R. Scott Obach

In vitro time-dependent inhibition (TDI) kinetic parameters for cytochrome P450 (CYP) 1A2, 2B6, 2C8, 2C9, 2C19, and 2D6, were determined in pooled human liver microsomes for 19 drugs (and 2 metabolites) for which clinical drug-drug interactions (DDI) are known. In vitro TDI data were incorporated into the projection of the magnitude of DDIs using mechanistic static models and Simcyp®. Results suggest that for the mechanistic static model, use of estimated average unbound exit concentration of the inhibitor from the liver resulted in a successful prediction of observed magnitude of clinical DDIs and was similar to Simcyp®. Overall, predictions of DDI magnitude (i.e., fold increase in AUC of a CYP-specific marker substrate) were within 2-fold of actual values. Geometric mean-fold errors were 1.7 and 1.6 for static and dynamic models, respectively. Projections of DDI from both models were also highly correlated to each other (r2 = 0.92). This investigation demonstrates that DDI can be reliably predicted from in vitro TDI data generated in HLM for several CYP enzymes. Simple mechanistic static model equations as well as more complex dynamic PBPK models can be employed in this process. Significance Statement Cytochrome P450 time-dependent inhibitors (TDI) can cause drug-drug interactions (DDI). An ability to reliably assess the potential for a new drug candidate to cause DDI is essential during drug development. In this report, TDI data for 19 drugs (and 2 metabolites) were measured and used in static and dynamic models to reliably project the magnitude of DDI resulting from inhibition of CYP1A2, 2B6, 2C8, 2C9, 2C19, and 2D6.

The metabolism of lufotrelvir, a novel phosphate prodrug of PF-00835231 for the treatment of COVID-19, was evaluated in healthy human volunteers and clinical trial participants with COVID-19 following intravenous infusion. The prodrug was completely converted to PF-00835231 that was subsequently cleared by hydrolysis, hydroxylation, ketoreduction, epimerization, renal clearance, and secretion into the feces. The main circulating metabolite was a hydrolysis product (M7) that was present at concentrations greater than PF-00835231, and this was consistent between healthy volunteers and participants with COVID-19. On administration of [¹⁴C]lufotrelvir, only 63% of the dose was obtained in excreta over 10 days and total drug-related material demonstrated a prolonged terminal phase half-life in plasma. A considerable portion of the labeled material was unextractable from fecal homogenate and plasma. The position of the carbon-14 atom in the labeled material was at a leucine carbonyl, and pronase digestion of the pellet derived from extraction of the fecal homogenate showed that [¹⁴C]leucine was released. SIGNIFICANCE STATEMENT Lufotrelvir is an experimental phosphate prodrug intravenous therapy investigated for the potential treatment of COVID-19 in a hospital setting. The overall metabolism of lufotrelvir was determined in human healthy volunteers and clinical trial participants with COVID-19. Conversion of the phosphate prodrug to the active drug PF-00835231 was complete and the subsequent metabolic clearance of the active drug was largely via amide bond hydrolysis. Substantial drug-related material was not recovered due to loss of the carbon-14 label to endogenous metabolism.

Cytochrome P450 reaction phenotyping to determine the fraction of metabolism values (f_m) for individual enzymes is a standard study in the evaluation of a new drug. However, there are technical challenges in these studies caused by shortcomings in the selectivity of P450 inhibitors and unreliable scaling procedures for recombinant P450 (rCYP) data. In this investigation, a two-step "qualitative-then-quantitative" approach to P450 reaction phenotyping is described. In the first step, each rCYP is tested qualitatively for potential to generate metabolites. In the second step, selective inhibitors for the P450s identified in step1 are tested for their effects on metabolism using full inhibition curves. Forty-eight drugs were evaluated in step 1 and there were no examples of missing an enzyme important to in vivo clearance. Five drugs (escitalopram, fluvastatin, pioglitazone, propranolol, and risperidone) were selected for full phenotyping in step2 to determine f_m values, with findings compared to f_m values estimated from single inhibitor concentration data and rCYP with intersystem-extrapolation-factor corrections. The two-step approach yielded f_m values for major drug clearing enzymes that are close to those estimated from clinical data: escitalopram and CYP2C19 (0.42 vs 0.36-0.82), fluvastatin and CYP2C9 (0.76 vs 0.76), pioglitazone and CYP2C8 (0.72 vs 0.73), propranolol and CYP2D6 (0.68 vs 0.37-0.56) and risperidone and CYP2D6 (0.60 vs 0.66-0.88). Reaction phenotyping data generated in this fashion should offer better input to physiologically-based pharmacokinetic models for prediction of DDI and impact of genetic polymorphisms on drug clearance. The qualitative-then-quantitative approach is proposed as a replacement to standard reaction phenotyping strategies. Significance Statement P450 reaction phenotyping is important for projecting drug-drug interactions and interpatient variability in drug exposure. However, currently recommended practices can frequently fail to provide reliable estimates of the fractional contributions of specific P450 enzymes (f_m) to drug clearance. In this report, we describe a two-step qualitative-then-quantitative reaction phenotyping approach that yields more accurate estimates of f_m.

Typically human absorption, distribution, metabolism, and excretion (ADME) studies are executed using radiolabeled (e.g., carbon‐14) material, the synthesis of which is a time‐consuming activity. In this study, we were able to assess the metabolism and excretion of unlabeled nirmatrelvir (PF‐07321332) within the first‐in‐human study via a novel application of quantitative fluorine (¹⁹F) nuclear magnetic resonance (NMR) spectroscopy in place of a standard radiolabel ADME study. Six healthy participants received a single 300‐mg oral dose of nirmatrelvir (in combination with ritonavir), and excreta were collected up to 10 days. Virtually all drug‐related material was recovered within 5 days, and mass balance was achieved with 84.9 ± 8.9% (range = 70.7–95.5%) of the administered dose recovered in urine and feces. The excretion of fluorine‐containing material in urine and feces was 47.0% and 33.7%, respectively. Unchanged nirmatrelvir represented 82.5% of the normalized drug‐related material with a carboxylic acid metabolite M5, derived from hydrolysis of the P2 amide bond, present at 12.1% of dose. Nirmatrelvir was the only drug‐related entity observed in plasma. Approximately 4.2% of the dose was excreted as metabolite M8 (measured by liquid chromatography–mass spectrometry), which was ¹⁹F NMR silent due to hydrolysis of the trifluoroacetamide moiety. Hydrolysis of nirmatrelvir to M5 and M8 was shown to occur in cultures of human gut microflora. This successful demonstration of quantitative ¹⁹F NMR spectroscopy to establish the mass‐balance, excretion, and metabolic profile of nirmatrelvir offers an advantageous means to execute human ADME studies for fluorine‐containing compounds early in drug development.