Evidence map›Paper›PMID 32659293›Full record

ReviewAntiviral research2020

Concerns about pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) studies in the new therapeutic area of COVID-19 infection.

Nicolas Venisse, Gilles Peytavin, Stephane Bouchet, Marie-Claude Gagnieu, Rodolphe Garraffo, Romain Guilhaumou, Caroline Solas, ANRS-AC43 Clinical Pharmacology Committee, SFPT Therapeutic Drug Monitoring and Treatment Personalization group

Abstract readReview
In one paragraph

Review in Antiviral research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Observational
  11. Article
  12. Observational
  13. Article
  14. Review
  15. Repurpose but also (nano)-reformulate! The potential role of nanomedicine in the battle against SARS-CoV2.Journal of controlled release : official journal of the Controlled Release Society · 2021
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nicolas VenisseINSERM CIC1402, University Hospital of Poitiers, University of Poitiers, 86021, Poitiers Cedex, France; Biology-Pharmacy-Public Health Department, University Hospital of Poitiers, 2 Rue de La Milétrie, 86021, Poitiers Cedex, France. Electronic address: Nicolas.venisse@chu-poitiers.fr.
Gilles PeytavinAP-HP, Bichat Claude Bernard Hospital, Pharmacology-Toxicology Department and IAME, INSERM, UMRS1137, Université de Paris, 75018, Paris, 7, France.
Stephane BouchetLaboratoire de Pharmaco-Toxicologie, Service de Pharmacologie Médicale, CHU De Bordeaux, INSERM U1219, F-33076, Bordeaux Cedex, France.
Marie-Claude GagnieuHospices Civils de Lyon, GHS-Centre de Biologie Sud, Pierre Bénite, Lyon, France.
Rodolphe GarraffoPharmacologie et Toxicologie Médicales, CHU et Faculté de Médecine de Nice, Université Côte D'Azur, Nice, France.
Romain GuilhaumouService de Pharmacologie Clinique et Pharmacovigilance, APHM, Institut de Neurosciences des Systèmes, Inserm UMR 1106, Université D'Aix-Marseille, 13005, Marseille, France.
Caroline SolasAix-Marseille Univ, APHM, Unité des Virus Emergents (UVE) IRD 190, INSERM 1207, Laboratoire de Pharmacocinétique et Toxicologie, Hôpital La Timone, 13005, Marseille, France. Electronic address: Caroline.SOLAS@ap-hm.fr.
ANRS-AC43 Clinical Pharmacology Committee, SFPT Therapeutic Drug Monitoring and Treatment Personalization group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of the COVID-19 pandemic, several drugs have been repurposed as potential candidates for the treatment of COVID-19 infection. While preliminary choices were essentially based on in vitro potency, clinical translation into effective therapies may be challenging due to unfavorable in vivo pharmacokinetic properties at the doses chosen for this new indication of COVID-19 infection. However, available pharmacokinetic and pharmacokinetic-pharmacodynamic studies suffer from severe limitations leading to unreliable conclusions, especially in term of dosing optimization. In this paper we propose to highlight these limitations and to identify some of the major requirements that need to be addressed in designing PK and PK-PD studies in this era of COVID. A special attention should be paid to pre-analytical and analytical requirements and to the proper collection of covariates affecting dose-exposure relationships (co-medications, use of specific organ support techniques and other clinical and para-clinical data). We also promote the development of population PK and PK-PD models specifically dedicated to COVID-19 patients since those previously developed for other diseases (SEL, malaria, HIV) and clinical situations (steady-state, non-ICU patients) are not representative of severe patients. Therefore, implementation of well-designed PK and PD studies targeted to COVID-19 patients is urgently needed. For that purpose we call for multi-institutional collaborative work and involvement of clinical pharmacologists in multidisciplinary research consortia.

Indexed as

Antiviral AgentsBetacoronavirusClinical Trials as TopicCoronavirus InfectionsCOVID-19COVID-19 Drug TreatmentData CollectionDose-Response Relationship, DrugDrug InteractionsHumansModels, BiologicalPandemicsPneumonia, ViralSARS-CoV-2Antiviral AgentsCOVID-19PharmacodynamicsPharmacokineticsPK-PD

Identifiers

PMID32659293
PMCPMC7351053

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.