Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework

Abstract

Background: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]).

Methods: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV.

Results: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred.

Conclusions: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Fig. 1

Visual representation of the sources of variability and impact on pharmacokinetics. Pharmacokinetic profiles of tacrolimus representing different sources of variability. The black lines represent the typical (no IIV) profiles for the CYP3A5 expressers (dashed line: CYP3A5*1/*1 or CYP3A5*1/*3) and the CYP3A5 non-expressers (solid line: CYP3A5*3/*3). The solid colored lines represent the profiles for two different patients, one CYP3A5 expresser (blue line: individual with CYP3A5*1/*1) and one CYP3A5 non-expresser (orange line: individual with CYP3A5*3/*3), deviating from their corresponding typical CYP3A5 profiles by IIV. The colored dots represent the measured concentrations for each patient, deviating from the underlying “true” patient concentration (line) by RUV. CYP cytochrome P450, IIV inter-individual variability, RUV residual unexplained error

Fig. 2

Percentage of patients benefiting from pharmacogenetic testing over TDM across different levels of IIV, IOV, and RUV. Each axis represents one source of variability—IIV (x-axis), IOV (z-axis), and RUV (y-axis) – used in the simulations. Each dot represents the result of one simulation. The color of the dots constitutes the percentage of patients for which the pharmacogenetic biomarker performed better than TDM in predicting exposure (C_trough or AUC) displayed as 25 ± 2.5% (blue), 50 ± 2.5% (green), and 75 ± 2.5% (orange) of 1000 simulated patients. The black diamonds represent the combination of true values (i.e., reported IIV, IOV, and RUV) from the original model. AUC area under the curve, C_trough trough concentration, IIV inter-individual variability, IOV inter-occasion variability, RUV residual unexplained error, TDM therapeutic drug monitoring

Fig. 3

Accuracy of the MIPD versus the NCA approach. Box plots display the population's 25th and 75th percentiles at the ends of each box, with whiskers extending to the 2.5th and 97.5th percentiles. The horizontal continuous lines cutting through each box represent the median values, whereas the dashed lines represent the 0.8 and 1.25 accuracy cutoffs. 5-FU 5-fluorouracil, AUC area under the curve, MIPD model-informed precision dosing, NCA non-compartmental analysis, pred prediction