Concurrent use of herbal and prescribed medicine by patients in primary health care clinics, South Africa.

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.

Prescription psychostimulants for cocaine use disorder: A review from molecular basis to clinical approach.

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.

Prescription drugs and mitochondrial metabolism.

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.

The G protein database, GproteinDb.

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.

Noncoding RNA Roles in Pharmacogenomic Responses to Aspirin: New Molecular Mechanisms for an Old Drug.

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.

Gaps and Disparities in Primary Prevention Statin Prescription During Outpatient Care.

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.

Baseline co-medications may alter the anti-tumoural effect of checkpoint inhibitors as well as the risk of immune-related adverse events.

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.

Active Components of Commonly Prescribed Medicines Affect Influenza A Virus-Host Cell Interaction: A Pilot Study.

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.

High-content, targeted RNA-seq screening in organoids for drug discovery in colorectal cancer.

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.

Development of a High-Throughput Screening Assay to Identify Inhibitors of the SARS-CoV-2 Guanine-N7-Methyltransferase Using RapidFire Mass Spectrometry.

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.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

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.

Medication Use Quality and Safety in Older Adults: 2019 Update.

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.

Comparative Toxicogenomics Database (CTD): update 2021.

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.

Quantitative Automated Assays in Living Cells to Screen for Inhibitors of Hemichannel Function.

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 Ca2+ 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, YFPQL, 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.

Docking Paradigm in Drug Design.

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.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.

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.

Inhibition of the ß-carbonic anhydrase from the protozoan pathogen Trichomonas vaginalis with sulphonamides.

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 ß-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 KIs 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.

DrugSpaceX: a large screenable and synthetically tractable database extending drug space.

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.