A toolkit to be used with mrgsolve for optimization of model parameters.
library(devtools)
install_github("mhismail/mrgoptim")This example shows a simultaneous fit of PK and PD data from five dose levels.
The data to be fit is an mrgsolve dataset. Required columns for fitting are:
- ID
- time
- evid
- cmt
- amt
- dv
data <- read.csv("inst/maintenance/data/optim-example.csv")
head(data). ID time cmt evid dv amt
. 1 1 0.00 1 1 NA 1000
. 2 1 0.04 1 0 49.73123 NA
. 3 1 0.04 2 0 4.91075 NA
. 4 1 0.10 1 0 31.21634 NA
. 5 1 0.10 2 0 5.49637 NA
. 6 1 0.25 1 0 24.10030 NA
Plot the data to get an idea of the profiles to be fit. cmt 1 is plasma concentration data and cmt 2 is PD data
ggplot(data, aes(x = time, y = dv, color = as.factor(ID))) +
geom_point() +
geom_line() +
facet_wrap("cmt") +
guides(color = FALSE)
The following model will be fit to these data:
- PK: 2 compartment model
- PD: Inhibitory Emax model with baseline
- Proportional error models for both PK and PD
code<-"
$PROB 2 cmt PK Model, Emax PD model
$PARAM
CL=10
VC = 20
VP = 20
Q=20
Emax = 60
BL = 50
EC50 = 10
gamma =1
sigma1 = 0.1
sigma2 = 0.1
$CMT X1 X2
$ODE
dxdt_X1 = -(Q+CL)/VC*X1+Q/VP*X2;
dxdt_X2 = Q/VC*X1-Q/VP*X2;
$TABLE
capture PK = X1/VC;
capture varPK = (PK*sigma1)*(PK*sigma1);
capture PD = BL-(pow(PK,gamma)*Emax)/(pow(PK,gamma)+pow(EC50,gamma));
capture varPD = (PD*sigma2)*(PD*sigma2);
capture ipred = NAN;
capture var = NAN;
if(self.cmt == 1) {
ipred = PK;
var = varPK;
}
if(self.cmt == 2) {
ipred = PD;
var = varPD;
}"
mod <- mcode("2cmtPK-Emax", code)Here, the predicted plasma concentrations, response, and variances were captured in the PK, PD, varPK, and varPD outputs, respectively. Predictions and variances are consolidated to a single column each. If cmt == 1 the predicted output, ipred, will be PK and prediction variance, var, varPK. If cmt == 2 the predicted output will be PD and prediction variance varPD.
Let's check how the initial parameter values fit the data.
out <- mod %>%
data_set(data) %>%
carry.out(cmt, dv) %>%
obsonly() %>%
mrgsim() %>%
as.data.frame()
ggplot(filter(out, cmt == 1), aes(x = time, y = ipred, color = as.factor(ID))) +
geom_line() +
geom_point(aes(y = dv)) +
guides(color = FALSE)
ggplot(filter(out, cmt == 2), aes(x = time, y = ipred, color = as.factor(ID))) +
geom_line() +
geom_point(aes(y = dv)) +
guides(color = FALSE)
Not terrible, should be good enough for initial estimates.
Now let's use mrgoptim to optimize the parameters and return parameter values and precision. Use the output, and var arguments to specify which columns in the model code correspond to the predicted values and variances. Specify which system parameters to optimize with the prms argument and variance parameters with the v_prms arguments.
fit <- mod %>%
data_set(data) %>%
mrgoptim(output = "ipred",
var = "var",
prms = c("CL",
"VC",
"VP",
"Q",
"Emax",
"BL",
"EC50",
"gamma"),
v_prms = c("sigma1", "sigma2"),
method = "newuoa")The function returns a list with some information about the optimization, the final objective function value (-LL), final parameter estimates, covariance and correlation matrices, CV percent, and output dataset.
print(fit). $par
. CL VC VP Q Emax
. 8.75373026 16.72026061 31.25670528 107.80938229 42.98965084
. BL EC50 gamma sigma1 sigma2
. 48.42448862 4.33966995 1.69630917 0.15724999 0.08954397
.
