The benefit-risk profile of mepolizumab in severe asthma was established through clinical trials in adults, leading to EU and US approvals in 2015. To support a pediatric indication (≥6 years), an extrapolation approach was implemented. This extrapolation and its validation were informed by data from Phase III trials in adults (N = 1841) and adolescents aged 12-17 years (N = 37) with severe asthma, an open-label trial in children aged 6-11 years (N = 36), and a trial in children aged 2-11 years (N = 32) and adolescents aged 12-17 years (N = 27) with eosinophilic esophagitis. Population pharmacokinetic and pharmacokinetic/pharmacodynamic meta-analyses demonstrated consistent mepolizumab pharmacokinetic and blood eosinophil reduction across age groups and diseases, with bodyweight the only covariate of exposure and absolute bioavailability also a covariate in children aged 6-11 years. Baseline blood eosinophil count and disease type were the only covariates of response. In severe asthma, mepolizumab reduced the annualized rate of clinically significant exacerbations by 40% in adolescents and 54% in adults versus placebo. Bootstrap resampling and Bayesian analyses supported similar efficacy between adolescents and adults, and responses in children aged 6-11 years were consistent with older groups. Safety evaluations revealed no unique safety concerns in pediatric patients with severe asthma. This extrapolation strategy, implemented as an innovative approach to pediatric clinical development of mepolizumab in severe asthma, was validated and provided the scientific basis for dosing recommendations and for EU regulatory approval in patients aged ≥6 years in 2018.

IL-5, a key mediator of type 2 inflammation, underlies various diseases, including severe asthma, CRSwNP, EGPA, and HES. Reduction in blood eosinophil count (BEC), a biomarker of IL-5 activity, is commonly used to evaluate the efficacy of anti-IL-5 biologic therapies. Model-informed drug development (MIDD) and quantitative decision making (QDM) were used to shorten the clinical development of depemokimab (an ultra-long-acting anti-IL-5 biologic). A Bayesian nonlinear mixed effects dose-time response model predicted the depemokimab dose in severe asthma achieving comparable BEC reductions to those observed in mepolizumab (an approved anti-IL-5 biologic) Phase III MUSCA and MENSA trials. Prespecified QDM go/no-go criteria were applied to assess success probability. Phase IIb efficacy-based trial simulations were conducted using negative binomial distribution to simulate individual annualized exacerbation rate. A depemokimab PK/PD (BEC) model predicted Phase III trial doses in CRSwNP/EGPA/HES. Single depemokimab doses were well-described by the Bayesian model; a single depemokimab dose ≥ 60 mg had probability ≥ 80% of exceeding Minimum (78%; MUSCA) and ≥ 10% probability of exceeding Target (84%; MENSA) values for trough BEC reduction from baseline vs. placebo. Clinical trial simulations demonstrated < 3% probability of more precise estimation of the Phase III dosing regimen with a conventional efficacy-based dose-ranging study. Depemokimab 100 mg for severe asthma/CRSwNP and 200 mg for EGPA/HES, administered subcutaneously every 26 weeks, were selected for Phase III trials. MIDD and QDM shortened the depemokimab development program by 2-3 years, emphasizing the potential of this approach for progressing new therapies from Phase I directly to Phase III.

Sotrovimab was well tolerated by children/adolescents (6 to <18 years) with mild-to-moderate COVID-19 at high risk of progression to severe disease. Pharmacokinetic parameters in adolescents (12 to <18 years) were generally similar to those reported in adult studies of sotrovimab.

Sotrovimab 500 mg administered by a single intravenous (IV) infusion has been granted special approval for emergency use in Japan for treatment of SARS-CoV-2 infection in adults and children aged ≥ 12 years weighing ≥ 40 kg. This Phase 1, single-dose study investigated the pharmacokinetics, safety, and tolerability of IV or intramuscular (IM) sotrovimab 500 mg doses versus placebo in healthy Japanese and Caucasian volunteers. This was a two-part, Phase 1, randomized, placebo-controlled, single-blind study. In Part 1, participants received a single sotrovimab 500 mg IV infusion or matching placebo on Day 1. In Part 2, participants received a single sotrovimab 500 mg IM dose or matching placebo on Day 1, administered as two 4 mL injections. There was no effect of ethnicity on the peak or total serum exposure of IV sotrovimab through Week 18; after adjusting for body weight, the point estimate and 90

Sotrovimab administered intramuscularly at 500 mg was noninferior to 500 mg administered intravenously for treatment of mild/moderate COVID-19 in patients at high risk, as measured by all-cause hospitalization >24 hours or death through day 29, and it was well tolerated. Intramuscular sotrovimab should provide easier outpatient access to COVID-19 treatment. Background Convenient administration of coronavirus disease 2019 (COVID-19) treatment in community settings is desirable. Sotrovimab is a pan-sarbecovirus dual-action monoclonal antibody formulated for intravenous (IV) or intramuscular (IM) administration for early treatment of mild/moderate COVID-19. Method This multicenter phase 3 study based on a randomized open-label design tested the noninferiority of IM to IV administration according to an absolute noninferiority margin of 3.5%. From June to August 2021, patients aged & GE;12 years with COVID-19, who were neither hospitalized nor receiving supplemental oxygen but were at high risk for progression, were randomized 1:1:1 to receive sotrovimab as a single 500-mg IV infusion or a 500- or 250-mg IM injection. The primary composite endpoint was progression to (1) all-cause hospitalization for >24 hours for acute management of illness or (2) all-cause death through day 29. Results Sotrovimab 500 mg IM was noninferior to 500 mg IV: 10 (2.7%) of 376 participants vs 5 (1.3%) of 378 met the primary endpoint, respectively (absolute adjusted risk difference, 1.06%; 95% CI, -1.15% to 3.26%). The 95% CI upper limit was lower than the prespecified noninferiority margin of 3.5%. The 250-mg IM group was discontinued early because of the greater proportion of hospitalizations vs the 500-mg groups. Serious adverse events occurred in <1% to 2% of participants across groups. Four participants experienced serious disease-related events and died (500 mg IM, 2/393, <1%; 250 mg IM, 2/195, 1%). Conclusions Sotrovimab 500-mg IM injection was well tolerated and noninferior to IV administration. IM administration could expand outpatient treatment access for COVID-19.

Increase in serum bile acids (BAs) in patients with primary biliary cholangitis (PBC) may play a causal role in cholestatic pruritus (itch). Linerixibat is a selective small molecule inhibitor of the ileal bile acid transporter, which blocks re-absorption of BAs in the gastrointestinal tract thereby lowering BAs in the systemic circulation and reducing itch. One consequence is excess BAs in the colon, leading to diarrhea and abdominal pain. GLIMMER (NCT02966834) was a placebo-controlled phase IIb dose-ranging trial of linerixibat once (q.d.) or twice daily (b.i.d.) in adults with moderate to severe pruritus and PBC. To determine the optimal dose for maximum itch reduction while minimizing diarrhea, a kinetic-pharmacodynamic (k-PD) model was developed using data from GLIMMER. The PD end point modeled was worst daily itch, derived from itch score reported by patients b.i.d. A proportional odds model was developed post hoc to indicate the probability of diarrhea occurrence, a patient-reported outcome (GI-5) recorded weekly. The final k-PD model successfully described the effects of linerixibat and placebo on itch. Model simulations were consistent with the observed dose-dependent increase in the average number of itch responders (patients with a ≥ 2-point improvement in itch). This was paralleled by a dose-dependent increase in the probability of higher diarrhea frequency scores. The b.i.d. dosing regimens led to a modest increase in the number of itch responders as compared with q.d. dosing. This quantitative framework highlights the trade-off between benefit and tolerability and supported the selection of 40 mg b.i.d. in the phase III GLISTEN trial (NCT04950127).

