The influence of flip‐flop in population pharmacokinetic analyses

The two permutations using a CL, V, ka and k, V, ka parameterization are shown in Table 1, where CL is clearance and V is volume of distribution and k and ka the “elimination” and “absorption” rate constants, respectively. TABLE 1 Possible permutations for a one- and two-compartment model Parameterization One compartment model CL, V, ka k, V, ka Permutation 1 CL ′ = CL k ′ = k V ′ = V V ′ = V k a ′ = k a k a ′ = k a Permutation 2 CL ′ = CL k ′ = k a V ′ = CL k a V ′ = V ∙ k / k a k a ′ = CL / V k a ′ = k Two-compartment model α, β, ka, k21, Vc Permutation 1 α ′ = α β ′ = β k a ′ = k a k 21 ′ = k 21 V c ′ = V c Permutation 2 α ′ = k a β ′ = β k a ′ = α k 21 ′ = k 21 V c ′ = V c ∙ α k a Permutation 3 α ′ = α β ′ = k a k a ′ = β k 21 ′ = k 21 V c ′ = V c ∙ β k a Abbreviations: CL, clearance; ka, absorption rate constant; k, elimination rate constant; k21, rate of transfer from peripheral to central compartments; V, volume of distribution; Vc, central volume of distribution. The univariate addition of CLcrCG to either CL or ka resulted in a statistically significant improvement in model fit. The methodology used to solve the issues of local identifiability due to flip-flop pharmacokinetics in population pharmacokinetic modeling ranged from methods that simply ignored flip-flop to studies that had applied constraints in the structural model. 6–8 In addition, only one study was identified that explicitly stated how constraints were applied to maintain a certain rank order among model parameters. 6 In conclusion,