Search

Background: The use of herbal medicine (HM) as a self-management practice for treating various diseases has gained popularity worldwide. Consumers co-administer herbal products with conventional medicine without the knowledge of possible herb-drug interaction (HDI)., Aim: This study aimed to assess patients' perception and use of HM and their knowledge of HDI., Setting: Participants attending primary health care (PHC) clinics in three provinces (Gauteng, Mpumalanga and Free State), South Africa, were recruited., Methods: Focus group discussions comprising a total of thirty (N = 30) participants were conducted using a semi-structured interview guide. Discussions were audio-recorded and then transcribed verbatim. Data were analysed using thematic content analysis., Results: Reasons for using HM, sources of information on HM, co-administration of HM and prescribed medicine, disclosure of the use of HM, PHC nurses' attitudes and not having time to engage were frequently discussed. Respondents' lack of knowledge and perceptions about HDI and their dissatisfaction with prescribed medicine because of experienced side effects were also discussed., Conclusion: Because of the lack of discussions and non-disclosure about HM in PHC clinics, patients are at risk of experiencing HDIs. Primary health care providers should regularly enquire about HM use on every patient, to identify and prevent HDIs. The lack of knowledge about HDIs by patients further compromises the safety of HM.Contribution: The results highlighted the lack of knowledge of HDI by patients thus assisting the healthcare stakeholders in South Africa to implement measures to educate patients attending PHC clinics.

Cocaine use is a public health concern in many countries worldwide, particularly in the Americas and Oceania. Overdose deaths involving stimulants, such as cocaine, have been increasing markedly in North America, especially with concurrent opioid involvement. To date, no pharmacological treatment is available to treat stimulant (including cocaine) use disorders. Prescription psychostimulants (PPs) could be useful to treat cocaine use disorder (CUD) as they share the pharmacological effects with cocaine, as evidenced by a recent meta-analysis that assessed 38 randomized clinical trials (RCTs). PPs were found to promote sustained abstinence and reduce drug use in patients with CUD. The aim of this paper is to provide a narrative review of the clinical pharmacology of PPs and comment on the current stage of evidence supporting PPs to treat CUD. We also propose a model of care that integrates PPs with evidence-based psychosocial interventions (such as cognitive-behavioural therapy [CBT] and contingency management [CM]), a harm reduction approach and case management with social support., (© 2023 Society for the Study of Addiction.)

Mitochondria are central to the physiology and survival of nearly all eukaryotic cells and house diverse metabolic processes including oxidative phosphorylation, reactive oxygen species buffering, metabolite synthesis/exchange, and Ca2+ sequestration. Mitochondria are phenotypically heterogeneous and this variation is essential to the complexity of physiological function among cells, tissues, and organ systems. As a consequence of mitochondrial integration with so many physiological processes, small molecules that modulate mitochondrial metabolism induce complex systemic effects. In the case of many commonly prescribed drugs, these interactions may contribute to drug therapeutic mechanisms, induce adverse drug reactions, or both. The purpose of this article is to review historical and recent advances in the understanding of the effects of prescription drugs on mitochondrial metabolism. Specific 'modes' of xenobiotic-mitochondria interactions are discussed to provide a set of qualitative models that aid in conceptualizing how the mitochondrial energy transduction system may be affected. Findings of recent in vitro high-throughput screening studies are reviewed, and a few candidate drug classes are chosen for additional brief discussion (i.e. antihyperglycemics, antidepressants, antibiotics, and antihyperlipidemics). Finally, recent improvements in pharmacokinetics models that aid in quantifying systemic effects of drug-mitochondria interactions are briefly considered., (© 2022 The Author(s).)

Two-thirds of signaling substances, several sensory stimuli and over one-third of drugs act via receptors coupling to G proteins. Here, we present an online platform for G protein research with reference data and tools for analysis, visualization and design of scientific studies across disciplines and areas. This platform may help translate new pharmacological, structural and genomic data into insights on G protein signaling vital for human physiology and medicine. The G protein database is accessible at https://gproteindb.org., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The 2018 American College of Cardiology/American Heart Association Guideline on the Management of Blood Cholesterol recommends statin therapy for eligible patients to reduce the risk of atherosclerotic cardiovascular disease (ASCVD). We extracted electronic health record data for patients with at least one primary care or cardiology visit between October 2018 and January 2020 at an urban, academic medical center in New York City. Clinical and demographic data were used to identify patients eligible for primary prevention statin therapy. Statin prescription status was extracted from the electronic health record, and multivariate logistic regression was used to assess the association between statin prescription and age, gender, race, ethnicity, and other clinical and demographic covariables. In 7,550 patients eligible for primary prevention statin therapy, 3,994 (52.9%) were prescribed statins on at least 1 visit. Statin prescription was highest in patients with diabetes mellitus (73.6%) and with a 10-year ASCVD risk ≥20% (60.6%) and was lowest for those with a 10-year ASCVD risk between 5% and 7.5% (18.7%). Compared with those never prescribed statins, patients prescribed statins were less likely to be women, mainly driven by lower statin prescription rates for women with diabetes. In a fully adjusted model, women remained less likely to be prescribed statin therapy (adjusted odds ratios 0.79, 95% confidence interval 0.71 to 0.88). In conclusion, primary prevention statin therapy remains underutilized., Competing Interests: Disclosures The authors have no conflicts of interest to declare., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Aspirin, as one of the most frequently prescribed drugs, can have therapeutic effects on different conditions such as cardiovascular and metabolic disorders and malignancies. The effects of this common cardiovascular drug are exerted through different molecular and cellular pathways. Altered noncoding RNA (ncRNA) expression profiles during aspirin treatments indicate a close relationship between these regulatory molecules and aspirin effects through regulating gene expressions. A better understanding of the molecular networks contributing to aspirin efficacy would help optimize efficient therapies for this very popular drug. This review is aimed at discussing and highlighting the identified interactions between aspirin and ncRNAs and their targeting pathways and better understanding pharmacogenetic responses to aspirin., Competing Interests: All the authors declare that they have no competing interests., (Copyright © 2021 Mohammad Amin Khazeei Tabari et al.)

