MuSyC_model: Fit a MuSyC Synergy Model

In maomlab/BayesPharma: Tools for Bayesian Analysis of Non-Linear Pharmacology Models

Fit a MuSyC Synergy Model

Description

The MuSyC synergy model is a bivariate functional form with Bliss and Loewe synergy models models as special cases described in (Meyer, et al., 2019) and (Wooten, et al., 2021).

The functional form is

    <response> ~ MuSyC(
      <treatment 1> - <logd1scale>,
      <treatment 2> - <logd2scale>,
      logE0,
      logC1, logE1, h1,
      logC2, logE2, h2,
      logE3, logalpha)

See MuSyC_robust() for the full mathematical description of the MuSyC function. By default the observed data (and therefore should be columns in the input data data.frame) are

• ⁠<treatment 1>⁠: logd1, the log10 of the dose as a molar concentration of treatment 1

• ⁠<treatment 2>⁠: logd2, the log10 of the dose as a molar concentration of treatment 2

• ⁠<response>⁠: response, with unspecified units

The logd1scale and logd2scale are used to center ⁠<treatment 1>⁠ and ⁠<treatment 2>⁠ to make fitting more numerically stable. If they are not in the input data, then they are taken to be the mean of ⁠<treatment 1>⁠ and ⁠<treatment 2>⁠ respectively.

The modeled parameters are

• logE0: the ⁠log(<response>)⁠ when ⁠<treatment 1> = 0⁠ and ⁠<treatment 2> = 0⁠

• logC1: the ⁠log(<treatment 1>)⁠ where when ⁠<treatment 2> = 0⁠, the ⁠<response>⁠ is halfway between E0 and E1

• logE1: the log(response) when ⁠<treatment 1> => Inf⁠ and ⁠<treatment 2> = 0⁠

• h1: the hill slope of the response with respect to ⁠<treatment 1>⁠ when ⁠<treatment 1> = C1⁠ and ⁠<treatment 2> = 0⁠. See MuSyC_hi_to_si() and MuSyC_si_to_hi() for converting between the slope (si) and hill slope (hi).

• logC2: the ⁠log(<treatment 2>)⁠ where when ⁠<treatment 1> = 0⁠, the ⁠<response>⁠ is halfway between E0 and E2

• logE2: the log(response) when ⁠<treatment 2> => Inf⁠ and ⁠<treatment 1> = 0⁠

• h2: the hill slope of the response with respect to ⁠<treatment 2>⁠ when ⁠<treatment 2> = C2⁠ and ⁠<treatment 1> = 0⁠. See MuSyC_hi_to_si() and MuSyC_si_to_hi() for converting between the slopec(si) and hill slope (hi).

• logE3: the log(response) when ⁠<treatment1 1> => Inf⁠ and ⁠<treatment 2> => Inf⁠, modeling the synergistic efficacy

• logalpha: the log of the synergistic potency alpha. When alpha > 1 the treatments are synergistic so that ⁠<treatment 1>`` shifts the response due to ⁠<treatment 2>⁠to lower doses and visa versa. When⁠alpha < 1⁠the treatments are antagonistic so that⁠<treatment 1>⁠shifts the response to⁠<treatment 2>' to higher doses and vise versa

Usage

MuSyC_model(
  data,
  prior = MuSyC_prior(),
  init = MuSyC_init(),
  formula = MuSyC_formula(),
  control = list(adapt_delta = 0.99, max_treedepth = 12),
  stanvars = c(MuSyC_stanvar(), MuSyC_genquant()),
  expose_functions = TRUE,
  ...
)

Arguments

data	data.frame of observed data. It must contain columns for the treatment, response and any additional predictors specified in the formula. See sigmoid_agonist_formula() for more details.
prior	brms::brmsprior() for the model parameters. To create a prior for the MuSyC model, use the MuSyC_prior() function.
init	function giving the initial values for the parameters. To specify the initial values, use the MuSyC_init() function.
formula	bpformula object. To create a formula for the MuSyC model, use the MuSyC_formula() function.
control	a named list of arguments to control the sampler's behavior. Adding max_treedepth and giving a greater value than 10 can improve model convergence.
stanvars	stanvars code for the MuSyC model.
expose_functions	logical. Expose the sigmoid function used in the model. This is needed e.g. for brms::loo_compare()
...	additional arguments passed to brms::brm()

References

Meyer, D.J., Wooten, D.J., Paudel B.B., Bauer, J., Hardeman, K.N., Westover, D., Lovly, C.M., Harris, L.A., Tyson D.R., Quaranta, V., Quantifying Drug Combination Synergy along Potency and Efficacy Axes, Cell Syst. 8, 2 (2019). https://doi.org/10.1016/j.cels.2019.01.003

Wooten, D.J., Meyer, C.T., Lubbock, A.L.R. et al. MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery. Nat Commun 12, 4607 (2021). https://doi.org/10.1038/s41467-021-24789-z

maomlab/BayesPharma documentation built on Aug. 24, 2024, 8:45 a.m.