Your SlideShare is downloading. ×
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Monolix4 monolix day2011
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Monolix4 monolix day2011

1,539

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,539
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
18
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. MONOLIX DAYDecember 12th, 2011La Maison de la Recherche, Paris
  • 2. Schedule 9.15: MONOLIX 4: presentation & demos, Marc Lavielle (Inria, POPIX) 10.15: Lixoft, status & future plans, Jérôme Kalifa (Lixoft) 10.45: Pause 11.00: New challenges for MONOLIX 1. An overview of POPIX and DDMoRe activities, Marc Lavielle (Inria, POPIX) 2. New challenges in oncology, Benjamin Ribba (Inria, NUMED) 12.15: Buffet 13.45: MONOLIX Guidance Committee meeting 16.30: End of the MONOLIX Day
  • 3. Some new features in MONOLIX 4 New graphics New MLXTRAN  full project programming  complex PK models  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 4. Some new features in MONOLIX 4 New graphics New MLXTRAN  full project programming  complex PK models  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 5. Some new features in MONOLIX 4 New graphics New MLXTRAN  complex PK models  full project programming  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 6. MLXTRAN for PK modelExample 1: oral administration, 1cpt, first order absorption PK model The data ID TIME AMT CONC 1 0 100 . 1 0.5 . 0.15 1 2 . 0.71 1 3 . 0.97 1 6 . 1.77 1 12 . 3.64 1 24 . 4.09 1 36 . 3.36 1 48 . 2.83 1 72 . 2.18 1 96 . 1.40 1 120 . 1.32
  • 7. MLXTRAN for PK modelExample 1: oral administration, 1cpt, first order absorption PK model The data MLXTRAN Full ODE ID TIME AMT CONC $INPUT 1 0 100 . psi = {ka, V, Cl} 1 0.5 . 0.15 1 2 . 0.71 1 3 . 0.97 $PK 1 6 . 1.77 compartment(amount=Ad) 1 12 . 3.64 iv(dpt=1, cmt=1) 1 24 . 4.09 1 36 . 3.36 1 48 . 2.83 $EQUATION 1 72 . 2.18 ddt_Ad = - ka*Ad 1 96 . 1.40 ddt_Ac = ka*Ad - k*Ac 1 120 . 1.32 Cc = Ac/V $OUTPUT output = Cc
  • 8. MLXTRAN for PK modelExample 1: oral administration, 1cpt, first order absorption PK model The data MLXTRAN MLXTRAN Full ODE Built-in functions ID TIME AMT CONC $INPUT $INPUT 1 0 100 . psi = {ka, V, Cl} 1 0.5 . 0.15 psi = {ka, V, Cl} 1 2 . 0.71 1 3 . 0.97 $PK $PK 1 6 . 1.77 compartment(amount=Ad) compartment(amount=Ac) 1 12 . 3.64 iv(dpt=1, cmt=1) 1 24 . 4.09 absorption(ka) 1 36 . 3.36 elimination(k=Cl/V) 1 48 . 2.83 $EQUATION Cc = Ac/V 1 72 . 2.18 ddt_Ad = - ka*Ad 1 96 . 1.40 ddt_Ac = ka*Ad - k*Ac 1 120 . 1.32 Cc = Ac/V $OUTPUT output = Cc $OUTPUT output = Cc
  • 9. MLXTRAN for PK modelExample 1: oral administration, 1cpt, first order absorption PK model The data MLXTRAN MLXTRAN Full ODE Built-in functions ID TIME AMT CONC $INPUT $INPUT 1 0 100 . psi = {ka, V, Cl} 1 0.5 . 0.15 psi = {ka, V, Cl} 1 2 . 0.71 1 3 . 0.97 $PK $PK 1 6 . 1.77 compartment(amount=Ad) Cc = pkmodel(ka, V, Cl) 1 12 . 3.64 iv(dpt=1, cmt=1) 1 24 . 4.09 1 36 . 3.36 $OUTPUT 1 48 . 2.83 $EQUATION output = Cc 1 72 . 2.18 ddt_Ad = - ka*Ad 1 96 . 1.40 ddt_Ac = ka*Ad - k*Ac 1 120 . 1.32 Cc = Ac/V $OUTPUT output = Cc
  • 10. MLXTRAN for PK modelExample 2: oral 1cpt, sequential zero order – first order absorptions PK model The data MLXTRAN Built-in functions $INPUT ID TIME AMT CONC psi = {Fr, Tk0, ka, V, Cl} 1 0 100 . 1 0.5 . 0.15 1 2 . 0.71 1 3 . 0.97 $PK 1 6 . 1.77 compartment(amount=Ac) 1 12 . 3.64 1 24 . 4.09 absorption(Tk0, p=Fr) 1 36 . 3.36 absorption(ka, Tlag=Tk0, p=1-Fr) 1 48 . 2.83 1 72 . 2.18 elimination(k=Cl/V) 1 96 . 1.40 1 120 . 1.32 Cc = Ac/V $OUTPUT output = Cc