Sulphonamides and their isosteres are classical inhibitors of the carbonic anhydrase (CAs, EC 4.2.1.1) metalloenzymes. The protozoan pathogen Trichomonas vaginalis encodes two such enzymes belonging to the β-class, TvaCA1 and TvaCA2. Here we report the first sulphonamide inhibition study of TvaCA1, with a series of simple aromatic/heterocyclic primary sulphonamides as well as with clinically approved/investigational drugs for a range of pathologies (diuretics, antiglaucoma, antiepileptic, antiobesity, and antitumor drugs). TvaCA1 was effectively inhibited by acetazolamide and ethoxzolamide, with K I s of 391 and 283 nM, respectively, whereas many other simple or clinically used sulphonamides were micromolar inhibitors or did not efficiently inhibit the enzyme. Finding more effective TvaCA1 inhibitors may constitute an innovative approach for fighting trichomoniasis, a sexually transmitted infection, caused by T. vaginalis .

Purpose: As gut microbiota composition is an important determinant of response to immune checkpoint inhibitors (ICIs), we examined the effect of various co-medications known for their interaction with microbiota, when given at ICI initiation., Patients and Methods: We identified patients with advanced cancer treated with ICI between May 2015 and September 2017 in our institution. Co-medications given within 1 month before or 1 month after the first administration of ICI were reviewed from medical records. Survival data were analysed with univariable Cox regression, and the combined effect of multiple factors was assessed with factor analysis of mixed data (FAMD). The impact of co-medications on immune-related adverse events (irAEs) occurrence was also assessed., Results: A total of 635 patients were included. Psychotropic drugs (41%), proton pump inhibitors (PPIs; 38%), angiotensin-converting enzyme (ACE) inhibitors and/or angiotensin II receptor blockers (ARBs; 32%), glucocorticoids (26%), antibiotics (24%), statins (21%) and morphine (20%) were the most prescribed co-medications. Baseline use of antibiotics, glucocorticoids >10 mg/day, PPIs, psychotropic drugs, morphine and insulin was associated with significantly shortened overall survival and decreased tumour response, whereas coadministration of statins, ACEs and/or ARBs, non-steroidal anti-inflammatory drugs, aspirin and oral antidiabetic drugs did not impact patient outcomes. Treatments that altered the response to ICI were also associated with a decreased incidence of irAEs. FAMD revealed the respective weight of each factor or co-medication on the oncological outcomes., Conclusion: Co-medications must be carefully assessed at the time of ICI initiation and clinicians aware of their possible deleterious effect, notably for PPIs, glucocorticoids, antibiotics and psychotropic drugs., Competing Interests: Conflict of interest statement M.B.B. reports consulting and advisory boards for BMS and MSD France. A.R. reports being a member of Global, European and/or French Advisory Board in genitourinary tumours and/or immunotherapy for Pfizer, Novartis, BMS, Roche, Astra Zeneca and MSD and received travel support from Pfizer, BMS, Roche, Astra Zeneca and MSD. S.P. reports consulting for BMS and travel support from MSD. R.V. reports being one of the investigators for a clinical trial from BMS, consulting and advisory boards for BMS and MSD and travel support from BMS and MSD. All the others authors declared no conflict of interest for this work., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Every year, millions of people are hospitalized and thousands die from influenza A virus (FLUAV) infection. Most cases of hospitalizations and death occur among the elderly. Many of these elderly patients are reliant on medical treatment of underlying chronic diseases, such as arthritis, diabetes, and hypertension. We hypothesized that the commonly prescribed medicines for treatment of underlying chronic diseases can affect host responses to FLUAV infection and thus contribute to the morbidity and mortality associated with influenza. Therefore, the aim of this study was to examine whether commonly prescribed medicines could affect host responses to virus infection in vitro. Methods: We first identified 45 active compounds from a list of commonly prescribed medicines. Then, we constructed a drug-target interaction network and identified the potential implication of these interactions for FLUAV-host cell interplay. Finally, we tested the effect of 45 drugs on the viability, transcription, and metabolism of mock- and FLUAV-infected human retinal pigment epithelial (RPE) cells. Results: In silico drug-target interaction analysis revealed that drugs such as atorvastatin, candesartan, and hydroxocobalamin could target and modulate FLUAV-host cell interaction. In vitro experiments showed that at non-cytotoxic concentrations, these compounds affected the transcription and metabolism of FLUAV- and mock-infected cells. Conclusion: Many commonly prescribed drugs were found to modulate FLUAV-host cell interactions in silico and in vitro and could therefore affect their interplay in vivo, thus contributing to the morbidity and mortality of patients with influenza virus infections.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) represents a significant threat to human health. Despite its similarity to related coronaviruses, there are currently no specific treatments for COVID-19 infection, and therefore there is an urgent need to develop therapies for this and future coronavirus outbreaks. Formation of the cap at the 5' end of viral RNA has been shown to help coronaviruses evade host defenses. Nonstructural protein 14 (nsp14) is responsible for N7-methylation of the cap guanosine in coronaviruses. This enzyme is highly conserved among coronaviruses and is a bifunctional protein with both N7-methyltransferase and 3'-5' exonuclease activities that distinguish nsp14 from its human equivalent. Mutational analysis of SARS-CoV nsp14 highlighted its role in viral replication and translation efficiency of the viral genome. In this paper, we describe the characterization and development of a high-throughput assay for nsp14 utilizing RapidFire technology. The assay has been used to screen a library of 1771 Food and Drug Administration (FDA)-approved drugs. From this, we have validated nitazoxanide as a selective inhibitor of the methyltransferase activity of nsp14. Although modestly active, this compound could serve as a starting point for further optimization.

