Could Metabolomics Be the Key to Unlocking Precision Dosing in the Clinic?

Field: Pharmacology, Toxicology and Pharmaceutics (Life Sciences)

In their report on the results of a prospective study collecting samples in pre-hematopoietic stem cell transplant (HCT) recipients, McCune et al.1 find a biomarker panel associating endogenous metabolomic compounds with busulfan clearance (Figure 1). Metabolome-guided predictive performance out-did a pretransplant test dose or personalizing based on genotype. Not only might this point to a future method for personalizing initial busulfan dose, but the paper highlights how metabolomic profiling could more broadly be used to give mechanistic insights into drug clearance pathways. In the hierarchy of “omics” technologies, one could think of metabolomics sitting at the closest point to drug metabolism. Whereas pharmacogenomics gives the recipe for enzyme type and possibly abundance via copy number, it does not inform researchers regarding enzyme expression, or, if so, whether it may be inhibited by an endogenous marker, for instance. Transcriptomics allows for an analysis of whether genes are expressed through the presence of RNA, but again the presence of RNA does not necessarily imply function. Proteomics and metabolomics applied to pharmacokinetics (PK) on the other hand seek to measure proteins and their metabolic substrates and products to find associations with pathways important in drug clearance. Metabolomic analyses can be either nontargeted metabolome-wide analyses, looking for qualitative association (presence/absence) by screening large numbers of compounds, or targeted, assessing a limited range of compounds focused on pathways of interest.2 In addition, metabolomics can be combined with other “omics” in an attempt to follow mechanistic pathways. A simple example of an endogenous substance predicting drug clearance was recently highlighted with the case of C-reactive protein (CRP). Midazolam clearance could be as much as 70% lower in patients with high vs. normal CRP levels.3 CRP is induced by the pro-inflammatory cytokine interleukin 6 (IL-6), which in turn may be responsible for inhibiting the activity of CYP3A4. The effects of the IL-6 receptor antagonist tocilizumab in reversing CYP3A4 suppression in chronic inflammatory conditions has long been known,4 and more recently has been noted during tocilizumab's use to treat acute inflammatory effects associated with COVID-19.5 The PK community is now beginning to harness proteomic and metabolomic signatures to seek out similar and other multifactor predictors of drug clearance. In this issue McCune et al.1 ask whether the metabolic products of proteins can predict busulfan clearance. To understand how the authors went about this, readers may have two questions that could be considered “black box” elements in their work. The first is on the prediction of clearance itself, and the second on details of the metabolomic panel and its bioinformatic processing. Busulfan therapeutic drug monitoring in HCT, usually through measuring concentrations on the first dose and adjusting one or more subsequent doses, is practiced in most centers, but remains without prospective randomized control trial (RCT) evidence of benefit. Furthermore different centers use different assays, sampling schedules, and PK parameter estimation methods, with attempts at standardization only recently being made.6 McCune et al.1 used a prior PK model to estimate a maximum a posteriori probability of individual clearance values from a drug exhibiting two compartment kinetics with rich sampling (i.e., more samples than model parameters). The prior model employs the rather odd approach of using fat-free mass, which itself has to be predicted from a model, as a covariate. Authors then predict a clearance in the nonstandard units mL/min/kg of normal-fat mass. Most of us would struggle to interpret PK parameters reported in this way, and the authors do not show the relative predictive performance on individual clearance that would have resulted had a simpler prior model been used. The relatively rich individual data imply there would likely be little loss in accuracy with a more interpretable model, but caution should be exercised with protocols using fewer samples per patient. While the dependent variable, clearance, is relatively certain then, the second question surrounds the covariates related to the metabolomic analysis. The methods presented by McCune et al.1 are somewhat uncertain with a commercial assay used to screen for 841 compounds, the identity of which were not reported, and bioinformatics undertaken with proprietary software. Nor were clear details given on how the targeted metabolomic signatures were treated as quantitative measures with the meaning of beta coefficients in Tables 2 and 3 not clearly defined. Despite this, the biomarkers found at the very least generate hypotheses for further study on the complex mechanisms of busulfan clearance. In a recent double-blind RCT studying the combination of the polymerase inhibitor favipiravir in combination with lopinavir/ritonavir in COVID-19, Lowe et al.7 discovered an unexpected drug–drug interaction. Favipiravir concentrations were significantly lower in patients also receiving lopinavir/ritonavir, a strong effect that persisted even when accounting for lower compliance in the protease inhibitor-containing arms. This interaction was observed despite the drugs apparently not sharing a common known clearance pathway.8 Perhaps a metabolomic analysis in drug–drug interaction studies could help elucidate such mechanisms. Therapeutic drug monitoring for optimizing drug efficacy was recently dealt a blow as model-based antimicrobial dose adjustments failed to improve outcomes compared with the standard of care.9 Whether metabolomics can be rapidly adopted in HCT and how much influence the heterogeneous conditioning regimes (patients also received cyclophosphamide, melphalan, fludarabine, and thiotepa), diagnoses, and transplant types have on outcomes in the absence of RCT data remains to be seen. Furthermore, the optimal timepoint to measure metabolomic signatures, and indeed the site to measure them from given that urine, for example, has been previously suggested,10 are also to be defined. Nevertheless, despite these uncertainties and need for substantial future work before metabolomic profiling becomes clinical routine for personalized medicine, the paper by McCune et al.1 points to a new way of looking at drug metabolism and drug–drug interaction studies in vivo.