model

Start

TASK

SimulationEstimation

Covariates

Interventions

TRIAL DESIGN

EXPLICIT

TRIAL DESIGN

DESIGN IN

A DATASET

Covariates,

Dosing records,

Observation records

Treatments,

Actions

DS

<TrialDesign>

<ModellingSteps>

Structural Model

Observations

Optimal Design

Design Spaces

Occasions

Arms

DS

DS

DS

<ModelDefinition>

Mapping to model

elements

Dataset

Monolix/NONMEM

Mapping to model

elementsModel

Definition



Parent Level


Fixed/Random Effcts


General

Matrix

Individual

Population

Distribution

General

Distribution

Var

iability

Ref

eren

ce

Covariate Model

Observation Model

Continuous



Structural Model

Algebraic Eqs

ODE

DDE

Initial Condition

History

PK Macros

Discrete

Count

Categorical

Time-To-Event

PMF

Hazard/Survival Fct

Censoring

Assignment

Distribution

General

Standard Var

.Ref

.


Parameter

15

Mathematical formalism 1 – standard continuous/discrete models

Distribution

Transformation

Interpolation

New covariate

Structured Type

Distribution Type

Equation Type

ODE

Algebraic Eq.

PK macros

Structured Type

Distribution Type

Equation Type

ObservationModel

ContinuousData

CountData

CategoricalData

TimeToEventData

StructuralModel ParameterModel

CovariateModel VariabilityModel

- Parameter related variability

- Residual error related variability

Usually

i.e. drug concentration as predicted by the structural model

Hierarchical structures for

16

Mathematical formalism 2 – structural/parameter/covariate/variability models

Distribution

Transformation

Interpolation

New covariate

Structured Type

Distribution Type

Equation Type

ODE

Algebraic Eq.

PK macros

Structured Type

Distribution Type

Equation Type

ObservationModel

ContinuousData

CountData

CategoricalData

TimeToEventData

StructuralModel ParameterModel

CovariateModel VariabilityModel

- Parameter related variability

- Residual error related variability

Usually

i.e. drug concentration as predicted by the structural model

Hierarchical structures for

17

PharmML coded example

The first three layers of the PharmML hierarchical structure are shown: (a) The root level ‘PharmML’, (b) the second level with ‘ModelDefinition’, ‘TrialDesign’ and ‘ModellingSteps’, and (c) the third level within ‘ModelDefinition’ and ‘TrialDesign’.

Zoom

in on

PharmML

18

PharmML coded example

PharmML

Zoom

in on


19

PharmML example – Parameter Model


PharmML

Log-normally distributed parameter

or in another representation as

Working with PharmML – step 2: define design or load dataset

20

DATA TYPE

Structural Model

Observation Model

Count DataObservation

Model

Parameter Model

Variability Model

Covariate Model

Residual ErrorCategorical

Data

TTE Data

DISCRETECONTINUOUS

Variability Model

Covariate Model

Parameter Model

Start

TASK


Covariates

Interventions

TRIAL DESIGN

EXPLICIT

TRIAL DESIGN

DESIGN IN

A DATASET

Covariates,

Dosing records,

Observation records

Treatments,

Actions

DS

<TrialDesign>

<ModellingSteps>

Structural Model

Observations

Optimal Design

Design Spaces

Occasions

Arms

DS

DS

DS

<ModelDefinition>

Mapping to model

elements

Dataset

Monolix/NONMEM

Mapping to model

elements

Trial Design


Mapping/Transformation

Column/Target/Category

AdministrationsBolus

Infusion

Washout

InterventionsVariable Reset

Actions

Interventions-Combinations

Individual Administrations

Dosing Time

Dose Amount

Lookup Table

Continuous Discrete

Observations

Observations Combinations

Observation

Indiv. Observations

CovariatesCovariate Model

Indiv. Covariates

Occasions Start/End Points

Arms

Design Spaces

ArmSize, DoseAmount/Times, Duration, Stage,

NumberArms/Samples/Times, ObsTimes, RefsReference to any design element with space

