Multidrug Interactions: Why Do They Occur and How to Handle?

A nonoptimized medication therapy (NOMT) event is an iatrogenic hazard or incident associated with medications and is a leading cause of death, serious injury, and illness. NOMT events are often related to multidrug interactions in patients with polypharmacy. In these patients, NOMT events can be avoided by using advanced clinical decision support systems and clinical interventions such as separating the time of administration of certain drugs during the day. At the individual level, medication reconciliation is a first logical step for reducing adverse side effects. Then, intersubject variability in drug response should be considered to optimize patient drug regimens. Furthermore, patient pharmacogenomic status information can help ensure appropriateness of drug therapy. However, in patients with polypharmacy, such information is most valuable when combined with phenoconversion probability. At a population level, the virtual addition of drugs to various drug regimens and the use of a medication risk score can help predict the risk of NOMT events. This review outlines some of the mechanisms behind multidrug interactions and their association with drug safety and NOMTs, polypharmacy and its impact on patient outcomes, the value of pharmacogenomics, and an assessment of simulation studies and the virtual addition of drugs to a drug regimen using real-world data.

Revisiting conclusions on the empiric segmentation of high-risk patients.

The authors provide feedback on generalizations made regarding interventions for high-risk populations in previous research.

Physiologically Based Pharmacokinetic Modeling to Assess the Impact of CYP2D6-Mediated Drug-Drug Interactions on Tramadol and O-Desmethyltramadol Exposures via Allosteric and Competitive Inhibition.

Tramadol is an opioid medication used to treat moderately severe pain. Cytochrome P450 (CYP) 2D6 inhibition could be important for tramadol, as it decreases the formation of its pharmacologically active metabolite, O-desmethyltramadol, potentially resulting in increased opioid use and misuse. The objective of this study was to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol pharmacokinetics using quinidine and metoprolol as prototypical perpetrator drugs. A physiologically based pharmacokinetic model for tramadol and O-desmethyltramadol was developed and verified in PK-Sim version 8 and linked to respective models of quinidine and metoprolol to evaluate the impact of allosteric and competitive CYP2D6 inhibition on tramadol and O-desmethyltramadol exposure. Our results show that there is a differentiated impact of CYP2D6 inhibitors on tramadol and O-desmethyltramadol based on their mechanisms of inhibition. Following allosteric inhibition by a single dose of quinidine, the exposure of both tramadol (51% increase) and O-desmethyltramadol (52% decrease) was predicted to be significantly altered after concomitant administration of a single dose of tramadol. Following multiple-dose administration of tramadol and a single-dose or multiple-dose administration of quinidine, the inhibitory effect of quinidine was predicted to be long (≈42 hours) and to alter exposure of tramadol and O-desmethyltramadol by up to 60%, suggesting that coadministration of quinidine and tramadol should be avoided clinically. In comparison, there is no predicted significant impact of metoprolol on tramadol and O-desmethyltramadol exposure. In fact, tramadol is predicted to act as a CYP2D6 perpetrator and increase metoprolol exposure, which may necessitate the need for dose separation.

Quantifying Anticholinergic Burden and Sedative Load in Older Adults with Polypharmacy: A Systematic Review of Risk Scales and Models.

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 medication-related 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.

Use of Drug Claims Data and a Medication Risk Score to Assess the Impact of CYP2D6 Drug Interactions among Opioid Users on Healthcare Costs.