Organoids allow the recapitulation of intestinal homeostasis and cancerogenesis in vitro; however, RNA sequencing (RNA-seq)-based methods for drug screens are missing. We develop targeted organoid sequencing (TORNADO-seq), a high-throughput, high-content drug discovery platform that uses targeted RNA-seq to monitor the expression of large gene signatures for the detailed evaluation of cellular phenotypes in organoids. TORNADO-seq is a fast, highly reproducible time- and cost-effective ($5 per sample) method that can probe cell mixtures and their differentiation state in the intestinal system. We apply this method to isolate drugs that enrich for differentiated cell phenotypes and show that these drugs are highly efficacious against cancer compared to wild-type organoids. Furthermore, TORNADO-seq facilitates in-depth insight into the mode of action of these drugs. Our technology can easily be adapted to many other systems and will allow for more systematic, large-scale, and quantitative approaches to study the biology of complex cellular systems., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

In vertebrates, intercellular communication is largely mediated by connexins (Cx), a family of structurally related transmembrane proteins that assemble to form hemichannels (HCs) at the plasma membrane. HCs are upregulated in different brain disorders and represent innovative therapeutic targets. Identifying modulators of Cx-based HCs is of great interest to better understand their function and define new treatments. In this study, we developed automated versions of two different cell-based assays to identify new pharmacological modulators of Cx43-HCs. As HCs remain mostly closed under physiological conditions in cell culture, depletion of extracellular Ca 2+ was used to increase the probability of opening of HCs. The first assay follows the incorporation of a fluorescent dye, Yo-Pro, by real-time imaging, while the second is based on the quenching of a fluorescent protein, YFP QL , by iodide after iodide uptake. These assays were then used to screen a collection of 2242 approved drugs and compounds under development. This study led to the identification of 11 candidate hits blocking Cx43-HC, active in the two assays, with 5 drugs active on HC but not on gap junction (GJ) activities. To our knowledge, this is the first screening on HC activity and our results suggest the potential of a new use of already approved drugs in central nervous system disorders with HC impairments.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Improving the quality of medication use and medication safety are important priorities for prescribers who care for older adults. The objective of this article was to identify four exemplary articles with this focus in 2019. We selected high-quality studies that moved the field of research forward and were not merely replication studies. The chosen articles cover domains related to aspects of suboptimal prescribing and medication safety. The first study used a nationally representative sample of Medicare beneficiaries to examine the continuation of medications with limited benefit in patients admitted for cancer and non-cancer diagnoses in hospice (domain: potentially inappropriate medications). The second study, a retrospective cohort study of older adults in Ontario, Canada, assessed the association between prescribing oral anticoagulants in an emergency department relative to not prescribing anticoagulants in the emergency department and their persistence at 6 months (domain: underuse of medications). The third study, a cluster randomized trial in Quebec, Canada, evaluated the effect of conducting electronic medication reconciliation on several outcomes including adverse drug events and medication discrepancies (domain: medication safety). Lastly, the fourth study, a retrospective study using national inpatient and outpatient Veteran Health Administration combined with clinical and Medicare Claims data, examined the effects of intensification of antihypertensive medications on older adults' likelihood for hospital re-admission and other important clinical outcomes (domain: medication safety). Collectively, this review succinctly highlights pertinent topics related to promoting safe use of medications and promotes awareness of optimizing older adults' medication regimens., (© 2021 The American Geriatrics Society.)

The public Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is an innovative digital ecosystem that relates toxicological information for chemicals, genes, phenotypes, diseases, and exposures to advance understanding about human health. Literature-based, manually curated interactions are integrated to create a knowledgebase that harmonizes cross-species heterogeneous data for chemical exposures and their biological repercussions. In this biennial update, we report a 20% increase in CTD curated content and now provide 45 million toxicogenomic relationships for over 16 300 chemicals, 51 300 genes, 5500 phenotypes, 7200 diseases and 163 000 exposure events, from 600 comparative species. Furthermore, we increase the functionality of chemical-phenotype content with new data-tabs on CTD Disease pages (to help fill in knowledge gaps for environmental health) and new phenotype search parameters (for Batch Query and Venn analysis tools). As well, we introduce new CTD Anatomy pages that allow users to uniquely explore and analyze chemical-phenotype interactions from an anatomical perspective. Finally, we have enhanced CTD Chemical pages with new literature-based chemical synonyms (to improve querying) and added 1600 amino acid-based compounds (to increase chemical landscape). Together, these updates continue to augment CTD as a powerful resource for generating testable hypotheses about the etiologies and molecular mechanisms underlying environmentally influenced diseases., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

