Inferring antifungal drug synergy from Candidozyma auris optical density data using Bayesian mechanistic modelling

Antifungal drug-resistant Candidozyma auris (C. auris) is a threat to human health worldwide. Combination antifungal drug therapy has emerged as a promising approach to combat drug-resistant C. auris because some drugs interact synergistically to increase fungal clearance when co-administered. Moreover, combination regimens that either rapidly act or completely kill C. auris could mitigate development of on-treatment resistance. However, traditional checkerboard methods to identify synergistic drug combinations only inspect fungal growth at a single timepoint. As a result, they cannot be used to estimate the rate of drug-action or to hypothesise on fungicidal or fungistatic drug-action. Mechanistic modelling would allow us to quantify time-dependent drug-action and infer killing or inhibitory action, but these models are usually fit to direct measurements of fungal growth whose collection is currently not scalable to many time-points and drug combinations. In this paper, we propose a Bayesian mechanistic modelling approach that could detect drug-synergy, estimate drug-action over time and investigate fungicidal or fungistatic drug-activity from optical density (OD600) data alone. OD600 is quicker and easier to collect than direct measurements of fungal growth and therefore more amenable to high-throughput susceptibility testing. By fitting our model to time-course OD600 data of a multi-drug-resistant C. auris isolate growing in mono- and combination drug regimens, we successfully inferred synergy between previously confirmed synergistic antifungal drugs (anidulafungin with manogepix or with 5-flucytosine) and linked our models inferred kinetic parameters to fungicidal and fungistatic action on C. auris growth, which matched drug-activity reported in literature where known. We validated that our model outperformed baseline logistic and Gompertz models using cross validation stratified by OD600 replicates. Our results represent the much-needed groundwork for identifying drug combinations for subsequent experimental testing for use in clinics based on their synergy, temporal drug-action and fungicidal or fungistatic activities inferred from OD600 data alone. Author SummaryThere is an urgent need to locate novel treatments to better treat antifungal drug-resistant Candidozyma auris infections. Combination therapy is a promising approach where two or more antifungal drugs are administered and interact synergistically to enhance fungal clearance. If these combinations are fast acting or eradicate fungi through killing, then they could also reduce the chance of resistance developing during treatment. The synergy of antifungal drug combinations is currently assessed by checkerboard methodologies that compare fungal growth under drug combinations to that under a single drug. However, checkerboard methodologies record only one time-point. Hence, they cannot evaluate drug combinations timeframe of action and follow-up studies are required to determine which combinations could optimally enhance killing. We developed a Bayesian mechanistic model that could detect synergy between drugs, estimate rates of drug-action and investigate killing and inhibition drug-action using only optical density (OD600) data of C. auris. OD600-based measurement of fungal growth is more amenable to large-scale drug testing than data typically used for mechanistic modelling, such as microscopy data. This work serves as a foundation for more targeted drug testing that identifies promising drug combinations based on their inferred drug-synergy and hypothesised killing (or inhibition) rates.