Cytochrome P450 2D6 (CYP2D6) activity is highly variable due to several factors, including genetic polymorphisms and drug-drug-gene interactions. Hydrocodone, oxycodone, codeine, and tramadol the most commonly prescribed CYP2D6-activated opioids for pain. However, the co-administration of CYP2D6 interacting drugs can modulate CYP2D6-medicated activation of these opioids, affecting drug analgesia, effectiveness, and safety, and can impact healthcare costs. A retrospective, observational cohort analysis was performed in a large (n = 50,843) adult population. This study used drug claims data to derive medication risk scores and matching propensity scores to estimate the effects of opioid use and drug-drug interactions (DDIs) on medical expenditures. 4088 individuals were identified as opioid users; 95% of those were prescribed CYP2D6-activated opioids. Among those, 15% were identified as being at risk for DDIs. Opioid users had a significant increase in yearly medical expenditure compared to non-opioid users ($2457 vs. $1210). In matched individuals, average healthcare expenditures were higher for opioid users with DDIs compared to those without DDIs ($7841 vs. $5625). The derived medication risk score was higher in CYP2D6 opioid users with interacting drug(s) compared to no DDI (15 vs. 12). Higher costs associated with CYP2D6 opioid use under DDI conditions suggest inadequate CYP2D6 opioid prescribing practices. Efforts to improve chronic opioid use in adults should reduce interacting drug combinations, especially among patients using CYP2D6 activated opioids.

Association of the MedWise Risk Score with health care outcomes.

OBJECTIVES: Older patients are especially vulnerable to drug-related problems due to multiple prescription drugs, which increases their risk of drug-drug interactions and adverse drug events (ADEs). This study aimed to examine outcomes associated with the MedWise Risk Score (MRS) in a Medicare Part D population, including total medical expenditures, ADEs, falls, mortality, emergency department (ED) visits, hospital admissions, and length of stay (LOS). STUDY DESIGN: Retrospective observational study. METHODS: The association between MRS and patient health outcomes was derived using drug claims data from 213,561 beneficiaries and medication risk stratification using outcomes data in 2018 with 1 year of follow-up. Analyses were conducted with the Max MRS and the Mean MRS calculated over the year. Analyses utilizing the Max MRS performed better, and results using the Max MRS are presented. Statistical analyses were performed using linear regression, logistic regression, negative binomial regression, and zero-inflated Poisson (ZIP) models. RESULTS: Of 203,630 patients studied (mean ±â€…SD age, 76.0 ±â€…8.0 years), 4.9%, 9.8%, 24.5%, and 15.5% experienced at least 1 ADE, fall, ED visit, and hospital admission, respectively, in 2018. The MRS was associated with an 8.5% change in total medical expenditure per 1-unit increase. The adjusted odds ratio (OR) of ADE was 1.058 (95% CI, 1.055-1.06)/unit MRS. ADEs, falls, and death were more likely in elevated MRS categories (eg, OR of 4.45 for ADEs [95% CI, 4.10-4.83], 5.51 for falls [95% CI, 5.17-5.87], and 4.42 for death [95% CI, 3.82-5.12], respectively forSevere MRS group). Our model predicts 7000 ED visits for every 100,000 patients per unit increase of the MRS. The ZIP models estimated ORs of 1.03 and 1.01 for hospital admissions and increase in hospital LOS, respectively, per MRS unit. CONCLUSIONS: This study shows that MRS was associated with health outcomes and therefore could be used to identify patients at increased risk of negative outcomes based primarily on their medication regimens.

Disease-Induced Modulation of Drug Transporters at the Blood-Brain Barrier Level.

The blood-brain barrier (BBB) is a highly selective and restrictive semipermeable network of cells and blood vessel constituents. All components of the neurovascular unit give to the BBB its crucial and protective function, i.e., to regulate homeostasis in the central nervous system (CNS) by removing substances from the endothelial compartment and supplying the brain with nutrients and other endogenous compounds. Many transporters have been identified that play a role in maintaining BBB integrity and homeostasis. As such, the restrictive nature of the BBB provides an obstacle for drug delivery to the CNS. Nevertheless, according to their physicochemical or pharmacological properties, drugs may reach the CNS by passive diffusion or be subjected to putative influx and/or efflux through BBB membrane transporters, allowing or limiting their distribution to the CNS. Drug transporters functionally expressed on various compartments of the BBB involve numerous proteins from either the ATP-binding cassette (ABC) or the solute carrier (SLC) superfamilies. Pathophysiological stressors, age, and age-associated disorders may alter the expression level and functionality of transporter protein elements that modulate drug distribution and accumulation into the brain, namely, drug efficacy and toxicity. This review focuses and sheds light on the influence of inflammatory conditions and diseases such as Alzheimer's disease, epilepsy, and stroke on the expression and functionality of the BBB drug transporters, the consequential modulation of drug distribution to the brain, and their impact on drug efficacy and toxicity.