ABSTRACT The clinical relevance of bacteriuria following antibiotic treatment of complicated urinary tract infections in clinical trials remains controversial. We evaluated the impact of urine pharmacokinetics on the timing of recurrent bacteriuria in a recently completed trial that compared oral tebipenem pivoxil hydrobromide to intravenous ertapenem. The urinary clearance and urine dwell time of ertapenem were prolonged relative to tebipenem and were associated with a temporal difference in the repopulation of bladder urine with bacteria following treatment, potentially confounding the assessment of efficacy.

Abstract Dostarlimab (JEMPERLI) is an anti‐programmed cell death protein‐1 (PD‐1) monoclonal antibody (mAb) which is approved by the US Food and Drug Administration for patients with recurrent/advanced mismatch repair‐deficient solid tumors, including endometrial cancer, following progression on prior treatment, with approval based on data from the phase I GARNET trial. To support dostarlimab dose regimen recommendations, we estimated and compared the potency of dostarlimab relative to anti–PD‐1 mAb pembrolizumab using both data published from the KEYNOTE‐001 trial of pembrolizumab and data from the GARNET trial. PD‐1 target engagement was assessed ex vivo in blood samples via a super antigen staphylococcal enterotoxin B stimulation assay and interleukin‐2 (IL‐2) stimulation ratios calculated for dostarlimab. A non‐linear mixed‐effect sigmoid maximum effect inhibitory model was fitted to dostarlimab IL‐2 stimulation ratios using extracted pembrolizumab data as informative priors. The estimated half‐maximal effective concentration was 1.95 μg ml−1 (95% credibility interval: 0.21–5.87) for dostarlimab and 1.59 μg ml−1 (95% confidence interval: 0.42–6.12) for pembrolizumab. These findings suggest dostarlimab and pembrolizumab to be equipotent for peripheral PD‐1 suppression based on analysis of ex vivo IL‐2 stimulation ratios. Accounting for a three‐fold dilution between serum and tumor, a target dostarlimab trough concentration of ~54 μg ml−1 would be needed for 90% suppression in the tumor. These data support the use of dostarlimab as a potent PD‐1 suppressor and the recommended dostarlimab monotherapy dose regimen of 500 mg Q3W ×4 cycles followed by 1000 mg Q6W thereafter in recurrent/advanced solid tumors.

Importance:Older patients and those with comorbidities who are infected with SARS-CoV-2 may be at increased risk of hospitalization and death. Sotrovimab is a neutralizing antibody for the treatment of high-risk patients to prevent COVID-19 progression.Objective:To evaluate the efficacy and adverse events of sotrovimab in preventing progression of mild to moderate COVID-19 to severe disease.Design, Setting, and Participants:Randomized clinical trial including 1057 nonhospitalized patients with symptomatic, mild to moderate COVID-19 and at least 1 risk factor for progression conducted at 57 sites in Brazil, Canada, Peru, Spain, and the US from August 27, 2020, through March 11, 2021; follow-up data were collected through April 8, 2021.Interventions:Patients were randomized (1:1) to an intravenous infusion with 500 mg of sotrovimab (n = 528) or placebo (n = 529).Main Outcomes and Measures:The primary outcome was the proportion of patients with COVID-19 progression through day 29 (all-cause hospitalization lasting >24 hours for acute illness management or death); 5 secondary outcomes were tested in hierarchal order, including a composite of all-cause emergency department (ED) visit, hospitalization of any duration for acute illness management, or death through day 29 and progression to severe or critical respiratory COVID-19 requiring supplemental oxygen or mechanical ventilation.Results:Enrollment was stopped early for efficacy at the prespecified interim analysis. Among 1057 patients randomized (median age, 53 years [IQR, 42-62], 20% were ≥65 years of age, and 65% Latinx), the median duration of follow-up was 103 days for sotrovimab and 102 days for placebo. All-cause hospitalization lasting longer than 24 hours or death was significantly reduced with sotrovimab (6/528 [1%]) vs placebo (30/529 [6%]) (adjusted relative risk [RR], 0.21 [95% CI, 0.09 to 0.50]; absolute difference, -4.53% [95% CI, -6.70% to -2.37%]; P < .001). Four of the 5 secondary outcomes were statistically significant in favor of sotrovimab, including reduced ED visit, hospitalization, or death (13/528 [2%] for sotrovimab vs 39/529 [7%] for placebo; adjusted RR, 0.34 [95% CI, 0.19 to 0.63]; absolute difference, -4.91% [95% CI, -7.50% to -2.32%]; P < .001) and progression to severe or critical respiratory COVID-19 (7/528 [1%] for sotrovimab vs 28/529 [5%] for placebo; adjusted RR, 0.26 [95% CI, 0.12 to 0.59]; absolute difference, -3.97% [95% CI, -6.11% to -1.82%]; P = .002). Adverse events were infrequent and similar between treatment groups (22% for sotrovimab vs 23% for placebo); the most common events were diarrhea with sotrovimab (n = 8; 2%) and COVID-19 pneumonia with placebo (n = 22; 4%).Conclusions and Relevance:Among nonhospitalized patients with mild to moderate COVID-19 and at risk of disease progression, a single intravenous dose of sotrovimab, compared with placebo, significantly reduced the risk of a composite end point of all-cause hospitalization or death through day 29. The findings support sotrovimab as a treatment option for nonhospitalized, high-risk patients with mild to moderate COVID-19, although efficacy against SARS-CoV-2 variants that have emerged since the study was completed is unknown.Trial Registration:ClinicalTrials.gov Identifier: NCT04545060.

Understanding who is at risk of progression to severe coronavirus disease 2019 (COVID-19) is key to clinical decision making and effective treatment. We study correlates of disease severity in the COMET-ICE clinical trial that randomized 1:1 to placebo or to sotrovimab, a monoclonal antibody for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (ClinicalTrials.gov 04545060). Laboratory parameters identify study participants at greater risk of severe disease, including a high neutrophil-to-lymphocyte ratio (NLR), a negative SARS-CoV-2 serologic test, and whole-blood transcriptome profiles. Sotrovimab treatment is associated with normalization of NLR and the transcriptomic profile and with a decrease of viral RNA in nasopharyngeal samples. Transcriptomics provides the most sensitive detection of participants who would go on to be hospitalized or die. To facilitate timely measurement, we identify a 10-gene signature with similar predictive accuracy. We identify markers of risk for disease progression and demonstrate that normalization of these parameters occurs with antibody treatment of established infection.

Conclusion:The concomitant HBsAg declines and ALT elevations indicate that a single, very low dose of IMC-I109V elicited on-target activity consistent with the TCR bispecific (ENVxCD3) mechanism of action, without adverse events.These results are encouraging for the prospect of identifying a tolerable and active treatment regimen with higher and repeat dosing.Enrolment in Part 1 dose escalation continues to evaluate this novel mechanism designed to eliminate HBV-positive hepatocytes.(Eudract no.

