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

An update on drug-drug interactions in older adults living with human immunodeficiency virus (HIV)

INTRODUCTION

People with HIV are living longer due to advances in antiretroviral therapy. With improved life expectancy comes an increased lifetime risk of comorbid conditions - such as cardiovascular disease and cancer - and polypharmacy. Older adults, particularly those living with HIV, are more vulnerable to drug interactions and adverse effects, resulting in negative health outcomes.

AREA COVERED

Antiretrovirals are involved in many potential drug interactions with medications used to treat common comorbidities and geriatric conditions in an aging population of people with HIV. We review the mechanisms and management of significant drug-drug interactions involving antiretroviral medications and non-antiretroviral medications commonly used among older people living with HIV. The management of these interactions may require dose adjustments, medication switches to alternatives, enhanced monitoring, and considerations of patient- and disease-specific factors.

EXPERT OPINION

Clinicians managing comorbid conditions among older people with HIV must be particularly vigilant to side effect profiles, drug-drug interactions, pill burden, and cost when optimizing treatment. To support healthier aging among people living with HIV, there is a growing need for antiretroviral stewardship, multidisciplinary care models, and advances that promote insight into the correlations between an individual, their conditions, and their medications.

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

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.

Outcomes of Drug Interactions Between Antiretrovirals and Co-Medications, Including Over-the-Counter Drugs: A Real-World Study

INTRODUCTION

The objective was to characterize real-world outcomes of drug-drug interactions (DDIs) between antiretrovirals (ARVs) and other drugs, including over-the-counter medications (OTC), and treatment outcomes in clinical practice.

METHODS

www.clinicalcasesDDIs.com is an open-access website for healthcare providers to consult and briefly describe real-world clinical cases on DDI with ARVs. We reviewed all the clinical cases reported to the website between March 2019 and May 2023.

RESULTS

A total of 139 cases were reported, mostly involving ritonavir or cobicistat (boosters; 74 cases), unboosted integrase inhibitors (InSTI; 29 cases), and non-nucleoside reverse transcriptase inhibitors (NNRTI; 23 cases). Central nervous system drugs (29 cases) and cardiovascular drugs (19 cases) were the most frequently described co-medications. Notably, OTC medications were implicated in 27 cases, including mineral supplements (11 cases), herbals (8 cases), weight loss drugs (4 cases), anabolic steroids (3 cases), and recreational drugs (1 case). OTC acted as the perpetrator drug in 21 cases, leading to loss of ARV efficacy in 17 instances (mineral supplements in 10 cases, weight loss drugs in 4 cases, herbals in 3 cases). Additionally, toxicity was reported in 4 out of 6 cases where OTC was considered the victim drug of the DDI (anabolic steroids in 3 cases, MDMA in 1 case).

CONCLUSIONS

Frequent unwanted outcomes resulting from DDIs between ARVs and OTC medications underscore the importance of integrating non-prescription drugs into medication reconciliation. The real-world data available through www.clinicalcasesDDIs.com serves as a valuable resource for assessing the clinical relevance of DDIs.

Concordance analysis of two databases to search for potential drug interactions in onco-hematologic patients

INTRODUCTION

Potential drug interactions exert a significant impact on patient safety, especially within intricate onco-hematological treatments, potentially resulting in toxicity or treatment failures. Despite the availability of databases for potential drug interaction investigation, persistent heterogeneity in concordance rates and classifications exists. The additional variability in database agreement poses further complexity, notably in critical contexts like onco-hematology.

AIM

To analyze the concordance of two databases for researching potential drug interaction in prescriptions for hematological patients at a University Hospital in the Midwest region of Brazil.

METHOD

Cross-sectional study developed in a Brazilian hospital. The search for potential drug interaction was conducted in Micromedex® and UpToDate®. The variables were: the presence of potential drug interaction, severity, mechanism, management, and documentation. Data was analyzed in terms of frequency (absolute and relative), Cohen's kappa, and Fleiss kappa.

RESULTS

The presence of potential drug interaction, showed a lack of concordance between the databases (k = -0.115 [95% CI: 0.361-0.532], p = 0.003). Regarding the mechanism, a strong agreement was observed (k = 0.805, p < 0.001 [95% CI: 0.550-0.941]). The management concordance showed a fair agreement, 46.8% (k = 0.22, p < 0.001 [95% CI: 0.099-0.341]). Stratifying the categories, significant concordance was observed in "Adjustment of dose + Monitoring" (k = 0.302, p = 0.018) and "Monitoring" (k = 0.417, p = 0.001), while other categories did not reach statistical significance.

CONCLUSION

Our study emphasizes the variability in potential drug interaction research, revealing disparities in severity classification, management recommendations, and documentation practices across databases.

Prevalence of acquired and transmitted HIV drug resistance in Iran: a systematic review and meta-analysis

BACKGROUND

There is no systematic review on the prevalence of HIV drug resistance (HIVDR) in Iran. We aimed to estimate the prevalence of HIVDR among people living with HIV (PLHIV) in Iran. We assessed HIVDR prevalence in antiretroviral therapy (ART) naïve PLHIV (i.e., those without a history of ART) and PLHIV receiving ART.

