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Analyze in vitro CYP induction data

indmod.Rd

[Experimental]

For each hepatocyte donor and CYP isoform, fit a hyperbolic Emax model to vehicle-normalized mRNA fold-change (FOLD) from an induction_experiment.

Usage

indmod(x, use_emax_obs = FALSE, individual_donors = TRUE)

Arguments

x

An induction_experiment object. A FOLD column is required.

use_emax_obs

If TRUE (default), constrain emax to the observed maximum fold-increase. If FALSE, emax is fitted.

individual_donors

If TRUE (default), fold_plot facets by donor and isoform. If FALSE, facets by isoform and colours donors.

Value

A named list:

  • data Nested tibble: raw curve, nlsLM fit, tidy coefficients, and emax_obs for each donor and isoform.

  • fold_plot ggplot of observed FOLD and the fitted curves.

  • ind_param Tidy coefficient table (term, estimate, …) by isoform and donor. With the default use_emax_obs = TRUE the only fitted term is ec50; emax_obs is a separate column.

  • downregulation Tibble flagging donor/object combinations with suspected downregulation.

Details

Fit Emax / EC50 curves to in vitro CYP mRNA induction

Only SAMPLE == "test" rows with CONC > 0 are used. The model is

$$f(C) = 1 + \frac{E_{max} \cdot C}{EC_{50} + C}$$

which is an Emax relationship with baseline 1 (vehicle) and Hill slope 1. Emax is fold-increase (fold-change minus 1), not fold-change. Observed Emax is max(FOLD) - 1.

By default (use_emax_obs = TRUE) that observed value is held fixed and only EC50 is estimated. If use_emax_obs = FALSE, both Emax and EC50 are estimated.

The fit assumes fold-change rises with concentration. Concentration- dependent down-regulation (see induction_downregulation()) is not modelled and will yield unusable parameters.

static_cyp_induction_risk() treats emax >= 2 as ICH ≥ 2-fold induction. Values from this function are fold-increase, so a 2.4-fold curve has emax 1.4 and would be called no risk if passed through unchanged.

See also

induction_experiment(), induction_downregulation()

Examples

indmod(examplinib_in_vitro_ind)
#> $downregulation
#> # A tibble: 6 × 8
#>   OBJECT DONOR     n min_fold max_fold fold_maxc   rho downregulation
#>   <chr>  <chr> <int>    <dbl>    <dbl>     <dbl> <dbl> <lgl>         
#> 1 CYP1A2 A         4    1.38      7.37      7.37 1     FALSE         
#> 2 CYP1A2 B         4    1.24      5.09      5.09 1     FALSE         
#> 3 CYP1A2 C         4    1.56      4.15      4.15 1     FALSE         
#> 4 CYP3A4 A         7    0.904     6.33      6.33 1     FALSE         
#> 5 CYP3A4 B         8    0.655     2.31      2.31 0.619 FALSE         
#> 6 CYP3A4 C         8    0.648     1.58      1.58 0.238 FALSE         
#> 
#> $data
#> # A tibble: 6 × 7
#> # Rowwise:  DONOR, OBJECT, ID
#>   DONOR OBJECT ID                     data emax_obs mod    modpar          
#>   <chr> <chr>  <chr>    <list<tibble[,5]>>    <dbl> <list> <list>          
#> 1 A     CYP1A2 CYP1A2_A            [4 × 5]    6.37  <nls>  <tibble [2 × 5]>
#> 2 A     CYP3A4 CYP3A4_A            [8 × 5]    5.33  <nls>  <tibble [2 × 5]>
#> 3 B     CYP1A2 CYP1A2_B            [4 × 5]    4.09  <nls>  <tibble [2 × 5]>
#> 4 B     CYP3A4 CYP3A4_B            [8 × 5]    1.31  <nls>  <tibble [2 × 5]>
#> 5 C     CYP1A2 CYP1A2_C            [4 × 5]    3.15  <nls>  <tibble [2 × 5]>
#> 6 C     CYP3A4 CYP3A4_C            [8 × 5]    0.585 <nls>  <tibble [2 × 5]>
#> 
#> $fold_plot