definition

Lookup Table

EXPL

ICIT

TRIA

L DESI

GN

Working with PharmML – step 3: define modelling step

21

SoftwareSettings

OutputFile

TargetToolReference

OP

TIO

NA

L

TASK


<ModellingSteps>

Optimal Design

ParametersToEstimate

Operation

ParametersToEstimate

OptimiseOn

FIM

Method

Cost

PriorInformation

Compute

OP

TIO

NA

L

ExternalDataSetRef

InterventionsRef

ObservationsRef

VariableAssignment

OP

TIO

NA

L

Modelling Steps

Software Settings

Estimation StepParameter Estimation

Algorithm

PropertyOperation

Intitial Estimate

Lower/Upper Bound

Design Evaluation

FIM

Optimize On

Method

CostDesign Optimisation Prior Information

Compute

Simulation StepObservations

Dataset/Observation Reference

22

Selected models 1

A pharmacodynamic model for a combined effect, E, a function of two

drug doses, d1 and d2, which does not have a closed form.

Count data models Drug-drug interaction models – ‘open’ form models

Markov models

23

Selected models 2 – (pregnant) woman PBPK model

Woman model implemented

already in PharmML

Bois et al.

<PharmML xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.pharmml.org/pharmml/0.8/PharmML" xsi:schemaLocation="http://www.pharmml.org/pharmml/0.8/PharmML  implementedBy="JamesBond" writtenVersion="0.8.1"> <ct:Name>Schematic representation of model structure</ct:Name> <ct:Description>Additional comments/description if required.</ct:Description> <IndependentVariable symbId="t"/> <ct:FunctionDefinition symbId="MyErrorModel"> ... </ct:FunctionDefinition> <ModelDefinition xmlns="http://www.pharmml.org/pharmml/0.8/ModelDefinition"> <VariabilityModel blkId="vm1" type="parameterVariability"> ... </VariabilityModel> <VariabilityModel blkId="vm2" type="residualError"> ... </VariabilityModel> <CovariateModel blkId="cm1"> <Covariate symbId="C1"> <Continuous> ... </Continuous> </Covariate> <Covariate symbId="C2"> <Categorical> ... </Categorical> </Covariate> </CovariateModel> <ParameterModel blkId="pm1"> <PopulationParameter symbId="pop_Psi"/> <RandomVariable symbId="eta_Psi"> <ct:VariabilityReference> <ct:SymbRef blkIdRef="vm1" symbIdRef="..."/> </ct:VariabilityReference> ... <Distribution> <po:ProbOnto name="DistributionCodeName"> ... </po:ProbOnto> </Distribution> </RandomVariable> <IndividualParameter symbId="Psi"> ... <RandomEffects> <ct:SymbRef symbIdRef="eta_Psi"/> </RandomEffects> </IndividualParameter> <Correlation> ... </Correlation> </ParameterModel> <StructuralModel blkId="sm1"> <ct:Variable symbId="f1"> ... </ct:Variable> <ct:DerivativeVariable symbId="f2"> ... </ct:DerivativeVariable> </StructuralModel> <ObservationModel blkId="om1"> <ContinuousData> <RandomVariable symbId="epsilon_Y"> <ct:VariabilityReference> <ct:SymbRef blkIdRef="vm2" symbIdRef="..."/> </ct:VariabilityReference> ... </RandomVariable> <Standard symbId="Y"> <Output> <ct:SymbRef blkIdRef="sm1" symbIdRef="f1"/> </Output> <ErrorModel> ... </ErrorModel> <ResidualError> <ct:SymbRef symbIdRef="epsilon_Y"/> </ResidualError> </Standard> </ContinuousData> </ObservationModel> </ModelDefinition>