Adverse drug event risk assessment by the virtual addition of COVID-19 repurposed drugs to Medicare and commercially insured patients' drug regimens: A drug safety simulation study.

Drug safety is generally established from clinical trials, by pharmacovigilance programs and during observational phase IV safety studies according to drug intended or approved indications. The objective of this study was to estimate the risk of potential adverse drug events (ADEs) associated with drugs repurposed for coronavirus disease 2019 (COVID-19) treatment in a large-scale population. Drug claims were used to calculate a baseline medication risk score (MRS) indicative of ADE risk level. Fictitious claims of repurposed drugs were added, one at a time, to patients' drug regimens to calculate a new MRS and compute a level of risk. Drug claims data from enrollees with Regence health insurance were used and sub-payer analyses were performed with Medicare and commercial insured groups. Simulated interventions were conducted with hydroxychloroquine and chloroquine, alone or combined with azithromycin, and lopinavir/ritonavir, along with terfenadine and fexofenadine as positive and negative controls for drug-induced Long QT Syndrome (LQTS). There were 527,471 subjects (56.6% women; mean [SD] age, 47 years [21]) were studied. The simulated addition of each repurposed drug caused an increased risk of ADEs (median MRS increased by two-to-seven points, p < 0.001). The increase in ADE risk was mainly driven by an increase in CYP450 drug interaction risk score and by drug-induced LQTS risk score. The Medicare group presented a greater risk overall compared to the commercial group. All repurposed drugs were associated with an increased risk of ADEs. Our simulation strategy could be used as a blueprint to preemptively assess safety associated with future repurposed or new drugs.

Risk Assessment of Drug-Induced Long QT Syndrome for Some COVID-19 Repurposed Drugs.

The risk-benefit ratio associated with the use of repurposed drugs to treat severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)-related infectious coronavirus disease 2019 (COVID-19) is complicated because benefits are awaited, not proven. A thorough literature search was conducted to source information on the pharmacological properties of 5 drugs and 1 combination (azithromycin, chloroquine, favipiravir, hydroxychloroquine, remdesivir, and lopinavir/ritonavir) repurposed to treat COVID-19. A risk assessment of drug-induced long QT syndrome (LQTS) associated with COVID-19 repurposed drugs was performed and compared with 23 well-known torsadogenic and 10 low torsadogenic risk compounds. Computer calculations were performed using pharmacokinetic and pharmacodynamic data, including affinity to block the rapid component of the delayed rectifier cardiac potassium current (IKr ) encoded by the human ether-a-go-go gene (hERG), propensity to prolong cardiac repolarization (QT interval) and cause torsade de pointes (TdP). Seven different LQTS indices were calculated and compared. The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database was queried with specific key words relating to arrhythmogenic events. Estimators of LQTS risk levels indicated a very high or moderate risk for all COVID-19 repurposed drugs with the exception for azithromycin, although cases of TdP have been reported with this drug. There was excellent agreement among the various indices used to assess risk of drug-induced LQTS for the 6 repurposed medications and 23 torsadogenic compounds. Based on our results, monitoring of the QT interval shall be performed when some COVID-19 repurposed drugs are used, as such monitoring is possible for hospitalized patients or with the use of biodevices for outpatients.

Illustrative and historic cases of phenoconversion.