The neurodegenerative Alzheimer's disease (AD) affects more than 30 million people worldwide. There is thus far no cure or prevention for AD. Aggregation of hyperphosphorylated tau in the brain correlates with the cognitive decline of patients of AD and other neurodegenerative tauopathies. Intracerebral injection of tau aggregates isolated from tauopathy brains causes similar pathology in the recipient mice, demonstrating the pathogenic role of abnormally phosphorylated tau. Compounds controlling the aggregation of hyperphosphorylated tau therefore are probable modulators for the disease. Here we report the use of recombinant hyperphosphorylated tau (p-tau) to identify potential tauopathy therapeutics and risk factors. Hyperphosphorylation renders tau prone to aggregate and to impair cell viability. Taking advantage of these two characters of p-tau, we performed a screen of a 1280-compound library, and tested a selective group of prescription drugs in p-tau aggregation and cytotoxicity assays. R-(-)-apomorphine and raloxifene were found to be p-tau aggregation inhibitors that protected p-tau-treated cells. In contrast, a subset of benzodiazepines exacerbated p-tau cytotoxicity apparently via enhancing p-tau aggregation. R-(-)apomorphine and raloxifene have been shown to improve cognition in animals or in humans, whereas benzodiazepines were linked to increased risks of dementia. Our results demonstrate the feasibility and potential of using hyperphosphorylated tau-based assays for AD drug discovery and risk factor identification.

Patients with COVID-19 are sometimes already being treated for one or more other chronic conditions, especially if they are elderly. Introducing a treatment against COVID-19, either on an outpatient basis or during hospitalization for more severe cases, raises the question of potential drug-drug interactions. Here, we analyzed the potential or proven risk of the co-administration of drugs used for the most common chronic diseases and those currently offered as treatment or undergoing therapeutic trials for COVID-19. Practical recommendations are offered, where possible., (© 2020 Société Française de Pharmacologie et de Thérapeutique.)

SARS-CoV-2 is the causative agent for the ongoing COVID19 pandemic, and this virus belongs to the Coronaviridae family. Like other members of this family, the virus possesses a positive-sense single-stranded RNA genome. The genome encodes for the nsp12 protein, which houses the RNA-dependent-RNA polymerase (RdRP) activity responsible for the replication of the viral genome. A homology model of nsp12 was prepared using the structure of the SARS nsp12 (6NUR) as a model. The model was used to carry out in silico screening to identify molecules among natural products, or Food and Drug Administration-approved drugs that can potentially inhibit the activity of nsp12. This exercise showed that vitamin B12 (methylcobalamin) may bind to the active site of the nsp12 protein. A model of the nsp12 in complex with substrate RNA and incoming NTP showed that vitamin B12 binding site overlaps with that of the incoming nucleotide. A comparison of the calculated energies of binding for RNA plus NTP and methylcobalamin suggested that the vitamin may bind to the active site of nsp12 with significant affinity. It is, therefore, possible that methylcobalamin binding may prevent association with RNA and NTP and thus inhibit the RdRP activity of nsp12. Overall, our computational studies suggest that methylcobalamin form of vitamin B12 may serve as an effective inhibitor of the nsp12 protein., (© 2020 International Union of Biochemistry and Molecular Biology.)

Pharmacogenetics (PGx) aims to improve drug therapy using the individual patients' genetic make-up. Little is known about the potential impact of PGx on the population level, possibly hindering implementation of PGx in clinical care. Therefore, we investigated how many patients use actionable PGx drugs, have actionable genotypes or phenotypes and which patients could benefit the most of PGx testing. We included PGx recommendations from two international PGx consortia (Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG)). Using data from publically accessible sales information drawn from the Danish Register of Medicinal Product Statistics (MEDSTAT), we identified the number of users of actionable prescription PGx drugs among the total Danish population in 2017. We estimated actionable genotypes or phenotypes based on reported frequencies from literature. We identified 49 drug-gene interactions related to 41 unique prescription drugs. The estimated median frequency of actionable genotypes or phenotypes among prescription drug users was 25% (interquartile range 7-26%). Six of 41 drugs were used more than twice as much in women. Actionable PGx drugs were most frequently used by 45-79 year old patients (62%), followed by 25-44 year old patients (18%). Almost half of the actionable PGx drugs (19/41) were psychotropics (i.e., antidepressants, antipsychotics, or psychostimulants). PGx testing can have a substantial impact on the population, as one in four prescription drug users has an actionable genotype or phenotype and could thus benefit from PGx testing. We advocate for prospective panel-based PGx testing at the time of the first PGx drug prescription ("as needed"), with PGx results ready prior to start of the first, and all future, therapies., (© 2020 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

In chronic kidney disease (CKD), the secretion of uremic toxins is compromised leading to their accumulation in blood, which contributes to uremic complications, in particular cardiovascular disease. Organic anion transporters (OATs) are involved in the tubular secretion of protein-bound uremic toxins (PBUTs). However, OATs also handle a wide range of drugs, including those used for treatment of cardiovascular complications and their interaction with PBUTs is unknown. The aim of this study was to investigate the interaction between commonly prescribed drugs in CKD and endogenous PBUTs with respect to OAT1-mediated uptake. We exposed a unique conditionally immortalized proximal tubule cell line (ciPTEC) equipped with OAT1 to a panel of selected drugs, including angiotensin-converting enzyme inhibitors (ACEIs: captopril, enalaprilate, lisinopril), angiotensin receptor blockers (ARBs: losartan and valsartan), furosemide and statins (pravastatin and simvastatin), and evaluated the drug-interactions using an OAT1-mediated fluorescein assay. We show that selected ARBs and furosemide significantly reduced fluorescein uptake, with the highest potency for ARBs. This was exaggerated in presence of some PBUTs. Selected ACEIs and statins had either no or a slight effect at supratherapeutic concentrations on OAT1-mediated fluorescein uptake. In conclusion, we demonstrate that PBUTs may compete with co-administrated drugs commonly used in CKD management for renal OAT1 mediated secretion, thus potentially compromising the residual renal function.