<TrialDesign xmlns="http://www.pharmml.org/pharmml/0.8/TrialDesign"> <ExternalDataSet toolName="yourToolName" oid="someOid"> <ColumnMapping> <ds:ColumnRef columnIdRef="COL1"/> <ct:SymbRef blkIdRef="blockId" symbIdRef="symbolID"/> </ColumnMapping> <ds:DataSet> <ds:Definition> <ds:Column columnId="COL1" columnType="..." columnNum="1"/>  </ds:Definition> <ds:ExternalFile oid="dataOid"> <ds:path>fileName.csv</ds:path> </ds:ExternalFile> </ds:DataSet> </ExternalDataSet> <Interventions> <Administration oid="adminOid"> ... </Administration> </Interventions> <Observations> <Observation oid="obsOIS"> <ObservationTimes> ... </ObservationTimes> <Continuous> <ct:SymbRef blkIdRef="om1" symbIdRef="Y"/> </Continuous> </Observation> </Observations> <Covariates> <CovariateModel oid="covOID"> ... </CovariateModel> <IndividualCovariates> ... </IndividualCovariates> </Covariates> <Occasions> <OccasionList oid="occOID"> ... </OccasionList> </Occasions> <DesignSpaces> <DesignSpace> ... </DesignSpace> </DesignSpaces> <Arms> <Arm oid="armOID"> <InterventionSequence> ... </InterventionSequence> <ObservationSequence> ... </ObservationSequence> </Arm> </Arms> </TrialDesign> <ModellingSteps xmlns="http://www.pharmml.org/pharmml/0.8/ModellingSteps"> <EstimationStep oid="estimStep"> <ExternalDataSetReference> <ct:OidRef oidRef="someOid"/> </ExternalDataSetReference> <ParametersToEstimate> <ParameterEstimation> <ct:SymbRef blkIdRef="blockId" symbIdRef="symbId"/> <InitialEstimate fixed="false"> ... </InitialEstimate> </ParameterEstimation> </ParametersToEstimate> <Operation order="1" opType="operationType1"> <Property name="property1"> <ct:Assign> ... </ct:Assign> </Property> ... </Operation> </EstimationStep> </ModellingSteps> </PharmML>

M O D E L D E F I N I T I O N

T R I A L D E S I G N

M O D E L L I N G

S T E P S

Variability Model

Covariate Model

Parameter Model

Structured Model

Observational Model

Dataset sourced

Explicitly coded

e.g. Estimation

<IndependentVariable symbId="t"/> <ModelDefinition> <VariabilityModel blkId="vm1" type="parameterVariability"> <Level symbId="indiv"/> </VariabilityModel> <VariabilityModel blkId="vm2" type="residualError"> <Level symbId="residual"/> </VariabilityModel> <CovariateModel blkId="cm3"> <Covariate symbId="WT"> <Continuous> <Transformation> <TransformedCovariate symbId="tW"/>  </Transformation> </Continuous> </Covariate> <Covariate symbId="Sex"> <Categorical> <Category catId="F"/> <Category catId="M"/> </Categorical> </Covariate> </CovariateModel> <StructuralModel blkId="sm1"> <ct:DerivativeVariable symbId="Ad"> <ct:Assign>  </ct:Assign> </ct:DerivativeVariable> </StructuralModel> <ObservationModel blkId="om1"> <ContinuousData> <Standard symbId="Y_obs">  </Standard> </ContinuousData> </ObservationModel> </ModelDefinition>

<TrialDesign> <ExternalDataSet toolName="NONMEM" oid="NMoid”> <ColumnMapping> <ds:ColumnRef columnIdRef="TIME"/> <ct:SymbRef symbIdRef="t"/> </ColumnMapping> <ColumnMapping> <ds:ColumnRef columnIdRef="ID"/> <ct:SymbRef blkIdRef="vm1" symbIdRef="indiv"/> </ColumnMapping> <ColumnMapping> <ds:ColumnRef columnIdRef="WT"/> <ct:SymbRef blkIdRef="cm1" symbIdRef="W"/> </ColumnMapping> <ColumnMapping> <ds:ColumnRef columnIdRef="SEX"/> <ct:SymbRef blkIdRef="cm1" symbIdRef="Sex"/> <ds:CategoryMapping> <ds:Map dataSymbol="0" modelSymbol="M"/> <ds:Map dataSymbol="1" modelSymbol="F"/> </ds:CategoryMapping> </ColumnMapping> <ColumnMapping> <ds:ColumnRef columnIdRef="AMT"/> <ct:SymbRef blkIdRef="sm1" symbIdRef="Ad"/> </ColumnMapping> <ColumnMapping> <ds:ColumnRef columnIdRef="Y"/> <ct:SymbRef blkIdRef="om1" symbIdRef="Y_obs"/> </ColumnMapping> <ds:DataSet> </ds:DataSet> </ExternalDataSet> </TrialDesign>

Y

1 1 1 ... 2 2 2 ...

