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.

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug-drug interactions

BACKGROUND

During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels.

METHODS

We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC.

RESULTS

This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences.

DISCUSSION AND CONCLUSION

Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more "fit for purpose" NLP methods could be developed and used to facilitate the drug development process.

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 to 9 other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from 2 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.

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.

A general procedure to select calibration drugs for lab-specific validation and calibration of proarrhythmia risk prediction models: An illustrative example using the CiPA model

INTRODUCTION

In response to the ongoing shift of the regulatory cardiac safety paradigm, a recent White Paper proposed general principles for developing and implementing proarrhythmia risk prediction models. These principles included development strategies to validate models, and implementation strategies to ensure a model developed by one lab can be used by other labs in a consistent manner in the presence of lab-to-lab experimental variability. While the development strategies were illustrated through the validation of the model under the Comprehensive In vitro Proarrhythmia Assay (CiPA), the implementation strategies have not been adopted yet.

METHODS

The proposed implementation strategies were applied to the CiPA model by performing a sensitivity analysis to identify a subset of calibration drugs that were most critical in determining the classification thresholds for proarrhythmia risk prediction.

RESULTS

The selected calibration drugs were able to recapitulate classification thresholds close to those calculated from the full list of CiPA drugs. Using an illustrative dataset it was shown that a new lab could use these calibration drugs to establish its own classification thresholds (lab-specific calibration), and verify that the model prediction accuracy in the new lab is comparable to that in the original lab where the model was developed (lab-specific validation).

DISCUSSION

This work used the CiPA model as an example to illustrate how to adopt the proposed model implementation strategies to select calibration drugs and perform lab-specific calibration and lab-specific validation. Generic in nature, these strategies could be generally applied to different proarrhythmia risk prediction models using various experimental systems under the new paradigm.

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction

hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed.

Methods

A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates.

Results

A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used.

Discussion

This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

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hERG potency/safety margin is a widely used nonclinical cardiac safety strategy.

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A new regulatory paradigm promotes the integration of nonclinical and clinical data.

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Lack of uncertainty quantification hindered using hERG potency in the new paradigm.

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A systematic method was established to address this limitation.

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Analysis supports using different safety margin thresholds in different context.

ReaxFF MD Simulations of Peptide-Grafted Gold Nanoparticles

Functionalized gold nanoparticles have critical applications in biodetection with surface-enhanced Raman spectrum and drug delivery. In this study, reactive force field molecular dynamics simulations were performed to study gold nanoparticles, which are modified with different short-chain peptides consisting of amino acid residues of cysteine and glycine in different grafting densities in the aqueous environment. Our study showed slight facet-dependent peptide adsorption on a gold nanoparticle with the 3 nm core diameter. Peptide chains prefer to adsorb on the Au(111) facet compared to those on other facets of Au(100) and Au(110). In addition to the stable thiol interaction with gold nanoparticle surfaces, polarizable oxygen and nitrogen atoms show strong interactions with the gold surface and polarize the gold nanoparticle surfaces with an overall positive charge. Charges of gold atoms vary according to their contacts with peptide atoms and lattice positions. However, at the outmost peptide layer, the whole functionalized Au nanoparticles exhibit overall negative electrostatic potential due to the grafted peptides. Moreover, simulations show that thiol groups can be deprotonated and subsequently protons can be transferred to water molecules and carboxyl groups.

Atomic-Level Simulation Study of n-Hexane Pyrolysis on Silicon Carbide Surfaces

Ethylene production plays a key role in the petrochemical industry. The severe operation conditions of ethylene thermal cracking, such as high-temperature and coke-formation, pose challenges for the development of new corrosion-resistant and coking-resistant materials for ethylene reactor radiant coils tubes (RCTs). We investigated the performance of ceramic materials such as silicon carbide (SiC) in severe pyrolysis conditions by using reactive force field molecular dynamics (ReaxFF MD) simulation method. Our results indicate that β-SiC surface remains fully stable at 1500 K, whereas increased temperature results in melted interface. At 2500 K, fully grown cross-linked-graphene-like polycyclic aromatic hydrocarbon coking structure on SiC surfaces was observed. Such coking was particularly severe in the carbon-side of the surface slab. The coking structures were mainly derived from surface atoms at the initial 3.0 ns, as a result of the loss of interfacial hydroxyl layer and further hydrothermal corrosion. The SiC substrate surface enhances the ethylene cracking rate and also leads to different intermediate-state compounds. Our fundamental research will have significant and broad impact on both petrochemical industry and academic research in materials science, petrochemistry, and combustion chemistry.

Protein-Ligand Interaction Detection with a Novel Method of Transient Induced Molecular Electronic Spectroscopy (TIMES): Experimental and Theoretical Studies

Protein-ligand interaction detection without disturbances (e.g., surface immobilization, fluorescent labeling, and crystallization) presents a key question in protein chemistry and drug discovery. The emergent technology of transient induced molecular electronic spectroscopy (TIMES), which incorporates a unique design of microfluidic platform and integrated sensing electrodes, is designed to operate in a label-free and immobilization-free manner to provide crucial information for protein-ligand interactions in relevant physiological conditions. Through experiments and theoretical simulations, we demonstrate that the TIMES technique actually detects protein-ligand binding through signals generated by surface electric polarization. The accuracy and sensitivity of experiments were demonstrated by precise measurements of dissociation constant of lysozyme and N-acetyl-d-glucosamine (NAG) ligand and its trimer, NAG₃. Computational fluid dynamics (CFD) computation is performed to demonstrate that the surface's electric polarization signal originates from the induced image charges during the transition state of surface mass transport, which is governed by the overall effects of protein concentration, hydraulic forces, and surface fouling due to protein adsorption. Hybrid atomistic molecular dynamics (MD) simulations and free energy computation show that ligand binding affects lysozyme structure and stability, producing different adsorption orientation and surface polarization to give the characteristic TIMES signals. Although the current work is focused on protein-ligand interactions, the TIMES method is a general technique that can be applied to study signals from reactions between many kinds of molecules.

Self-Assembled Monolayers of an Azobenzene Derivative on Silica and Their Interactions with Lysozyme

The capability of the photoresponsive isomerization of azobenzene derivatives in self-assembled monolayer (SAM) surfaces to control protein adsorption behavior has very promising applications in antifouling materials and biotechnology. In this study, we performed an atomistic molecular dynamics (MD) simulation in combination with free-energy calculations to study the morphology of azobenzene-terminated SAMs (Azo-SAMs) grafted on a silica substrate and their interactions with lysozyme. Results show that the Azo-SAM surface morphology and the terminal benzene rings' packing are highly correlated with the surface density and the isomer state. Higher surface coverage and the trans-isomer state lead to a more ordered polycrystalline backbone as well as more ordered local packing of benzene rings. On the Azo-SAM surface, water retains a high interfacial diffusivity, whereas the adsorbed lysozyme is found to have extremely low mobility but a relative stable secondary structure. The moderate desorption free energy (∼60 kT) from the trans-Azo-SAM surface was estimated by using both the nonequilibrium-theorem-based Jarzynski's equality and equilibrium umbrella sampling.