#> 
#> $ind_param
#> # A tibble: 12 × 8
#>    OBJECT DONOR term  emax_obs estimate std.error statistic  p.value
#>    <chr>  <chr> <chr>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1 CYP1A2 A     ec50     6.37     37.3     29.2      1.28   0.329   
#>  2 CYP1A2 A     emax     6.37     30.1     19.0      1.58   0.254   
#>  3 CYP1A2 B     ec50     4.09      8.71     1.92     4.53   0.0455  
#>  4 CYP1A2 B     emax     4.09      7.62     0.917    8.31   0.0142  
#>  5 CYP1A2 C     ec50     3.15      2.00     0.168   11.9    0.00696 
#>  6 CYP1A2 C     emax     3.15      3.75     0.108   34.8    0.000826
#>  7 CYP3A4 A     ec50     5.33    100     1166.       0.0857 0.935   
#>  8 CYP3A4 A     emax     5.33    171.    1938.       0.0880 0.933   
#>  9 CYP3A4 B     ec50     1.31    100     2811.       0.0356 0.973   
#> 10 CYP3A4 B     emax     1.31     20.9    562.       0.0371 0.972   
#> 11 CYP3A4 C     ec50     0.585   100     5760.       0.0174 0.987   
#> 12 CYP3A4 C     emax     0.585     8.64   477.       0.0181 0.986   
#> 

indmod(induction_experiment(examplinib_in_vitro_ind, "examplinib"))
#> $downregulation
#> # A tibble: 6 × 8
#>   OBJECT DONOR     n min_fold max_fold fold_maxc   rho downregulation
#>   <chr>  <chr> <int>    <dbl>    <dbl>     <dbl> <dbl> <lgl>         
#> 1 CYP1A2 A         4    1.38      7.37      7.37 1     FALSE         
#> 2 CYP1A2 B         4    1.24      5.09      5.09 1     FALSE         
#> 3 CYP1A2 C         4    1.56      4.15      4.15 1     FALSE         
#> 4 CYP3A4 A         7    0.904     6.33      6.33 1     FALSE         
#> 5 CYP3A4 B         8    0.655     2.31      2.31 0.619 FALSE         
#> 6 CYP3A4 C         8    0.648     1.58      1.58 0.238 FALSE         
#> 
#> $data
#> # A tibble: 6 × 7
#> # Rowwise:  DONOR, OBJECT, ID
#>   DONOR OBJECT ID                     data emax_obs mod    modpar          
#>   <chr> <chr>  <chr>    <list<tibble[,5]>>    <dbl> <list> <list>          
#> 1 A     CYP1A2 CYP1A2_A            [4 × 5]    6.37  <nls>  <tibble [2 × 5]>
#> 2 A     CYP3A4 CYP3A4_A            [8 × 5]    5.33  <nls>  <tibble [2 × 5]>
#> 3 B     CYP1A2 CYP1A2_B            [4 × 5]    4.09  <nls>  <tibble [2 × 5]>
#> 4 B     CYP3A4 CYP3A4_B            [8 × 5]    1.31  <nls>  <tibble [2 × 5]>
#> 5 C     CYP1A2 CYP1A2_C            [4 × 5]    3.15  <nls>  <tibble [2 × 5]>
#> 6 C     CYP3A4 CYP3A4_C            [8 × 5]    0.585 <nls>  <tibble [2 × 5]>
#> 
#> $fold_plot

