Table 1.

Different nonlinear model and parameter estimates that best describe the data corresponding to only one drug sensitive cell types based on the data obtained from LINCS.

Cells	Model	Parameters (Std. error*)	Death rate
Alpelisib-sensitive cells	Exponential decay model: E(C) = l + (u − l) · e−C/e	l=0.0126 h−1 (5 · 10−4) u=0.023 h−1 (2 · 10−4) e=3.047 μM (0.414)	0.0146 h−1
Neratinib-sensitive cells	5-parameter log-logistic function: $E\left(C\right)=E_{max}+\frac{E_0-E_{max}}{{\left[[1+{\left(\frac{C}{E{C}_{50}}\right)}^h\right]}^f}$	Emax = −0.02 h−1 (0.055) E0 = 0.0148 h−1 (1.3 · 10−4) h = 0.3 (0.055) EC50 = 0.63 μM (1.295) f = 0.086 (0.147)	0.0093 h−1
Trametinib-sensitive cells	4-parameter log-logistic function: $E\left(C\right)=E_{max}+\frac{E_0-E_{max}}{1+{\left(\frac{C}{E{C}_{50}}\right)}^h}$	Emax = 0.0098 h−1(0.001) E0 = 0.0122 h−1 (2.5· 10−4) h = 0.43 (0.284) EC50 = 0.7 μM (1.56)	0.0096 h−1
Dactolisib-sensitive cells	Exponential decay model: E(C) = l + (u − l) · e−C/k	l = 0.00531 h−1 (4.8 · 10−4) u=0.01480 h−1 (4.9 · 10−4) k=0.03495 μM (0.007)	0.0105 h−1
Lapatinib-sensitive cells	3-parameter Weibull function: $E\left(C\right)=E_0·e^{-e^{h(loglogC-loglogb)}}$	E0 = 0.0136 h−1 (4.2· 10−4) h = 2.32 (0.785) b= 30.38 μM (11.31)	0.008 h−1
Selumetinib-sensitive cells	4-parameter log-logistic function: $E\left(C\right)=E_{max}+\frac{E_0-E_{max}}{1+{\left(\frac{C}{E{C}_{50}}\right)}^h}$	Emax = 0.0142 h−1 (4.6· 10−5) E0 = 0.01432 h−1 (2.6· 10−5) h = 4.4 (5.6) EC50 = 2.13 μM (1.45)	0.0089 h−1
Sacaratinib-sensitive cells	4-parameter Weibull function: $E\left(C\right)=l+\left(u-l\right)·(1-e^{-e^{h(loglogC-loglogb)}}$	u = 0.0118 h−1 (1.1· 10−4) l = 0.011 h−1 (1.9· 10−4) h = −3.37 (2.91) b= 0.91 μM (0.196)	0.009 h−1
NVP-TAE684-sensitive cells	3-parameter log-normal model: E(C) = E0 · ϕ(h(log log C − log log b)) ϕ: cumulative distribution function of the standard normal distribution	E0 = 0.0127 h−1 (6.7· 10−4) h = −2.09 (2.37) b= 3.94 μM (0.906)	0.0092 h−1

^*Std. Error: Standard error