Abstract

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

Highlights

  • In recent years, fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates due to growing populations of patients with weakened immune systems, for example due to cancer, organ transplant or Acquired Immune Deficiency Syndrome (AIDS)

  • Drug combinations represent a promising strategy for overcoming fungal drug resistance and treating complex diseases

  • We evaluated the predictive performance of NLLSS using leave-one-out cross validation (LOOCV)

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Summary

Introduction

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates due to growing populations of patients with weakened immune systems, for example due to cancer, organ transplant or Acquired Immune Deficiency Syndrome (AIDS). In these patients, infections caused by Candida, Aspergillus and Cryptococcus neoformans fungi strains may take the form of potentially lethal blood stream infections, lung infections and other infections. CRx-102 is a novel synergistic drug candidate combination comprised of dipyridamole and low-dose prednisolone. These advantages have increasingly driven researchers towards the search for safe and effective combinatorial drugs [5,6,7,14]

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