  • 11. MLXTRAN for PK modelExample 3: IV bolus 2cpt, Michaelis Menten elimination PK model MLXTRAN Built-in functions $INPUT psi = {k12, k21, V, Vm, Km} $PK compartment(cmt=1, amount=Ac) iv(dpt=1, cmt=1) peripheral(k12, k21) elimination(cmt=1, Vm, Km) Cc = Ac/V $OUTPUT output = Cc
  • 12. MLXTRAN for PK modelExample 3: IV bolus 2cpt, Michaelis Menten elimination PK model MLXTRAN MLXTRAN Built-in functions Mixed ODE/Built-in functions $INPUT $INPUT psi = {k12, k21, V, Vm, Km} psi = {k12, k21, V, Vm, Km} $PK $PK compartment(cmt=1, amount=Ac) compartment(cmt=1, amount=Ac) iv(dpt=1, cmt=1) iv(dpt=1, cmt=1) peripheral(k12, k21) peripheral(k12, k21) elimination(cmt=1, Vm, Km) $EQUATION Cc = Ac/V ddt_Ac = -Vm*Ac/(V*Km + Ac) Cc = Ac/V $OUTPUT $OUTPUT output = Cc output = Cc
  • 13. MLXTRAN for PK modelExample 3: IV bolus 2cpt, Michaelis Menten elimination PK model MLXTRAN MLXTRAN Built-in functions Mixed ODE/Built-in functions $INPUT $INPUT psi = {k12, k21, V, Vm, Km} psi = {k12, k21, V, Vm, Km} $PK $PK Cc = pkmodel(k12 , k21, V, Vm, Km) compartment(cmt=1, amount=Ac) iv(dpt=1, cmt=1) peripheral(k12, k21) $OUTPUT output = Cc $EQUATION ddt_Ac = -Vm*Ac/(V*Km + Ac) Cc = Ac/V $OUTPUT output = Cc
  • 14. MLXTRAN for PK modelExample 4: multiple administrations & multiple compartments PK model
  • 15. MLXTRAN for PK modelExample 4: multiple administrations & multiple compartments PK model ID TIME AMT CONC DPT 1 0 2 . 3 1 0.5 0 229 . 1 1 0 142 . 1 4 0 17.5 . 1 6 7 . 1 1 6.5 0 8.1 . 1 7 0 192 . 1 9 0 189 . 1 12 7 . 2 1 13 0 50 . 1 15 0 201 .
  • 16. MLXTRAN for PK modelExample 4: multiple administrations & multiple compartments PK model MCL Built-in functions $INPUT psi = {Tk01, F1, Tk02, F2, kl, k, V, Vm, Km} $PK compartment(cmt=1, amount=Al) compartment(cmt=2, amount=Ac) absorption(dpt=1 , cmt=1 , Tk0=Tk01 , p=F1) absorption(dpt=2 , cmt=2 , Tk0=Tk02 , p=F2) ID TIME AMT CONC DPT 1 0 2 . 3 absorption(dpt=3 , cmt=2 ) 1 0.5 0 229 . elimination(cmt=1, k) 1 1 0 142 . 1 4 0 17.5 . elimination(cmt=2, Vm, Km) 1 6 7 . 1 transfer(from=1, to=2, kt=kl) 1 6.5 0 8.1 . 1 7 0 192 . Cc=Ac/V 1 9 0 189 . 1 12 7 . 2 1 13 0 50 . $OUTPUT 1 15 0 201 . output = Cc
  • 17. Some new features in MONOLIX 4 New graphics New MLXTRAN  complex PK models  full project programming  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 18. Full MLXTRAN for PK/PD model Model Coding Language$DATA path="%MLXPROJECT%/", file="warfarin_data.txt", headers={ID, TIME, DOSE, Y, YTYPE, COV, SEX},$VARIABLE wt, lwt = log(wt/70) [use=cov] sex [use=cov, type=cat]$INDIVIDUAL default={distribution=logNormal, iiv=yes}, Tlag, ka, V={covariate=lwt}, Cl, Imax={distribution=logitNormal, iiv=no}, C50, Rin, kout$STRUCTURAL_MODEL file="mlxt:turnover2_mlxt", path="%MLXPROJECT%/libraryMLXTRAN", output={Cc, E}$OBSERVATIONS Concentration = {type=continuous, prediction=Cc, error=comb1}, Effect = {type=continuous, prediction=E, error=constant}
  • 19. Full MLXTRAN for PK/PD model Model Coding Language Task Execution Language$DATA path="%MLXPROJECT%/", $TASKS file="warfarin_data.txt", globalSettings={ headers={ID, TIME, DOSE, Y, YTYPE, COV, SEX}, settingsAlgorithms="%MLXPROJECT%/pkpd_algo.xmlx" , settingsGraphics="%MLXPROJECT%/pkpd_graphics.xmlx",$VARIABLE resultFolder="%MLXPROJECT%/pkpd_project" }, wt, lwt = log(wt/70) [use=cov] estimatePopulationParameters( sex [use=cov, type=cat] initialValues={ POP_V = 10,$INDIVIDUAL POP_Cl = 0.1, default={distribution=logNormal, iiv=yes}, POP_Imax = 0.5 }), Tlag, ka, V={covariate=lwt}, Cl, Imax={distribution=logitNormal, iiv=no}, C50, Rin, kout estimateFisherInformationMatrix( method={ linearization} ),$STRUCTURAL_MODEL file="mlxt:turnover2_mlxt", estimateIndividualParameters( path="%MLXPROJECT%/libraryMLXTRAN", method={ conditionalMean, conditionalMode} ), output={Cc, E} estimateLogLikelihood($OBSERVATIONS method={linearization, importanceSampling} ) Concentration = {type=continuous, prediction=Cc, error=comb1}, Effect = {type=continuous, prediction=E, error=constant}