METHOD

We systematically searched Scopus, PubMed, Web of Science, Embase, Iranian databases (Iranian Medical Research Information System, Magiran, and Scientific Information Database), the references of studies, and Google Scholar until March 2023. A random-effects model was used to calculate a point estimate and 95% confidence interval (95% CI) for the prevalence of HIVDR in PLHIV.

RESULTS

Among 461 potential publications, 22 studies were included in the meta-analysis. The pooled prevalence of acquired HIVDR in PLHIV receiving ART was 34% (95% CI: 19, 50) for nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs), 27% (95% CI: 15, 41) for non-nucleoside reverse transcriptase inhibitors (NNRTIs), and 9% (95% CI: 3, 18) for protease inhibitors (PIs). The pooled prevalence of acquired HIVDR in treatment failure PLHIV was 50% (95% CI: 31, 69) for NRTIs, 49% (95% CI: 29, 69) for NNRTIs, 11% (95% CI: 2, 24) for PIs, and 1% (95% CI: 0, 4) for integrase inhibitors (INIs). The pooled prevalence of transmitted HIVDR in ART-naïve people was 3% (95% CI; 1, 6) for NRTIs, 5% (95% CI: 2, 9) for NNRTIs, and 0 for PIs and INIs.

CONCLUSION

The prevalence of HIVDR was relatively high in both ART-naïve PLHIV and those receiving ART. Without universal pretreatment HIVDR testing and more frequent routine HIV viral load testing among PLHIV who are on ART, the HIVDR prevalence might increase in PLHIV in Iran.

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

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.

Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: Drugs.com®, Lexicomp®, and Epocrates®

BACKGROUND

Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument.

OBJECTIVES

This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®.

METHODS

This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss' kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases.

RESULTS

From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study.

CONCLUSION

Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers.

Characteristics of 2-drug regimen users living with HIV-1 in a real-world setting: A large-scale medical claim database analysis in Japan

Background

Regimen simplification to 2-drug antiretroviral therapy (2-ART) may address potential tolerability issues, increase adherence, and reduce toxicity and potential drug-drug-interactions among people living with HIV-1 (PLWH). However, real-world treatment patterns and characteristics of 2-ART users are unclear.

Methods

This retrospective observational cohort study employed a large-scale medical claim database of Japanese hospitals to extract data on 4,293 PLWH aged ≥18 years with diagnosis of HIV and treated with any ART regimens between April 2008 and April 2019. A 2-ART cohort was compared with a 3-drug antiretroviral therapy (3-ART) cohort in terms of population characteristics, comorbid conditions, and treatment patterns. Treatment switching rates were calculated for each cohort followed by sensitivity analysis to confirm the robustness of the findings.

Results

There were 94 individuals identified in the 2-ART cohort. Compared to the standard 3-ART cohort (n = 3,993), the 2-ART cohort was older (median age 53 [IQR 44–64] vs 42 years [IQR 35–50]), with a lower proportion of males (87.2% vs 93.8%), higher Charlson Comorbidity Index (CCI) (median score 6 [IQR 5–8] vs 5 [IQR 4–6]), more co-medications (median 6 [IQR 4–11] vs 3 [IQR 2–7]), and a higher percentage of AIDS-defining conditions (66.0% vs 42.8%). The most common 2-ART were protease inhibitor (PI) + integrase strand transfer inhibitor (INSTI) and non-nucleoside reverse transcriptase inhibitor (NNRTI) + INSTI (33.0% and 31.9%, respectively). Overall, most of the regimens were nucleoside reverse transcriptase inhibitor (NRTI)-sparing (71.3%), with a decreasing trend over time (76.2% to 70.2%). ART regimen switch occurred more often in the 2-ART cohort than in the 3-ART cohort (33.0% vs 21.2%).

Conclusion

The profiles of individuals on 2-ART in Japan were demonstrated to be complex. Most were treated with NRTI-sparing regimens which may reflect an effort to reduce treatment-related toxicities.

Clinical standards for the dosing and management of TB drugs

BACKGROUND: Optimal drug dosing is important to ensure adequate response to treatment, prevent development of drug resistance and reduce drug toxicity. The aim of these clinical standards is to provide guidance on 'best practice´ for dosing and management of TB drugs.METHODS: A panel of 57 global experts in the fields of microbiology, pharmacology and TB care were identified; 51 participated in a Delphi process. A 5-point Likert scale was used to score draft standards. The final document represents the broad consensus and was approved by all participants.RESULTS: Six clinical standards were defined: Standard 1, defining the most appropriate initial dose for TB treatment; Standard 2, identifying patients who may be at risk of sub-optimal drug exposure; Standard 3, identifying patients at risk of developing drug-related toxicity and how best to manage this risk; Standard 4, identifying patients who can benefit from therapeutic drug monitoring (TDM); Standard 5, highlighting education and counselling that should be provided to people initiating TB treatment; and Standard 6, providing essential education for healthcare professionals. In addition, consensus research priorities were identified.CONCLUSION: This is the first consensus-based Clinical Standards for the dosing and management of TB drugs to guide clinicians and programme managers in planning and implementation of locally appropriate measures for optimal person-centred treatment to improve patient care.

