Driving Efficiency: Leveraging Model-Informed Approaches in 505(b)(2) Regulatory Actions.

Introduced by the Hatch-Waxman Amendments of 1984, 505(b)(2) applications permit the US Food and Drug Administration to rely, for approval of a new drug application, on information from studies not conducted by or for the applicant and for which the applicant has not obtained a right of reference. This pathway is designed to circumvent the unnecessary duplication of studies already conducted on a previously approved drug. It can lead to a considerably more efficient and expedited route to approval compared to a traditional development path. Model-informed drug development refers to the utilization of a diverse array of quantitative models in drug development to streamline the decision-making process. In this approach, diverse quantitative models that integrate knowledge of physiology, disease processes, and drug pharmacology are employed to address drug development challenges and guide regulatory decisions. Integration of these model-informed approaches into 505(b)(2) regulatory submissions and decision-making can further expedite the approval of new drugs. This article discusses some applications of model-informed approaches that were used to support 505(b)(2) drug development and regulatory actions. Specifically, various quantitative models such as population pharmacokinetic and exposure-response models have been employed to provide evidence of effectiveness, guide dosing in subgroups such as subjects with hepatic or renal impairment, and inform policies. These case study examples collectively underscore the significance of model-informed approaches in drug development and regulatory decisions associated with 505(b)(2) submissions.

Physiologically based modeling reveals different risk of respiratory depression after fentanyl overdose between adults and children.

Despite a rapid increase in pediatric mortality rate from prescription and illicit opioids, there is limited research on the dose-dependent impact of opioids on respiratory depression in children, the leading cause of opioid-associated death. In this article, we extend a previously developed translational model to cover pediatric populations by incorporating age-dependent pharmacokinetic, pharmacodynamic, and physiological changes compared to adults. Our model reproduced previous perioperative clinical findings that adults and children have similar risk of respiratory depression at the same plasma fentanyl concentration when specific endpoints (minute ventilation, CO2 tension in the blood) were used. However, our model points to a potential caveat that, in a perioperative setting, routine use of mechanical ventilation and supplemental oxygen maintained the blood and tissue oxygen partial pressures in patients and prevented the use of oxygen-related endpoints to evaluate the consequences of respiratory depression. In a community setting when such oxygenation procedures are not immediately available, our model suggests that the higher oxygen demand and reduced cerebrovascular reactivity could make children more susceptible to severe hypoxemia and brain hypoxia, even with the same plasma fentanyl concentration as adults. Our work indicates that when developing intervention strategies to protect children from opioid overdose in a community setting, these pediatric-specific factors may need to be considered.

Intranasal Naloxone Repeat Dosing Strategies and Fentanyl Overdose: A Simulation-Based Randomized Clinical Trial.

