The Metabolism and Disposition of Brepocitinib in Humans and Characterization of the Formation Mechanism of an Aminopyridine Metabolite.

Brepocitinib is an oral once-daily Janus kinase 1 and Tyrosine kinase 2 selective inhibitor currently in development for the treatment of several autoimmune disorders. Mass balance and metabolic profiles were determined using accelerator mass spectrometry in six healthy male participants following a single oral 60 mg dose of 14C-brepocitinib (∼300 nCi). The average mass balance recovery was 96.7% ± 6.3%, with the majority of dose (88.0% ± 8.0%) recovered in urine and 8.7% ± 2.1% of the dose recovered in feces. Absorption of brepocitinib was rapid, with maximal plasma concentrations of total radioactivity and brepocitinib achieved within 0.5 hours after dosing. Circulating radioactivity consisted primarily of brepocitinib (47.8%) and metabolite M1 (37.1%) derived from hydroxylation at the C5' position of the pyrazole ring. Fractional contributions to metabolism via cytochrome P450 enzymes were determined to be 0.77 for CYP3A4/5 and 0.14 for CYP1A2 based on phenotyping studies in human liver microsomes. However, additional clinical studies are required to understand the potential contribution of CYP1A1. Approximately 83% of the dose was eliminated as N-methylpyrazolyl oxidative metabolites, with 52.1% of the dose excreted as M1 alone. Notably, M1 was not observed as a circulating metabolite in earlier metabolic profiling of human plasma from a multiple ascending dose study with unlabeled brepocitinib. Mechanistic studies revealed that M1 was highly unstable in human plasma and phosphate buffer, undergoing chemical oxidation leading to loss of the 5-hydroxy-1-methylpyrazole moiety and formation of aminopyrimidine cleavage product M2. Time-dependent inhibition and trapping studies with M1 yielded insights into the mechanism of this unusual and unexpected instability. SIGNIFICANCE STATEMENT: This study provides a detailed understanding of the disposition and metabolism of brepocitinib, a JAK1/TYK2 inhibitor for atopic dermatitis, in humans as well as characterization of clearance pathways and pharmacokinetics of brepocitinib and its metabolites.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

An improved method for cytochrome p450 reaction phenotyping using a sequential qualitative-then-quantitative approach.

Cytochrome P450 reaction phenotyping to determine the fraction of metabolism values (fm) 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 fm values, with findings compared to fm values estimated from single inhibitor concentration data and rCYP with intersystem-extrapolation-factor corrections. The two-step approach yielded fm 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 (fm) to drug clearance. In this report, we describe a two-step qualitative-then-quantitative reaction phenotyping approach that yields more accurate estimates of fm.

Defining the Selectivity of Chemical Inhibitors Used for Cytochrome P450 Reaction Phenotyping: Overcoming Selectivity Limitations with a Six-Parameter Inhibition Curve-Fitting Approach.

The utility of chemical inhibitors in cytochrome P450 (CYP) reaction phenotyping is highly dependent on their selectivity and potency for their target CYP isoforms. In the present study, seventeen inhibitors of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4/5 commonly used in reaction phenotyping were evaluated for their cross-enzyme selectivity in pooled human liver microsomes. The data were evaluated using a statistical desirability analysis to identify (1) inhibitors of superior selectivity for reaction phenotyping and (2) optimal concentrations for each. Among the inhibitors evaluated, α-naphthoflavone, furafylline, sulfaphenazole, tienilic acid, N-benzylnirvanol, and quinidine were most selective, such that their respective target enzymes were inhibited by ~95% without inhibiting any other CYP enzyme by more than 10%. Other commonly employed inhibitors, such as ketoconazole and montelukast, among others, were of insufficient selectivity to yield a concentration that could adequately inhibit their target enzymes without affecting other CYP enzymes. To overcome these shortcomings, an experimental design was developed wherein dose response data from a densely sampled multi-concentration inhibition curve are analyzed by a six-parameter inhibition curve function, allowing accounting of the inhibition of off-target CYP isoforms inhibition and more reliable determination of maximum targeted enzyme inhibition. The approach was exemplified using rosiglitazone N-demethylation, catalyzed by both CYP2C8 and 3A4, and was able to discern the off-target inhibition by ketoconazole and montelukast from the inhibition of the targeted enzyme. This methodology yields more accurate estimates of CYP contributions in reaction phenotyping. Significance Statement Isoform-selective chemical inhibitors are important tools for identifying and quantifying enzyme contributions as part of a CYP reaction phenotyping assessment for projecting drug-drug interactions. However, currently employed practices fail to adequately compensate for shortcomings in inhibitor selectivity and the resulting confounding impact on estimates of the CYP enzyme contribution to drug clearance. In this report, we describe a detailed IC50 study design with 6-parameter modeling approach that yields more accurate estimates of enzyme contribution.

Discovery of SARS-CoV-2 papain-like protease (PLpro) inhibitors with efficacy in a murine infection model.

Vaccines and first-generation antiviral therapeutics have provided important protection against COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there remains a need for additional therapeutic options that provide enhanced efficacy and protection against potential viral resistance. The SARS-CoV-2 papain-like protease (PLpro) is one of the two essential cysteine proteases involved in viral replication. While inhibitors of the SARS-CoV-2 main protease have demonstrated clinical efficacy, known PLpro inhibitors have, to date, lacked the inhibitory potency and requisite pharmacokinetics to demonstrate that targeting PLpro translates to in vivo efficacy in a preclinical setting. Here, we report the machine learning-driven discovery of potent, selective, and orally available SARS-CoV-2 PLpro inhibitors, with lead compound PF-07957472 (4) providing robust efficacy in a mouse-adapted model of COVID-19 infection.

The Role of Alcohol Dehydrogenase in Drug Metabolism: Beyond Ethanol Oxidation.

Alcohol dehydrogenases (ADHs) are most known for their roles in oxidation and elimination of ethanol. Although less known, ADHs also play a critical role in the metabolism of a number of drugs and metabolites that contain alcohol functional groups, such as abacavir (HIV/AIDS), hydroxyzine (antihistamine), and ethambutol (antituberculosis). ADHs consist of 7 gene family numbers and several genetic polymorphic forms. ADHs are cytosolic enzymes that are most abundantly found in the liver, although also present in other tissues including gastrointestinal tract and adipose. Marked species differences exist for ADHs including genes, proteins, enzymatic activity, and tissue distribution. The active site of ADHs is relatively small and cylindrical in shape. This results in somewhat narrow substrate specificity. Secondary alcohols are generally poor substrates for ADHs. In vitro-in vivo correlations for ADHs have not been established, partly due to insufficient clinical data. Fomepizole (4-methylpyrazole) is a nonspecific ADH inhibitor currently being used as an antidote for the treatment of methanol and ethylene glycol poisoning. Fomepizole also has the potential to treat intoxication of other substances of abuse by inhibiting ADHs to prevent formation of toxic metabolites. ADHs are inducible through farnesoid X receptor (FXR) and other transcription factors. Drug-drug interactions have been observed in the clinic for ADHs between ethanol and therapeutic drugs, and between fomepizole and ADH substrates. Future research in this area will provide additional insights about this class of complex, yet fascinating enzymes.