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Hands on session

Hands on session. Pravin Jadhav, Pharmacometrics. The material in this part of the presentation is prepared using NONMEM help guides ACCP website (http://www.accp1.org/pharmacometrics/index.html). Where to find help to learn NONMEM software?. Depending on your installation version

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Hands on session

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  1. Hands on session Pravin Jadhav, Pharmacometrics The material in this part of the presentation is prepared using NONMEM help guides ACCP website (http://www.accp1.org/pharmacometrics/index.html) Hands on session

  2. Where to find help to learn NONMEM software? • Depending on your installation version • Version VI- C:\nmvi\guides and C:\nmvi\help • Version V- C:\nmv\guides and C:\nmv\help • NMUSERS • Listserve: http://www.cognigencorp.com/nonmem/nm/ • ACCP website • http://www.accp1.org/pharmacometrics/index.html • NONMEM VI features • http://www.ecpag.org/presentations/2006/0725/1_TomLudden_NONMEM_VI.pdf Hands on session

  3. Introduction to NONMEM • What is NONMEM? • A computer program to analyze data using a NONlinear Mixed Effects Model • Focus on Population Pharmacokinetic data, however, can handle several other problem sets as well • Developed by the NONMEM Project Group at the University of California, San Francisco (UCSF) • Written in ANSI FORTRAN 77 • Two NONMEM programs available- SINGLE and DOUBLE (default) precision Hands on session

  4. NONMEM Architecture File of NM-TRAN records Begins with “$” Data File ASCII format • Two major components are distributed with NONMEM • NM-TRAN (NONMEM Translator) • a preprocessor to NONMEM that translates inputs specified in a more user-friendly way to the formats required by NONMEM • PREDPP (“PRED for Population Pharmacokinetics”) • Specialized PRED subroutine for use with NONMEM for pharmacokinetic data • Collection of FORTRAN subroutines NM-TRAN Several Input files for NONMEM NONMEM PREDPP ADVAN and TRANS PK ERROR NONMEM Output Hands on session

  5. Model input file (NM-TRAN Control Records) Independent variable Dependent variable Covariates • NM-TRAN Control Language Translation service provides flexible order of these records and allows abbreviations of the record names, for example, $EST ($ESTIMATION), $COV ($COVARIANCE) or option names METH (METHOD), SIG (SIGDIGITS) etc. • No “Tabs” in NONMEM! ASCII format only Hands on session

  6. Data file (passed to NM-TRAN preprocessor) Data item Data record • When using PREDPP, data preprocessor can supply missing items such as MDV even if user does not supply in dataset/$INPUT record • Note: • TIME should be sorted chronologically and records are added according to events. • The dose (AMT) is specified at the actual dosing time. Don’t confuse with DOSE column that is intended to be used as a covariate Hands on session

  7. Building a control stream and data file • Record 1- description of the problem • Record 2- location of the dataset. If not the same directory, requires file path • NONMEM VI incorporates • More flexible use of IGNORE option- For example, IGNORE=(BW.GT.100) • Adds ACCEPT option- For example, ACCEPT=(BW.LE.100) Hands on session

  8. Building a control stream and data file • Record 3- names of data items (in the order) on each data record • Quick Notes: • Names of the data items in the $INPUT record are the ones supplied to NMTRAN • Remember what IGNORE option does? Hands on session

  9. Building a control stream and data file • ID- Identification number • TIME- Time associated with the event described in the data record • AMT- Dose Amount (AMT) data item for PREDPP • Dose given at the specified TIME • CONCNGML- Dependent Variable (DV) • observed concentration of drug in ng/mL • EVID- Event Identification data item for PREDPP (takes values 0, 1, 2, 3, 4) • MDV- Missing Dependent Variable data items for NONMEM (takes values 0, 1) Hands on session

  10. Building a control stream and data file • What if an additional of 50 mg was given at 12 hr? • Method 1: • Explicitly specify dosing at the specified TIME • Need additional record if dosing and observation occurred at identical times Observed Concentrations Hands on session

  11. Building a control stream and data file • Method 2: • Use of ADDL (Additional Dose data item for PREDPP) and II (Interdose Interval data item for PREDPP) • The units of II should be consistent with those of TIME • If II value contains a colon (:), it is assumed to be a clock time (hh:min) Hands on session

  12. Building a control stream and data file • Record 4: the pharmacokinetic model to be used (PREDPP) • PREDPP subroutines • ADVAN1: One compartment model with intravenous administration • ADVAN2: One compartment model with first order administration • ADVAN3: Two compartment model with intravenous administration • ADAVN4: Two compartment model with first order administration • ADVAN5 & 7: General linear models • ADVAN6, 8 & 9: General nonlinear models (Differential equations) • ADVAn10: One Compartment Model with Michaelis-Menten Elimination • ADVAN11: Three compartment model with intravenous administration • ADVAN12: Three compartment model with first order administration Hands on session