  • 20. Some new features in MONOLIX 4 New graphics New MLXTRAN  complex PK models  full project programming  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 21. MLXTRAN for Time-To-Event model Example 1 constant hazard model$INPUTpsi = Hbase$OBSERVATIONadverseEvent = {type=event, hazard=Hbase/365)$OUTPUToutput = adverseEvent
  • 22. MLXTRAN for Time-To-Event model Example 1 Example 2 constant hazard model Joint PK-RTTE model$INPUT $INPUTpsi = Hbase psi = {ka, V, Cl, gamma} $PK$OBSERVATION Cc = pkmodel(ka, V, Cl)adverseEvent = {type=event, hazard=Hbase/365) $OBSERVATION$OUTPUT Hemorrhaging= {type=event, hazard=gamma*Cc)output = adverseEvent $OUTPUT output = {Cc, Hemorrhaging}
  • 23. Full MLXTRAN for joint PK-RTTE model Model Coding Language$DATA path="%MLXPROJECT%/", file="pkrtte_data.txt", headers={ID,TIME,DOSE,Y,YTYPE,CENS},$INDIVIDUAL default={ distribution = logNormal, iiv = yes }, ka, V, Cl, gamma$STRUCTURAL_MODEL file="mlxt:pkrtte_mlxt", path="%MLXPROJECT%/libraryMLXTRAN", output={Cc, Hemorrhaging }$OBSERVATIONS Concentration = { type=continuous, prediction=Cc, error=comb1}, Hemorrhaging = { type=event}
  • 24. Full MLXTRAN for joint PK-RTTE model Model Coding Language Task Execution Language$DATA $TASKS path="%MLXPROJECT%/", globalSettings={ file="pkrtte_data.txt", settingsAlgorithms="%MLXPROJECT%/pkrtte_algo.xmlx" , headers={ID,TIME,DOSE,Y,YTYPE,CENS}, settingsGraphics="%MLXPROJECT%/pkrtte_graphics.xmlx", resultFolder="%MLXPROJECT%/pkrtte_project" },$INDIVIDUAL default={ distribution = logNormal, iiv = yes }, estimatePopulationParameters( ka, V, Cl, gamma initialValues={ POP_ka = 1,$STRUCTURAL_MODEL POP_V = 10, file="mlxt:pkrtte_mlxt", POP_Cl = 0.1, path="%MLXPROJECT%/libraryMLXTRAN", POP_gamma = 0.005 }), output={Cc, Hemorrhaging } estimateFisherInformationMatrix($OBSERVATIONS method={ stochasticApproximation} ), Concentration = { type=continuous, prediction=Cc, error=comb1}, Hemorrhaging = { type=event} estimateIndividualParameters( method={ conditionalMode } ),
  • 25. Some new features in MONOLIX 4 New graphics New MLXTRAN  full project programming  complex PK models  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 26. Some new features in MONOLIX 4 New graphics New MLXTRAN  full project programming  complex PK models  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 27. Some new features in MONOLIX 4 New graphics New MLXTRAN  full project programming  complex PK models  (repeated) time-to-event models Workflows Convergence assessment Batch mode and scripts
  • 28. Monolix Batch ModesRunning a single Monolix project through a shell with a simple command line Using the Standalone version of Monolix Under linux Under windows monolix.sh –nowin –p myproject.mlxtran –f run monolix.bat –nowin –p myproject.mlxtran –f run Using the Matlab Version of Monolix matlab –wait –nosplash –nodesktop –r “monolix(„-nowin‟,‟-p‟,‟myproject.mlxtran‟,‟-f‟,‟run‟,‟-destroy‟),exit”
  • 29. Monolix Batch Modes Use PSMLX as a command line helperHelp user to run Monolix on numerous projects stored into a directory perl toolsRunner.pl –tool=execute –config=myconfig.ini –input-directories=/home/gandalf/myprojects_dir/ ; myconfig.ini [path] ; matlab path matlab=/opt/matlab ; monolix path monolix=/opt/Monolix-4.1.0-matlab2009a-linux64/matlab/ [monolix] ; monolix version (here we do not use standalone) standalone=false [program-generic-options] ; number of instances of monolix run in same time thread=4

×