. $fval
. [1] 194.7881
.
. $feval
. [1] 4853
.
. $ierr
. [1] 0
.
. $msg
. [1] "Normal exit from newuoa"
.
. $cov
. [,1] [,2] [,3] [,4]
. [1,] 4.050254e-02 3.319504e-02 6.356155e-02 2.402288e-01
. [2,] 3.319504e-02 1.870666e+00 -1.321098e+00 -1.095217e+01
. [3,] 6.356155e-02 -1.321098e+00 2.732482e+00 9.718232e+00
. [4,] 2.402288e-01 -1.095217e+01 9.718232e+00 1.578101e+02
. [5,] -2.106478e-03 1.679978e-01 -2.395963e-01 2.567654e-02
. [6,] -2.375067e-03 1.133761e-01 -1.283697e-01 -2.769804e-01
. [7,] -1.543825e-02 -3.406453e-02 -6.295146e-02 -4.753888e-02
. [8,] -1.393394e-03 -6.280719e-03 2.534692e-02 6.536450e-03
. [9,] 5.673031e-04 1.083587e-03 2.025655e-03 6.986798e-03
. [10,] -1.240847e-10 -9.252575e-10 1.400150e-09 4.637098e-09
. [,5] [,6] [,7] [,8]
. [1,] -2.106478e-03 -2.375067e-03 -1.543825e-02 -1.393394e-03
. [2,] 1.679978e-01 1.133761e-01 -3.406453e-02 -6.280719e-03
. [3,] -2.395963e-01 -1.283697e-01 -6.295146e-02 2.534692e-02
. [4,] 2.567654e-02 -2.769804e-01 -4.753888e-02 6.536450e-03
. [5,] 5.136528e+00 4.429163e+00 -5.655071e-01 -2.385901e-01
. [6,] 4.429163e+00 3.970299e+00 -5.239931e-01 -1.875052e-01
. [7,] -5.655071e-01 -5.239931e-01 9.221572e-02 2.623165e-02
. [8,] -2.385901e-01 -1.875052e-01 2.623165e-02 1.640934e-02
. [9,] -1.981223e-09 -1.024343e-09 -2.812404e-04 3.630618e-10
. [10,] -5.144395e-04 -5.794714e-04 1.801624e-09 9.501174e-10
. [,9] [,10]
. [1,] 5.673031e-04 -1.240847e-10
. [2,] 1.083587e-03 -9.252575e-10
. [3,] 2.025655e-03 1.400150e-09
. [4,] 6.986798e-03 4.637097e-09
. [5,] -1.981223e-09 -5.144395e-04
. [6,] -1.024343e-09 -5.794714e-04
. [7,] -2.812404e-04 1.801624e-09
. [8,] 3.630618e-10 9.501174e-10
. [9,] 2.162539e-04 -1.058478e-12
. [10,] -1.058478e-12 6.788920e-05
.