Background The GSK3732394 multivalent protein was developed as a novel, long-acting, antiretroviral biologic treatment regimen with three independent, non–cross-resistant mechanisms for inhibiting HIV-1 entry. Methods A single-centre, Phase 1, double-blind, randomized, placebo-controlled study was conducted in healthy volunteers, using a 2-part adaptive study design: in Part 1, participants were randomized to receive subcutaneous injection of GSK3732394 or placebo (3:1) as single ascending doses (10-mg starting dose); in Part 2, participants were intended to receive multiple ascending doses. Primary and secondary objectives included safety, pharmacokinetics (PK) and pharmacodynamics (PD; cluster of differentiation four receptor occupancy [CD4 RO]) of GSK3732394 in healthy adults; PK/PD results in healthy volunteers were used to project HIV-1 treatment success. Results The most frequently reported adverse event was injection site reactions (ISRs; 8/18 [44%]). Most ISRs were mild (Grade 1–2; n = 7); one participant experienced a Grade 3 ISR (erythema ≥10 cm). All ISRs were delayed in onset (after Day 10). GSK3732394 demonstrated linear PK across all cohorts. Clearance was faster than expected, and PK/PD results were lower than expected, with the maximum dose investigated (80 mg) achieving mean trough CD4 RO of ∼25% on Day 7. The study was terminated as the PK/PD model linking PK and CD4 RO indicated that the maximum planned doses would not achieve the desired therapeutic profile. Conclusions This study demonstrated successful deployment of PK/PD dose relationships in the design and conduct of clinical trials by leveraging the findings toward predicting probability of success, resulting in appropriate early termination ( ClinicalTrials.gov , NCT03984812).

Abstract Background COVID-19 disproportionately results in hospitalization and death in older patients and those with underlying comorbidities. Sotrovimab is a pan-sarbecovirus monoclonal antibody that binds a highly conserved epitope of the SARS-CoV-2 receptor binding domain and has an Fc modification that increases half-life. Sotrovimab retains activity against UK, S. Africa, Brazil, India, New York and California variants in vitro. Objectives To evaluate the efficacy and safety of treatment with sotrovimab in high-risk, non-hospitalized patients with mild/moderate COVID-19, as part of the COMET-ICE clinical trial. Methods Multicenter, double-blind, phase 3 trial in non-hospitalized patients with symptomatic COVID-19 and ≥1 risk factor for disease progression were randomized 1:1 to an IV infusion of sotrovimab 500 mg or placebo. The primary efficacy endpoint was the proportion of patients with COVID-19 progression, defined as hospitalization > 24 hours or death, due to any cause, ≤29 days of randomization. Results The study met the pre-defined primary efficacy endpoint in a preplanned interim analysis: the risk of COVID-19 progression was significantly reduced by 85% (97.24% CI, 44% to 96%; P = 0.002) in 583 patients. In the final intention-to-treat analysis (N = 1057), the adjusted relative risk reduction was 79% (95% CI, 50% to 91%; p< 0.001) through Day 29 in recipients of sotrovimab (n=528) vs. placebo (n=529). Treatment with sotrovimab (ITT) resulted in a numerical reduction in the need for ER visits for illness management, hospitalization for acute illness management (any duration) or death (any cause) compared to placebo. No participants on sotrovimab required ICU admission, compared to 9 participants on placebo, of whom 4 participants required mechanical ventilation. No participants who received sotrovimab died, compared to 4 participants on placebo. The incidence of adverse events was similar between treatment arms and SAEs were numerically more common in the placebo arm. Conclusion Treatment with sotrovimab 500 mg IV resulted in a clinically and statistically significant reduction in progression of COVID-19 to hospitalization or death in patients with mild/moderate disease and was well-tolerated. Study funding GSK & VIR; NCT04545060 Disclosures Jaynier Moya, MD, VIR Biotechnology (Other Financial or Material Support, Jaynier Moya received non-financial support for serving as a clinical trial investigator for Vir Biotechnology) Diego Rodrigues Falci, MD, MSc, PhD, Gilead Sciences (Grant/Research Support, Scientific Research Study Investigator, Speaker's Bureau)GSK (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member)MSD (Speaker's Bureau)Pfizer (Speaker's Bureau)United Medical (Speaker's Bureau, Other Financial or Material Support) Joel Solis, MD, VIR Biotechnology (Other Financial or Material Support, Joel Solis received non-financial support for serving as a clinical trial investigator for Vir Biotechnology) Hanzhe Zheng, PhD, VIR Biotechnology (Employee) Nicola Scott, MSc, GlaxoSmithKline (Employee, Shareholder) Andrea L. Cathcart, PhD, Gilead (Shareholder)VIR (Employee, Shareholder) Christy Hebner, PhD, Vir Biotechnology (Employee, Shareholder) Jennifer Sager, PhD, GSK (Other Financial or Material Support)Vir Biotechnology (Employee, Shareholder) Erik Mogalian, PharmD, PhD, Vir Biotechnology (Employee, Shareholder) Daren Austin, PhD, GlaxoSmithKline (Employee, Shareholder) Amanda Peppercorn, MD, GlaxoSmithKline (Employee) Elizabeth L. Alexander, MD, MSc, GlaxoSmithKline (Grant/Research Support, Other Financial or Material Support)VIR Biotechnology (Employee, Shareholder, GSK pharmaceuticals) Wendy W. Yeh, MD, Vir Biotechnology (Employee) Almena Free, MD, Amgen (Scientific Research Study Investigator)Astra Zeneca (Scientific Research Study Investigator)Cardurian (Scientific Research Study Investigator)Coherus (Scientific Research Study Investigator)Freenome (Scientific Research Study Investigator)GlaxoSmithKline/Vir (Scientific Research Study Investigator)Ionis (Scientific Research Study Investigator)Kowa (Scientific Research Study Investigator)New Amsterdam (Scientific Research Study Investigator)Regenacy (Scientific Research Study Investigator)Romark (Scientific Research Study Investigator)Scynexis (Scientific Research Study Investigator) Cynthia Brinson, MD, Abbvie (Scientific Research Study Investigator)BI (Scientific Research Study Investigator)Gilead Sciences Inc. (Scientific Research Study Investigator, Advisor or Review Panel member, Speaker's Bureau, Personal fees)GSK (Scientific Research Study Investigator)Novo Nordisk (Scientific Research Study Investigator)ViiV Healthcare (Scientific Research Study Investigator, Advisor or Review Panel member, Speaker's Bureau) Melissa Aldinger, PharmD, VIR Biotechnology (Employee) Adrienne Shapiro, MD, PhD, Vir Biotechnology (Scientific Research Study Investigator)