Intersubject variability in drug response, whether related to efficacy or toxicity, is well recognized clinically. Over the years, drug selection from our pharmacologic armamentarium has moved from providers' preferred choices to more personalized treatments as clinicians' decisions are guided by data from clinical trials. Since the advent of more accessible and affordable pharmacogenomic (PGx) testing, the promise of precise pharmacotherapy has been made. Results have accumulated in the literature with numerous examples demonstrating the value of PGx to improve drug response or prevent drug toxicity. Unfortunately, limited availability of reimbursement policies has dampened the enthusiasm of providers and organizations. The clinical application of PGx knowledge remains difficult for most clinicians under real-world conditions in patients with numerous chronic conditions and polypharmacy. This may be due to phenoconversion, a condition where there is a discrepancy between the genotype-predicted phenotype and the observed phenotype. This condition complicates the interpretation of PGx results and may lead to inappropriate recommendations and clinical decision making. For this reason, regulatory agencies have limited the transfer of information from PGx laboratories directly to consumers, especially recommendations about the use of certain drugs. This mini-review presents cases (mexiletine, propafenone, clopidogrel, warfarin, codeine, procainamide) from historical observations where drug response was modified by phenoconversion. The cases illustrate, from decades ago, that we are still in great need of advanced clinical decision systems that cope with conditions associated with phenoconversion, especially in patients with polypharmacy.

Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice.

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.

Risk of Adverse Drug Events Following the Virtual Addition of COVID-19 Repurposed Drugs to Drug Regimens of Frail Older Adults with Polypharmacy.

Determination of the risk-benefit ratio associated with the use of novel coronavirus disease 2019 (COVID-19) repurposed drugs in older adults with polypharmacy is mandatory. Our objective was to develop and validate a strategy to assess risk for adverse drug events (ADE) associated with COVID-19 repurposed drugs using hydroxychloroquine (HCQ) and chloroquine (CQ), alone or in combination with azithromycin (AZ), and the combination lopinavir/ritonavir (LPV/r). These medications were virtually added, one at a time, to drug regimens of 12,383 participants of the Program of All-Inclusive Care for the Elderly. The MedWise Risk Score (MRSTM) was determined from 198,323 drug claims. Results demonstrated that the addition of each repurposed drug caused a rightward shift in the frequency distribution of MRSTM values (p < 0.05); the increase was due to an increase in the drug-induced Long QT Syndrome (LQTS) or CYP450 drug interaction burden risk scores. Increases in LQTS risk observed with HCQ + AZ and CQ + AZ were of the same magnitude as those estimated when terfenadine or terfenadine + AZ, used as positive controls for drug-induced LQTS, were added to drug regimens. The simulation-based strategy performed offers a way to assess risk of ADE for drugs to be used in people with underlying medical comorbidities and polypharmacy at risk of COVID-19 infection without exposing them to these drugs.

ACE2 as a Therapeutic Target for COVID-19; its Role in Infectious Processes and Regulation by Modulators of the RAAS System.

Angiotensin converting enzyme 2 (ACE2) is the recognized host cell receptor responsiblefor mediating infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). ACE2bound to tissue facilitates infectivity of SARS-CoV-2; thus, one could argue that decreasing ACE2tissue expression would be beneficial. However, ACE2 catalytic activity towards angiotensin I (AngI) and II (Ang II) mitigates deleterious effects associated with activation of the renin-angiotensinaldosteronesystem (RAAS) on several organs, including a pro-inflammatory status. At the tissuelevel, SARS-CoV-2 (a) binds to ACE2, leading to its internalization, and (b) favors ACE2 cleavage toform soluble ACE2: these actions result in decreased ACE2 tissue levels. Preserving tissue ACE2activity while preventing ACE2 shredding is expected to circumvent unrestrained inflammatoryresponse. Concerns have been raised around RAAS modulators and their effects on ACE2expression or catalytic activity. Various cellular and animal models report conflicting results invarious tissues. However, recent data from observational and meta-analysis studies in SARS-CoV-2-infected patients have concluded that RAAS modulators do not increase plasma ACE2 levels orsusceptibility to infection and are not associated with more severe diseases. This review presentsour current but evolving knowledge of the complex interplay between SARS-CoV-2 infection, ACE2levels, modulators of RAAS activity and the effects of RAAS modulators on ACE2 expression.