Transcript Leveraging Prior Knowledge in Guiding Drug Development and
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)
October 09, 2008 Hands on session [email protected]
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Where to find help to learn NONMEM software?
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Depending on your installation version
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Version VI- C:\nmvi\guides and C:\nmvi\help Version V- C:\nmv\guides and C:\nmv\help NMUSERS
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Listserve: http://www.cognigencorp.com/nonmem/nm/ ACCP website
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http://www.accp1.org/pharmacometrics/index.html
NONMEM VI features
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http://www.ecpag.org/presentations/2006/0725/1_TomLudden_NONM EM_VI.pdf
October 09, 2008 Hands on session [email protected]
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Introduction to NONMEM
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What is NONMEM?
– A computer program to analyze data using a
NON
linear
M
ixed
E
ffects
M
odel • 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
October 09, 2008 Hands on session [email protected]
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NONMEM Architecture
File of NM-TRAN records Begins with “$” Data File ASCII format NM-TRAN Several Input files for NONMEM NONMEM PREDPP ADVAN and TRANS PK ERROR NONMEM Output October 09, 2008 Hands on session
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Two major components are distributed with NONMEM
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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
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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 October 09, 2008 Hands on session [email protected]
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Data file (passed to NM-TRAN preprocessor)
Data record Data item
When using PREDPP, data preprocessor can supply missing items such as MDV even if user does not supply in dataset/$INPUT record
Note:
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TIME should be sorted chronologically and records are added according to
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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 October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Record 1- description of the problem
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Record 2- location of the dataset. If not the same directory, requires file path
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NONMEM VI incorporates
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More flexible use of IGNORE option- For example, IGNORE=(BW.GT.100) Adds ACCEPT option- For example, ACCEPT=(BW.LE.100) October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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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?
October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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ID- Identification number TIME- Time associated with the event described in the data record AMT- Dose Amount (AMT) data item for PREDPP
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Dose given at the specified TIME CONCNGML- Dependent Variable (DV)
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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) October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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What if an additional of 50 mg was given at 12 hr?
Method 1:
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Explicitly specify dosing at the specified TIME Need additional record if dosing and observation occurred at identical times Observed Concentrations October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Method 2:
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Use of ADDL (Additional Dose data item for PREDPP) and II (Interdose Interval data item for PREDPP)
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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) October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Record 4: the pharmacokinetic model to be used (PREDPP)
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PREDPP subroutines
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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 October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Record 5: Specification of the PK parameters.
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3 lines specifying CL, V and S1 are so called “abbreviated code” The entire block is called $PK block
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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.
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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 October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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If we did not use TRANS2 in this example
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The abbreviated code would need code for K, the rate constant of elimination (K=CL/V)
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PREDPP needs the values of microconstants, rather than physiological-based pharmacokinetic parameters such as clearance
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See ..\help\trans(1-6).ppp for the choice of translator routines
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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 October 09, 2008 Hands on session [email protected]
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The (user) choice of the model in the NONMEM ouput
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Building a control stream and data file
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Record 6: user’s specification of the (statistical) model for the lack of fit of the pharmacokinetic model to the data
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The model is specified in the abbreviated code the $ERROR record, along with this line of abbreviated code is called the $ERROR block October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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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 October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Record 10: Instructions for the NONMEM Estimation Step
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METHOD
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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) October 09, 2008 Hands on session [email protected]
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Building a control stream and data file
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Record 11: Instructions for NONMEM Covariance Step Record 12: Requests that NONMEM generate a table October 09, 2008 Hands on session [email protected]
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NONMEM output
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NONMEM output
Scaled Transformed Parameters (STP) Gradient vector of the objective function with respect to the STP Objective function values
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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. October 09, 2008 Hands on session [email protected]
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NONMEM output
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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)
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NONMEM output
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. (1 st level random effect) (1.c) Residual variance estimates (2 nd level random effect) October 09, 2008 Hands on session Note that all estimates (including IIV and residual variance) have standard error estimated.
“Variability versus Uncertainty” Slide 9 of 57
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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
October 09, 2008 Hands on session [email protected]
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Model diagnostics
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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!
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Model diagnostics
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Create a working database
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File > New >
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Browse to intended working folder and click “OK” Import the run into CENSUS environment
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Runs > Import Run…
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Browse to NONMEM output and click “Open” Alternatively, click October 09, 2008 Click each tab and explore Hands on session [email protected]
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Model diagnostics
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Select the Run (only one here) and click
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Here are graphic options available October 09, 2008 Hands on session [email protected]
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Model diagnostics
Compare these plots to your homework plots- 1.h.i and 1.h.ii
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Model diagnostics
Compare these plots to your homework plots- 1.d.i
October 09, 2008 Hands on session [email protected]
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Model diagnostics
Compare these plots to your homework plots 1.h.iii and 1.h.iv
October 09, 2008 Hands on session [email protected]
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Model diagnostics
Compare these plots to your homework plots 1.h.iii and 1.h.iv
October 09, 2008 Hands on session [email protected]
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Model diagnostics
Compare these plots to your homework plots- 1.e.i
Only BW plots are shown here October 09, 2008 Hands on session [email protected]
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For the next session
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One compartment PK model for oral administration (HW#3) and two compartment PK model for IV administration (HW#4)
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Submit control records and NONMEM output Summarize PK parameters Review covariate model (for HW#2)
Will be available by Tuesday (October 14 th )
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