CYP perpetration risk as per mechanistic-static modeling

CYP perpetration risk as per mechanistic-static modeling

Usage

mech_stat_cyp_risk(
  perp,
  cyp_inh,
  cyp_ind,
  cyp_tdi = NULL,
  d = 1,
  include_induction = TRUE,
  substr = cyp_reference_substrates,
  cyp_kdeg = cyp_turnover
)

Arguments

perp

The perpetrator object.

cyp_inh

CYP inhibition data as data frame. The following fields are expected:

• 'name' The name of the perpetrator compound.

• 'cyp' The CYP enzyme as (upper case) character.

• 'ki' The \(K_i\) in \(\mu M\) as numeric.

• 'source' Optional source information as character.

cyp_ind

The CYP induction data as data frame. The following fields are expected:

• 'name' The name of the perpetrator compound as character.

• 'cyp' The CYP enzyme as (upper case) character.

• 'emax' The \(E_{max}\), i.e., the maximum induction effect determined in vitro as numeric.

• 'ec50' The \(EC_{50}\) in \(\mu M\) as numeric.

• 'maxc' The maximal concentration in \(\mu M\) tested in the in vitro assay as numeric.

• 'source' Optional source information as character.

cyp_tdi

The CYP TDI data as data frame. The following fields are expected:

• 'name' The perpetrator compound name as character.

• 'cyp' The CYP enzyme as character.

• 'ki' The \(K_I\) in \(\mu M\) as numeric.

• 'kinact' The \(k_{inact}\) in 1/h as numeric.

• 'source' Optional source information as character,

d

Scaling factor, defaults to 1.

include_induction

Switch to define whether induction effects should be included in the calculation (C-terms as per the FDA guideline)

substr

The CYP probe substrates to be used as data frame, defaults to cyp_reference_substrates. The data frame is expected to have the following fields:

• 'cyp' The CYP enzyme as (upper case) character.

• 'substrate' The substrate name as character.

• 'fgut' The fraction of the drug escaping gut metabolism.

• 'fm' The fraction of the drug that undergoes hepatic metabolism.

• 'fmcyp' The fraction metabolized by the respective CYP enzyme.

cyp_kdeg

The CYP turnover data as data frame. Defaults to the built-in reference data, cyp_turnover.

Value

A data frame.

Details

In this approach, AUC ratios for CYP-specific probe substrates are calculated based on their known intestinal and hepatic metabolism. Direct (competitive) and time-dependent inhibition terms, as well as enzyme induction terms are considered. For details, refer to section 7.5.1.2 of the ICH M12 guideline.

$$AUCR = \frac{1}{A_g*B_g*C_g* \left(1-F_g \right)+F_g} * \frac{1}{A_h*B_h*C_h*f_m+\left(1-f_m\right)}$$

The individual terms are:

Reversible inhibition

$$A_g = \frac{1}{1+\frac{I_g}{K_i}}$$

$$A_h = \frac{1}{1+\frac{I_h}{K_i}}$$

Time-dependent inhibition

$$B_g = \frac{k_{deg,g}}{k_{deg,g} + \frac{I_g*k_{inact}}{I_g+K_I}}$$

$$B_h = \frac{k_{deg,h}}{k_{deg,h} + \frac{I_h*k_{inact}}{I_h+K_I}}$$

Induction

$$C_g = 1 + \frac{d*E_{max}*I_g}{I_g+EC_{50}}$$

$$C_h = 1 + \frac{d*E_{max}*I_h}{I_h+EC_{50}}$$

with the hepatic inlet concentration \(I_h=I_{max,inlet,u}\) and the intestinal concentration \(I_g=I_{enteric,u}\), see key_concentrations().

\(d\) is a scaling factor for the CYP induction term with a standard value of 1. A different value can be used if warranted by prior experience with the experimental setup.

See also

mech_stat_cyp_risk_table()

