Path to another drug against COVID-19 The rapid development of vaccines has been crucial in battling the ongoing COVID-19 pandemic. However, access challenges remain, breakthrough infections occur, and emerging variants present increased risk. Developing antiviral therapeutics is therefore a high priority for the treatment of COVID-19. Some drug candidates in clinical trials act against the viral RNA-dependent RNA polymerase, but there are other viral enzymes that have been considered good targets for inhibition by drugs. Owen et al . report the discovery and characterization of a drug against the main protease involved in the cleavage of polyproteins involved in viral replication. The drug, PF-07321332, can be administered orally, has good selectivity and safety profiles, and protects against infection in a mouse model. In a phase 1 clinical trial, the drug reached concentrations expected to inhibit the virus based on in vitro studies. It also inhibited other coronaviruses, including severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, and could be in the armory against future viral threats. —VV

The worldwide outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an established global pandemic. Alongside vaccines, antiviral therapeutics are an important part of the healthcare response to counter the ongoing threat presented by COVID-19. Here, we report the discovery and characterization of PF-07321332, an orally bioavailable SARS-CoV-2 main protease inhibitor with in vitro pan-human coronavirus antiviral activity, and excellent off-target selectivity and in vivo safety profiles. PF-07321332 has demonstrated oral activity in a mouse-adapted SARS-CoV-2 model and has achieved oral plasma concentrations exceeding the in vitro antiviral cell potency, in a phase I clinical trial in healthy human participants. Clinical Trial Registration ID #: NCT04756531

dinucleotide phosphate, reduced form; ROE, rotating Overhauser effect; V max , maximal velocity; K M , substrate concentration at half-maximal velocity; CL int,app , apparent intrinsic clearance; CL int,app,scaled , scaled apparent intrinsic clearance; f u,mic , fraction unbound in liver microsomes; f CL , fractional clearance; f m , fraction metabolized; f a , fraction of the po dose absorbed from the gastrointestinal lumen; CL renal , renal clearance; Vd ss , steady state distribution volume; C max , maximal plasma concentration; T max , time to reach C max ; AUC, area under the plasma concentration-time curve; t 1/2 , half-life; P app , apparent absorptive permeability; MDCKII-LE, low efflux Madin-Darby canine kidney cells; BCRP, breast cancer resistant protein; MDR1, multidrug resistant gene 1; f u,p , plasma unbound fraction; BPR, blood to plasma ratio; CL hep , in

Quantitative characterization of UDP-glucuronosyltransferase (UGT) enzymes is valuable in glucuronidation reaction phenotyping, predicting metabolic clearance and drug-drug interactions using extrapolation exercises based on pharmacokinetic modeling. Different quantitative proteomic workflows have been employed to quantify UGT enzymes in various systems, with reports indicating large variability in expression, which cannot be explained by interindividual variability alone. To evaluate the effect of methodological differences on end-point UGT abundance quantification, eight UGT enzymes were quantified in 24 matched liver microsomal samples by two laboratories using stable isotope-labeled (SIL) peptides or quantitative concatemer (QconCAT) standard, and measurements were assessed against catalytic activity in seven enzymes (n = 59). There was little agreement between individual abundance levels reported by the two methods; only UGT1A1 showed strong correlation [Spearman rank order correlation (Rs) = 0.73, P < 0.0001; R 2 = 0.30; n = 24]. SIL-based abundance measurements correlated well with enzyme activities, with correlations ranging from moderate for UGTs 1A6, 1A9, and 2B15 (Rs = 0.52-0.59, P < 0.0001; R 2 = 0.34-0.58; n = 59) to strong correlations for UGTs 1A1, 1A3, 1A4, and 2B7 (Rs = 0.79-0.90, P < 0.0001; R 2 = 0.69-0.79). QconCAT-based data revealed generally poor correlation with activity, whereas moderate correlations were shown for UGTs 1A1, 1A3, and 2B7. Spurious abundance-activity correlations were identified in the cases of UGT1A4/2B4 and UGT2B7/2B15, which could be explained by correlations of protein expression between these enzymes. Consistent correlation of UGT abundance with catalytic activity, demonstrated by the SIL-based dataset, suggests that quantitative proteomic data should be validated against catalytic activity whenever possible. In addition, metabolic reaction phenotyping exercises should consider spurious abundance-activity correlations to avoid misleading conclusions.