0 10 20 ... 0 12 21 ...

0 1.5 6.8 ... 0 2

5.9 ...

65 65 65 ... 72 72 72 ...

100 . . ...

100 . . ...

TIMEID WT AMT

1 1 1 ... 0 0 0 ...

SEX

M O D E L D E F I N I T I O N

T R I A L D E S I G N

Model – data mapping

SBML in DDMoRe environment

PharmML

Tools generating PharmML

(MDL-IDE or infix2pharmml)

SBML

PFIM PopED

OED Tools



NONMEM

Estimation Tools


SO

Encoding of model, trial design and modelling tasks

Recording of results from various tasks

Interoperability in SB/QSP and PMX ca. 1 year ago

Interoperability in Systems Biology

SBtoolbox2

SBML

PySCeS

SBW

CellDesigner

Copasi

and >200(!) other tools

AMIGO Toolbox

BioModels Database

Interoperability in Pharmacometrics

PharmML

NONMEM

simulx

PopED

PFIM

MATLAB

MONOLIX

R

WinBUGS

SIMCYP Simulator

Selected SBML compatible software tools Current DDMoRe target tools

Where PharmML and SBML overlap

Model Definition



Parent Level


Fixed/Random Effcts


General

Matrix

Individual

Population

Distribution

General

Distribution

Var

iability

Ref

eren

ce

Covariate Model

Observation Model

Continuous



Structural Model

Algebraic Eqs

ODE

DDE

Initial Condition

History

PK Macros

Discrete

Count

Categorical

Time-To-Event

PMF

Hazard/Survival Fct

Censoring

Assignment

Distribution

General

Standard Var

.Ref

.


Parameter

SBML

model

Modelling Steps

Estimation Step

Design Evaluation

Design Optimisation

Simulation Step

Model Definition

Variability Model

Parameter Model

Covariate Model

Observation Model

Structural Model

Design Spaces

Trial Design


Interventions

Observations

Covariates

Occasions

ArmsEXPL

ICIT

TRIA

L DESIG

N

DDMoRe (Cyprotex & SBML) provides bi-directional

translator between PharmML and SBML

Structure of PharmML:

model definition, trial design

and modelling steps

Interoperability in SB/QSP and PMX today

Interoperability in Systems Biology

SBtoolbox2

SBML

PySCeS

SBW

CellDesigner

Copasi

and >200(!) other tools

AMIGO Toolbox

BioModels Database

Interoperability in Pharmacometrics

PharmML

NONMEM

simulx

PopED

PFIM

MATLAB

MONOLIX

R

WinBUGS

SIMCYP Simulator

Selected SBML compatible software tools Current DDMoRe target tools

DDMoRe

Bi-directional

converter

SB – QSP – PMX: data types and objectives

Thanks to the available converters between SBML and PharmML, these two

exchange formats have the potential to cover the entire spectrum of M&S in

Systems Biology, Quantitative Systems Pharmacology and Pharmacometrics.

On behalf of the DDMoRe consortium

SO – Standard Output PharmML






SO


SO - storing and retrieving typical M&S results

The Standard Output (SO) represents a tool-independent format for storing typical output produced in a pharmacometric workflow. It aims at:

providing a flexible storage structure for typical results of M&S analyses performed in any DDMoRe target tool;

enabling effective data flow across tasks to ensure optimal interactions among software tools and, then, extend the modeling capabilities of the workflow;

facilitating information retrieval for post-processing and reporting, by allowing immediate access to M&S results.

