I have data for a compound given by oral administration at 25 and 75 mg/kg. The AUC exposure at 75 mg/kg is 7-fold higher than what is produced by the 25 mg/kg dose. I know that this compound has an efflux ratio of 7 in the Caco-2 model. If I fit the two dose groups separately using a simple 1-compartment model with first order absorption & elimination I see major differences in the volume & clearance parameters between the 25 mg/kg dose (12 L/kg & 4.7 L/hr-kg) and 75 mg/kg dose (4.7 L/kg & 1.8 L/hr-kg). Noncompartment analysis shows both dose levels give similar slopes for the terminal phase and I note from the 1-C analysis that both dose levels give similar Ka values and Tmax values. I assume that absorption is saturable while elimination is simple first order. At the low dose absorption would be limited by the efflux mechanism while at high dose it would be saturated with first order-like absorption. However, I cannot see anyway to to implement this kind of model. I tried to set up a parallel input using two Aa absorption compartments, one controlled by a saturable (Km/Vmax) process while the other by first order absorption (Ka). I am assuming that both absorption compartments are really the same dose. While this model “runs” it seems obvious from both the initial estimates tool and the convergence results that it does not account for nonlinearity in exposure. The DV versus PRED plot shows two groups of concentrations (low and high) with only one really fit by the unity line. I have spent the better part of this day looking through Pubmed and here with no insights in sight. Is anyone aware of any publications on this? Do you have any suggestions?
Richard I’m sorry your message last summer was overlooked you CAN do this graphically; When setting it up, add a flow from Aa to A1, then a second one from A1 to Aa. Make the second one saturable. For initial estimates, fit a high dose profile and a low dose profile. Ka is the Ka from the high dose profile. Set the Km around the Cmax for the low dose. You’ll have to take few guesses at the Vm. Try freezing the other parameters and fitting the model with naïve pooled engine. 
which gives the base code below. Simon test(){ deriv(A1 = - (Cl * C) + (Aa * Ka)- (VMax * C / (C + Km))) urinecpt(A0 = (Cl * C)) deriv(Aa = - (Aa * Ka) + (VMax * C / (C + Km))) C = A1 / V dosepoint(Aa, idosevar = AaDose, infdosevar = AaInfDose, infratevar = AaInfRate) error(CEps = 1) observe(CObs = C + CEps) stparm(V = tvV * exp(nV)) stparm(Cl = tvCl * exp(nCl)) stparm(Ka = tvKa * exp(nKa)) stparm(VMax = tvVMax * exp(nVMax)) stparm(Km = tvKm * exp(nKm)) fixef(tvV = c(, 1, )) fixef(tvCl = c(, 1, )) fixef(tvKa = c(, 1, )) fixef(tvVMax = c(, 1, )) fixef(tvKm = c(, 1, )) ranef(diag(nV, nKa, nCl, nKm, nVMax) = c(1, 1, 1, 1, 1)) }