Importance: Older patients and those with underlying comorbidities infected with SARS-CoV-2 may be at increased risk of hospitalization and death from COVID-19. Sotrovimab is a neutralizing antibody designed for treatment of high-risk patients to prevent COVID-19 progression. Objective: To evaluate the efficacy and safety of sotrovimab in preventing progression of mild to moderate COVID-19 to severe disease. Design: Randomized, double-blind, multicenter, placebo-controlled, phase 3 study. Setting: 57 centers in 5 countries. Participants: Nonhospitalized patients with symptomatic, mild to moderate COVID-19 and at least 1 risk factor for disease progression. Intervention: Patients were randomized (1:1) to an intravenous infusion of sotrovimab 500 mg or placebo. Main Outcomes and Measures: The primary efficacy outcome was the proportion of patients with COVID-19 progression, defined as all-cause hospitalization longer than 24 hours for acute illness management or death through day 29. Key secondary outcomes included the proportion of patients with COVID-19 progression, defined as emergency room visit, hospitalization of any duration, or death, and proportion of patients developing severe/critical respiratory COVID-19 requiring supplemental oxygen. Results: Among 1057 patients randomized (sotrovimab, 528; placebo, 529), all-cause hospitalization longer than 24 hours or death was significantly reduced with sotrovimab (6/528 [1%]) vs placebo (30/529 [6%]) by 79% (95% CI, 50% to 91%; P<.001). Secondary outcome results further demonstrated the effect of sotrovimab in reducing emergency room visits, hospitalization of any duration, or death, which was reduced by 66% (95% CI, 37% to 81%; P<.001), and severe/critical respiratory COVID-19, which was reduced by 74% (95% CI, 41% to 88%; P=.002). No patients receiving sotrovimab required high-flow oxygen, oxygen via nonrebreather mask, or mechanical ventilation compared with 14 patients receiving placebo. The proportion of patients reporting adverse events was similar between treatment groups; sotrovimab was well tolerated, and no safety concerns were identified. Conclusions and Relevance: Among nonhospitalized patients with mild to moderate COVID-19, a single 500-mg intravenous dose of sotrovimab prevented progression of COVID-19, with a reduction in hospitalization and need for supplemental oxygen. Sotrovimab is a well-tolerated, effective treatment option for patients at high risk for severe morbidity and mortality from COVID-19.

Aims To compare the airway potency, systemic activity and therapeutic index of three inhaled corticosteroids that differ in glucocorticoid receptor binding affinity, physicochemical and pharmacokinetic properties. Methods This escalating‐dose, placebo‐controlled, cross‐over study randomised adults with asthma to 1 or 2 treatment periods with ≥25 days washout in‐between. Each treatment period comprised five 7‐day dose escalations (μg/d): fluticasone furoate (FF; 25 → 100 → 200 → 400 → 800), fluticasone propionate (FP; 50 → 200 → 500 → 1000 → 2000), budesonide (BUD; 100 → 400 → 800 → 1600 → 3200) or placebo. Airway hyperresponsiveness to adenosine‐5'‐monophosphate (AMP PC 20 ) was assessed on day 8. Plasma cortisol was assessed on day 1 (predose baseline) and from pre‐PM dose on day 6 to pre‐PM dose day 7 (24‐h weighted mean). Results Fifty‐four subjects were randomised. FF showed greater airway potency than FP and BUD (AMP PC 20 dose at which 50% of the maximum effect is achieved [ED 50 ] values: 48.52, 1081.27 and 1467.36 μg/d, respectively). Systemic activity (cortisol suppression) ED 50 values were 899.99, 1986.05 and 1927.42 μg/d, respectively. The therapeutic index (ED 50 cortisol suppression/ED 50 AMP PC 20 ) was wider for FF (18.55) than FP (1.84) and BUD (1.31). FF 100 μg/d and 200 μg/d were both comparable in terms of airway potency with high doses of FP (≥1000 μg twice daily [BID]) and BUD (≥1500 μg/BID). The systemic activity of FF 100 μg/d and 200 μg/d (cortisol suppression: 7.41% and 14.28%, respectively) was comparable with low doses of FP (100 μg/BID and 250 μg/BID) and BUD (100 μg/BID and 200 μg/BID). Conclusion This study provides evidence that FF can provide more protection against airway hyperresponsiveness, with less systemic activity, than FP or BUD. This suggests that all inhaled corticosteroids are not therapeutically similar and may differ in their therapeutic index. (203162; NCT02991859).