32

SO structure (latest spec…)

33

Raw ResultsDataFile

GraphicsFile

Tool Settings

Task Information

NumberChains

OutputFilePath

NumberIterations

RunTime

Severity

Content

Name

ToolName

Message

Diagnostic IndividualParams

Diagnostic StructuralModel

Model Diagnostic

IndivObsPredict

VPC

RandomEffects

RelationIndiv ParamsCovariates

PDFIndivParams

Optimal Design

Design

FIM

CovarianceMatrix

SimulatedData

ParamPrecision

Tests

Criteria

Op

tim

alD

esig

nB

lock

Individual Estimates

EtaShrinkage

RandomEffects

EffectMeang

EffectMedian

EffectMode

Estimates

Mean

Median

Mode

AsymptoticCI

RelatStandardError

StandardError

PercentilesCI

PosteriorDistribution

CovarianceMatrix

CorrelationMatrix

ConditionNumber

StandardError

StandardDeviation

AsymptoticCIEstimation

Population Estimates

OtherMethod

MLE

Median

Mean

OFMeasures

Deviance

IndivContribToLL

Information Criteria

AIC

BIC

DIC

LogLikelihood

Predictions

ResidualsEpsShrinkage

ResidualTable

Precision Population Estimates

MLE

Bayesian

OtherMethod

FIM

CovarianceMatrix

CorrelationMatrix

PercentilesCI

PosteriorDistrib

StandDevPosterior

BayesianPosteriorMedian

PosteriorMean

Precision Individual Estimates

StandardDeviation

EstimatesDistrib

PercentilesCI

Likelihood

ToolObjFunction

Simulations

SimulatedProfiles

RandomEffects

IndivParameters

Population Parameters

RawResultsFile

Dosing

Covariates

Sim

ula

tio

nB

lock

Regressors

Standard Output (SO) – Structure

34

<?xml version="1.0" encoding="UTF-8"?> <SO xmlns="http://www.pharmml.org/so/0.3/StandardisedOutput" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ds="http://www.pharmml.org/pharmml/0.8/Dataset" xmlns:ct="http://www.pharmml.org/pharmml/0.8/CommonTypes" xmlns:po="http://www.pharmml.org/probonto/ProbOnto" xsi:schemaLocation="http://www.pharmml.org/so/0.3/StandardisedOutput http://www.pharmml.org/so/0.3/StandardisedOutput" implementedBy="MJS" writtenVersion="0.3.1" metadataFile="warfarin_PK_ODE_SO_FULL.rdf" id="i1"> <PharmMLRef name="warfarin_PK_ODE.xml"/> <SOBlock blkId="SO1"> <ToolSettings>...</ToolSettings> <RawResults>...</RawResults> <TaskInformation>...</TaskInformation> <Estimation> <PopulationEstimates>...</PopulationEstimates> <PrecisionPopulationEstimates>...</PrecisionPopulationEstimates> <IndividualEstimates>...</IndividualEstimates> <PrecisionIndividualEstimates>...</PrecisionIndividualEstimates> <Residuals>...</Residuals> <Predictions>...</Predictions> <OFMeasures>...</OFMeasures> </Estimation> <ModelDiagnostic> <DiagnosticStructuralModel>...</DiagnosticStructuralModel> <DiagnosticIndividualParams>...</DiagnosticIndividualParams> </ModelDiagnostic>

<Simulation> <SimulationBlock replicate="1"> <SimulatedProfiles extFileNo="1">...</SimulatedProfiles> <IndivParameters>...</IndivParameters> <RandomEffects>...</RandomEffects> <Covariates>...</Covariates> <Regressors>...</Regressors> <PopulationParameters>...</PopulationParameters> <Dosing>...</Dosing> <RawResultsFile oid="rawR">...</RawResultsFile> </SimulationBlock>  </Simulation> <OptimalDesign type="evaluation"> <OptimalDesignBlock blockNumber="1"> <FIM>...</FIM> <CovarianceMatrix>...</CovarianceMatrix> <ParameterPrecision>...</ParameterPrecision> <Criteria>...</Criteria> <Tests>...</Tests> <SimulatedData oid="SimDataOid">...</SimulatedData> <Design oid="desingOid">...</Design> </OptimalDesignBlock>  </OptimalDesign> </SOBlock>  </SO>

Tool settings

Raw results

Task information

Estimation

Model diagnostic

Simulation

Optimal design

Estimation section

35

This section is designed to store typical output of interest resulting from an estimation task.