  13. Building a control stream and data file • Record 5: Specification of the PK parameters. • 3 lines specifying CL, V and S1 are so called “abbreviated code” • The entire block is called $PK block • The amount A in the observation compartment at the time of observation, divided by the value of a parameter S, is used as the prediction. • In this example A (mg) in CENTRAL compartment will be divided by S1 to match units for observations (ng/mL) and units for predictions (ng/mL). With out scaling factor, the predictions will be passed to $ERROR as mg/L or ug/mL Hands on session

  14. Building a control stream and data file • If we did not use TRANS2 in this example • The abbreviated code would need code for K, the rate constant of elimination (K=CL/V) • PREDPP needs the values of microconstants, rather than physiological-based pharmacokinetic parameters such as clearance • See ..\help\trans(1-6).ppp for the choice of translator routines • TRANS2 Used with ADVAN1 and ADVAN2. • TRANS3 Used with ADVAN3 and ADVAN4. • TRANS4 Used with ADVAN3, ADVAN4, ADVAN11, ADVAN12 • TRANS5 Used with ADVAN3 and ADVAN4. • TRANS6 Used with ADVAN3, ADVAN4, ADVAN11, ADVAN12 Hands on session

  15. The (user) choice of the model in the NONMEM ouput Hands on session

  16. Building a control stream and data file • Record 6: user’s specification of the (statistical) model for the lack of fit of the pharmacokinetic model to the data • The model is specified in the abbreviated code • the $ERROR record, along with this line of abbreviated code is called the $ERROR block Hands on session

  17. Building a control stream and data file • Record 7: Information on possible values of THETA (Format: lower bound, initial estimate, upper bound) • Record 8: Information on initial estimate of variance of ETA • Record 9: Information on initial estimate of variance of ERR In the current specification, upper bound is understood to be unlimited Hands on session

  18. Building a control stream and data file • Record 10: Instructions for the NONMEM Estimation Step • METHOD • For example; 0 (FIRST ORDER (FO) method; requires POSTHOC option to get individual estimates); 1 (FIRST ORDER CONDITIONAL ESTIMATION (FOCE)); HYBRID (Use conditional estimates for the etas during the computation of the objective function, with the exception of those etas listed in the ZERO option) Review $ESTIMATION from NONMEM help folder ($estimat.ctl) Hands on session

  19. Building a control stream and data file • Record 11: Instructions for NONMEM Covariance Step • Record 12: Requests that NONMEM generate a table Hands on session

  20. NONMEM output Documentation Hands on session

  21. NONMEM output Scaled Transformed Parameters (STP) Search Summary Gradient vector of the objective function with respect to the STP Objective function values • Note that gradient vector at the last iteration is smaller than that at the 0th iteration • Gradient equal zero or unexpectedly large gradient usually means problem with NONMEM estimation- For example, over parameterization or numerical constraints etc. Hands on session

  22. NONMEM output • Minimum objective function value: a goodness of fit statistic • For example sum of squares (and as with a sum of squares, the lower the value, the better the fit) Hands on session

  23. NONMEM output Parameter estimates and standard error estimates CL and V estimates: compare that to your homework mean CLi and Vi (1.c) Inter individual variability (IIV) in CL and V: compare square root of that to homework estimate of SD. (1st level random effect) (1.c) Residual variance estimates (2nd level random effect) Note that all estimates (including IIV and residual variance) have standard error estimated. “Variability versus Uncertainty” Slide 9 of 57 Hands on session

  24. NONMEM output Basic output of NONMEM's Covariance Step. Under asymptotic theory, describes the variability under the assumed model of the parameter estimates across (imagined) replicated data sets, using the design of the real data set. Review covarian.out The correlation matrix is the variance-covariance matrix in correlation form Correlation between TH1 and TH2 = 0.00718/ (sqrt(0.00387)*sqrt(0.386)) Review correlat.out Hands on session

  25. Model diagnostics • We will use CENSUS (http://census.sourceforge.net/) for this exercise but learning some programming language (for example, R/S-Plus/SAS etc.) will widen your skills sets and offer flexibility CENSUS Snapshot Double click the chicken! Hands on session

  26. Model diagnostics Alternatively, click • Create a working database • File > New > • Browse to intended working folder and click “OK” • Import the run into CENSUS environment • Runs > Import Run… • Browse to NONMEM output and click “Open” Click each tab and explore Hands on session

  27. Model diagnostics • Select the Run (only one here) and click • Here are graphic options available Hands on session

  28. Model diagnostics Compare these plots to your homework plots- 1.h.i and 1.h.ii Hands on session

  29. Model diagnostics Compare these plots to your homework plots- 1.d.i Hands on session

  30. Model diagnostics Compare these plots to your homework plots- 1.h.iii and 1.h.iv Hands on session

  31. Model diagnostics Compare these plots to your homework plots- 1.h.iii and 1.h.iv Hands on session

  32. Model diagnostics Compare these plots to your homework plots- 1.e.i Only BW plots are shown here Hands on session

  33. For the next session • One compartment PK model for oral administration (HW#3) and two compartment PK model for IV administration (HW#4) • Submit control records and NONMEM output • Summarize PK parameters • Review covariate model (for HW#2) Will be available by Tuesday (October 14th) Hands on session

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