NONMEM 7.3. Overview - ICON plc

NONMEM®NONMEM ® is a nonlinear mixed effects modeling tool used in population pharmacokinetic– pharmacodynamic (PK/PD) analysis. The software was developed by the NONMEM ® ProjectGroup at the University of California, San Francisco. The PK/PD modeling community has reliedon the use of the NONMEM ® statistical software for over 30 years.Drug level PK data and drug response PD data are typicallycollected from clinical studies of pharmaceutic agents.Proper modeling of these data involves accounting for bothunexplainable between- and within-subject effects (randomeffects), as well as measured concomitant effects (fixedeffects). Such modeling is especially useful when there areonly a few PK or PD measurements from each individualsampled in the population, or when the data collectiondesign varies considerably between these individuals.The appropriate statistical analysis with NONMEM ® usingthe appropriate model helps pharmaceutical companiesdetermine appropriate dosing strategies for their products,and increase their understanding of drug mechanismsand interactions. NONMEM ® can also simulate data for avariety of these population PK/PD problems. The continueddevelopment of NONMEM ® is important to our customers.The changes that are incorporated into new versions is inresponse to customer requests, and from understandingby our developers of some improvements that are needed,such as increased incidence of success in solving problems,greater speed, smaller memory usage, and parallelcomputing of a single problem.The SoftwareNONMEM ® is an evolving program, reflecting testedmethodological and programming improvements. Thesoftware consists of three parts:(i)(ii)NONMEM ® itself, the basic and very general nonlinearregression programPREDPP, a very powerful package of subroutineshandling population PK data as well as general linearand nonlinear models, which can free the user fromcoding standard kinetic type equations him/herselfwhile simultaneously allowing complicated patient-typedata to be easily analyzed(iii)NM-TRAN, a preprocessor allowing control andother needed inputs to be specified in a user-friendlymanner. Both NONMEM® and NM-TRAN are batchtype programs.NONMEM ® provides an extensive set of output files withresults placed in table format for easy incorporation intopost-processing statistical and graphical software.NONMEM ® 7.2 includes the following enhancements:1. Parallel computing of a single problem over multiplecores or computers. The computation of a singleproblem that can take many hours or days may bedistributed over two or more cores and/or computers tocomplete in a shorter time.2. Memory dynamically allocated according to problemsize. No need to recompile the NONMEM ® programfor unusually large problems. Memory is automaticallysized according to the number of parameters andnumber of subjects. User may override programgenerated suggested values using a statement in thecontrol stream. Often for moderate sized problems, thisresults in much smaller memory usage, compared tothe standard memory usage in NONMEM ® 7.1.2 andearlier. Particularly helpful for parallel computing whenusing multiple cores on a single computer.3. Control stream files may be written in mixed case.4. Variance matrix parameters output in covariance andcorrelation format.5. Variance matrix parameters may be input in covariance,correlation, or Cholesky format.®6. XML markup version of the NONMEM report file.7. Features to facilitate stochastic differential equations(SDE).www.iconplc.com