#> 
#> $ind_param
#> # A tibble: 12 × 8
#>    OBJECT DONOR term  emax_obs estimate std.error statistic  p.value
#>    <chr>  <chr> <chr>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1 CYP1A2 A     ec50     6.37     37.3     29.2      1.28   0.329   
#>  2 CYP1A2 A     emax     6.37     30.1     19.0      1.58   0.254   
#>  3 CYP1A2 B     ec50     4.09      8.71     1.92     4.53   0.0455  
#>  4 CYP1A2 B     emax     4.09      7.62     0.917    8.31   0.0142  
#>  5 CYP1A2 C     ec50     3.15      2.00     0.168   11.9    0.00696 
#>  6 CYP1A2 C     emax     3.15      3.75     0.108   34.8    0.000826
#>  7 CYP3A4 A     ec50     5.33    100     1166.       0.0857 0.935   
#>  8 CYP3A4 A     emax     5.33    171.    1938.       0.0880 0.933   
#>  9 CYP3A4 B     ec50     1.31    100     2811.       0.0356 0.973   
#> 10 CYP3A4 B     emax     1.31     20.9    562.       0.0371 0.972   
#> 11 CYP3A4 C     ec50     0.585   100     5760.       0.0174 0.987   
#> 12 CYP3A4 C     emax     0.585     8.64   477.       0.0181 0.986   
#> 
indmod(induction_experiment(examplinib_in_vitro_ind1, "examplinib"))
#> Warning: Check for down-regulation of CYP1A2, CYP2B6 and CYP3A4 by examplinib
#> $downregulation
#> # A tibble: 9 × 8
#>   OBJECT DONOR     n min_fold max_fold fold_maxc   rho downregulation
#>   <chr>  <chr> <int>    <dbl>    <dbl>     <dbl> <dbl> <lgl>         
#> 1 CYP1A2 A         4    0.087    0.951     0.087  -1   TRUE          
#> 2 CYP1A2 B         4    0.188    1.54      0.188  -0.8 TRUE          
#> 3 CYP1A2 C         4    0.072    1.09      0.072  -1   TRUE          
#> 4 CYP2B6 A         4    0.188    1.07      0.188  -0.8 TRUE          
#> 5 CYP2B6 B         4    0.341    1.36      0.341  -0.8 TRUE          
#> 6 CYP2B6 C         4    0.107    0.978     0.107  -1   TRUE          
#> 7 CYP3A4 A         4    0.285    0.973     0.285  -0.2 TRUE          
#> 8 CYP3A4 B         4    0.293    1.79      0.293  -0.8 TRUE          
#> 9 CYP3A4 C         4    0.286    1.06      0.286  -0.8 TRUE          
#> 
#> $data
#> # A tibble: 9 × 7
#> # Rowwise:  DONOR, OBJECT, ID
#>   DONOR OBJECT ID                     data emax_obs mod    modpar          
#>   <chr> <chr>  <chr>    <list<tibble[,4]>>    <dbl> <list> <list>          
#> 1 A     CYP1A2 CYP1A2_A            [4 × 4]  -0.0490 <nls>  <tibble [2 × 5]>
#> 2 A     CYP2B6 CYP2B6_A            [4 × 4]   0.0690 <nls>  <tibble [2 × 5]>
#> 3 A     CYP3A4 CYP3A4_A            [4 × 4]  -0.0270 <nls>  <tibble [2 × 5]>
#> 4 B     CYP1A2 CYP1A2_B            [4 × 4]   0.535  <nls>  <tibble [2 × 5]>
#> 5 B     CYP2B6 CYP2B6_B            [4 × 4]   0.356  <nls>  <tibble [2 × 5]>
#> 6 B     CYP3A4 CYP3A4_B            [4 × 4]   0.79   <nls>  <tibble [2 × 5]>
#> 7 C     CYP1A2 CYP1A2_C            [4 × 4]   0.0920 <nls>  <tibble [2 × 5]>
#> 8 C     CYP2B6 CYP2B6_C            [4 × 4]  -0.0220 <nls>  <tibble [2 × 5]>
#> 9 C     CYP3A4 CYP3A4_C            [4 × 4]   0.0600 <nls>  <tibble [2 × 5]>
#> 
#> $fold_plot

#> 
#> $ind_param
#> # A tibble: 18 × 8
#>    OBJECT DONOR term  emax_obs estimate std.error statistic p.value
#>    <chr>  <chr> <chr>    <dbl>    <dbl>     <dbl>     <dbl>   <dbl>
#>  1 CYP1A2 A     ec50   -0.0490    5.16      3.21     1.61    0.249 
#>  2 CYP1A2 A     emax   -0.0490   -1.44      0.388   -3.72    0.0652
#>  3 CYP1A2 B     ec50    0.535   100      3575.       0.0280  0.980 
#>  4 CYP1A2 B     emax    0.535    -7.92    260.      -0.0304  0.978 
#>  5 CYP1A2 C     ec50    0.0920    5.66      5.95     0.951   0.442 
#>  6 CYP1A2 C     emax    0.0920   -1.54      0.730   -2.11    0.169 
#>  7 CYP2B6 A     ec50    0.0690   14.6      31.6      0.463   0.689 
#>  8 CYP2B6 A     emax    0.0690   -2.09      2.92    -0.716   0.548 
#>  9 CYP2B6 B     ec50    0.356   100      2850.       0.0351  0.975 
#> 10 CYP2B6 B     emax    0.356    -6.55    172.      -0.0382  0.973 
#> 11 CYP2B6 C     ec50   -0.0220    6.96      2.41     2.89    0.102 
#> 12 CYP2B6 C     emax   -0.0220   -1.54      0.262   -5.88    0.0277
#> 13 CYP3A4 A     ec50   -0.0270    0         0.155    0       1     
#> 14 CYP3A4 A     emax   -0.0270   -0.300     0.234   -1.28    0.328 
#> 15 CYP3A4 B     ec50    0.79      0         0.449    0       1     
#> 16 CYP3A4 B     emax    0.79      0.212     0.478    0.443   0.701 
#> 17 CYP3A4 C     ec50    0.0600  100      1417.       0.0706  0.950 
#> 18 CYP3A4 C     emax    0.0600   -6.83     88.9     -0.0768  0.946 
#>