It is organized into seven main SO elements:

Population Estimates

Precision of Population Estimates

Individual Estimates

Precision of Individual Estimates

Residuals

Predictions

Likelihood

Some of these elements are in turn composed of child and grandchild elements to allow the separation among different estimation techniques (e.g., MLE, Bayesian, Bootstrap).

Population estimates

36

<PopulationEstimates> stores the population parameter estimates obtained from MLE or Bayesian estimation as well as Bootstrap.

ProbOnto Ontology and Knowledge Base of Probability Distributions

BUGS

Monolix

STAN

BER1

BER2BBIN1

BIN1

BIN2

CATO1

CATU1

CMP1

DP1

GNB1

GP2

GP3

GEOM1

HGEOM1

IB1

NB1

NB2

NB3

NB4

NB5

NB6

OL1

POI1

POI2

UD1

UD2

ZINB1

ZIGP1

ZIP1

B1

BS1

BUR1

CAU1

CHIS1

ERL1

EXP1EXP2

EMG1

F1

FR1

FR2

GAM1

GAM2

GP1

GOM1

GUM1

HN1

HS1

IGAM1

IGAU1

LAP1

LAP2

LOGITN1LOGL1

LOGL2

LOGN1LOGN2

LOGN3

LOGN4

LOGN5

LOGN6

LOGU1

LOM1

NAK1

N1

N2

N3

NIG1

PARI1

PARII1

RAY1

RIC1

SICS1

SKN1

SN1

SU1

ST1

ST2TR1

TRU1

TN1

U1

VM1

WB1

WB2

WDM1

NONMEM

ProbOntoISF

Z(a)

CHF

H(x)

SF

S(x)HF

h(x)PPF

G(a)

CDF

F(x)PDF

f(x)

URL: probonto.org

Re-parameterization relationships

38 Figure: Re-parameterization relationships implemented in ProbOnto and

their support in target languages/tools.

Interoperability background: various tools support different parameterisations, e.g. log-normal distribution

When moving model from tool to tool

–> re-parameterisation is needed

ProbOnto stores the formulas

ISF

Z(a)

CHF

H(x)

SF

S(x)HF

h(x)PPF

G(a)

CDF

F(x)PDF

f(x)

LogNormal5

( , )

LogNormal4

LogNormal6

LogNormal1

LogNormal2

LogNormal3 v( , )

( , )

cv m( , )

g m( , )

m( , )

PML

PML

PML

LogNormal7

( , )N N

MDL

MDL

MDL

MDL

MDL

LN1…LN7 re-parameterisations

39

ISF

Z(a)

CHF

H(x)

SF

S(x)HF

h(x)PPF

G(a)

CDF

F(x)PDF

f(x)

screenshots from the ProbOnto 2.0 specification

ProbOnto – coverage of univariates

17 distributions with alternative parameterisations

ISF

Z(a)

CHF

H(x)

SF

S(x)HF

h(x)PPF

G(a)

CDF

F(x)PDF

f(x)

Application of ProbOnto in PharmML

41 PMF implemented explicitly –> error prone process and limited interoperability

PMF implemented using ProbOnto –> full interoperability by using the code names of the distribution and its parameters

ISF

Z(a)

CHF

H(x)

SF

S(x)HF

h(x)PPF

G(a)

CDF

F(x)PDF

f(x)

Problem description:

Count data models require the specification of

the according PMF (probability mass function),

here e.g. for the Zero-inflated Poisson

How to write PharmML

PharmML

generator, e.g.

