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Computational determination of toxicity risks associated with a selection of approved drugs having demonstrated
Maral Aminpour1, Williams Ernesto Miranda Delgado2, Soren Wacker2
1Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1Z2, Canada.
Background:
The emergence and rapid spread of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in thelate 2019 has caused a devastating global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19). Although vaccines have been and are being developed, they are not accessible to everyone and not everyone can receive these vaccines. Also, it typically takes more than 10 years until a new therapeutic agent is approved for usage. Therefore, repurposing of known drugs can lend itself well as a key approach for significantly expediting the development of new therapies for COVID-19.
Methods:
We have incorporated machine learning-based computational tools and in silico models into the drug discovery process to predict Adsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles of 90 potential drugs for COVID-19 treatment identified from two independent studies mainly with the purpose of mitigating late-phase failures because of inferior pharmacokinetics and toxicity.
Results:
Here, we summarize the cardiotoxicity and general toxicity profiles of 90 potential drugs for COVID-19 treatment and outline the risks of repurposing and propose a stratification of patients accordingly. We shortlist a total of five compounds based on their non-toxic properties.
Conclusion:
In summary, this manuscript aims to provide a potentially useful source of essential knowledge on toxicity assessment of 90 compounds for healthcare practitioners and researchers to find off-label alternatives for the treatment for COVID-19. The majority of the molecules discussed in this manuscript have already moved into clinical trials and thus their known pharmacological and human safety profiles are expected to facilitate a fast track preclinical and clinical assessment for treating COVID-19.
Insights
Drug repurposing accelerates COVID-19 treatment development. Machine learning identified 90 potential drugs, with five non-toxic compounds shortlisted for further clinical assessment.
Area of Science:
- Pharmacology and Toxicology
- Computational Drug Discovery
Background:
- The COVID-19 pandemic necessitates rapid development of effective treatments.
- Vaccine accessibility and development timelines highlight the need for alternative therapeutic strategies.
- Drug repurposing offers a faster pathway to identify viable COVID-19 therapies.
Purpose of the Study:
- To assess the toxicity profiles of 90 potential COVID-19 repurposed drugs.
- To mitigate late-stage drug development failures due to poor pharmacokinetics and toxicity.
- To identify safe and effective drug candidates for COVID-19 treatment.
Main Methods:
- Utilized machine learning and in silico models to predict ADMET profiles.
- Evaluated cardiotoxicity and general toxicity of 90 candidate drugs.
- Stratified patient populations based on repurposing risks.
Main Results:
- Summarized toxicity profiles for 90 potential COVID-19 drugs.
- Identified five compounds with favorable non-toxic properties.
- Outlined risks associated with drug repurposing for COVID-19.
Conclusions:
- Provides essential toxicity data for 90 compounds to aid researchers and clinicians.
- Facilitates expedited preclinical and clinical assessment of repurposed drugs.
- Selected compounds with known safety profiles are poised for rapid clinical evaluation.
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