A drug-disease model for predicting survival in an Ebola outbreak.


Journal

Clinical and translational science
ISSN: 1752-8062
Titre abrégé: Clin Transl Sci
Pays: United States
ID NLM: 101474067

Informations de publication

Date de publication:
10 2022
Historique:
revised: 30 06 2022
received: 12 04 2022
accepted: 11 07 2022
pubmed: 28 7 2022
medline: 21 10 2022
entrez: 27 7 2022
Statut: ppublish

Résumé

REGN-EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN-EB3's inhibition of Ebola virus to survival in a non-human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single- and multiple-dose REGN-EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine [ALT] and aspartate aminotransferases [AST], blood urea nitrogen [BUN], and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration-dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN-EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug-disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.

Identifiants

pubmed: 35895082
doi: 10.1111/cts.13383
pmc: PMC9579403
doi:

Substances chimiques

atoltivimab, maftivimab, and odesivimab-ebgn drug combination 0
Creatinine AYI8EX34EU
Antiviral Agents 0
Aspartate Aminotransferases EC 2.6.1.1
Antibodies, Monoclonal 0
Alanine OF5P57N2ZX

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

2538-2550

Informations de copyright

© 2022 Regeneron Pharmaceuticals. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

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Auteurs

Masood Khaksar Toroghi (MK)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Nidal Al-Huniti (N)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

John D Davis (JD)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

A Thomas DiCioccio (AT)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Ronda Rippley (R)

Formerly of Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Alina Baum (A)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Christos A Kyratsous (CA)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Sumathi Sivapalasingam (S)

Formerly of Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Joel Kantrowitz (J)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

Mohamed A Kamal (MA)

Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA.

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Classifications MeSH