. $cor
. [,1] [,2] [,3] [,4]
. [1,] 1.000000e+00 1.205963e-01 1.910622e-01 9.502032e-02
. [2,] 1.205963e-01 1.000000e+00 -5.843302e-01 -6.374333e-01
. [3,] 1.910622e-01 -5.843302e-01 1.000000e+00 4.679950e-01
. [4,] 9.502032e-02 -6.374333e-01 4.679950e-01 1.000000e+00
. [5,] -4.618288e-03 5.419641e-02 -6.395382e-02 9.018502e-04
. [6,] -5.922745e-03 4.160175e-02 -3.897375e-02 -1.106547e-02
. [7,] -2.526122e-01 -8.201659e-02 -1.254078e-01 -1.246175e-02
. [8,] -5.404895e-02 -3.584807e-02 1.197019e-01 4.061898e-03
. [9,] 1.916867e-01 5.387455e-02 8.333064e-02 3.782064e-02
. [10,] -7.483017e-08 -8.210397e-08 1.028006e-07 4.480006e-08
. [,5] [,6] [,7] [,8]
. [1,] -4.618288e-03 -5.922745e-03 -2.526122e-01 -5.404895e-02
. [2,] 5.419641e-02 4.160175e-02 -8.201659e-02 -3.584807e-02
. [3,] -6.395382e-02 -3.897375e-02 -1.254078e-01 1.197019e-01
. [4,] 9.018502e-04 -1.106547e-02 -1.246175e-02 4.061898e-03
. [5,] 1.000000e+00 9.807883e-01 -8.216763e-01 -8.218114e-01
. [6,] 9.807883e-01 1.000000e+00 -8.659872e-01 -7.346088e-01
. [7,] -8.216763e-01 -8.659872e-01 1.000000e+00 6.743379e-01
. [8,] -8.218114e-01 -7.346088e-01 6.743379e-01 1.000000e+00
. [9,] -5.944517e-08 -3.495842e-08 -6.297863e-02 1.927317e-07
. [10,] -2.754858e-02 -3.529556e-02 7.200479e-07 9.001843e-07
. [,9] [,10]
. [1,] 1.916867e-01 -7.483017e-08
. [2,] 5.387455e-02 -8.210396e-08
. [3,] 8.333064e-02 1.028006e-07
. [4,] 3.782064e-02 4.480005e-08
. [5,] -5.944518e-08 -2.754858e-02
. [6,] -3.495842e-08 -3.529556e-02
. [7,] -6.297863e-02 7.200479e-07
. [8,] 1.927317e-07 9.001843e-07
. [9,] 1.000000e+00 -8.735736e-09
. [10,] -8.735735e-09 1.000000e+00
.
. $CVPercent
. CL VC VP Q Emax BL EC50
. 2.299048 8.180034 5.288536 11.652277 5.271945 4.114780 6.997546
. gamma sigma1 sigma2
. 7.551626 9.351718 9.201613
.
. $fitted_data
. ID time cmt dv pred var
. 1 1 0.04 1 49.7312300 46.1003147 52.551973613
. 2 1 0.04 2 4.9107500 6.2016949 0.308385183
. 3 1 0.10 1 31.2163430 33.5938060 27.906133760
. 4 1 0.10 2 5.4963700 6.7302043 0.363186093
. 5 1 0.25 1 24.1003020 21.7211546 11.666674021
. 6 1 0.25 2 7.0547400 8.0622724 0.521179887
. 7 1 0.50 1 18.6064240 18.0427039 8.049788997
. 8 1 0.50 2 8.2386300 8.9546019 0.642932353
. 9 1 1.00 1 17.2169040 16.3015873 6.571144825
. 10 1 1.00 2 8.9477600 9.5524400 0.731646586
. 11 1 1.50 1 15.3247450 14.9250210 5.508218439
. 12 1 1.50 2 9.5888900 10.1443358 0.825125370
. 13 1 2.00 1 12.4596430 13.6658848 4.618030163
. 14 1 2.00 2 10.2978000 10.8088575 0.936768540
. 15 1 3.00 1 11.9853760 11.4573415 3.246003400
. 16 1 3.00 2 11.9391000 12.3792821 1.228750290
. 17 1 4.00 1 9.3736800 9.6057209 2.281608793
. 18 1 4.00 2 13.8959000 14.3004622 1.639731946
