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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.

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Niclas Jonsson

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  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)

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