IntroductionThis communication provides the justification supporting the selection of the mepolizumab 300 mg subcutaneous (SC) dose in patients with eosinophilic granulomatosis with polyangiitis (EGPA) or hyper-eosinophilic syndrome (HES).In rare diseases, the conduct of formal dose-ranging studies for determination of dosing recommendations is challenging due to limitations in access to the patient population. In such context, borrowing pharmacology information from other diseases that share a similar pathophysiology can be used to support dose selection for investigation, when the paucity of study patients requires innovative dose selection approaches. There are a number of common and rare diseases that are associated with elevated levels of blood and tissue eosinophils. Because reduction of blood eosinophils is the known pharmacologic effect of mepolizumab, blood eosinophil count data collected across multiple mepolizumab studies conducted in various eosinophilic diseases were leveraged to justify the dosing regimen proposed for investigation in the mepolizumab pivotal Phase III studies in patients with EGPA or HES. The aim was to select a mepolizumab dose that maximizes the pharmacologic effect to afford maximal efficacy in these rare diseases.MethodsA dose–response meta-analysis of blood eosinophil data collected from 16 mepolizumab studies (GSK IDs, MHE100185, MHE100901, MEA112997, MEA115575, SB-240563/006, MEA115588, MEA115661, MEA115666, MEA114092, SB-240563/001, SB-240563/035, SB-240563/017, MEE103226, MEE103219, SB-240563/018, and MEA115705) was conducted by using SAS version 9.2 (SAS Institute, Inc, Cary, North Carolina). These studies encompassed various eosinophilic diseases (including asthma of varying severity, HES, and eosinophilic esophagitis) and healthy subjects. The end point for all studies was defined as the absolute blood eosinophil count 4 weeks after the last dose. All doses were converted to SC equivalent doses based on mepolizumab SC absolute bioavailability.1Pouliquen IJ Kornmann O Barton SV Price JA Ortega HG. Characterization of the relationship between dose and blood eosinophil response following subcutaneous administration of mepolizumab.Int J Clin Pharmacol Ther. 2015; 53: 1015-1027Crossref PubMed Scopus (64) Google Scholar Absolute blood eosinophil count was logarithmically transformed before analysis, and baseline blood eosinophil count was included as a covariate in the model. The best model was defined as the model with the lowest Bayesian information criteria.The nonlinear maximal response (Emax) model was of the form:log(absolutebloodeosinophilcount)=β0+β1DosenDosen+(eβ2)nwhere β0 is an intercept, β1 is the maximal response attributable to mepolizumab, and β2 is the logarithm of the dose providing half-maximal drug effect. Hill function slope, n, was fixed to unity for parsimony.The final model was then inverted and used to represent the relationship between mepolizumab dose, D, and blood eosinophil count at baseline to achieve various absolute target counts.ResultsFrom the models tested, the model with the lowest Bayesian information criteria was a nonlinear Emax model with subject-level random effects (on β0 [intercept] and β1 [Emax]) and baseline blood eosinophil count included as a covariate on β1 (Emax) as well as on β2 (dose providing half-maximal drug effect).The relationship between mepolizumab dose and blood eosinophil count at baseline for various absolute target counts derived from the final nonlinear Emax model is represented in Figure 1. It clearly shows that the higher the baseline blood eosinophil count, the higher the mepolizumab dose required to achieve the same absolute target counts. For example, for a baseline count of 2000 cells/μL (or 2 giga [GI]/L), an SC dose of 100 mg is predicted to achieve a target absolute count of 300 cells/μL (or 0.3 GI/L), whereas an SC dose of 300 mg is predicted to achieve a target absolute count of 200 cells/μL (or 0.2 GI/L), affording a greater reduction. For a baseline count of 1000 cells/μL (or 1 GI/L), an SC dose of 100 mg is predicted to achieve a target absolute count of 150 cells/μL (or 0.15 GI/L).DiscussionIn the rare hypereosinophilic diseases EGPA and HES, characterized by elevated blood eosinophil counts at diagnosis (>1000 cells/μL [or 1 GI/L] and >1500 cells/μL [or 1.5 GI/L], respectively), the goal was to identify a dose that would maximize the reduction in blood eosinophil counts. The dose–response modeling exercise conducted provided evidence that blood eosinophil count at baseline is an important determinant of the overall mepolizumab pharmacologic response. The graphical representation of the mepolizumab dose required to achieve target absolute counts as a function of the baseline blood eosinophil count further illustrated that a higher mepolizumab dose is justified in eosinophilic diseases characterized by substantially higher blood eosinophil counts at diagnosis to maximize the pharmacology effect. Furthermore, exploration of mepolizumab pharmacology in a dose-ranging study conducted in subjects with severe eosinophilic asthma showed that the additional pharmacology benefit between a 250 mg intravenous dose (∼300 mg subcutaneously) and a 750 mg intravenous dose was marginal.2Pavord ID Korn S Howarth P Bleecker ER Buhl R Keene ON et al.Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial.Lancet. 2012; 380: 651-659Abstract Full Text Full Text PDF PubMed Scopus (1545) Google Scholar A dose of 300 mg subcutaneously was therefore selected for investigation in the pivotal Phase III EGPA and HES studies to ensure therapeutic benefit across the wide spectrum of blood eosinophils displayed by patients with EGPA and HES. Dose prediction from the modeling exercise was subsequently confirmed: in the pivotal Phase III studies conducted in EGPA3Wechsler ME Akuthota P Jayne D Khoury P Klion A Langford CA et al.Mepolizumab or placebo for eosinophilic granulomatosis with polyangiitis.N Engl J Med. 2017; 376: 1921-1932Crossref PubMed Scopus (439) Google Scholar and HES,4Roufosse F Kahn JE Rothenberg ME Wardlaw AJ Klion AD Yun Kirby S et al.Efficacy and safety of mepolizumab in hypereosinophilic syndrome: a phase III, randomized, placebo-controlled trial.J Allergy Clin Immunol. 2020; 146 (05): 1397Abstract Full Text Full Text PDF PubMed Scopus (45) Google Scholar mepolizumab 300 mg SC every 4 weeks was found to be efficacious and well tolerated.ConclusionsA higher mepolizumab dose of 300 mg subcutaneously every 4 weeks is justified in eosinophilic diseases characterized by a substantially higher blood eosinophil count at diagnosis (compared with the therapeutic SC dose of 100 mg in severe asthma with an eosinophilic phenotype). This 300 mg SC dose allows maximal pharmacologic effect and ensures therapeutic benefit across the wide spectrum of blood eosinophils displayed in this patient population, with no substantial additional pharmacologic benefit afforded beyond this dose, regardless of the absolute count at baseline. IntroductionThis communication provides the justification supporting the selection of the mepolizumab 300 mg subcutaneous (SC) dose in patients with eosinophilic granulomatosis with polyangiitis (EGPA) or hyper-eosinophilic syndrome (HES).In rare diseases, the conduct of formal dose-ranging studies for determination of dosing recommendations is challenging due to limitations in access to the patient population. In such context, borrowing pharmacology information from other diseases that share a similar pathophysiology can be used to support dose selection for investigation, when the paucity of study patients requires innovative dose selection approaches. There are a number of common and rare diseases that are associated with elevated levels of blood and tissue eosinophils. Because reduction of blood eosinophils is the known pharmacologic effect of mepolizumab, blood eosinophil count data collected across multiple mepolizumab studies conducted in various eosinophilic diseases were leveraged to justify the dosing regimen proposed for investigation in the mepolizumab pivotal Phase III studies in patients with EGPA or HES. The aim was to select a mepolizumab dose that maximizes the pharmacologic effect to afford maximal efficacy in these rare diseases.

Conventional epidemiological models require estimates of important parameters including incubation time and case fatality rate that may be unavailable in the early stage of an epidemic. For the ongoing SARS-COV-2 epidemic, with no previous population exposure, alternative prediction methods less reliant on assumptions may prove more effective in the near-term. We present three methods used to provide early estimates of likely SARS-COV-2 epidemic progression. During the first stage of the epidemic, growth rate charts revealed the UK, Italy and Spain as outliers, with differentially increasing growth of deaths over cases. A novel data-driven time-series model was then used to near-cast 7-day future cases and deaths with much greater precision. Finally, an epidemio-statistical model was used to bridge from near-casting to forecasting the future course of the global epidemic. By applying multiple approaches to global SARS-COV-2 data, coupled with mixed-effects methods, countries further ahead in the epidemic provide valuable information for those behind. Using current daily global data, we note convergence in near-term predictions for Italy signifying an appropriate call on the future course of the global epidemic. For the UK and elsewhere, prediction of peak and eventual time to resolution is now possible.