HIV-1 Reservoir Persistence and Decay: Implications for Cure Strategies

PURPOSE OF REVIEW

Despite suppressive antiretroviral therapy (ART), a viral reservoir persists in individuals living with HIV that can reignite systemic replication should treatment be interrupted. Understanding how HIV-1 persists through effective ART is essential to develop cure strategies to induce ART-free virus remission.

RECENT FINDINGS

The HIV-1 reservoir resides in a pool of CD4-expressing cells as a range of viral species, a subset of which is genetically intact. Recent studies suggest that the reservoir on ART is highly dynamic, with expansion and contraction of virus-infected cells over time. Overall, the intact proviral reservoir declines faster than defective viruses, suggesting enhanced immune clearance or cellular turnover. Upon treatment interruption, rebound viruses demonstrate escape from adaptive and innate immune responses, implicating these selective pressures in restriction of virus reactivation. Cure strategies employing immunotherapy are poised to test whether host immune pressure can be augmented to enhance reservoir suppression or clearance. Alternatively, genomic engineering approaches are being applied to directly eliminate intact viruses and shrink the replication-competent virus pool. New evidence suggests host immunity exerts selective pressure on reservoir viruses and clears HIV-1 infected cells over years on ART. Efforts to build on the detectable, but insufficient, reservoir clearance via empiric testing in clinical trials will inform our understanding of mechanisms of viral persistence and the direction of future cure strategies.

Evaluation of the Potency of Anti-HIV and Anti-HCV Drugs to Inhibit P-Glycoprotein Mediated Efflux of Digoxin in Caco-2 Cell Line and Human Precision-Cut Intestinal Slices

The inhibition of P-glycoprotein (ABCB1) could lead to increased drug plasma concentrations and hence increase drug toxicity. The evaluation of a drug’s ability to inhibit ABCB1 is complicated by the presence of several transport-competent sites within the ABCB1 binding pocket, making it difficult to select appropriate substrates. Here, we investigate the capacity of antiretrovirals and direct-acting antivirals to inhibit the ABCB1-mediated intestinal efflux of [³H]-digoxin and compare it with our previous rhodamine123 study. At concentrations of up to 100 µM, asunaprevir, atazanavir, daclatasvir, darunavir, elbasvir, etravirine, grazoprevir, ledipasvir, lopinavir, rilpivirine, ritonavir, saquinavir, and velpatasvir inhibited [³H]-digoxin transport in Caco-2 cells and/or in precision-cut intestinal slices prepared from the human jejunum (hPCIS). However, abacavir, dolutegravir, maraviroc, sofosbuvir, tenofovir disoproxil fumarate, and zidovudine had no inhibitory effect. We thus found that most of the tested antivirals have a high potential to cause drug–drug interactions on intestinal ABCB1. Comparing the Caco-2 and hPCIS experimental models, we conclude that the Caco-2 transport assay is more sensitive, but the results obtained using hPCIS agree better with reported in vivo observations. More inhibitors were identified when using digoxin as the ABCB1 probe substrate than when using rhodamine123. However, both approaches had limitations, indicating that inhibitory potency should be tested with at least these two ABCB1 probes.

A retrospective evaluation of drug–drug interactions in patients admitted to Internal Medicine Departments in Palestinian Hospitals

Objective:

To measure the prevalence and identify risk factors associated with drug–drug interactions among patients admitted to internal medicine departments in Palestinian hospitals

Methods:

A retrospective cross-sectional observational study was conducted. Data were obtained from patient files from the internal medicine departments in Palestinian hospitals from 1 September 2017, to 31 March 2018. The data collected included patient gender, age, length of hospitalization, medications prescribed, and the number of medications. The digital clinical decision support system IBM Micromedex® was used to assess potential drug–drug interactions.

Results:

The number of patients included in this study is 513. The total number of potential drug–drug interactions detected in study participants is 1558. The average number of potential drug–drug interactions per patient was found to be 3 ± 3.9. Among study participants, 66.1% (n = 339) were found to have potential drug–drug interactions in their current medications. The most commonly encountered drug–drug interactions type was “major” drug–drug interaction, which was encountered in 43.6% (n = 681) of total detected drug–drug interactions. Other types of drug–drug interactions were encountered in 42% (n = 647), 14% (n = 224), and 0.4% (n = 6) which were moderate, minor, and contraindicated drug–drug interactions, respectively. Patients’ age, number of medications, and length of hospitalization were associated with the increased risk of potential drug–drug interactions.

Conclusion:

The results indicated a high prevalence of potential drug–drug interactions in Palestinian hospitals, associated with polypharmacy, increased age, and increased length of hospitalization. Therefore, managing patient medication by a drug expert such as a clinical pharmacist to identify and resolve potential drug–drug interactions will possibly decrease the high prevalence of drug–drug interactions, prevent patient harm, and decrease the cost of hospitalization.