Importance: Questions have emerged as to whether standard intranasal naloxone dosing recommendations (ie, 1 dose with readministration every 2-3 minutes if needed) are adequate in the era of illicitly manufactured fentanyl and its derivatives (hereinafter, fentanyl). Objective: To compare naloxone plasma concentrations between different intranasal naloxone repeat dosing strategies and to estimate their effect on fentanyl overdose. Design, Setting, and Participants: This unblinded crossover randomized clinical trial was conducted with healthy participants in a clinical pharmacology unit (Spaulding Clinical Research, West Bend, Wisconsin) in March 2021. Inclusion criteria included age 18 to 55 years, nonsmoking status, and negative test results for the presence of alcohol or drugs of abuse. Data analysis was performed from October 2021 to May 2023. Intervention: Naloxone administered as 1 dose (4 mg/0.1 mL) at 0, 2.5, 5, and 7.5 minutes (test), 2 doses at 0 and 2.5 minutes (test), and 1 dose at 0 and 2.5 minutes (reference). Main Outcomes and Measures: The primary outcome was the first prespecified time with higher naloxone plasma concentration. The secondary outcome was estimated brain hypoxia time following simulated fentanyl overdoses using a physiologic pharmacokinetic-pharmacodynamic model. Naloxone concentrations were compared using paired tests at 3 prespecified times across the 3 groups, and simulation results were summarized using descriptive statistics. Results: This study included 21 participants, and 18 (86%) completed the trial. The median participant age was 34 years (IQR, 27-50 years), and slightly more than half of participants were men (11 [52%]). Compared with 1 naloxone dose at 0 and 2.5 minutes, 1 dose at 0, 2.5, 5, and 7.5 minutes significantly increased naloxone plasma concentration at 10 minutes (7.95 vs 4.42 ng/mL; geometric mean ratio, 1.95 [1-sided 97.8% CI, 1.28-∞]), whereas 2 doses at 0 and 2.5 minutes significantly increased the plasma concentration at 4.5 minutes (2.24 vs 1.23 ng/mL; geometric mean ratio, 1.98 [1-sided 97.8% CI, 1.03-∞]). No drug-related serious adverse events were reported. The median brain hypoxia time after a simulated fentanyl 2.97-mg intravenous bolus was 4.5 minutes (IQR, 2.1-∞ minutes) with 1 naloxone dose at 0 and 2.5 minutes, 4.5 minutes (IQR, 2.1-∞ minutes) with 1 naloxone dose at 0, 2.5, 5, and 7.5 minutes, and 3.7 minutes (IQR, 1.5-∞ minutes) with 2 naloxone doses at 0 and 2.5 minutes. Conclusions and Relevance: In this clinical trial with healthy participants, compared with 1 intranasal naloxone dose administered at 0 and 2.5 minutes, 1 dose at 0, 2.5, 5, and 7.5 minutes significantly increased naloxone plasma concentration at 10 minutes, whereas 2 doses at 0 and 2.5 minutes significantly increased naloxone plasma concentration at 4.5 minutes. Additional research is needed to determine optimal naloxone dosing in the community setting. Trial Registration: ClinicalTrials.gov Identifier: NCT04764630.

A metagenomic analysis for combination therapy of multiple classes of antibiotics on the prevention of the spread of antibiotic-resistant genes.

Antibiotics used systemically to treat infections may have off-target effects on the gut microbiome, potentially resulting in the emergence of drug-resistant bacteria or selection of pathogenic species. These organisms may present a risk to the host and spread to the environment with a risk of transmission in the community. To investigate the risk of emergent antibiotic resistance in the gut microbiome following systemic treatment with antibiotics, this metagenomic analysis project used next-generation sequencing, a custom-built metagenomics pipeline, and differential abundance analysis to study the effect of antibiotics (ampicillin, ciprofloxacin, and fosfomycin) in monotherapy and different combinations at high and low doses, to determine the effect on resistome and taxonomic composition in the gut of Balb/c mice. The results showed that low-dose monotherapy treatments showed little change in microbiome composition but did show an increase in expression of many antibiotic-resistant genes (ARGs) posttreatment. Dual combination treatments allowed the emergence of some conditionally pathogenic bacteria and some increase in the abundance of ARGs despite a general decrease in microbiota diversity. Triple combination treatment was the most successful in inhibiting emergence of relevant opportunistic pathogens and completely suppressed all ARGs after 72 h of treatment. The relative abundances of mobile genetic elements that can enhance transmission of antibiotic resistance either decreased or remained the same for combination therapy while increasing for low-dose monotherapy. Combination therapy prevented the emergence of ARGs and decreased bacterial diversity, while low-dose monotherapy treatment increased ARGs and did not greatly change bacterial diversity.

Informing a Comprehensive Risk Assessment of Infant Drug Exposure From Human Milk: Application of a Physiologically Based Pharmacokinetic Lactation Model for Sotalol.

Characterization of infant drug exposure through human milk is important and underexplored. Because infant plasma concentrations are not frequently collected in clinical lactation studies, modeling and simulation approaches can integrate physiology, available milk concentrations, and pediatric data to inform exposure in breastfeeding infants. A physiologically based pharmacokinetic model was built for sotalol, a renally eliminated drug, to simulate infant drug exposure from human milk. Intravenous and oral adult models were built, optimized, and scaled to an oral pediatric model for a breastfeeding-relevant age group (<2 years). Model simulations captured the data that were put aside for verification. The resulting pediatric model was applied to predict the impacts of sex, infant body size, breastfeeding frequency, age, and maternal dose (240 and 433 mg) on drug exposure during breastfeeding. Simulations suggest a minimal effect of sex or frequency on total sotalol exposure. Infants in the 90th percentile in height and weight have predicted exposures ≈20% higher than infants of the same age in the 10th percentile due to increased milk intake. The simulated infant exposures increase throughout the first 2 weeks of life and are maintained at the highest concentrations in weeks 2-4, with a consistent decrease observed as infants age. Simulations suggest that breastfeeding infants will have plasma concentrations in the lower range observed in infants administered sotalol. With further validation on additional drugs, physiologically based pharmacokinetic modeling approaches could use lactation data to a greater extent and provide comprehensive information to support decisions regarding medication use during breastfeeding.

