Neurological and Psychiatric Adverse Effects of Antimicrobials

Bangert, Madison K; Hasbun, Rodrigo

doi:10.1007/s40263-019-00649-9

Cited by 39 publications

(51 citation statements)

References 255 publications

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“…We were interested in exploring the use of all PHARMAC‐funded drugs and combinations of these at the population level, including mainly antimicrobials, antihistamines, antihypertensive drugs, anticholinergic drugs, antidepressant drugs, antipsychotics, benzodiazepines, diuretics, opioids, and nonsteroidal anti‐inflammatory medications 2,3,19,23–27,30,31 …”

Section: Methodsmentioning

confidence: 99%

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

Chyou

Nishtala

2021

Pharmacoepidemiology and Drug

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Background Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication‐induced delirium in older adults is well understood, limited population‐level evidence is available, particularly on combinations of medications associated with delirium in older adults. Objectives We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case‐time‐control design. Method We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005–2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti‐inflammatory medications, were considered drugs of interest. The first‐time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case‐time‐control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l‐day with a 35‐day washout period) that are statistically associated with delirium, and the association was tested by computing a time‐trend adjusted matched odds‐ratio (MOR) and its 95% confidence interval (CI). Results We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case‐time‐control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR = 6.17; 95%CI = [2.05–18.54]), haloperidol (MOR = 4.81; 95%CI = [3.16–6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR = 3.94; 95%CI = [3.03–5.10]), and fentanyl exposure (MOR = 3.46; 95%CI [2.05–9.21]). Conclusion The association rule method applied to a case‐time‐control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.

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Section: Methodsmentioning

confidence: 99%

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

Chyou

Nishtala

2021

Pharmacoepidemiology and Drug

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“…Neurotoxicity is also encountered in adults exposed to antibiotics and presents in multiple ways including headache, delirium, psychosis, and seizure. 45,46 In adult mice, antibiotic exposure reduces the expression of tightjunction proteins of brain-blood vessels and increases BBB permeability, which induces monocyte recruitment to the brain and causes brain disorder. 47 The five nonabsorbable antibiotics used in the study substantially reduce the abundances of phylum Firmicutes and Bacteroidetes, which are replaced by Proteobacteria, and significantly increases the Firmicutes to Bacteroidetes ratio, indicating a sign of gut dysbiosis.…”

Section: Antibiotic-induced Neurotoxicity and Gut Dysbiosismentioning

confidence: 99%

Connecting the dots: Targeting the microbiome in drug toxicity

Luo

Zhou

2021

Medicinal Research Reviews

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The gut microbiota has a vast influence on human health and its role in initiating, aggravating, or ameliorating diseases is beginning to emerge. Recently, its contribution to heterogeneous toxicological responses is also gaining attention, especially in drug-induced toxicity. Whether they are orally administered or not, drugs may interact with the gut microbiota directly or indirectly, which leads to altered toxicity. Present studies focus more on the unidirectional influence of how xenobiotics disturb intestinal microbial composition and functions, and thus induce altered homeostasis. However, interactions between the gut microbiota and xenobiotics are bidirectional and the impact of the gut microbiota on xenobiotics, especially on drugs, should not be neglected. Thus, in this review, we focus on how the gut microbiota modulates drug toxicity by highlighting the microbiome, microbial enzyme, and microbial metabolites. We connect the dots between drugs, the microbiome, microbial enzymes or metabolites, drug metabolites, and host

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“…The positive hepatotoxicity by pkCSM is only based on the similarity of structural features to compounds with liver-associated adverse effects, whereas DS, which showed no possible hepatotoxicity, uses additional information related to dose concentrations to establish the probability of hepatotoxicity. According to the info generated in this investigation, Decoquinate RMB041 shows a promising safety profile compared to that of other first-and second line antitubercular drugs which exibit cardiotoxic, hepatotoxic and/or neurotoxic effects (32,(39)(40)(41).…”

Section: Absorption Distribution Metabolism Elimination and Toxicitymentioning

confidence: 99%

In silicodrug discovery strategies identified ADMET properties of decoquinate RMB041 and its potential drug targets againstMycobacterium Tuberculosis

Knoll

Walt

Loots

2021

Preprint

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The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the thirteen identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated to the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication.

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Neurological and Psychiatric Adverse Effects of Antimicrobials

Cited by 39 publications

References 255 publications

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

Connecting the dots: Targeting the microbiome in drug toxicity

In silicodrug discovery strategies identified ADMET properties of decoquinate RMB041 and its potential drug targets againstMycobacterium Tuberculosis

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