How can users benefit from the DDMoRe common language standard and - - PowerPoint PPT Presentation

On behalf of the DDMoRe consortium

How can users benefit from the DDMoRe common language standard and interoperability framework?

World Conference on Pharmacometrics 2016

Phylinda LS Chan, Lutz O Harnisch, Peter A Milligan, Mike K Smith

Typical modelling problems today

Limited access to existing modelling knowledge Non-compatible software languages Waste of time and resources, more errors working in a non-integrated/non-compatible environment

2

DDMoRe Consortium in a nutshell

3

23 million € budget 26 partners Start March 2011

5.5 years Until Aug 2016

4

DDMoRe’s Vision

Improve quality, efficiency and cost effectiveness of Model-Informed Drug Discovery and Development (MID3) and therapeutic use

5

DDMoRe Platform: “Step by step”

“User perspective“

(June 2016)

Scientific Question  Task & Workflow

8

Executable files

DDMoRe Platform: “Step by step”

“Integration manager”

“User perspective“

(June 2016) “Translator” “Task manager”

DDMoRe Platform

“User perspective“

(July 2016)

“User perspective“

(July 2016)

PharmML & SO – Big Picture

Pharmacometric Markup Language & Standardized Output

MDL – ProbOnto

11

Model Description Language

Structure

12

Model Description Language

Structure

13

Modularity – example workflow

▪ Estimation = Data + Parameters + MODEL + Monolix Task Properties ▪ Bayesian estimation = Data + Priors + MODEL + BUGS Task Properties ▪ VPC = Data + Final Parameters + MODEL + NONMEM Task Properties ▪ Prediction / simulation = Design + Final Parameters + MODEL + Simulation Task Properties ▪ Optimal design / evaluation = Design + Final Parameters + MODEL + PFIM / PopED Task Properties

14

MDL – How is it different?

• Definition of dataset variables and their use in the model

15

DATA_INPUT_VARIABLES{ ID : { use is id } TIME : { use is idv } WT : { use is covariate } AGE : { use is covariate } SEX : { use is catCov withCategories{female when 0, male when 1} } AMT : { use is amt, variable = GUT } DVID : { use is dvid } DV : { use is dv, define={1 in DVID as CP_obs, 2 in DVID as PCA_obs} } MDV : { use is mdv } }# end DATA_INPUT_VARIABLES

MDL – How is it different?

▪ To support interoperability , some relationships need to be more explicit (when they can be)...

16

INDIVIDUAL_VARIABLES{ CL : { type is linear, trans is ln, pop = POP_CL, fixEff = [{coeff =BETA_CL_WT, cov =logtWT}], ranEff = ETA_CL ) ... } # end INDIVIDUAL_VARIABLES

𝐷𝑀𝑗 = 𝐷𝑀 × 𝑋𝑈 70

𝛾𝐷𝑀

× 𝑓𝜃𝑗 log 𝐷𝑀𝑗 = 𝑚𝑝𝑕 𝐷𝑀 × 𝑋𝑈 70

𝛾𝐷𝑀

+ 𝜃𝑗

MDL – Some things are easier…

▪ MDL uses the ProbOnto knowledge base for statistical distributions used in Random Variable Definitions.

17

MODEL_PREDICTION{ lnLAMBDA = ln(BASECOUNT) + BETA*CP LAMBDA = exp(lnLAMBDA) } RANDOM_VARIABLE_DEFINITION(level=DV){ Y ~ Poisson1(rate=LAMBDA) } OBSERVATION{ :: {type is count, variable = Y} }# end ESTIMATION

Model/script editor with advanced editing and syntax checking features Task console (R) Project explorer where standardized

• utputs are

accessible

MDL-IDE

Manages all modelling tasks

18

• Warfarin Pop PK used as the basis.
• UseCase1 – Structural model with differential

equations

• UseCase2 – Analytical solution
• UseCase3 – Joint model of PK and PD (>1 outcome)
• UseCase4 – IV and oral administration
• UseCase5 – Covariate models including categorical

covariate

• UseCase6 – Correlation between V, CL, KA
• UseCase7 – Structural model specified by

compartments

• UseCase8 – Between-occasion variability
• UseCase9 – Infusion rates
• UseCase10 – 2 distribution compartments
• UseCase11 – Poisson count
• UseCase12 – Categorical outcome
• UseCase13 – Binary outcome
• UseCase14 – Time to event (Exact time of event known)
• UseCase15 – log-transformed DV
• UseCase16 – BLQ handling
• UseCase17 – Steady State PK
• UseCase16 – Interpolation of covariates
• UseCase19 – L2 handling
• UseCase20 – Transit compartment for absorption
• UseCase21 – Mixture models
• UseCase22 – Complex PK (>1 absorption compartment)
• UseCase23 – Conditional observation model

Use Cases

Demonstrate functionality, model features

19

Interoperable UseCase Valid MDL In progess

Typical modelling problems addressed by DDMoRe products today

Limited access to existing modelling knowledge Non-compatible software languages Waste of time and resources, more errors working in a non-integrated/non-compatible environment

21

In Summary

• In support of MID3, DDMoRe products provide a vital

improvement to transparency in model informed decision making, enhancing knowledge-sharing and scientific communication.

• Deliverables of the DDMoRe project provide
• a quantitative framework for prediction and extrapolation,
• centred on knowledge and inference generated from integrated models of

compound, mechanism and disease level data,

• hence improving the quality, efficiency and cost effectiveness of decision

making.

22

More information about DDMoRe

• Official webpage: http://ddmore.eu/
• For updates on project, products, news, newsletters, publications, blog,

events, forums, FAQ, etc.

• Follow DDMoRe: Twitter
• Videos:
• DDMoRe vision: https://www.youtube.com/watch?v=zxsNOewJ84g
• Podcasts: https://www.youtube.com/watch?v=kgGw4uc2hmw
• MDL IDE installation: https://www.youtube.com/watch?v=kgGw4uc2hmw
• DDMoRe: an introduction and demo (ISOP workshop):

https://www.youtube.com/watch?v=7FmrPKAhFKM

23

How do I get started?

• Drug and Disease Model Repository

http://repository.ddmore.eu/

• Interoperability Framework
• Installation and User Cases:

http://ddmore.eu/beta-release-interoperability-framework

• MDL User Guide: http://www.ddmore.eu/instructions/mdl-user-guide
• DDMoRe Foundation
• Contact the Board members: Marylore Chenel, Lutz Harnisch, Mats

Karlsson, Paolo Magni, Peter Milligan.

24

Participants

25

are a unique combination of model builders, model users, software developers and teachers

On behalf of the DDMoRe consortium

THANK YOU…

26