A Bioanalytical Method for Quantification of N-nitrosodimethylamine (NDMA) in Human Plasma and Urine with Different Meals and following Administration of Ranitidine.

Control of N-nitrosoamine impurities is important for ensuring the safety of drug products. Findings of nitrosamine impurities in some drug products led FDA to develop new guidance providing recommendations for manufacturers towards prevention and detection of nitrosamine impurities in pharmaceutical products. One of these products, ranitidine, also had a published in vivo study, which has since been retracted by its authors, suggesting a potential for in vivo conversion of ranitidine to the probable human carcinogen, N-nitrosodimethylamine (NDMA). FDA subsequently initiated a randomized, double-blind, placebo-controlled, crossover clinical investigation to assess the potential for in vivo conversion of ranitidine to NDMA with different meals. A bioanalytical method toward characterization of NDMA formation was needed as previously published methods did not address potential NDMA formation after biofluid collection. Therefore, a bioanalytical method was developed and validated as per FDA's Bioanalytical Method Validation guidance. An appropriate surrogate matrix for calibration standards and quality control sample preparation for both liquid matrices (human plasma and urine) was optimized to minimize the artifacts of assay measurements and monitor basal NDMA levels. Interconversion potential of ranitidine to NDMA was monitored during method validation by incorporating the appropriate quality control samples. The validated methods for NDMA were linear from 15.6 pg/mL to 2000 pg/mL. Low sample volumes (2 mL for urine and 1 mL for plasma) made this method suitable for clinical study samples and helped to evaluate the influence of ranitidine administration and meal types on urinary excretion of NDMA in human subjects.

Pharmacodynamic Biomarkers for Biosimilar Development and Approval: A Workshop Summary.

The US Food and Drug Administration (FDA) Biosimilars Guidance describes how biosimilars may be approved based on clinical pharmacokinetic and pharmacodynamic (PD) biomarker data, without comparative clinical studies with efficacy end points. This type of clinical development program, however, has only been implemented for a small number of FDA-approved biosimilar products over the last decade. To encourage the use of PD biomarkers in biosimilar development and approval, the Duke-Margolis Center for Health Policy collaborated with the FDA to host a two-day virtual public workshop entitled "Pharmacodynamic Biomarkers for Biosimilar Development and Approval" on September 20-21, 2021. The public workshop was a forum for global regulators, biopharmaceutical developers, and academic researchers to discuss the current and future role of PD biomarkers in improving the efficiency of biosimilar development and approval. The workshop objectives included: (i) discuss the current and potential future state of leveraging PD biomarkers for biosimilar development and approval; (ii) summarize the FDA's initiatives to advance biosimilar development; (iii) describe stakeholders' experience with PD biomarkers in biosimilar development; and (iv) explain research efforts to promote broader application of PD biomarkers in biosimilar development. This document summarizes presentations and panel discussions from each session of the two-day September 2021 public workshop covering the application of PD biomarkers for biosimilar development.

Considerations for Use of Pharmacodynamic Biomarkers to Support Biosimilar Development - (III) A Randomized Trial with Interferon Beta-1a Products.

