Comparison of non-compartmental and mixed effect modelling methods for establishing bioequivalence for the case of two compartment kinetics and censored concentrations

Abstract

Non-compartmental analysis (NCA) is regarded as the standard for establishing bioequivalence, despite its limitations and the existence of alternative methods such as non-linear mixed effects modelling (NLMEM). Comparisons of NCA and NLMEM in bioequivalence testing have been limited to drugs with one-compartment kinetics and have included a large number of different approaches. A simulation tool was developed with the ability to rapidly compare NCA and NLMEM methods in determining bioequivalence using both R and NONMEM and applied to a drug with two-compartment pharmacokinetics. Concentration-time profiles were simulated where relative bioavailability, random unexplained variability (RUV) at the lower limit of quantification (LLOQ) differed between simulations. NLMEM analyses employed either the M1 or M3 methods for dealing with values below the LLOQ. It was used to elucidate the impact of changes in (i) RUV at the LLOQ, (ii) the extent of censoring data below the LLOQ and (iii) the concentration sampling times. The simulations showed NLMEM having a consistent 20-40% higher accuracy and sensitivity in identifying bioequivalent studies when compared to NCA, while NCA was found to have a 1-10% higher specificity than NLMEM. Increasing data censoring by increasing the LLOQ resulted in decreases of ~10% to the accuracy and sensitivity of NCA, with minimal effects on NLMEM. The tool provides a platform for comparing NCA and NLMEM methods and its use can be extended beyond the scenarios reported here. In the situations examined it is seen that NLMEM is more accurate than NCA and may offer some advantages in the determination of bioequivalence.

Tramadol Steady-State Pharmacokinetics of Immediate-Release Capsules and Sustained-Release Tablets in Dogs

“…Handling values below the LLOQ can highly influence the results of the analysis. Especially the AUC is highly affected by how one deals with these LLOQ data [16]. The influence is most pronounced in datasets with a large percentage of data below the LLOQ coupled with other low concentrations [17].…”

“…Replacing those values with LLOQ/2 is a common way to handle these data in NCA. Several studies in recent years have compared different methods of handling LLOQ in NCA [16][17][18][19]. These studies all refer to the methods about handling data below LLOQ in pharmacokinetic modeling, described by Beal [20], and it has since been shown that handling data that are below the LLOQ as fixed-point censored data (called method 3, M3) gives the best and least biased results in pharmacokinetic modeling, especially when a high percentage (>30%) of the data are below the LLOQ [21].…”

“…The concentrations below LLOQ were replaced by LLOQ/2, i.e., 2.5 µg/L before the NCA was executed. Replacing LLOQ by LLOQ/2 is a common way of handling these data in the NCA [16][17][18][19]. Leaving these out would have resulted in an overestimation of some of the parameter values, especially of the area under the concentration-time curve (AUC).…”

Optimizing Lead Compounds: The Role of Artificial Intelligence in Drug Discovery

“…One such strategy uses Bayesian optimization, which efficiently selects compounds based on predictive models' uncertainty estimates for experimental testing. Bayesian optimization iteratively explores the chemical space by balancing the exploitation of known regions with the exploration of uncertain areas, thereby maximizing the discovery of promising lead candidates [45][46][47].…”

Applying Hierarchical Generalized Additive Models to Non-Compartmental Analysis of Pharmacokinetic Data

“…In veterinary applications for example, arbitrary decisions such as dropping censored observations are generally abundant (Woodward and Whittem, 2019). In population pharmacokinetics censored observations may be rigorously handled via an appropriate likelihood function (Beal, 2001), but this of course is not present in NCA, which may lead to poor performance (Hughes, Upton and Foster, 2017).…”