1 / 11

Niclas Jonsson

Estimating and forecasting in vivo drug disposition and effects using distributed computer systems. Niclas Jonsson. Pharmacometrics. Describes the dynamic interaction between drugs and individuals using quantitative models.

leala
Télécharger la présentation

Niclas Jonsson

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Estimating and forecasting in vivo drug disposition and effects using distributed computer systems Niclas Jonsson

  2. Pharmacometrics • Describes the dynamic interaction between drugs and individuals using quantitative models. • The models are highly non-linear and are based on pato-physiological and pharmacological knowledge. • The models are used in clinical drug development with the goal to make more efficient use of available data and to optimize future clinical trials.

  3. Cross-disciplinary field Pharmacokinetics, Pharmacodynamics, Pharmacology Numerical issues (simulation, optimized computations, hardware strategies) Pharmacometrics Statistics/Mathematics (Theory, model formulation) (Pato-)physiologi (Disease states and progression)

  4. Main application - NONMEM • NON-linear Mixed Effects Modeling • Old (fashioned) FORTRAN 77 program • First version appeared 1980 • Still the most widely used • Single threaded…

  5. Scope of problems to be solved • Typical data consist of plasma concentration-drug effect-time data from tens to thousands of patients • Run times for a single model fit varies depending on model complexity and amount of data • <1 min Short • >10 min Moderate • > Days Long • A typical analysis involves 30-100 runs

  6. Future trends • More computer intensive methods, e.g. • Stepwise variable selection (40-300 runs) • Cross-validation (100-500 runs) • Bootstrapping (200-2000 runs) • Monte Carlo simulations • Combinations of the above • More mechanistically based models, i.e. more complex models (>Days) • Wider use of pharmacometrics in commercial and academic research. • Parallelized “NONMEM”

  7. Impact of parallelization/distributed computing • Present software (NONMEM) does not allow for parallel execution of single runs. • Most computer intensive methods do lend themselves to parallelization! • Distributed computing solutions will allow us to investigate the properties of methods that will be tractable to the regular users 5+ years from now.

  8. Our current environment • 20+ users • The majority in applied work • ~5 in theoretical research • Cluster (since 2001) of 15+ CPUs • Load balancing • Parallel execution of multiple runs

  9. Current hardware Fast Ethernet 200 GB File Server 5 double CPU Computational Servers 5 Workstations Desktops/ Laptops

  10. Software • Red Hat Linux 7.3 • openMOSIX kluster patch for kernel 2.4.19 • Perl 5.6.1 • Perl-speaks-NONMEM+Parallel::Forkmanager • In house support library for parallelization of multiple NONMEM runs. • Recently completed.

  11. Technical requirements • Computational resources needed: • Fall 2003 and onwards, as much as possible… • Inter-processor relative processing speed: • Inter-processor>processor • Primary memory: • For some applications 1Gb is too little • Secondary storage: • Up to 200 Gb for months • Data access frequency: • ? • Status of software to be used: • Depends on porting issues (help needed?) • Late beta stage (PsN)

More Related