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Population Data-Driven Formulation of a COVID-19 Therapeutic
Heather R Campbell1, Regan Cecil2, Robert A Lodder1
1Department of Pharmaceutical Sciences, University of Kentucky, Lexington, KY, USA.
Medrxiv : the Preprint Server for Health Sciences
|August 4, 2020
Summary
Computer modeling of the US population will reduce preclinical studies for an Ebola antiviral (BSN389) repurposed for COVID-19. This approach aims to accelerate the therapeutic candidate
Area of Science:
- Computational biology
- Pharmacology
- Infectious diseases
Background:
- Repurposing existing antiviral drugs can accelerate clinical development for new indications.
- Ebola antiviral BSN389 is being investigated for efficacy against COVID-19.
- Preclinical studies are time-consuming and resource-intensive.
Approach:
- Utilize computer modeling of the US population, leveraging National Health and Nutrition Examination Survey (NHANES) data.
- Simulate the pharmacokinetic and pharmacodynamic properties of BSN389 in a diverse population.
- Predict potential efficacy and safety profiles to inform clinical trial design.
Key Points:
- Computer modeling can effectively predict therapeutic outcomes.
- NHANES data provides a robust foundation for population-based simulations.
- BSN389 shows promise as a repurposed therapeutic for COVID-19.
Conclusions:
- Computer modeling significantly reduces the need for extensive preclinical formulation and toxicology studies.
- This strategy expedites the advancement of BSN389 to clinical trials for COVID-19 treatment.
- Accelerated development pathways are crucial for addressing public health emergencies.
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