Initial code:
code <- "
$PARAM @annotated
TVCL:  0.9 : Clearance
TVV1: 10.0 : Central volume
V2  : 10.0 : Peripheral volume of distribution
Q   :  1.0 : Intercompartmental clearance

ETA1: 0 : Clearance (L/h)
ETA2: 0 : Central volume (L)

$PARAM @annotated @covariates
BW : 70 : Body weight (kg)

$OMEGA 0.3 0.3
$SIGMA
0.05 // proportional
0.1 // additive

$CMT @annotated
CENT  : Central compartment (mg/L)[ADM, OBS]
PERIPH: Peripheral compartment ()

$TABLE
double DV = (CENT/V1) *(1 + EPS(1)) + EPS(2);

$MAIN
double CL = TVCL * exp(ETA1 + ETA(1)) * pow(BW / 70, 1.2) ;
double V1 = TVV1 * exp(ETA2 + ETA(2)) ;
double K12 = Q / V1  ;
double K21 = Q / V2  ;
double K10 = CL / V1 ;

$ODE
dxdt_CENT   =  K21 * PERIPH - (K10 + K12) * CENT ;
dxdt_PERIPH =  K12 * CENT - K21 * PERIPH ;

$CAPTURE DV CL
"

my_model <- mcode("Example_model", code)

my_data <- data.frame(ID = 1, time = c(0,6,15,24), evid = c(1, rep(0,3)), cmt = 1, amt = c(100, rep(0,3)), 
                      rate = c(20, rep(0,3)), DV = c(NA, 3.9, 1.1, 2), mdv = c(1,0,0,1), BW = 90)
my_data

my_est <- mapbayest(my_model, data = my_data)


print(my_est)

plot(my_est)

Please modify the initial code to:
1.	Update the PK model to include non-linear elimination using a Michaelis-Menten equation. Change the parameters as follows: set TVCL to 7, TVV1 to 40, TVV2 to 30, TVQ to 50, Vmax to 0.0, and Km to 0.005.
2.	Read data from a file called "Hosseini_et_al_2018.txt". Use this data for the parameter estimation instead of the manually defined dataset.
3.	After performing Bayesian parameter estimation, capture and print the individual parameter estimates (CL, V1, V2, Q), their covariances, and the posterior distributions.
4.	Save the estimation results to a CSV file called "Results.csv".
5.	Include visualizations such as observed vs predicted concentration plots and allow for simulating different dosing regimens based on posterior parameter distributions.
6.	Eliminated BW. I do not need it.