The scope of this article is to review the effects on sleep of prescription drugs that are commonly prescribed for chronic insomnia in adults. The following groups are discussed: benzodiazepines and its receptor agonists, the dual orexin receptor antagonist suvorexant, melatonin and its receptor agonists, sedating antidepressants, and antipsychotics. Together with the neurobiologic and pharmacologic properties of these drugs, clinical effects are described, including subjective and objective effects on sleep duration, continuity, and architecture. Medical prescription information is given when available. Recently published American and European guidelines for the treatment of insomnia serve as reference frame., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Effects of drug treatment of clinical Alzheimer-type dementia (CATD) are uncertain., Purpose: To summarize evidence on the effects of prescription drugs and supplements for CATD treatment., Data Sources: Electronic bibliographic databases (inception to November 2019), ClinicalTrials.gov (to November 2019), and systematic review bibliographies., Study Selection: English-language trials of prescription drug and supplement treatment in older adults with CATD that report cognition, function, global measures, behavioral and psychological symptoms of dementia (BPSD), or harms. Minimum treatment was 24 weeks (≥2 weeks for selected BPSD)., Data Extraction: Studies with low or medium risk of bias (ROB) were analyzed. Two reviewers rated ROB. One reviewer extracted data; another verified extraction accuracy., Data Synthesis: Fifty-five studies reporting non-BPSD outcomes (most ≤26 weeks) and 12 reporting BPSD (most ≤12 weeks) were analyzed. Across CATD severity, mostly low-strength evidence suggested that, compared with placebo, cholinesterase inhibitors produced small average improvements in cognition (median standardized mean difference [SMD], 0.30 [range, 0.24 to 0.52]), no difference to small improvement in function (median SMD, 0.19 [range, -0.10 to 0.22]), no difference in the likelihood of at least moderate improvement in global clinical impression (median absolute risk difference, 4% [range, 2% to 4%]), and increased withdrawals due to adverse events. In adults with moderate to severe CATD receiving cholinesterase inhibitors, low- to insufficient-strength evidence suggested that, compared with placebo, add-on memantine inconsistently improved cognition and improved global clinical impression but not function. Evidence was mostly insufficient about prescription drugs for BPSD and about supplements for all outcomes., Limitation: Most drugs had few trials without high ROB, especially for supplements, active drug comparisons, BPSD, and longer trials., Conclusion: Cholinesterase inhibitors and memantine slightly reduced short-term cognitive decline, and cholinesterase inhibitors slightly reduced reported functional decline, but differences versus placebo were of uncertain clinical importance. Evidence was mostly insufficient on drug treatment of BPSD and on supplements for all outcomes., Primary Funding Source: Agency for Healthcare Research and Quality. (PROSPERO: CRD42018117897).

Accumulating evidence has shown that drug-target interactions (DTIs) play a crucial role in the process of genomic drug discovery. Although biological experimental technology has made great progress, the identification of DTIs is still very time-consuming and expensive nowadays. Hence it is urgent to develop in silico model as a supplement to the biological experiments to predict the potential DTIs. In this work, a new model is designed to predict DTIs by incorporating chemical sub-structures and protein evolutionary information. Specifically, we first use Position-Specific Scoring Matrix (PSSM) to convert the protein sequence into the numerical descriptor containing biological evolutionary information, then use Discrete Cosine Transform (DCT) algorithm to extract the hidden features and integrate them with the chemical sub-structures descriptor, and finally utilize Rotation Forest (RF) classifier to accurately predict whether there is interaction between the drug and the target protein. In the 5-fold cross-validation (CV) experiment, the average accuracy of the proposed model on the benchmark datasets of Enzymes, Ion Channels, GPCRs and Nuclear Receptors reached 0.9140, 0.8919, 0.8724 and 0.8111, respectively. In order to fully evaluate the performance of the proposed model, we compare it with different feature extraction model, classifier model, and other state-of-the-art models. Furthermore, we also implemented case studies. As a result, 8 of the top 10 drug-target pairs with the highest prediction score were confirmed by related databases. These excellent results indicate that the proposed model has outstanding ability in predicting DTIs and can provide reliable candidates for biological experiments.

During pregnancy, there are several physiological changes during each trimester that can affect the absorption, distribution, metabolism, and elimination of drugs. Although there is a potential need to understand the pharmacokinetics and pharmacodynamics of drugs in pregnant patients, therapeutic drug monitoring is not well established for various drug classes due to ethical and safety concerns regarding the neonate. Potential risks from in utero drug exposure to the fetus may impact growth and development and may cause malformations or teratogenesis. The clinician must consider the benefits of drug treatment for the pregnant mother versus the risk to the fetus, before prescribing medications during pregnancy. The objective of this review is to aid clinicians, pharmacists, and laboratorians in understanding the pharmacokinetic and pharmacodynamic changes during pregnancy, to provide drug class recommendations for monitoring therapy throughout pregnancy via therapeutic drug monitoring, and to highlight the recent directives of governing agencies on maternal and fetal health.