MID3: Mission Impossible, or Model-Informed, Drug Discovery and Development? At the 2019 American Society for Clinical Pharmacology and Therapeutics (ASCPT) annual meeting, point-counterpoint discussions were held on key challenges that limit, and future directions that enhance the adoption of model-informed drug discovery and development (MID3) across the drug discovery, development, regulatory, and utilization continuum. We envision that the opportunities discussed and lessons learned from having contrasting perspectives on issues that lack consensus may aid our discipline in more effectively implementing MID3 principles. The evolution of the science and application of quantitative approaches over the past 50 years in drug discovery, development, regulatory approval, and clinical utilization (DDRU) is incontrovertible. Numerous publications have extensively documented case studies demonstrating the impact of modeling and simulation (M&S) in decision making in industry, regulatory, and practice settings.1-7 At the same time, there appears to be consensus within the community that the discipline needs to continue to evolve from one-off case studies to a paradigm of systematic, best practices-driven approaches to improving decision making across the DDRU continuum. Over the past two decades, significant efforts have been made to appropriately frame the scope and promise of the quantitative discipline of pharmacometrics. This is apparent in the evolution of the terms used to describe the discipline, from M&S, to model-based drug development and model-based drug discovery, to the current usage: MID3. Marshall and colleagues have described MID3 as a "quantitative framework for prediction and extrapolation centered on knowledge and inference generated from integrated models of compound, mechanism, and disease level data aimed at improving the quality, efficiency, and cost effectiveness of decision making."1 It should be noted that in the context of regulatory decision making, reference is often made to MIDD, which excludes the discovery term in MID3. There has been tangible progress towards the goal of MID3 as "business as usual." Best practices have been developed for MID3 with the objective of improving implementation, standardization, and acceptance to various stakeholders. Reviews of the literature and standard practice across organizations have generally affirmed the documented standards, acknowledged the modest improvements in organizational awareness, and set expectations for future wider use and impact, and have also highlighted areas for further improvement.8 There have been significant developments in the regulatory domain, with model-informed drug development (MIDD) being formally noted in the Prescription Drug User Fee Act VI, highlighting model-informed decisions made in the areas of extrapolation and dose optimization, inference about efficacy, clinical trial design, and informing policy.9 Additionally, the US Food and Drug Administration (FDA) has implemented a new Model-Informed Drug Development Paired Meeting Pilot Program. Their early experience has been encouraging and may ultimately help achieve transformative applications of MIDD approaches in drug development programs as a matter of routine.10, 11 While there is a general appreciation of the positive impact of MID3 on the quality and efficiency of decision making and its potential to have a significant impact on the well-documented research and development (R&D) productivity challenges3-6, MID3 in practice can be isolated and inconsistently applied across the community, with its full potential yet unrealized. We posit that this may be due to a number of factors ranging from unresolved scientific and technical issues, lack of standardized processes, operational and organizational barriers, and educational and knowledge gaps both within the scientific and clinical research community.12-14 Moreover, there is continued lack of clarity of the return on investment among decision makers, the novelty and ever-increasing complexity of treatment modalities, and need for more precisely addressing the needs of patient populations. To tackle a few of these foundational challenges, an interactive discussion using a point-counterpoint format was held at the ASCPT 2019 annual meeting. The format was chosen to encourage critical thinking based on opposing views from a panel of experts, who occasionally took positions that were extreme and not necessarily aligned with their own personal opinions. The diversity of perspectives was felt to be particularly helpful as the selected topics lack consensus within the community and may have multiple plausible/correct answers depending on the context (Table 1). The debate themes were chosen to be of broad value to students, early career members, and experienced scientists within and outside the clinical pharmacology and drug development community at ASCPT, and regardless of the organization and setting in which they reside. The themes ranged from those that baselined the state of the art by defining success now and into the future (theme 1), behaviors and approaches to delivering useful models for the right questions and decisions (themes 2 and 3), looking into the future in terms of underserved areas of great potential (theme 4), disruptive innovations (theme 5), and educational and organizational opportunities to prepare and position the discipline for success (themes 6 and 7, respectively). To encourage interactivity, the audience was polled prior to the session, and after each debate and in real time, to assess level of agreement between the panelists and audience, and to what extent the debates changed audiences' perceptions. The presentation slides from the session can be found in the Supplementary Material. The assessment of whether MID3 has been a success can be made based on the following four considerations (Figure 1). First, the evidence is clear that pharmacometricians are highly sought after to help inform important decisions that span across the DDRU continuum. The fact that pharmaceutical companies, regulatory authorities, and other domains have recognized and created dedicated departments to formalize input from quantitative disciplines is a primary indicator of the power and influence that scientists in the field of pharmacometrics have gained over the past 40-plus years. Second, because of the demonstrated ability to integrate data to generate knowledge, pharmacometricians today have clearly defined career paths and leadership opportunities as well as organizational infrastructure that enable them to climb the career ladder, take up increasingly complex challenges, and expand the spheres of influence. Third, perhaps the most important metric of success of MID3 progression is the level of impact that scientists in the discipline have had on advancing public health. There are numerous, well-documented innovations in the areas of dose optimization, alternative approaches to evidence generation, novel clinical trial designs, and drug approvals that would not have been possible without applying pharmacometric concepts to problem solving. Finally, from an economic standpoint, there has been a sustained investment in the area over the past 20-plus years and the current demand for pharmacometric scientists continues to outstrip supply. It is important to contextualize the speed and depth of these achievements and recognize that the field is still in its nascent stages. This is evident when compared with related disciplines such as statistics, where for instance, the concept of confidence intervals was introduced in 1937 and it was not until 1962 that the regulatory amendment to expectations around the efficacy of medicines was introduced. Therefore, the progression and translation of techniques and methodologies into the critical path in drug development can take decades. MID3 has been a smashing success when viewed from this context. Moreover, the future holds tremendous promise as new developments and opportunities emerge in the areas of physiologically-based pharmacokinetics/pharmacodynamics, quantitative systems pharmacology, real-world evidence, decision-support systems, and patient care. The problem with MID3 as a tool supporting clinical and regulatory decision making is that the beauty is in the eye of the beholder. The points made in favor of it represent a self-assessment that is biased with what may be close to the heart. However, when looking at the facts from an external perspective, it is readily apparent that critical decisions continue to be made using P values and rarely embrace the principles founded in the learn-and-confirm paradigm, which is the foundation of MID3. The fact that M&S is undervalued is reflected in its conspicuous absence from the pyramid of evidence-based medicine that includes not just randomized controlled trials and systematic reviews but also observational studies, uncontrolled cohort/case studies, and animal research.15 Furthermore, MID3 is essentially disconnected from the promised land of artificial intelligence (big data and personalized medicine). A literature search has highlighted that even the zebrafish model is advertised as a predictive tool for personalized medicine, whereas the term MID3 is barely mentioned, despite the fact that data integration and knowledge generation can play a central role in the development of dosing algorithms. From a regulatory perspective, it should be noted that the 2018 review on new therapy approvals by the FDA's Center of Drug Evaluation and Research does not mention the role of M&S, pharmacometrics, or MID3 as an enabler of the successful programs, raising questions regarding whether it is a core element of the modern toolkit for innovation. This is not to say that MID3 has had no impact, as the volume of ad hoc case studies clearly demonstrate. Unfortunately, one could argue that drug development decision making would not be very different if pharmacometrics did not exist as a discipline. Therefore, for MID3 to emerge as a core driver of innovation, the discipline needs to rechannel its resources to a renewed focus on knowledge generation and management and less on developing productivity tools (e.g., standardizing its outputs). In contrast to what has been observed with Big Data, MID3 has fallen short of expectations. One needs a similar momentum to ensure MID3 becomes mainstream, elevating the drug developing and decision-making process. Pharmacometricians enjoy the operational aspects and the challenge of model development—modeling is fun! The first inclination is to immediately start model building upon receipt of data (Figure 2). An analysis plan may exist, but such plans generally contain template text with vague objectives rather than focusing on the development, clinical, and/or regulatory question that M&S will support. However, even if the question is clear and the modeler begins with the end in mind, it is easy to be distracted by interesting trends in the data, or the obsession with getting a perfect fit. Pharmacometricians often add unnecessary complexity to models to "get the line through the points." It's uncomfortable to accept that a model does not have a perfect fit. For example, if a model is effectively estimating the exposure metric (area under the curve (AUC)) for subsequent pharmacokinetics/pharmacodynamics modeling, one may consider the model complete. But we are often compelled to try complex absorption models to better characterize Cmax and improve the fit. Indeed, the diagnostics look better, but this adds time and complexity and reduces understanding and credibility. Thus, while the model "fits," it is of limited or no value if it does not answer the question for which it was developed or is not available before the deadline. To ensure that M&S adds value (and to avoid doing more than what is required), pharmacometricians must communicate with their project teams before any data analysis starts to understand the key strategic development questions, clinical context, available data, assumptions, and decision criteria. A M&S plan should be developed accordingly and shared with (and agreed upon by) the team. It is also imperative to regularly check in with the team during model development as well to ensure that the model being developed remains consistent with their needs and to course correct as needed. Lastly, pharmacometricians must become better scientific communicators whose objective should be to impact and influence quantitative decisions, rather than impress (or more likely confuse) teams with technical progress. Modelers often relay the features and processes associated with model development evaluation (e.g., goodness of fit, parameter tables), using technical "lingo" that is incomprehensible to other stakeholders. Unfortunately, this usually obliterates any impact that the model may have. For pharmacometricians to be successful, they must understand WHY M&S is being used, WHAT questions should be answered, WHO will use the results, and HOW they will be used. Thus, the primary limitation to success of MID3 is communication: We need to talk more and model less! While the importance of communication is fully acknowledged, it is equally critical, as noted by Alexander Pope in his poem "An Essay on Criticism" composed in 1711, "a little learning is a dang'rous thing," to understand that communication without subject mastery and elucidation of the strengths and weaknesses can be misleading at best and dangerous at worst. In fact, politicians are a great example of such a setting. One needs therefore to eliminate the root causes or the primary limitations for the successful implementation of MID3 by the ad hoc nature of MID3. Instead, models should become an integral part of the evidence synthesis framework, in a similar continuum to what is currently done for systematic reviews. One should integrate knowledge, building up on prior evidence, and by doing so summarize and scrutinize the predictive performance, strengths, and limitations of the models. The real problem pharmacometricians face is not poor communication, it is the deception of perception within the community that performs and presents models without full understanding of context, assumptions, and limitations. Evidence synthesis is more than fitting lines through data points. In this regard, the MID3 community is a long way away from the successes of prognostic, predictive, and diagnostic models, which are based on extensive and continuous data collection and exhaustive characterization of model performance. Because currently models are not continuously verified and improved upon, the ability to build trust by demonstrating reliability over time is absent. It therefore stands to reason that pharmacometric models are not seen by decision makers and stakeholders as predictive instruments with the appropriate performance attributes around specificity, sensitivity, and predictive performance. This has the consequence of eroding trust and credibility. The need of the hour is to recognize that the core competency of MID3 is professional excellence. MID3 thus needs to become a process at the enterprise level, with a well-defined development path for scientists in the field, as to ensure the acquisition of core competencies across different knowledge domains. 