********** MODEL NAME model_1cpt_linear_abs1_racemix_mixture_2_enantiomers_and_metabolites ********** MODEL NOTES PK model for simulation of drug concentration in central compartment with following characteristics: Compartments: 6 gut, liver, rr, ss, metrr and metss Elimination : linear Absorption : first order (with lag time) Unit convention Dose: mg Concentration: ug/mL Time: hours The annotation of the parameter units is consistent with the given unit convention. Units of the inputs (dose) and outputs (concentration) in the dataset for parameter estimation need to match the unit convention. ********** MODEL STATES d/dt(Ad) = -K12*Ad + Fabs1*INPUT1 d/dt(Liver) = K12*Ad - K23*Liver - K24*Liver - K25*Liver - K26*Liver + K32*RR - K42*SS d/dt(RR) = K23*Liver - K32*RR - K30*RR d/dt(SS) = K24*Liver - K42*SS - K40*SS d/dt(RRmet) = K25*Liver - K50*RRmet d/dt(SSmet) = K26*Liver - K60*SSmet Ad(0) = 0 Liver(0) = 0 ********** MODEL PARAMETERS Fabs1 = 1 # Relative bioavailability (fraction) K12 = 1.4 # Absorption rate parameter (1/hour) Tlag1 = 0 # Absorption lag time (hours) CLRR = 7 # Apparent clearance RR (L/hour) VcRR = 35 # Apparent central volume RR (L) K23 = 0.4 # Transfer liver to RR K32 = 0.2 # Transfer RR to liver CLSS = 5 # Apparent clearance SS (L/hour) VcSS = 3.4 # Apparent central volume SS (L) K24 = 0.7 # Transfer liver to SS K42 = 0.5 # Transfer SS to liver K25 = 0.3 # Transfer liver to metabolite RR CLRRmet = 5.32 # Apparent clearance metabolite RR (L/hour) K26 = 0.2 # Transfer liver to metabolite SS CLSSmet = 3.2 # Apparent clearance metabolite SS (L/hour) ********** MODEL VARIABLES % Calculation of concentration in central compartment Cc = Liver VcRRmet = VcRR # AVOID IDENTIFIABILITY ISSUES VcSSmet = VcSS # AVOID IDENTIFIABILITY ISSUES K30 = CLRR/VcRR K40 = CLSS/VcSS K50 = CLRRmet/VcRRmet K60 = CLSSmet/VcSSmet % Defining an output (only needed when interfacing with NLME % parameter estimation tools such as NONMEM and MONOLIX) OUTPUT1 = Cc # Compound concentration in Liver (ug/mL) ********** MODEL REACTIONS ********** MODEL FUNCTIONS ********** MODEL EVENTS