The US Food and Drug Administration (FDA) has taken steps to bring efficiency to the development of biosimilars, including establishing guidance for the use of pharmacokinetic and pharmacodynamic (PD) similarity study data without a comparative clinical study with efficacy end point(s). To better understand the potential role for PD biomarkers in biosimilar development and inform best practices for biomarker selection and analysis, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants. Eighty-four healthy participants (n = 12 per dose arm) received either placebo or one of three doses of either interferon ß-1a (7.5-30 µg) or pegylated interferon ß-1a (31.25-125 µg) to evaluate the maximum change from baseline and the baseline-adjusted area under the effect curve for the biomarkers neopterin in serum and myxovirus resistance protein 1 in blood. Both PD biomarkers increased following product administration with clear separation from baseline (neopterin: 3.4-fold and 3.9-fold increase for interferon ß-1a and pegylated interferon ß-1a, respectively; myxovirus resistance protein 1: 19.0-fold and 47.2-fold increase for interferon ß-1a and pegylated interferon ß-1a, respectively). The dose-response curves support that therapeutic doses were adequately sensitive to detect differences in both PD biomarkers for consideration in a PD similarity study design. Because baseline levels of both biomarkers are low compared with on-treatment values, there was little difference in using PD measures adjusted to baseline compared with the results without baseline adjustment. This study illustrates potential methodologies for evaluating PD biomarkers and an approach to address information gaps when limited information is publicly available for one or more PD biomarkers.

Evaluating the Utility of Proteomics for the Identification of Circulating Pharmacodynamic Biomarkers of IFNß-1a Biologics.

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon-beta 1a products (IFNß-1a) or pegylated-IFNß-1a (pegIFNß-1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNß-1a and pegIFNß-1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty-one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4-fold maximal change from baseline. We identified previously reported candidates, beta-2microglobulin and interferon-induced GTP-binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I-TAC, C1QC, and IP-10) with CVs ranging from 26%-129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNß1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers.

Considerations for Use of Pharmacodynamic Biomarkers to Support Biosimilar Development - (I) A Randomized Trial with PCSK9 Inhibitors.

US Food and Drug Administration (FDA) guidance outlines how biosimilars can be developed based on pharmacokinetic (PK) and pharmacodynamic (PD) similarity study data in lieu of a comparative clinical efficacy study. There is a paucity of PD comparability studies in biosimilar development, leaving open questions about how best to plan these studies. To that end, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants to evaluate approaches to address information gaps, inform analysis best practices, and apply emerging technologies in biomarker characterization. Seventy-two healthy participants (n = 8 per arm) received either placebo or one of four doses of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors alirocumab (15-100 mg) or evolocumab (21-140 mg) to evaluate the maximum change from baseline (ΔPDmax ) and the baseline-adjusted area under the effect curve (AUEC) for the biomarkers low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apoB) in serum. We investigated approaches to minimize variability in PD measures. Coefficient of variation was lower for LDL-C than apoB at therapeutic doses. Modeling and simulation were used to establish the dose-response relationship and provided support that therapeutic doses for these products are adequately sensitive and are on the steep part of the dose-response curves. Similar dose-response relationships were observed for both biomarkers. ΔPDmax plateaued at lower doses than AUEC. In summary, this study illustrates how pilot study data can be leveraged to inform appropriate dosing and data analyses for a PK and PD similarity study.

Considerations for Use of Pharmacodynamic Biomarkers to Support Biosimilar Development - (II) A Randomized Trial with IL-5 Antagonists.

The US Food and Drug Administration (FDA) guidance describes how pharmacodynamic (PD) biomarkers can be used to address residual uncertainty and demonstrate no clinically meaningful differences between a proposed biosimilar and its reference product without relying on clinical efficacy end point(s). Pilot studies and modeling can inform dosing for such PD studies. To that end, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants to evaluate approaches to address information gaps, inform best practices for analysis of biomarker samples and study results, and apply emerging technologies in biomarker characterization. Seventy-two healthy participants (n = 8 per arm) received either placebo or 1 of 4 doses of the interleukin-5 inhibitors mepolizumab (3-24 mg) or reslizumab (0.1-0.8 mg/kg). A clinical study using doses lower than approved therapeutic doses was combined with modeling and simulation to evaluate the dose-response relationship of the biomarker eosinophils. There was no dose-response relationship for eosinophil counts due to variability, although the mepolizumab 24 mg and reslizumab 0.8 mg/kg doses showed clear effects. Published indirect-response models were used to explore eosinophil data across doses from this study and the unstudied therapeutic doses. Simulations were used to calculate typical PD metrics, such as baseline-adjusted area under the effect curve and maximum change from baseline. The simulation results demonstrate sensitivity of eosinophils as a PD biomarker and indicate doses lower than the approved doses would have PD responses overlapping with variability in the placebo arm. The simulation results further highlight the utility of model-based approaches in supporting use of PD biomarkers in biosimilar development.

