Assessing ADME gene coverage: An observational study on chloroquine therapy for COVID-19
This observational study aimed to compare various genotyping and enrichment platforms to determine the most comprehensive coverage for genome-wide association studies, specifically targeting new therapeutic approaches against coronaviruses using chloroquine. Pharmacogenomic studies have become essential for understanding individual drug responses, and optimal platform selection is critical for identifying relevant genetic variants. We developed Python scripts to assess the coverage rates of these platforms, focusing on the absorption, distribution, metabolism, and excretion (ADME) genes involved in drug absorption, distribution, metabolism, and excretion. Additionally, the PLINK tool was employed to evaluate single nucleotide polymorphisms in linkage disequilibrium with ADME variants, providing insights into the extended coverage achieved through correlation with these variants. Among the genotyping platforms analyzed, Axiom genotyping and SureSelect enrichment platforms demonstrated the most extensive coverage of the genome and key pharmacogenomic regions. These platforms effectively captured a significant proportion of ADME gene variants, which are crucial for predicting individual responses to chloroquine. The extensive coverage provided by the Axiom and SureSelect platforms supports their use in the design of pharmacogenomic studies, potentially revealing new therapeutic targets for combating coronaviruses, including through the use of chloroquine treatment. The results highlight the importance of selecting appropriate genotyping and enrichment technologies for maximizing the impact of pharmacogenomic research.
Authors
- Loubna Khalki (ORCID: https://orcid.org/0000-0001-8612-8432)
- Youness Limami (ORCID: https://orcid.org/0000-0002-8816-3001)
- Nabil Zaïd (ORCID: https://orcid.org/0000-0002-9272-6591)
- Younes Zaid (ORCID: https://orcid.org/0000-0001-8750-9106)
- Oussama Badad (ORCID: https://orcid.org/0000-0002-3891-5201)
- Mounia Oudghiri (ORCID: https://orcid.org/0000-0002-5815-1579)
- Abdallah Naya (ORCID: https://orcid.org/0000-0001-6882-5162)
- Banacer Himmi (ORCID: https://orcid.org/0000-0002-8086-2473)
- Lamyaa Benchikhi
- Naoual El Abboudi
Institutions
- Southern Illinois University Carbondale (US)
- Mohammed V University (MA)
- Instituts Supérieurs des Professions Infirmières et Techniques de Santé (MA)
- Université Mohammed VI des Sciences et de la Santé (MA)
- University of Hassan II Casablanca (MA)
Publication Details
- Journal
- Medicine
- Published
- 2025-09-12
- DOI
- https://doi.org/10.1097/md.0000000000044340
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00