System RequirementsThe NONMEM ® computer program is written anddistributed in ANSI FORTRAN 95 code, and therefore,can be used with most hardware and operating systemsincorporating a Fortan 95 compiler adhering to the ANSIstandard. It has been shown to operate with Intel FortranCompiler 9.0 or greater for Windows or Linux, and gFortranfor Windows or Linux (Intel Fortran compiler is preferred).Since a NONMEM® run can take considerable CPU time,perhaps many hours, depending on the speed of thecomputer and the size of the problem, it is advisable to usea fast machine. At least 1 and preferably 2 GB of memoryshould be available for exclusive use of NONMEM ® andNMTRAN programs.LicensingThe NONMEM ® program is available on a CD ROM, whichtogether with the documentation and all updates andadditions to the program, will be delivered for a licenseroyalty fee to be paid annually. This fee is subject to changefrom year to year, and at each anniversary the licensee at itsoption may choose not to renew the license.NONMEM 7.3 will include the following enhancements1. More mixed effects levels, with random effects acrossgroups of individuals such as clinical site, may nowbe modeled. Sites themselves may be additionallygrouped, such as by country, etc.2. Easy to code inter-occasion variability. ETA’s canbe referenced by an index variable related to theinter-occasion data item.3. Symbolic reference to thetas, etas, and epsilons.4. Priors for SIGMA matrix. Prior information may beadded to SIGMA parameters (assumes inverse Wishartdistributed).5. Certain options may be optimized for Monte Carlo (MC)and Expectation-Maximization (EM) methods. ForSAEM and IMP, NONMEM assesses best ISAMPLEvalues for each subject. For IMP, NONMEM assessesbest IACCEPT and DF values for each subject.For BAYES and SAEM, NONMEM assesses bestCINTERVAL based on the degree of Markov chaincorrelation across iterations.6. An AUTO option determines best set of options for MC/EM methods.7. Perform Monte Carlo search of population parameters,and select the set that provides the lowest startingobjective function for an estimation.8. Perform Monte Carlo search for initial best estimatesof etas for each subject. Together with a MonteCarlo search of best initial population parameters, thisprovides a global search technique for the traditionalmethods, with less reliance on starting position forincidence of success.9. FOCE/Laplace and ITS may be assessed using onlynumerical eta derivatives for search of best etas and/oreta Hessian matrix assessment. This feature relaxes therequirement that analytic derivatives be computed forFOCE and Laplace by either NMTRAN or the user.10. Conditional Individual Weighted Residual (CIWRES)added to residual variance diagnostics. Includesevaluation for L2 correlated data as well.11. Boot-strap simulations may be performed in NONMEM.12. Example control stream files modeling populationdensities of individual parameters that are t-distributed.13. Nelder-Mead optimization option for best fit individualetas, particularly useful to improve robustness forimportance sampling.14. Option to use either eigenvalue square root or Choleskysquare root algorithms for assessing weighted residualdiagnostics.15. Option to have etabar and eta shrinkage informationinclude only subjects which influence the etas.16. Subscripted variables may be used in abbreviatedcode, with fewer restrictions on DOWHILE. Additionally,correlation of residual variables using CORRL2 maynow be written in abbreviated code.17. Enhanced non-parametric analysis methods, suchas extended grid of support points, use of an outsizeinter-subject variance to obtain support points that fitoutlier subjects better, and built-in bootstrap analysismethods for obtaining empirical confidence ranges tonon-parametric probability parameters.www.iconplc.com

The following example demonstrates NONMEM’s ability to perform an MCMC Bayesiananalysis on a complex PK/PD problem.We compare Bayesian analysis performed in NONMEM ®7 with WinBUGS/Blackbox V1.4 for data simulated usingreceptor-mediated clearance and indirect response modeltypically used in antibody therapeutics. Data were simulatedwith 17 PK and 18 PD observations for each of 50 subjectsreceiving a bolus of drug, followed by short infusion a weeklater. The PK model has 2 compartments (Vc, k12, k21)with first-order (k10) and receptor-mediated clearance (Vm,Kmc). The PD model is indirect response, with receptorsgenerated by zero order process (k03), and removed byfirst order process (k30) or via drug-receptor complex (Vm,Kmc). There are 46 population parameters, variances/co-variances, and intra-subject error coefficients. NM7 usesa Gibbs or Metropolis-Hastings algorithm to implementthe MCMC procedure. Bayesian analysis consisted of4000 burn-in followed by 30,000 sample iterations, anduninformative priors were supplied. Extensive randommixing occurred in the sampling history for all parameters.Mean differences between sorted samples from NM7 andWinBUGS were typically

Example of Bayesian History Plotand Cumulative Distribution for Parameter k10NONMEM Sampling1.5000E-011.3511E-011.2022E-011.0533E-01mu[2] IHEIA29.0432E-027.5539E-026.0646E-0201.5339E-011.3860E-011.2380E-011.0901E-0160001200018000IterationWinbugs Sampling24000300009.4218E-027.9425E-026.4631E-020600012000180002400030000IterationFor more detailed information contact +1 410 696 3100or email us at IDSSoftware@iconplc.comICON Development SolutionsDivisional Headquarters7740 Milestone ParkwayHanover, MD 21076Tel: +1 410 696 3000Fax: +1 410 480 0776www.iconplc.com