Model-Based Approach to Selecting Pegfilgrastim Dose for Pharmacokinetic and Pharmacodynamic Similarity Studies in Biosimilar Development.

This study applied modeling and simulation (M&S) approaches to evaluate the sensitivity of pegfilgrastim pharmacokinetics (PKs) and pharmacodynamics (PDs) to changes in dose amount, and linear or nonlinear clearance (CL) over pegfilgrastim subcutaneous dose of 2-6 mg. A previously published model was adapted to better describe pegfilgrastim PK and PD data in healthy subjects and used in simulation. Nonlinear CL accounts for 98% and 77%, respectively, of the total CL of pegfilgrastim at 2 and 6 mg. The sensitivity analyses showed: (i) PK of 2 and 6 mg doses are similarly sensitive to detect differences for a 5% change in dose; (ii) PK of 2 mg dose is more sensitive to changes in receptor binding affinity, a model parameter for nonlinear CL, and a product quality attribute characterized with orthogonal methods as part of demonstrating analytical similarity between products; (iii) PK of approved 6 mg dose is more sensitive to changes in linear CL, which has not been associated with any specific product quality attributes, and (iv) the PDs are not sensitive to changes in linear or nonlinear CL. Taken together, our analyses support that the approved pegfilgrastim dose of 6 mg is appropriate for detecting differences between a biosimilar and the reference products in pegfilgrastim PK and PD similarity studies. The described M&S approaches can be adopted to support dose selection for biosimilars with nonlinear PK and complex PK-PD interplay.

Pharmacodynamic Biomarkers Evidentiary Considerations for Biosimilar Development and Approval.

A biosimilar is a biological product that is highly similar to and has no clinically meaningful differences from a US Food and Drug Administration (FDA)-approved reference product. The development and approval of biosimilars is critical to enhancing the availability of safe, effective, and affordable treatment options for patients. Utilization of pharmacodynamic (PD) biomarkers can help streamline biosimilar development programs as the current process can be costly and time-consuming. Whereas PD biomarkers have not been prominently used across biosimilar approvals to date, moving forward, there is ample opportunity to increase the use of PD biomarkers in biosimilar development programs in place of comparative clinical studies with efficacy end point(s). This includes utilizing PD biomarkers that were not used as surrogate end points in approval of reference products. This mini-review summarizes how PD biomarkers have been used in biosimilar development programs to date and then discusses evidentiary considerations for PD biomarkers. In addition, study design considerations for clinical pharmacokinetic and PD assessment of proposed biosimilars are discussed. Finally, the FDA's applied regulatory science activities related to PD biomarkers for biosimilars conducted in support of the FDA's Biosimilars Action Plan are reviewed. This included conducting three clinical studies to address information gaps about PD biomarkers for biosimilars and inform general methodological best practices. In summary, enhancing our understanding of key evidentiary considerations and optimal study designs for incorporating PD biomarkers in the evaluation of proposed biosimilars can help bring more treatment options to patients faster.

Effect of Paroxetine or Quetiapine Combined With Oxycodone vs Oxycodone Alone on Ventilation During Hypercapnia: A Randomized Clinical Trial.

