CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.
Lena PariggerAndreas KrassniggMichael HetmannAnna HofmannKarl GruberGeorg SteinkellnerChristian C GruberPublished in: Viruses (2024)
Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.
Keyphrases
- infectious diseases
- zika virus
- aedes aegypti
- dengue virus
- climate change
- drug discovery
- clinical trial
- drug administration
- sars cov
- human health
- drug induced
- current status
- stem cells
- transcription factor
- molecular docking
- adverse drug
- risk assessment
- cancer therapy
- mesenchymal stem cells
- health insurance
- microbial community
- emergency department
- drug delivery
- deep learning
- genome wide
- protein protein
- neural network
- molecular dynamics simulations
- binding protein
- gene therapy