Dataset Information

Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19.

ABSTRACT:

Introduction

In the randomized double-blind placebo-controlled CounterCOVID study, oral imatinib treatment conferred a positive clinical outcome and a signal for reduced mortality in COVID-19 patients. High concentrations of alpha-1 acid glycoprotein (AAG) were observed in these patients and were associated with increased total imatinib concentrations.

Aims

This post-hoc study aimed to compare the difference in exposure following oral imatinib administration in COVID-19 patients to cancer patients and assess assocations between pharmacokinetic (PK) parameters and pharmacodynamic (PD) outcomes of imatinib in COVID-19 patients. We hypothesize that a relatively higher drug exposure of imatinib in severe COVID-19 patients leads to improved pharmacodynamic outcome parameters.

Methods

648 total concentration plasma samples obtained from 168 COVID-19 patients were compared to 475 samples of 105 cancer patients, using an AAG-binding model. Total trough concentration at steady state (Ct_trough) and total average area under the concentration-time curve (AUCt_ave) were associated with ratio between partial oxygen pressure and fraction of inspired oxygen (P/F), WHO ordinal scale (WHO-score) and liberation of oxygen supplementation (O₂lib). Linear regression, linear mixed effects models and time-to-event analysis were adjusted for possible confounders.

Results

AUCt_ave and Ct_trough were respectively 2.21-fold (95%CI 2.07-2.37) and 1.53-fold (95%CI 1.44-1.63) lower for cancer compared to COVID-19 patients. Ct_trough, not AUCt_ave, associated significantly with P/F (β=-19,64; p-value=0.014) and O₂lib (HR 0.78; p-value= 0.032), after adjusting for sex, age, neutrophil-lymphocyte ratio, dexamethasone concomitant treatment, AAG and baseline P/F-and WHO-score. Ct_trough, but not AUCt_ave associated significantly with WHO-score. These results suggest an inverse relationship between PK-parameters, Ct_trough and AUCt_ave, and PD outcomes.

Conclusion

COVID-19 patients exhibit higher total imatinib exposure compared to cancer patients, attributed to differences in plasma protein concentrations. Higher imatinib exposure in COVID-19 patients did not associate with improved clinical outcomes. Ct_trough and AUCt_ave inversely associated with some PD-outcomes, which may be biased by disease course, variability in metabolic rate and protein binding. Therefore, additional PKPD analyses into unbound imatinib and its main metabolite may better explain exposure-response.

SUBMITTER: Baalbaki N

PROVIDER: S-EPMC9979628 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19.

Baalbaki Nadia N Duijvelaar Erik E Said Medhat M MM Schippers Job J Bet Pierre M PM Twisk Jos J Fritchley Sarah S Longo Cristina C Mahmoud Kazien K Maitland-van der Zee Anke H AH Bogaard Harm Jan HJ Swart Eleonora L EL Aman Jurjan J Bartelink Imke H IH

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences 20230302

<h4>Introduction</h4>In the randomized double-blind placebo-controlled CounterCOVID study, oral imatinib treatment conferred a positive clinical outcome and a signal for reduced mortality in COVID-19 patients. High concentrations of alpha-1 acid glycoprotein (AAG) were observed in these patients and were associated with increased total imatinib concentrations.<h4>Aims</h4>This post-hoc study aimed to compare the difference in exposure following oral imatinib administration in COVID-19 patients t ...[more]

PMID: 36870577

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Project description:ObjectiveTo discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.MethodsWe propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach.ResultsAccuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed.ConclusionWe showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.

Dataset Information

Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19.

Introduction

Aims

Methods

Results

Conclusion

Publications

Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19.

Similar Datasets

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