Importance: Opioids can cause severe respiratory depression by suppressing feedback mechanisms that increase ventilation in response to hypercapnia. Following the addition of boxed warnings to benzodiazepine and opioid products about increased respiratory depression risk with simultaneous use, the US Food and Drug Administration evaluated whether other drugs that might be used in place of benzodiazepines may cause similar effects. Objective: To study whether combining paroxetine or quetiapine with oxycodone, compared with oxycodone alone, decreases the ventilatory response to hypercapnia. Design, Setting, and Participants: Randomized, double-blind, crossover clinical trial at a clinical pharmacology unit (West Bend, Wisconsin) with 25 healthy participants from January 2021 through May 25, 2021. Interventions: Oxycodone 10 mg on days 1 and 5 and the following in a randomized order for 5 days: paroxetine 40 mg daily, quetiapine twice daily (increasing daily doses from 100 mg to 400 mg), or placebo. Main Outcomes and Measures: Ventilation at end-tidal carbon dioxide of 55 mm Hg (hypercapnic ventilation) using rebreathing methodology assessed for paroxetine or quetiapine with oxycodone, compared with placebo and oxycodone, on days 1 and 5 (primary) and for paroxetine or quetiapine alone compared with placebo on day 4 (secondary). Results: Among 25 participants (median age, 35 years [IQR, 30-40 years]; 11 female [44%]), 19 (76%) completed the trial. The mean hypercapnic ventilation was significantly decreased with paroxetine plus oxycodone vs placebo plus oxycodone on day 1 (29.2 vs 34.1 L/min; mean difference [MD], -4.9 L/min [1-sided 97.5% CI, -∞ to -0.6]; P = .01) and day 5 (25.1 vs 35.3 L/min; MD, -10.2 L/min [1-sided 97.5% CI, -∞ to -6.3]; P < .001) but was not significantly decreased with quetiapine plus oxycodone vs placebo plus oxycodone on day 1 (33.0 vs 34.1 L/min; MD, -1.2 L/min [1-sided 97.5% CI, -∞ to 2.8]; P = .28) or on day 5 (34.7 vs 35.3 L/min; MD, -0.6 L/min [1-sided 97.5% CI, -∞ to 3.2]; P = .37). As a secondary outcome, mean hypercapnic ventilation was significantly decreased on day 4 with paroxetine alone vs placebo (32.4 vs 41.7 L/min; MD, -9.3 L/min [1-sided 97.5% CI, -∞ to -3.9]; P < .001), but not with quetiapine alone vs placebo (42.8 vs 41.7 L/min; MD, 1.1 L/min [1-sided 97.5% CI, -∞ to 6.4]; P = .67). No drug-related serious adverse events were reported. Conclusions and Relevance: In this preliminary study involving healthy participants, paroxetine combined with oxycodone, compared with oxycodone alone, significantly decreased the ventilatory response to hypercapnia on days 1 and 5, whereas quetiapine combined with oxycodone did not cause such an effect. Additional investigation is needed to characterize the effects after longer-term treatment and to determine the clinical relevance of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT04310579.

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology - A Proof-of-Concept Model Development for Remdesivir.

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. The disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest.

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first-responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro, hypoxia-induced cardiac arrest in vivo, and opioid-induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid-induced respiratory depression, including those caused by newly emerging synthetic opioids.

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science.

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

Effects of sedative psychotropic drugs combined with oxycodone on respiratory depression in the rat.

Following a decision to require label warnings for concurrent use of opioids and benzodiazepines and increased risk of respiratory depression and death, the US Food and Drug Administratioin (FDA) recognized that other sedative psychotropic drugs may be substituted for benzodiazepines and be used concurrently with opioids. In some cases, data on the ability of these alternatives to depress respiration alone or in conjunction with an opioid are lacking. A nonclinical in vivo model was developed that could detect worsening respiratory depression when a benzodiazepine (diazepam) was used in combination with an opioid (oxycodone) compared to the opioid alone based on an increased arterial partial pressure of carbon dioxide (pCO2 ). The current study used that model to assess the impact on respiration of non-benzodiazepine sedative psychotropic drugs representative of different drug classes (clozapine, quetiapine, risperidone, zolpidem, trazodone, carisoprodol, cyclobenzaprine, mirtazapine, topiramate, paroxetine, duloxetine, ramelteon, and suvorexant) administered alone and with oxycodone. At clinically relevant exposures, paroxetine, trazodone, and quetiapine given with oxycodone significantly increased pCO2 above the oxycodone effect. Analyses indicated that most pCO2 interaction effects were due to pharmacokinetic interactions resulting in increased oxycodone exposure. Increased pCO2 recorded with oxycodone-paroxetine co-administration exceeded expected effects from only drug exposure suggesting another mechanism for the increased pharmacodynamic response. This study identified drug-drug interaction effects depressing respiration in an animal model when quetiapine or paroxetine were co-administered with oxycodone. Clinical pharmacodynamic drug interaction studies are being conducted with these drugs to assess translatability of these findings.