cyp_reference_substrates

cyp_turnover

Examples

mech_stat_cyp_risk(examplinib_parent, examplinib_cyp_inhibition_data,
  examplinib_cyp_induction_data)
#>       cyp   substrate   kiu fgut   fm fmcyp         Ag        Ah Bg Bh       Cg
#> 1  CYP1A2  tizanidine    NA 1.00 0.95  0.98 1.00000000 1.0000000  1  1 1.000000
#> 2  CYP2B6        <NA>    NA   NA   NA    NA 1.00000000 1.0000000  1  1 1.000000
#> 3  CYP2C8 repaglinide 11.00 1.00 1.00  0.61 0.62551298 0.9827625  1  1 1.000000
#> 4  CYP2C9  S-warfarin  0.60 1.00 1.00  0.91 0.08350072 0.7566792  1  1 1.000000
#> 5 CYP2C19  omeprazole  0.25 1.00 1.00  0.87 0.03657341 0.5644126  1  1 1.000000
#> 6  CYP2D6 desipramine    NA 1.00 1.00  0.85 1.00000000 1.0000000  1  1 1.000000
#> 7  CYP3A4   midazolam 12.50 0.57 0.96  1.00 0.65494520 0.9847995  1  1 6.884569
#>         Ch      aucr  risk
#> 1 1.000000 1.0000000 FALSE
#> 2 1.000000        NA    NA
#> 3 1.000000 1.0106266 FALSE
#> 4 1.000000 1.2843927  TRUE
#> 5 1.000000 1.6102050  TRUE
#> 6 1.000000 1.0000000 FALSE
#> 7 1.773674 0.2321612  TRUE
mech_stat_cyp_risk(examplinib_parent, examplinib_cyp_inhibition_data,
  examplinib_cyp_induction_data, examplinib_cyp_tdi_data)
#>       cyp   substrate   kiu fgut   fm fmcyp         Ag        Ah        Bg
#> 1  CYP1A2  tizanidine    NA 1.00 0.95  0.98 1.00000000 1.0000000 1.0000000
#> 2  CYP2B6        <NA>    NA   NA   NA    NA 1.00000000 1.0000000 1.0000000
#> 3  CYP2C8 repaglinide 11.00 1.00 1.00  0.61 0.62551298 0.9827625 1.0000000
#> 4  CYP2C9  S-warfarin  0.60 1.00 1.00  0.91 0.08350072 0.7566792 1.0000000
#> 5 CYP2C19  omeprazole  0.25 1.00 1.00  0.87 0.03657341 0.5644126 1.0000000
#> 6  CYP2D6 desipramine    NA 1.00 1.00  0.85 1.00000000 1.0000000 1.0000000
#> 7  CYP3A4   midazolam 12.50 0.57 0.96  1.00 0.65494520 0.9847995 0.4348241
#>          Bh       Cg       Ch      aucr  risk
#> 1 1.0000000 1.000000 1.000000 1.0000000 FALSE
#> 2 1.0000000 1.000000 1.000000        NA    NA
#> 3 1.0000000 1.000000 1.000000 1.0106266 FALSE
#> 4 1.0000000 1.000000 1.000000 1.2843927  TRUE
#> 5 1.0000000 1.000000 1.000000 1.6102050  TRUE
#> 6 1.0000000 1.000000 1.000000 1.0000000 FALSE
#> 7 0.4757909 6.884569 1.773674 0.8446594 FALSE
mech_stat_cyp_risk(examplinib_parent, examplinib_cyp_inhibition_data,
  examplinib_cyp_induction_data, examplinib_cyp_tdi_data,
  include_induction = FALSE)
#>       cyp   substrate   kiu fgut   fm fmcyp         Ag        Ah        Bg
#> 1  CYP1A2  tizanidine    NA 1.00 0.95  0.98 1.00000000 1.0000000 1.0000000
#> 2  CYP2B6        <NA>    NA   NA   NA    NA 1.00000000 1.0000000 1.0000000
#> 3  CYP2C8 repaglinide 11.00 1.00 1.00  0.61 0.62551298 0.9827625 1.0000000
#> 4  CYP2C9  S-warfarin  0.60 1.00 1.00  0.91 0.08350072 0.7566792 1.0000000
#> 5 CYP2C19  omeprazole  0.25 1.00 1.00  0.87 0.03657341 0.5644126 1.0000000
#> 6  CYP2D6 desipramine    NA 1.00 1.00  0.85 1.00000000 1.0000000 1.0000000
#> 7  CYP3A4   midazolam 12.50 0.57 0.96  1.00 0.65494520 0.9847995 0.4348241
#>          Bh Cg Ch     aucr  risk
#> 1 1.0000000  1  1 1.000000 FALSE
#> 2 1.0000000  1  1       NA    NA
#> 3 1.0000000  1  1 1.010627 FALSE
#> 4 1.0000000  1  1 1.284393  TRUE
#> 5 1.0000000  1  1 1.610205  TRUE
#> 6 1.0000000  1  1 1.000000 FALSE
#> 7 0.4757909  1  1 2.948311  TRUE
mech_stat_cyp_risk(examplinib_parent, examplinib_cyp_inhibition_data, NULL)
#>       cyp   substrate   kiu fgut   fm fmcyp         Ag        Ah Bg Bh Cg Ch
#> 1  CYP1A2  tizanidine    NA 1.00 0.95  0.98 1.00000000 1.0000000  1  1  1  1
#> 2  CYP2B6        <NA>    NA   NA   NA    NA 1.00000000 1.0000000  1  1  1  1
#> 3  CYP2C8 repaglinide 11.00 1.00 1.00  0.61 0.62551298 0.9827625  1  1  1  1
#> 4  CYP2C9  S-warfarin  0.60 1.00 1.00  0.91 0.08350072 0.7566792  1  1  1  1
#> 5 CYP2C19  omeprazole  0.25 1.00 1.00  0.87 0.03657341 0.5644126  1  1  1  1
#> 6  CYP2D6 desipramine    NA 1.00 1.00  0.85 1.00000000 1.0000000  1  1  1  1
#> 7  CYP3A4   midazolam 12.50 0.57 0.96  1.00 0.65494520 0.9847995  1  1  1  1
#>       aucr  risk
#> 1 1.000000 FALSE
#> 2       NA    NA
#> 3 1.010627 FALSE
#> 4 1.284393  TRUE
#> 5 1.610205  TRUE
#> 6 1.000000 FALSE
#> 7 1.191612 FALSE