MDL-IDE or

infix2pharmml

PharmML

Monolix NONMEM

winBUGS, openBUGS

Matlab

R

Simulx

PFIM

PopED Simcyp Simulator

Two option for now

MDL-IDE – new human readable and writable language

Infix2pharmml – online converter translating expressions from the usual mathematical infix notation into the corresponding PharmML markup

http://infix2pharmml.sourceforge.net

43

http://infix2pharmml.sourceforge.net

Modelling Description Language Structure

44

Objects

Object Group

Language MDL

MCL Modelling Object Group

Model Parameter Data Design Task Properties

MOG

TEL Task Object Group

TEL

functions R code

The MDL is divided into two language components:

• Model Coding Language (MCL) describes the input needed for a modelling task and will constitute the Modelling Object Group (MOG)

• Task Execution Language (TEL) Task Object Group is an R script comprising R code (e.g. prepare data, plot model diagnostics etc.) and TEL functions to execute tasks e.g. estimate and extract MCL items for manipulation e.g. initial values, MLE estimates.

MCL – Warfarin Pop PK

45

Key idea: Interoperability

• Automated translation to NMTRAN, MLXTRAN

MCL Editor: Syntax highlighting

Scope for multiple levels of hierarchy

Explicit definition of random effect distributions

Explicitly linear relationship between population, fixed effect and random effect model

• Facilitates interoperability with Monolix

• MU referencing in NONMEM

combinedError residual model

Infix2pharmml

DDMoRe Model Repository

47


Public open-source collaborative model development platform available at http://repository.ddmore.eu/

Stores and disseminates models encoded in open formats (e.g. PharmML)

Facilitates reuse of models

Private models can be shared with individual collaborators or whole teams; choice of read-only and editing rights that a collaborator can be granted

Allows models to be updated while preserving the development history

Provides basic means of browsing and searching the existing content

48

http://repository.ddmore.eu/

Conclusions

PharmML and SO cover both model definition and tool output and have the potential to improve the way pharmacometricians work today by

• Facilitating smooth and lossless transmission of models between tools.

• Enabling complex workflows based on standardised model and output definition.

• Improving reproducibility of research.

• Easier reporting and bug tracking.

• Interaction with regulatory agencies.

• Facilitating the use of existing models, see e.g. BioModels database of computational models of biological processes (SBML).

• Standards stimulate development of new tools and methods.

ProbOnto

• Facilitates encoding, exchange and annotation of NLME models

Future directions

PharmML

Monolix

NONMEMwinBUGS

Matlab

R

Simulx

PFIM

PopEDSimcyp Simulator

FORTRAN

Berkeley Madonna

DDMoRe foundation – post August 2016

• keeping products updated

• developing further the software infrastructure

Write your own translator for Berkeley Madonna or acslX or hire an expert

What makes a standard a standard?

https://twitter.com/chronaki/status/742625753225940993

Found on Twitter:

SO Nadia Terranova, Marc Lavielle, Mike K Smith, Emmanuelle

Comets, Kajsa Harling, Rikard Nordgren, Duncan Edwards, Andrew Hooker, Celine Sarr, France Mentre, Florent Yvon, Maciej J Swat

53

PharmML

ProbOnto

Swat MJ, S Moodie, SM Wimalaratne, NR Kristensen, M Lavielle, A Mari, P Magni, MK Smith, R Bizzotto, L Pasotti, E Mezzalana, E Comets, C Sarr, N Terranova, E Blaudez, P Chan, J Chard, K Chatel, M Chenel, D Edwards, C Franklin, T Giorgino, M Glont, P Girard, P Grenon, K Harling, AC Hooker, R Kaye, R Keizer, C Kloft, JN Kok, N Kokash, C Laibe, C Laveille, G Lestini, F Mentre, A Munafo, R Nordgren, HB Nyberg, ZP Parra-Guillen, E Plan, B Ribba, G Smith, IF Troconiz, F Yvon, PA Milligan, L Harnisch, M Karlsson, H Hermjakob and N Le Novère

Maciej J Swat, Pierre Grenon, Sarala M Wimalaratne

Contributions

RawResults

TollSettings

Task Information

EstimationModel Diagnostic

Simulations

Optimal Design

Modelling Steps

Estimation Step

Design Evaluation

Design Optimisation

Simulation Step

Model Definition

Variability Model

Parameter Model

Covariate Model

Observation Model

Structural Model

Design Spaces

Trial Design


Interventions

Observations

Covariates

Occasions

ArmsEXPL

ICIT

TRIA

L DESIG

N

54

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