13 The competencies required to address existing limitations are lacking and cannot be compensated for by communicating better or more. Pharmacometric models range from the oversimplified compartment models to complex quantitative systems pharmacology models where it is impossible to estimate every parameter with precision (Figure 3). Are these "wrong" models dangerous, though? It depends! If the only purpose of a model was to be descriptive, a cubic spline would be sufficient; however, one can't extrapolate from a model whose sole purpose is to "connect the dots." Empirical models have a useful place in MID3 but must be used with caution; if a model optimized only based on goodness-of-fit is used to extrapolate and make subsequent drug development or regulatory decisions, danger can arise. Some illustrative examples are highlighted below. Indirect response models with two different mechanisms represent similar though not identically shaped response-time curves, and could, in theory, both be successfully fit to data at hand.16 If a naïve modeler chooses a model based on goodness-of-fit rather than the underlying mechanism, they may select the wrong one whose parameters will not have physiological/pharmacological meaning, and extrapolation to make decisions on study design or dosing may have future negative consequences. There is an ongoing debate in the pharmacometrics community around fixing versus estimating allometric scaling exponents; one against the latter is that if the population being modeled has a relatively narrow weight range, an estimated exponent will likely not be representative of a wider patient population. If the model is used to extrapolate to another population (e.g., adults to children), the subsequent estimate of clearance in the new population may be off by several fold, propagating the error to the calculation of AUC, and subsequent estimates of dose in the new population. In population pharmacokinetics, spurious covariates that are statistically significant but not clinically relevant may be added to the model if modelers let the data rather than the clinical question drive the selection. A dose modification for an irrelevant subgroup ultimately may lead to an FDA-mandated label change! Similarly, rejecting a known important/influential covariate based on lack of statistical significance may lead to a decision based on incomplete information. Lastly, pharmacometrics is a very heterogeneous field. Modelers with excellent technical skills but limited understanding of pharmacology or physiology may not be aware that they are using the wrong model or getting parameter values that are impossible or not physiologically reasonable, because they are relying on goodness of fit to indicate that the model is appropriate. In addition, they may make incorrect modifications that violate the biology to decrease computational complexity, which improves the fit but invalidates model utility. A wrong model may lead to incorrect dose predictions or incomplete understanding of the mechanism of action, which may cause real harm to patients. Decisions based on the faulty model may bring an ineffective or unsafe drug forward, or suggest study designs that will fail, making patients wait that much longer to get a drug that they need. Therefore, wrong models can indeed be dangerous! However, there seems to be no good reason to treat pharmacometrics (and M&S in general) different from any other scientific discipline and instead inculcate Karl Popper's universal principles and consider models as hypotheses which are testable and falsifiable.17 Viewed in this way, a wrong model becomes a rejected hypothesis which may be very impactful in the learning phase of drug development. In particular, if the model is mechanistic, it tells us that our current understanding of biology, pathophysiology, and disease is incorrect. Based on this, the model can be modified and/or expanded and becomes the next hypothesis which can be tested in the next experiment/trial. In this extended version of Sheiner's learn and confirm paradigm, the difference between having a model or not becomes the difference between having an explicit, quantifiable, and testable hypothesis or not. It is difficult to see how anyone could not be in favor of the former. Thus, we propose that the pharmacometrics mantra changes to "all models are hypotheses" and that the field embraces the concept that "wrong models can be very useful." The first point to consider is that the decision-making process in pharmaceutical R&D is broken (Figure 4). Relative to other science-based industries, it can be argued that the rigor with which decisions are made in drug development pales in comparison to the scientific excellence with which data are gathered from clinical trials and other data sources. Decisions are often too dependent on individual intuition and power structures, often limited to a statistical assessment at the protocol level without any integration of totality of evidence, leading to a lack of objective quantitative assessments in many cases. The most urgent need for our industry is to evolve toward the formal decision processes used in other science-based industries, driving all decisions with quantitative analyses. This requires a fundamental change to the drug development decision-making process, which is currently an empirical and opaque exercise that involves subjective intuition based on clinical trial results and several factors external to the trial observations, with uncertain assumptions about the translation of these results. The proposed approach would align with those used by other science-based industries by making assumptions explicit and transparent, considering the clinical trial data as a single element of the decision, augmented by prior knowledge, and utilizing model-based projections of clinically meaningful outcomes to quantitatively characterize all inputs to that decision. In other words, this is a model-based projection of decision criteria informed by a clinical trial, where the clinical trial data is input into the decision but not the ultimate decision criterion itself. When viewed in this context, it becomes clear that every clinical trial becomes an opportunity to provide a valuable data point to the decision process. Therefore, by extension, all decisions, not just trials, would need to be informed by simulations with the mechanics in place to deliver decision guidance at the time of the top line report of the trial. This has been accomplished in other industries and can be achieved in drug development with a change in mindset (proactive and long-term oriented vs. reactive and short term) and a shift in our area of focus from late-stage to early-stage drug development. This change in emphasis is aimed at conditions where the uncertainty in the decision space and, thereby, the probability of inaccurate decision making is largest. In doing so, the totality of all available evidence can be integrated and utilized in an effective and transparent manner to inform decisions and ultimately improve R&D productivity. While M&S is routinely deployed to understand exposure-response, support dose selection, and extrapolate to special populations, its application in supporting trial designs has been less systematic. We can all agree that gaining efficiency in drug development is critical to improving the probably of success and reducing the cost of development. There are many approaches other than M&S to enhance the efficiency of the study design, including implementation of futility analysis, supporting design with Bayesian approach, and/or using an adaptive design. Implementing a full M&S approach requires adequate resources, planning, and relevant data and models that can be used to predict study outcome. It should be considered a unique tool, ideal in certain situations, but should not be considered essential for every program. For certain established therapeutic areas, some aspects of the study design such as duration of treatment may be set by regulatory expectations, while other programs which have the potential to be transformational require companies to react rapidly and initiate the next clinical study as soon as possible to provide breakthrough drugs to patients. M&S still lack enough automation, standardization, and efficiency to become a routine deliverable to support ALL clinical studies rather than be deployed when it can have greatest impact. The need to prioritize investment toward industrializing mainstream pharmacometric approaches cannot be overemphasized, particularly as the majority of the audience considered MID3 not to be a smashing success (Figure 5). An analogy can be drawn to the evolution of the automobile industry. The first commercially used internal combustion engine was invented in 1859, and the modern version of it in 1876. However, horse carriages continued to be popular for several decades, and it was not until 1913 when the Ford motor company incorporated its first moving assembly line, triggering large-scale production and industrialization, which led to the success of the technology. Pharmacometrics is facing a similar situation today as it not a sustaining technology but a disruptive innovation. This is still a relatively new market, for which many may not even be aware of the need and certainly may not realize its full potential. The discipline has brought tools together from mathematics, statistics, pharmacology, physical chemistry, and medicine domains to solve new and complex problems to optimize therapy, and such disruptive innovations can only become mainstream if they are more accessible. A useful example here is the Kaplan-Meier analysis of survival data, which is based on relatively complex statistical principles, and yet is used widely by many scientists and clinicians, including those not trained in statistics, due to accessibility and interpretability. Similarly, there are widely-used pharmacometric methodologies (e.g., population PK modeling of sparse data, application of sigmoidal drug effect models) that do not need greater sophistication but unfortunately consume disproportionate time and resources due to lack of standardization. Therefore, the success of the disciplines depends on standardizing routine pharmacometric applications and making them predictable. Here, the emphasis around standardization is beyond documentation of the methodology and processes, which most organizations already do, but the consistent application of models that have stood the test of time (e.g., sigmoidal drug effect models) and which enable the majority of drug development decisions (e.g., phase 3 dose selection) today. It is proposed that this will not only enhance trust and credibility among stakeholders but will create the much-needed but scarcely available space to embrace new tools and ultimately allow the discipline to expand its influence into newer areas. Do we need to focus more on developing sophisticated tools and methods? Yes, we do. In fact, the analogy of the internal combustion engine illustrates this need as it will likely be obsolete within the next 10 years. Automation of methods without innovation leads to stagnation as this allows us to not to think about the problem. More important, the impact of MID3 today is limited to a small set of problems. Pharmacometrics as a tool has been marginalized to a small part of the M&S sandbox, limited to addressing traditional questions such as dose selection and dose adjustments for labeling. Thus, when resources are geared toward automation of the status quo, we limit future innovation and the scope of influence on drug development decision making. Advancements in quantitative sciences create a much larger M&S playground. We would limit ourselves if we don't expand beyond the typical pharmacometrics sandbox. Our discipline should embrace new methods, such as systems biology/pharmacology, artificial intelligence, machine learning, computational genomics, big data, Bayesian networks, etc. Although we need to be beware of unfounded enthusiasm (hype), a thoughtful application of these new methods has the potential to broaden the scope of influence and increase the opportunity to interact with other disciplines. For example, these tools and methods could serve as bridges to other disciplines, as the quantitative underpinning will help address new questions in in areas such as translational medicine, health economics, digital health, portfolio strategies, and real-world evidence. Innovating at the intersection of multiple disciplines has historically led to novel solutions in this discipline, and continuing to emphasize that over automation of current methods is more likely to have a positive impact on drug development and patients' lives. We have seen the FDA's MIDD pilot program, but perhaps missed the FDA's original pilot program in Statistical Inference? Oh, that's right, there wasn't one. In fact, after Neyman-Pearson methods of statistical inference were introduced in 1933, they were rapidly adopted as a means of interpreting data (Figure 6). Without a shadow of doubt, it is statistical thinking that has done more to influence drug development from the foundations of the FDA, through to the requirements for demonstration of efficacy in 1962 (Kefauver-Harris Drug Amendments). That does not mean that models are not of interest—inference falls into two camps: Interpolation (which is really the realm of statistics) and extrapolation outside the "data comfort zone" (pharmacometrics). Technology now allows the simple application of models by the uninformed hobbyist. Want to fit population-PK models—aka "nonlinear mixed-effects statistical models" (some of the MOST sophisticated statistics)—no problem, there is a software that can do that for you; even better, one does not even have to see an equation! Pharmacometricians must blend the application of models—with all their assumptions and flaws, with a thorough grounding in the methods of statistical inference necessary for decision making. Without the former, there will be no means of extrapolation. Without the latter there can be no acceptance. Pharmacometricians, by proper training in mathematics and statistics, should know the limitations of models—the pharmacometrician knows that the data is described by a poly-exponential function (the model), nothing more, but does not have to ascribe physiological importance to the parameters of the model. The nonexpert believes that there is an underly

