Developing a preclinical test platform that incorporates interpatient variability to lower the attrition rate in antimicrobial drug development

01 January 2019 → 31 December 2022
Research Foundation - Flanders (FWO)
Research disciplines
  • Medical and health sciences
    • Biomarker discovery and evaluation
    • Drug discovery and development
    • Medicinal products
    • Pharmaceutics
    • Pharmacognosy and phytochemistry
    • Pharmacology
    • Pharmacotherapy
    • Toxicology and toxinology
    • Other pharmaceutical sciences
In vitro interpatient variability cystic fibrosis microbiome airway eptihelial cells
Project description

The high attrition rate in drug development is a major issue for the pharmaceutical industry. Of particular concern is the high failure rate in clinical phase 2 and 3. At this point, failure is disastrous from an economic point of view, leading to an outrageous general cost to bring a compound to the market. Providing predictive in vitro tests in a preclinical stage is generally acknowledged to lower the chances of failure in clinical phases. Yet, these test platforms are not readily available. The present project suggests an approach in which highly variable factors in the patient population with respiratory diseases, are taken into account in in vitro assays. In this way, an “in vitro patient population” can be created. The highly variable cystic fibrosis (CF) population is used as an example. The patient-specific characteristics that are incorporated are airway epithelial cells and the collection of all microorganisms in the airways (termed the lung microbiome). We will focus on the most important therapeutic agents for CF patients to validate and test the in vitro models. An approximation of a clinical validation will be performed, based on the results of published clinical trials in the CF population. In a final stage, the potential of new compounds with therapeutic potential will be assessed using the developed test platform. The outcome of this project will represent an important step in integrating more predictive test platforms in preclinical drug development.