. 19 1 6.00 1 7.7204113 6.7518405 1.127263886
. 20 1 6.00 2 18.7456000 19.2287332 2.964654297
. 21 1 8.00 1 4.6017323 4.7458541 0.556942055
. 22 1 8.00 2 24.5406000 25.3016087 5.132973019
. 23 1 12.00 1 3.0028117 2.3447622 0.135949885
. 24 1 12.00 2 35.9189000 37.2331430 11.115579456
. 25 2 0.04 1 22.0138820 34.5752360 29.560485167
. 26 2 0.04 2 7.0795900 6.6702248 0.356741517
. 27 2 0.10 1 13.6405360 25.1953545 15.697200243
. 28 2 0.10 2 10.3125000 7.5058019 0.451717486
. 29 2 0.25 1 13.2921580 16.2908659 6.562504136
. 30 2 0.25 2 10.7249000 9.5565970 0.732283518
. 31 2 0.50 1 12.7862020 13.5320279 4.528006311
. 32 2 0.50 2 11.2202000 10.8878645 0.950513133
. 33 2 1.00 1 11.0903130 12.2261905 3.696268964
. 34 2 1.00 2 12.2981000 11.7614367 1.109158114
. 35 2 1.50 1 10.5310550 11.1937658 3.098372872
. 36 2 1.50 2 13.4954000 12.6122397 1.275431521
. 37 2 2.00 1 9.1342760 10.2494136 2.597641967
. 38 2 2.00 2 14.8131000 13.5511563 1.472398673
. 39 2 3.00 1 7.9227580 8.5930062 1.825876912
. 40 2 3.00 2 17.7958000 15.7041705 1.977437275
. 41 2 4.00 1 6.1166954 7.2042907 1.283404950
. 42 2 4.00 2 21.1754000 18.2189457 2.661455401
. 43 2 6.00 1 4.5425880 5.0638803 0.634085930
. 44 2 6.00 2 28.5158000 24.1319575 4.669364891
. 45 2 8.00 1 2.4414084 3.5593905 0.313279893
. 46 2 8.00 2 35.3294000 30.5095372 7.463524286
. 47 2 12.00 1 1.3998046 1.7585716 0.076471809
. 48 2 12.00 2 43.8288000 40.7868697 13.338698646
. 49 3 0.04 1 19.8496480 23.0501573 13.137993380
. 50 3 0.04 2 8.4846800 7.8244835 0.490889866
. 51 3 0.10 1 13.6405360 16.7969031 6.976533493
. 52 3 0.10 2 9.2260800 9.3672384 0.703551444
. 53 3 0.25 1 13.2921580 10.8605773 2.916668537
. 54 3 0.25 2 10.9996000 12.9239708 1.339259216
. 55 3 0.50 1 10.8070680 9.0213520 2.012447252
. 56 3 0.50 2 13.2726000 15.0731484 1.821715944
. 57 3 1.00 1 8.7865870 8.1507936 1.642786206
. 58 3 1.00 2 15.6679000 16.4209665 2.162071935
. 59 3 1.50 1 8.5621790 7.4625105 1.377054611
. 60 3 1.50 2 17.1228000 17.6889473 2.508861505
. 61 3 2.00 1 7.3310900 6.8329424 1.154507541
. 62 3 2.00 2 18.4746000 19.0396844 2.906646396
. 63 3 3.00 1 6.1963344 5.7286708 0.811500852
. 64 3 3.00 2 21.3042000 21.9587750 3.866241043
. 65 3 4.00 1 5.1034656 4.8028605 0.570402198
. 66 3 4.00 2 24.3006000 25.0853487 5.045602070
. 67 3 6.00 1 3.7901090 3.3759203 0.281815983
. 68 3 6.00 2 30.3706000 31.4400011 7.925703414
. 69 3 8.00 1 2.3484780 2.3729271 0.139235517
. 70 3 8.00 2 35.8188000 37.0646662 11.015212957
. 71 3 12.00 1 1.6561556 1.1723811 0.033987470
. 72 3 12.00 2 43.0923000 44.2130877 15.673803650
. 73 4 0.04 1 12.4596430 11.5250787 3.284498345
. 74 4 0.04 2 11.5892000 12.3212568 1.217258267
. 75 4 0.10 1 8.5621790 8.3984515 1.744133373