Background: Human-targeted drugs may exert off-target effects or can be repurposed to modulate the gut microbiota. However, our understanding of such effects is limited due to a lack of rapid and scalable assay to comprehensively assess microbiome responses to drugs. Drugs and other compounds can drastically change the overall abundance, taxonomic composition, and functions of a gut microbiome., Results: Here, we developed an approach to screen compounds against individual microbiomes in vitro, using metaproteomics to both measure absolute bacterial abundances and to functionally profile the microbiome. Our approach was evaluated by testing 43 compounds (including 4 antibiotics) against 5 individual microbiomes. The method generated technically highly reproducible readouts, including changes of overall microbiome abundance, microbiome composition, and functional pathways. Results show that besides the antibiotics, the compounds berberine and ibuprofen inhibited the accumulation of biomass during in vitro growth of the microbiota. By comparing genus and species level-biomass contributions, selective antibacterial-like activities were found with 35 of the 39 non-antibiotic compounds. Seven of the compounds led to a global alteration of the metaproteome, with apparent compound-specific patterns of functional responses. The taxonomic distributions of altered proteins varied among drugs, i.e., different drugs affect functions of different members of the microbiome. We also showed that bacterial function can shift in response to drugs without a change in the abundance of the bacteria., Conclusions: Current drug-microbiome interaction studies largely focus on relative microbiome composition and microbial drug metabolism. In contrast, our workflow enables multiple insights into microbiome absolute abundance and functional responses to drugs. The workflow is robust, reproducible, and quantitative and is scalable for personalized high-throughput drug screening applications.

Background: Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter., Objective: This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse-related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described., Methods: We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes-abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data., Results: Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271)., Conclusions: Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks., (©Karen O'Connor, Abeed Sarker, Jeanmarie Perrone, Graciela Gonzalez Hernandez. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.02.2020.)

Adverse pharmacokinetic interactions between illicit substances and clinical drugs are of a significant health concern. Illicit substances are taken by healthy individuals as well as by patients with medical conditions such as mental illnesses, acquired immunodeficiency syndrome, diabetes mellitus and cancer. Many individuals that use illicit substances simultaneously take clinical drugs meant for targeted treatment. This concomitant usage can lead to life-threatening pharmacokinetic interactions between illicit substances and clinical drugs. Optimal levels and activity of drug-metabolizing enzymes and drug-transporters are crucial for metabolism and disposition of illicit substances as well as clinical drugs. However, both illicit substances and clinical drugs can induce changes in the expression and/or activity of drug-metabolizing enzymes and drug-transporters. Consequently, with concomitant usage, illicit substances can adversely influence the therapeutic outcome of coadministered clinical drugs. Likewise, clinical drugs can adversely affect the response of coadministered illicit substances. While the interactions between illicit substances and clinical drugs pose a tremendous health and financial burden, they lack a similar level of attention as drug-drug, food-drug, supplement-drug, herb-drug, disease-drug, or other substance-drug interactions such as alcohol-drug and tobacco-drug interactions. This review highlights the clinical pharmacokinetic interactions between clinical drugs and commonly used illicit substances such as cannabis, cocaine and 3, 4-Methylenedioxymethamphetamine (MDMA). Rigorous efforts are warranted to further understand the underlying mechanisms responsible for these clinical pharmacokinetic interactions. It is also critical to extend the awareness of the life-threatening adverse interactions to both health care professionals and patients.

Competing Interests: Forschungsunterstützung: ThermoScience, Forschungskolleg Geriatrie der Robert-Bosch-Stiftung, Wissenschaftsforum Geriatrie, Deutsche Bank, Innovationsfonds des G-BA. Vortragshonorare: Pfizer Pharma, Bayer Health Care, Mundipharma, Astellas Pharma, MSD, BLÄK, AO Trauma Europe, Dr. Pfleger GmbH, Grünenthal, Novartis.

Competing Interests: Der Referent war von 2004 bis 2006 aufgrund einer Tätigkeit für die Fa. AstraZeneca beurlaubt. Seit 01.01.2007 ist er wieder Professor für Klinische Pharmakologie an der Universität Heidelberg in Mannheim. Vor und nach dieser Zeit war und ist er als Gutachter, Berater und Referent für Sanofi-Aventis, Bayer, Boehringer Ingelheim, Novartis, Takeda, Roche, Pfizer, Bristol-Myers Squibb, Daichii-Sankyo, Lilly, Otsuka, Novo-Nordisk, Shire and LEO Pharma tätig.

