Innovative Randomized Phase 1 Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID-19 Trial of Nirmatrelvir
openalex(2022)
Pfizer Worldwide Research | Pfizer Global Product Development | Pfizer Clinical Research Unit
Abstract
ABSTRACT Background COVID-19 is a continued leading cause of hospitalization and death. Safe and efficacious COVID-19 antivirals are needed urgently. Nirmatrelvir (PF-07321332), the first orally bioavailable, SARS-CoV-2 M pro inhibitor against the coronaviridae family, has demonstrated potent preclinical antiviral activity and benign safety profile. Methods We report safety, tolerability, and pharmacokinetic data of nirmatrelvir with and without ritonavir as a pharmacokinetic enhancer, from an accelerated randomized, double-blind, placebo-controlled, phase 1 study. Two interleaving single-ascending dose (SAD) cohorts were evaluated in a 3-period crossover. Multiple-ascending dose (MAD) with nirmatrelvir/ritonavir twice daily (BID) dosing was evaluated over 10 days in 5 parallel cohorts. Safety was assessed, including in a supratherapeutic exposure cohort. Dose and dosing regimen for clinical efficacy evaluation in phase 2/3 clinical trials were supported by integrating modelling and simulations of SAD/MAD data with nonclinical data and a quantitative systems pharmacology model (QSP). Results In SAD, MAD, and supratherapeutic exposure cohorts, nirmatrelvir/ritonavir was safe and well tolerated. Nirmatrelvir exposure and half-life were considerably increased by ritonavir, enabling selection of nirmatrelvir/ritonavir dose and regimen for phase 2/3 trials (300/100 mg BID), to achieve concentrations continuously above those required for 90% inhibition of viral replication in vitro. The QSP model suggested that a 5-day regimen would significantly decrease viral load in SARS-CoV-2-infected patients and prevent development of severe disease, hospitalization, and death. Conclusions An innovative and seamless trial design expedited establishment of phase 1 safety and pharmacokinetics of nirmatrelvir/ritonavir, enabling high confidence in phase 2/3 dose selection and accelerated pivotal trials’ initiation. NCT04756531
MoreTranslated text
Key words
Corona Virus
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Nirmatrelvir Combined with Ritonavir for Preventing and Treating COVID-19
COCHRANE DATABASE OF SYSTEMATIC REVIEWS 2022
被引用49
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话