. 76 4 0.10 2 15.2531000 16.0108845 2.055433157
. 77 4 0.25 1 6.0380800 5.4302887 0.729167134
. 78 4 0.25 2 22.4137000 22.8909566 4.201463496
. 79 4 0.50 1 4.8461100 4.5106760 0.503111813
. 80 4 0.50 2 26.3215000 26.2253130 5.514600681
. 81 4 1.00 1 4.1493260 4.0753968 0.410696552
. 82 4 1.00 2 28.2763000 28.0740330 6.319494235
. 83 4 1.50 1 3.9400852 3.7312553 0.344263653
. 84 4 1.50 2 29.8210000 29.6685425 7.057731509
. 85 4 2.00 1 3.6458411 3.4164712 0.288626885
. 86 4 2.00 2 31.3240000 31.2314679 7.820913896
. 87 4 3.00 1 3.2874105 2.8643354 0.202875213
. 88 4 3.00 2 34.1655000 34.2052528 9.381198350
. 89 4 4.00 1 2.4100301 2.4014302 0.142600549
. 90 4 4.00 2 36.7227000 36.8945731 10.914345338
. 91 4 6.00 1 1.7898185 1.6879601 0.070453996
. 92 4 6.00 2 40.8320000 41.2137459 13.619365928
. 93 4 8.00 1 1.2141426 1.1864635 0.034808879
. 94 4 8.00 2 43.6434000 44.1355155 15.618852258
. 95 4 12.00 1 0.7236866 0.5861905 0.008496868
. 96 4 12.00 2 46.5103000 47.0305396 17.735058824
. 97 5 0.04 1 7.6211843 5.7625393 0.821124586
. 98 5 0.04 2 18.7133000 21.8571747 3.830546665
. 99 5 0.10 1 3.9913847 4.1992258 0.436033343
. 100 5 0.10 2 27.7209000 27.5292713 6.076620868
. 101 5 0.25 1 3.1622777 2.7151443 0.182291784
. 102 5 0.25 2 36.7104000 35.0550870 9.853143510
. 103 5 0.50 1 2.3790550 2.2553380 0.125777953
. 104 5 0.50 2 38.5164000 37.7704627 11.438717147
. 105 5 1.00 1 2.0903776 2.0376984 0.102674138
. 106 5 1.00 2 39.7587000 39.0894217 12.251554626
. 107 5 1.50 1 1.7668147 1.8656276 0.086065913
. 108 5 1.50 2 40.8483000 40.1368026 12.916898761
. 109 5 2.00 1 1.4741421 1.7082356 0.072156721
. 110 5 2.00 2 41.8187000 41.0913747 13.538609224
. 111 5 3.00 1 1.3121320 1.4321677 0.050718803
. 112 5 3.00 2 43.4296000 42.7358220 14.643902705
. 113 5 4.00 1 1.1090313 1.2007151 0.035650138
. 114 5 4.00 2 44.6577000 44.0566755 15.563101671
. 115 5 6.00 1 0.7720411 0.8439801 0.017613498
. 116 5 6.00 2 46.2530000 45.9075356 16.898208693
. 117 5 8.00 1 0.4484204 0.5932318 0.008702219
. 118 5 8.00 2 47.1037000 47.0029613 17.714265547
. 119 5 12.00 1 0.2379055 0.2930953 0.002124217
. 120 5 12.00 2 47.7698000 47.9844868 18.461816026
Lets check how the optimized parameters fit the data.
out_fit <- mod %>%
param(fit$par) %>%
carry.out(cmt, dv) %>%
data_set(data) %>%
obsonly() %>%
mrgsim() %>%
as.data.frame()
ggplot(filter(out_fit, cmt == 1), aes(x = time, y = ipred, color = as.factor(ID))) +
geom_line() +
geom_point(aes(y = dv)) +
guides(color = FALSE)
ggplot(filter(out_fit, cmt == 2), aes(x = time, y = ipred, color = as.factor(ID))) +
geom_line() +
geom_point(aes(y = dv)) +
guides(color = FALSE)
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