Rhinacanthin-C is a major active constituent in Rhinacanthus nasutus (L.) Kurz, a plant widely used in herbal remedies. Its potential for pharmacokinetic herb-drug interaction may exist with drug transporters and drug metabolizing enzymes. This study assessed the possibility for rhinacanthin-C-mediated drug interaction by determining its inhibitory effects against major human efflux and influx drug transporters as well as various human cytochrome P450(CYP) isoforms. Rhinacanthin-C demonstrated a moderate permeability through the Caco-2 monolayers [P app (AP-to-BL) = 1.26 × 10 -6 cm/s]. It significantly inhibited transport mediated by both P-glycoprotein (P-gp) (IC 50 = 5.20 µ M) and breast cancer resistance protein (BCRP) (IC 50 = 0.83 µ M) across Caco-2 and BCRP-overexpressing Madin-Darby canine kidney II cells (MDCKII) cells. This compound also strongly inhibited uptake mediated by organic anion-transporting polypeptide 1B1 (OATP1B1) (IC 50 = 0.70 µ M) and OATP1B3 (IC 50 = 3.95 µ M) in OATP1B-overexpressing HEK cells. In addition to its inhibitory effect on these drug transporters, rhinacanthin-C significantly inhibited multiple human CYP isoforms including CYP2C8 (IC 50 = 4.56 µ M), 2C9 (IC 50 = 1.52 µ M), 2C19 (IC 50 = 28.40 µ M), and 3A4/5 (IC 50 = 53 µ M for midazolam and IC 50 = 81.20 µ M for testosterone), but not CYP1A2, 2A6, 2B6, 2D6, and 2E1. These results strongly support a high propensity for rhinacanthin-C as a perpetrator of clinical herb-drug interaction via inhibiting various influx and efflux drug transporters (i.e., P-gp, BCRP, OATP1B1, and OATP1B3) and CYP isoforms (i.e., CYP2C8, CYP2C9, and CYP2C19). Thus, the potential for significant pharmacokinetic herb-drug interaction should be addressed when herbal products containing rhinacanthin-C are to be used in conjunction with other prescription drugs., (Copyright © 2019 by The American Society for Pharmacology and Experimental Therapeutics.)

The use of antifungal agents in clinical settings is limited by the appearance of drug resistance and adverse side effects. There is, therefore, an urgent need to develop new drugs to strengthen the treatment of invasive fungal diseases. The aim of this study is to describe the potential repurposing of ribavirin as an adjunct therapy against Candida spp. Primary screening of a Prestwick Chemical library against Candida albicans ATCC 90028 and fluconazole-resistant Candida albicans strains was performed. Subsequently, we evaluated the responses of 100 Candida sp. strains to ribavirin, an antiviral agent, using the broth microdilution method as recommended by CLSI. We checked the involvement of efflux pump activity in the development of ribavirin resistance. We studied time-kill curves and performed a checkerboard assay for a ribavirin-antifungal combination study. Twenty-one nonstandard antifungal compounds were identified, including ribavirin. Ribavirin had antifungal activity in vitro against 63 Candida strains, including strains of C. albicans , C. parapsilosis , and C. tropicalis , with MICs ranging from 0.37 to 3.02 μg/ml, while MICs for C. krusei , C. glabrata , C. lusitaniae , and some C. albicans strains remained high (≥24.16 μg/ml). No relation was observed between efflux pump activity and ribavirin resistance. Ribavirin exhibited fungistatic activity against multidrug-resistant (MDR) C. albicans and fungicidal activity against a C. parapsilosis strain. In addition, ribavirin acted synergistically with azoles against Candida strains for which ribavirin MICs were <24.4 μg/ml. This study highlights the potential clinical application of ribavirin, alone or in association with other antifungal agents, as an adjunct anti- Candida drug., (Copyright © 2019 American Society for Microbiology.)

The interaction between drug use, the microbiome, and the host is complex and multidimensional. Drugs and the microbiota may be risk factors or protective factors for disease. These interactions may explain interpersonal variations in drug efficacy and toxicity, but also interpersonal variations in microbiota composition and functioning, and potential (long-term) side effects from drugs., (Copyright © 2019 Elsevier Inc. All rights reserved.)

A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objective/Background : Poor sleep and nonmedical use (NMU) of prescription drugs (NMUPD) are both common among college students. Since lack of sleep adversely influences academic performance, this study examined the association between NMUPD and subjective sleep quality among college students. Participants : Students who completed the American College Health Association-National College Health Assessment data (Fall 2010, Spring 2011; N  = 135,874). Methods : Associations were examined between NMUPD in four classes over the past 12 months (antidepressant, painkillers, sedatives, and stimulants), and five aspects of sleep quality (Enough Sleep, Early Awakening, Daytime Sleepiness, Difficulty Falling Asleep, and Problem With Daytime Sleepiness) in the past seven days. Results : Any NMUPD (at least one class), NMU of stimulants specifically, and NMU of painkillers specifically were associated with getting fewer days of Enough Sleep (OR: 0.86, 0.93, and 0.84 respectively), more days of Early Awakening (OR: 1.28, 1.10, and 1.28 respectively), Daytime Sleepiness (OR: 1.23, 1.13, and 1.16 respectively), and Difficulty Falling Asleep (OR:1.32, 1.10, and 1.27 respectively; p  < .0001, each). NMU of sedatives was significantly associated with having Problem With Daytime Sleepiness (OR: 1.10), more days of Early Awakening (OR: 1.12), and Difficulty Falling Asleep (OR: 1.17; p  < .0001). Conclusions : NMUPD is associated with poor sleep among college students. Therefore, behavioral medicine screening and treatment of this vulnerable population should consider sleep health, NMUPD, and the potential that these problems may be comorbid.

Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space of inhibited gene functions and enabled rational design of drug combinations. This approach is applicable to other therapeutic areas and can unveil unprecedented insights into drug tolerance, side effects, and repurposing. The compendium of drug-associated metabolome profiles is available at https://zampierigroup.shinyapps.io/EcoPrestMet, providing a valuable resource for the microbiological and pharmacological communities., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs., Results: We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.

