Potential drug-drug interactions of antiretrovirals and antimicrobials detected by three databases

Vivithanaporn, Pornpun; Kongratanapasert, Teetat; Suriyapakorn, Bovornpat; Songkunlertchai, Pichayut; Mongkonariyawong, Patpicha; Limpikirati, Patanachai; Khemawoot, Phisit

doi:10.1038/s41598-021-85586-8

Cited by 19 publications

(11 citation statements)

References 32 publications

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“…The two electronic databases reported 488/3,000 (16%) pairs of potential DDIs. This value is between our two previous potential DDI reports of antiretrovirals/antimicrobials (3%-4%) and metabolic syndrome medications (18%-20%) [12,13]. Considering the severity of potential DDIs reported by Micromedex, 191/271 pairs (70%) were reported as contraindicated or major in severity, compared with 70% for antiretrovirals/antimicrobials and 20% for metabolic syndrome medications.…”

Section: Plos Onementioning

confidence: 44%

Potential drug–drug interactions of frequently prescribed medications in long COVID detected by two electronic databases

Meakleartmongkol,

Tangpanithandee,

Vanavivit

et al. 2023

PLoS ONE

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Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to a wide range of acute and chronic complications including long COVID, a well-known chronic sequela. Long COVID often necessitates long-term treatment, which may lead to an increased potential for drug–drug interactions (DDIs). The objective of this study was to assess potential DDIs among frequently prescribed medications in long COVID by using two electronic databases. Sixty frequently prescribed agents were selected from Thailand’s National List of Essential Medicine 2022 for potential DDI analysis by Micromedex and Drugs.com. From these databases, 488 potential DDIs were identified. There were 271 and 434 DDI pairs based on Micromedex and Drugs.com, respectively. Among these DDIs, 77 pairs were labeled as contraindicated or major by both databases. The most common mechanisms for these serious interactions are cytochrome P450 (CYP) inhibition (45%), CYP induction (19%), and QT interval prolongation (7.8%). Based on Fleiss’ kappa (0.073), there was only slight agreement of the DDI severity classifications between these two databases. In conclusion, a large number of potential DDIs were detected among frequently prescribed medications for long COVID. Health care providers should be aware of these DDIs, particularly those that are deemed as contraindicated or major. These DDIs are most likely to cause significant adverse events in patients with long COVID because polypharmacy is common.

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Section: Plos Onementioning

confidence: 44%

Potential drug–drug interactions of frequently prescribed medications in long COVID detected by two electronic databases

Meakleartmongkol,

Tangpanithandee,

Vanavivit

et al. 2023

PLoS ONE

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“…One of such is DDI of lopinavir/ritonavir versus buprenorphine where both lopinavir and ritonavir may increase the concentration of buprenorphine, and thus monitoring and adjusting dose are recommended by the NICE 35 , 36 ; another example is DDI of darunavir/cobicistat/emtricitabine/tenofovir alafenamide (DRV/c/FTC/TAF) versus clotrimazole where antifungals like clotrimazole inhibiting CYP3A may decrease the clearance of darunavir and cobicistat, and thus clinical monitoring is recommended by the European Medicines Agency 37 . Interestingly, Vivithanaporn et al 38 reported a significant inconsistency in severity grading across three major DDI databases – Drugs.com, Micromedex and the Liverpool HIV Interaction. A number of drug pairs between ARVs and antimicrobials were shown to have poor concordance in the DDI recognition, e.g., minor or none by Micromedex and Drugs.com but major (Amber/Red) by the Liverpool HIV Interaction database (e.g.…”

Section: Discussionmentioning

confidence: 99%

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Pham,

Ghafoor,

Grañana-Castillo

et al. 2024

npj Syst Biol Appl

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Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

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“…ART adherence in Iran in previous studies ranged from 54.4-85.0% (10,11). Fourth, since the self-medication of drugs in Iran is high (67%) (12,13), drug-drug interaction with other medicines including antibiotics, herbal medicine, or even sedatives can be another reason for the high prevalence of HIV drug resistance (14,15).…”

Section: Discussionmentioning

confidence: 99%

Prevalence of acquired and transmitted HIV drug resistance in Iran: A systematic review and Metaanalysis

Mirzaei

Eybpoosh

Mehrabi

et al. 2023

Preprint

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Background There are no national-level surveys about HIV drug resistance (HIVDR) in Iran. Therefore, this investigation aimed to estimate the prevalence of HIVDR among antiretroviral therapy (ART) naïve people (those without a history of ART) and people receiving ART.Method We systematically searched Scopus, PubMed, Web of Science, Embase, Iranian database, the references of studies, and google scholar till March, 2023. A random-effect model was implemented to calculate point estimate and 95% confidence interval (95% CI) for the prevalence of HIVDR in people living with HIV (PLHIV).Results Among 461 potential publications, 22 studies were included in the meta-analysis. The pooled prevalence of acquired HIVDR in people receiving ART was 34.0% (95% CI: 19.0%-50.0%) for nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), 27.0% (95% CI: 15.0% -41.0%) for non-nucleoside reverse transcriptase inhibitors (NNRTIs), and 9.0% (95% CI: 3.0% -18.0%) for protease inhibitors (PIs). The pooled prevalence of Acquired HIVDR in treatment failure PLHIV was 50.0% (95% CI: 31.0% − 69.0%) for NRTI, 49.0% (95% CI: 29.0%-69.0%) for NNRTI, 11.0% (95% CI: 2.0% − 24.0%) for PI, and 1.0% (95% CI: 0–4.0%) for integrase inhibitors (INIs). The pooled prevalence of transmitted HIVDR in treatment naïve people was 3.0% (95% CI; 1.0%-6.0%) for NRTI, 5.0% (95% CI: 2.0%-9.0%) for NNRTI, and 0 for PIs and INIs.Conclusion The prevalence of HIVDR in Iranian treatment naïve and people receiving ART was relatively high. Without universal pretreatment HIVDR testing and more frequent routine viral load testing among PLHIV who are on ART, these numbers may go up.

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Potential drug-drug interactions of antiretrovirals and antimicrobials detected by three databases

Cited by 19 publications

References 32 publications

Potential drug–drug interactions of frequently prescribed medications in long COVID detected by two electronic databases

Potential drug–drug interactions of frequently prescribed medications in long COVID detected by two electronic databases

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Prevalence of acquired and transmitted HIV drug resistance in Iran: A systematic review and Metaanalysis

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