In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug interactions. The majority of currently available drugs are metabolized by CYP450 enzymes. Interactions due to shared CYP450-mediated metabolic pathways for two or more drugs are frequent, especially through reversible or irreversible CYP450 inhibition. The magnitude of these interactions depends on several factors, including varying affinity and concentration of substrates, time delay between the administration of the drugs, and mechanisms of CYP450 inhibition. Various types of CYP450 inhibition (competitive, non-competitive, mechanism-based) have been observed clinically, and interactions of these types require a distinct clinical management strategy. This review focuses on mechanism-based inhibition, which occurs when a substrate forms a reactive intermediate, creating a stable enzyme–intermediate complex that irreversibly reduces enzyme activity. This type of inhibition can cause interactions with drugs such as omeprazole, paroxetine, macrolide antibiotics, or mirabegron. A good understanding of mechanism-based inhibition and proper clinical management is needed by clinicians when such drugs are prescribed. It is important to recognize mechanism-based inhibition since it cannot be prevented by separating the time of administration of the interacting drugs. Here, we provide a comprehensive overview of the different types of mechanism-based inhibition, along with illustrative examples of how mechanism-based inhibition might affect prescribing and clinical behaviors.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by multiple hallmarks including extracellular amyloid (Aβ) plaques, neurofibrillary tangles, dysfunctional blood−brain barrier (BBB), neuroinflammation, and impaired autophagy. Thus, novel strategies that target multiple disease pathways would be essential to prevent, halt, or treat the disease. A growing body of evidence including our studies supports a protective effect of oleocanthal (OC) and extra-virgin olive oil (EVOO) at early AD stages before the onset of pathology. In addition, we reported previously that OC and EVOO exhibited such effect by restoring the BBB function; however, the mechanism(s) by which OC and EVOO exert such an effect and whether this effect extends to a later stage of AD remain unknown. In this work, we sought first to test the effect of OC-rich EVOO consumption at an advanced stage of the disease in TgSwDI mice, an AD mouse model, starting at the age of 6 months for 3 months treatment, and then to elucidate the mechanism(s) by which OC-rich EVOO exerts the observed beneficial effect. Overall findings demonstrated that OC-rich EVOO restored the BBB function and reduced AD-associated pathology by reducing neuroinflammation through inhibition of NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome and inducing autophagy through activation of AMP-activated protein kinase (AMPK)/Unc-51-like autophagy activating kinase 1 (ULK1) pathway. Thus, diet supplementation with OC-rich EVOO could provide beneficial effect to slow or halt the progression of AD.

Background Patients taking medication with high anticholinergic and sedative properties are at increased risk of experiencing poor cognitive and physical outcomes. Therefore, precise quantification of the cumulative burden of their drug regimen is advisable. There is no agreement regarding which scale to use to simultaneously quantify the burden associated with medications.Objectives The objective of this review was to assess the strengths and limitations of available tools to quantify medicationrelated anticholinergic burden and sedative load in older adults. We discuss specific limitations and agreements between currently available scales and models and propose a comprehensive table combining drugs categorized as high, moderate, low, or no anticholinergic or sedative activity as excerpted from the selected studies. Methods A targeted search was carried out using the National Library of Medicine through PubMed using medical subject heading terms and text words around the following search terms: (anticholinergic OR sedative) AND (load OR burden OR scale) for studies published between 1 January 1945 and 5 June 2021. In addition, the following databases were searched using the same terms: MEDLINE-EBSCO, APA PsycInfo, CINAHL Plus, Cochrane Library, Scopus, OAIster, OVID-MEDLINE, Web of Science, and Google Scholar. Screening by titles was followed by an abstract and full-text review. After blind evaluation, agreement between reviewers was reached to establish drug characteristics and categories. Results After 3163 articles were identified, 13 were included: 11 assigned risk scores to anticholinergic drugs and two to sedative drugs. Considerable variability between anticholinergic scales was observed; scales included between 27 and 548 drugs. We generated a comprehensive table combining the anticholinergic and sedative activities of drugs evaluated and proposed a categorization of these drugs based on available scientific and clinical evidence. Our table combines information about 642 drugs and categorizes 44, 25, 99, and 474 drugs as high, moderate, low, or no anticholinergic and sedative activity, respectively. Conclusions Variability and inconsistency exists among scales used to categorize drugs with anticholinergic or sedative burden. In this review, we provide a comprehensive table that proposes a new categorization of these drugs. A longitudinal study will be required to validate the new proposed anticholinergic and sedative burden catalog in an evidence-based manner.

Diabetes mellitus is a metabolic disease that causes a hyperglycemic status which leads, over time, to serious damage to the heart, blood vessels, eyes, kidneys and nerves. The most frequent form of diabetes is type 2 diabetes mellitus (T2DM) which is often part of a metabolic syndrome (hyperglycaemia, hypertension, hypercholesterolemia, abdominal obesity) that usually requires the use of several medications from different drug classes to bring each of these conditions under control. T2DM is associated with an increase in inflammatory markers such as interleukin-6 (IL-6) and the tumor necrosis factor alpha (TNF-α). Higher levels of IL-6 and TNF-α are associated with a downregulation of several drug metabolizing enzymes, especially the cytochrome P450 (P450) isoforms CYP3As and CYP2C19. A decrease in these P450 isoenzymes may lead to unexpected rise in plasma levels of substrates of these enzymes. It could also give rise to a mismatch between the genotypes determined for these enzymes, the predicted phenotypes based on these genotypes and the phenotypes observed clinically. This phenomenon is described as phenoconversion. Phenoconversion typically results from either a disease (such as T2DM) or concomitant administration of medications inducing or inhibiting (including competitive or non-competitive inhibition) a P450 isoenzyme used by other substrates for their elimination. Phenoconversion could have a significant impact on drug effects and genotypic-focused clinical outcomes. As the aging population is exposed to polypharmacy along with inflammatory comorbidities, consideration of phenoconversion related to drug metabolizing enzymes is of importance when applying pharmacogenomic results and establishing personalized and more precise drug regimens.

Fluoxetine is still one of the most widely used antidepressants in the world. The drug is extensively metabolized by several cytochrome P450 (CYP450) enzymes and subjected to a myriad of CYP450-mediated drug interactions. In a multidrug regimen, preemptive mitigation of drug–drug interactions requires knowledge of fluoxetine actions on these CYP450 enzymes. The major metabolic pathway of fluoxetine leading to the formation of its active metabolite, norfluoxetine, is mediated by CYP2D6. Fluoxetine and norfluoxetine are strong affinity substrates of CYP2D6 and can inhibit, potentially through various mechanisms, the metabolism of other sensitive CYP2D6 substrates. Remarkably, fluoxetine-mediated CYP2D6 inhibition subsides long after fluoxetine first passes through the liver and even remains long after the discontinuation of the drug. Herein, we review pharmacokinetic and pharmacogenetic information to help us understand the mechanisms underlying the prolonged inhibition of CYP2D6 following fluoxetine administration. We propose that long-term inhibition of CYP2D6 is likely a result of competitive inhibition. This is due to strong affinity binding of fluoxetine and norfluoxetine to the enzyme and unbound fluoxetine and norfluoxetine levels circulating in the blood for a long period of time because of their long elimination half-life. Additionally, we describe that fluoxetine is a CYP2C9 substrate and a mechanism-based inhibitor of CYP2C19.