Background: Phthalates are used as excipients in some drug products, and up to a 50-fold increased urinary excretion of phthalate metabolites compared to non-users has been demonstrated in users of such products. In vitro studies have demonstrated that phthalates stimulate mechanisms involved in gastric cancer development. We therefore examined the association between cumulative phthalate exposure from drug products and the risk of gastric adenocarcinomas. Methods: Using the Danish Cancer Registry, we identified all patients with incident gastric adenocarcinoma from 2008 to 2015 ( n  = 1525). Cancer cases were matched to 10 controls. Linking information retrieved from nationwide Danish registries, we determined individual cumulative phthalate exposure to the ortho -phthalates diethyl phthalate (DEP), dibutyl phthalate (DBP) and enteric phthalate polymers from prescription drugs. The association between cumulative phthalate exposure and gastric adenocarcinoma was estimated using conditional logistic regression, adjusting for socioeconomical status and drugs or comorbidities known or suspected to modify the risk of gastric adenocarcinoma. Results: No association was seen for the risk of gastric adenocarcinomas among individuals with high cumulative exposure to ortho -phthalates (exceeding 500 mg) (OR adj 1.22, 95% CI: 0.84-1.77). Likewise, no associations were observed individually for DEP (OR adj 1.06 95% CI: 0.63-1.76) or DBP (OR adj 1.32 95% CI: 0.78-2.23). Cumulative exposure to enteric phthalate polymers exceeding 10,000 mg, did not reveal an association with gastric adenocarcinoma (OR adj 0.79, 95% CI: 0.54-1.16) and no association was seen for individual compounds. Additionally, no dose-response pattern was observed across exposure strata ( p  = .39, test for trend). Conclusion: We did not find an increased risk of gastric adenocarcinoma among Danish users of phthalate-containing drug products. Our study is limited by a low number of cases exposed to high cumulative doses of phthalates.

The nonmedical use of prescription drugs to improve cognitive performance has gained attention due to concerns over its social and political implications as well as side effects and long-term health consequences. Some researchers expect a future trend of an instrumental use of drugs for cognitive enhancement (CE). Thus, getting insights about causes of CE-drug consumption is warranted before the prevalence increases. Because perceived stress is ubiquitous in universities and may decrease cognitive performance, one reaction to cope with stress and its consequences might be the instrumental use of drugs for CE, especially if other resources, such as social support, are lacking. With a prospective design, randomly selected students from four German universities were invited to a web-based survey and reinterviewed after 6 months (N = 2,203). Results show a 6-month prevalence rate of self-reported CE-drug use of about 2%. Higher reported chronic stress is positively associated with CE-drug use. Although social support has no main effect, stress-buffering effects were found. In men with low stress, more support is associated with a higher chance of self-reported CE-drug use. These findings can inform intervention and prevention strategies such as changes in drug regulation or sensitizing (potential) users to unwanted health consequences., (© 2018 John Wiley & Sons, Ltd.)

Objective: A recurrent observation is that associations between self-reported and objective medication adherence measures are often weak to moderate. Our aim was therefore to identify patients with different profiles on self-reported and objective adherence measures., Study Design and Setting: This was an observational study of 221 community pharmacy patients who were dispensed antidepressants. Adherence profiles were estimated with Latent Profile Analysis (LPA) using data on self-reported adherence (Medication Adherence Rating Scale) complemented with data on medication beliefs (perceived necessity and concerns measured with the Beliefs about Medicines Questionnaire) and data from objective adherence measures (electronic monitoring of medication taking and the Medication Possession Ratio calculated from pharmacy dispensing data)., Results: 'Goodness-of-fit' statistics indicated the presence of three classes: "concordantly high adherent" (83%, high adherence on all measures), "concordantly suboptimal adherent" (11%, low adherence on all measures), and "discordant" (6%, high self-reported adherence but lower adherence on objective measures)., Conclusion: Most patients had concordant outcomes on self-reported and objective measures of adherence. A small discordant class had high self-reported but low objective adherence. LPA will enable sensitivity analyses in future studies, for example excluding patients from the discordant class.

Purpose: This study aims to estimate (i) the prevalence of potentially serious alcohol-medication interactions in a nationally representative sample of older adults using the Potentially Serious Alcohol-Medication Interactions in Older adults (POSAMINO) criteria, and (ii) whether POSAMINO prevalence changes over time., Methods: A prospective cohort study of adults aged ≥ 65 years, using data from the first three waves of The Irish Longitudinal Study on Ageing (TILDA). All 38 POSAMINO criteria were applied at each wave using respondents' information on regular medications and alcohol consumption. Multilevel logistic regression and negative binomial models were used to investigate whether the prevalence of POSAMINO varied over time., Results: The overall prevalence of POSAMINO was 18% at baseline, with 8% at risk of one potentially serious alcohol-medication interaction, and 10% at risk of two or more. The most common POSAMINO involved cardiovascular (CVS) agents (15% baseline; 11% wave 2; 14% wave 3), followed by central nervous system (CNS) agents (4% baseline; 4% wave 2; 5% wave 3). Prevalence of any POSAMINO (AOR 0.94, 95% CI 0.81, 1.08) or number of POSAMINO criteria (AIRR 0.97, 95% CI 0.91, 1.04) did not change over time. Any POSAMINO and number of POSAMINO were associated with younger age, male sex and number of medications and chronic conditions., Conclusions: Potentially serious alcohol-medication interactions occurred in 18% of older adults in this study. Alcohol screening and brief interventions should be considered for high-risk groups at the point of prescribing, particularly among younger older adults, men and as patients receive more medications or develop additional illnesses.