With regard to the current coronavirus disease 2019 (COVID-19) outbreak, the recent publications of inhibitory concentrations for a range of antiviral treatments1-4 permits the evaluation of possible translation of therapeutic utility to humans. Using estimated half-maximal viral inhibitory concentration (IC50) values and published pharmacokinetic exposure measures (area under the curve, clearance, and population variability) sourced from prescribing information,5-7 published studies8-13 and published regulatory agency report,14 it is possible to calculate doses necessary to inhibit virus in vivo based on well-established translation of anti-infective agents (assuming free drug hypothesis and translational serum albumin protein binding to humans). Table 1 shows the inhibitory potencies, exposures, and predicted average concentration population range for 10 drugs tested in vitro.1-4 Of note, we find that only 4 drugs (favipiravir, nitazoxanide, chloroquine, and hydroxychloroquine) are predicted to achieve in vivo average concentrations in excess of in vitro IC50 values. Three drugs (nitazoxanide, chloroquine, and hydroxychloroquine) are predicted to match IC80s, (defined as 4× IC50), and only hydroxychloroquine to match IC90 (9× IC50). An increased daily dose of 500-mg remdesivir, or 2500-mg niclosamide is also predicted to match in vitro IC50s in half of patients. At a population level, clinical doses necessary to provide 90% inhibition in at least 90% of patients (as opposed to 50%) are not accessible for any agent except for hydroxychloroquine. Further clinical evaluation of nitazoxanide (1000 mg/day), hydroxychloroquine (620 mg/day), chloroquine (600 mg/day), remdesivir (500 mg/day), and niclosamide (2500 mg/day) appear merited based on in vitro to in vivo translational evidence. These clinical studies must be conducted using proper study design (randomized, double blinded, etc) and must assess efficacy and toxicity using appropriate measures and evaluation of safety and tolerability. Caution is additionally advised for agents that are renally cleared, where exposure may be higher than indicated in our population analysis. The authors are employees and stockholders of GlaxoSmithKline.