Why Do You Keep Telling Me That Drug-Drug Interactions Are Important in Psychopharmacology When I Do Not See Them in My Clinical Practice?: My Failure to Convince Clinicians

Optimizing antidepressant and clozapine co-prescription in clinical practice: A systematic review and expert recommendations

OBJECTIVES

To synthesize the information relevant for clinical practice on clozapine-antidepressant co-prescription concerning pharmacokinetic drug-drug interactions (DDI), adverse drug reactions (ADRs) associated with the co-prescription, antidepressant add-on for clozapine-resistant symptoms and antidepressant add-on for clozapine-induced ADRs.

METHODS

Articles were identified with MEDLINE, Web of Sciences and PsycINFO search from inception through April 2023. Data were synthesized narratively.

RESULTS

ADRs are most often induced by the co-prescription of antidepressants that inhibit CYP enzymes (fluvoxamine, fluoxetine, paroxetine). Fluvoxamine add-on is hazardous because of its potent inhibition of clozapine metabolism and has few indications (lowering daily number of clozapine tablets, reducing norclozapine-induced metabolic disturbances and other dose-dependent clozapine-induced ADRs). ADR frequency may be reduced by therapeutic drug monitoring and knowledge of other factors impacting clozapine metabolism (pneumonia, inflammation, smoking, etc.). Improvement of negative symptoms is the most documented beneficial effect of antidepressant add-on for clozapine-resistant psychotic symptoms. The add-on antidepressant should be chosen according to its safety profile regarding DDI with clozapine: antidepressants inhibiting clozapine metabolism or increasing the anticholinergic load should be avoided. Other indications of antidepressant add-on (affective or obsessive compulsive symptoms, sialorrhea, and enuresis) are poorly documented.

CONCLUSION

Antidepressant add-on to clozapine is associated with potential benefits in clozapine users as this strategy may contribute to reduce the burden of clozapine-resistant symptoms or of clozapine-induced ADRs. Further studies are needed to determine whether antidepressant add-on can reduce the risk of clozapine discontinuation.

Relationship between clinical efficacy and plasma concentration-dose ratio of risperidone in patients with schizophrenia

This study aimed to retrospectively explore the relationship between clinical efficacy and plasma concentration-dose ratio of risperidone (RIS) in 252 patients with schizophrenia taking RIS orally. After the same dose of RIS treatment, the plasma concentration of RIS/9-hydroxyrisperidone (9-OH-RIS), the total plasma concentration of RIS, and the ratio of the steady-state plasma concentration to the daily dose of the total active product (C/D) showed individual difference. The RIS plasma concentration was significantly higher in patients taking high doses than those taking lower doses ( P  = 0.003). There was a statistically significant difference in C/D ratio between males and females ( P  = 0.003). There were significant differences in ratio of C/D and the total plasma concentration of RIS between patients under 60 years and over 60 years ( P  = 0.016; P  = 0.005). Logistic regression analysis showed that the therapeutic effect and adverse reactions of RIS were correlated with the ratio of C/D in patients with schizophrenia ( P  = 0.038; P  < 0.001). It has been suggested that the importance of monitoring of the plasma concentration of RIS in patients with schizophrenia and the ratio of C/D may be used as the reference for RIS personalized treatment.

Drug-drug interactions between COVID-19 drug therapies and antidepressants

INTRODUCTION

Antidepressants are widely used for the pharmacological treatment of anxiety and mood disorders. Since the eruption of the SARS-COV-2 pandemic and the later development of targeted treatments against COVID-19, inevitably many patients receive antidepressants as well as targeted treatments against COVID-19 against COVID-19. Co-administration of antidepressants with COVID-19 therapeutics has the potential of drug-drug interactions, of varying severity and clinical significance.

AREAS COVERED

This is a curated narrative review of the current state of the art regarding drug-drug interactions between COVID-19 therapeutics and medications licensed for the pharmacotherapy of depression. A systematic search of electronic databases, using as keywords the international nonproprietaty names of currently approved COVID-19 therapeutics and antidepressants was performed, and additionally online interaction checker tools were consulted. Derived data were synthesized for each COVID-19 therapeutic and presented with up-to-date guidance.

EXPERT OPINION

Several COVID-19 therapeutics have potential for drug-drug interactions with antidepressants. Remdesivir and Nirmatrelvir-Ritonavir have the higher risk, whereas several monoclonal antibodies appear safer. The most serious drug-drug interactions (serotonin syndrome and QTc prolongation) require close monitoring; however, DDI toward reducing the efficacy of antidepressants may be difficult to recognize. As COVID-19 treatment protocols take precedence, psychiatrists should exert flexibility in antidepressant use and proactively monitor treatment progress.

Optimization of Therapy in Patients with Epilepsy and Psychiatric Comorbidities: Key Points

Psychiatric disorder comorbidity in patients with epilepsy (PWE) is very frequent with a mean percentage prevalence of up to 50% and even higher. Such a high frequency suggests that epilepsy and psychiatric disorders might share common pathological pathways. Various aspects contribute in making the matter very complex from a therapeutic point of view. Some antiseizure medications (ASMs), namely valproic acid, carbamazepine, and lamotrigine, have mood-stabilising effects and are routinely used for the treatment of bipolar disorder in patients who do not have epilepsy. Pregabalin and, to a lesser extent, gabapentin, exerts anxiolytic effects. However, several ASMs, in particular levetiracetam, topiramate, and perampanel, may contribute to psychiatric disorders, including depression, aggressive behaviour, and even psychosis. If these ASMs are prescribed, the patient should be monitored closely. A careful selection should be made also with psychotropic drugs. Although most of these can be safely used at therapeutic doses, bupropion, some tricyclic antidepressants, maprotiline, and clozapine may alter seizure threshold and facilitate epileptic seizures. Interactions between ASMs and psychotropic medication may make it difficult to predict individual response. Pharmacokinetic interactions can be assessed with drug monitoring and are consequently much better documented than pharmacodynamic interactions. Another aspect that needs a careful evaluation is patient adherence to treatment. Prevalence of non-adherence in PWE and psychiatric comorbidities is reported to reach values even higher than 70%. A careful evaluation of all these aspects contributes in optimizing therapy with a positive impact on seizure control, psychiatric wellbeing, and quality of life.

Pharmacokinetic drug interactions with oral haloperidol in adults: dose correction factors from a combined weighted analysis

INTRODUCTION

Pharmacokinetic (PK) drug-drug interactions (DDIs) of oral haloperidol, a first-generation antipsychotic, are systematically reviewed.

AREAS COVERED

After exclusions, the search for DDIs with oral haloperidol provided 47 articles as victim and 7 as perpetrator. Changes in mean haloperidol concentration-to-dose (C/D) ratios after weighting each study's size were used to calculate the effects of other drugs (inhibitors/inducers) on haloperidol. These changes of haloperidol C/D ratio were used to estimate dose-correction factors (<1 for inhibitors and >1 for inducers).

EXPERT OPINION

A box summarizes our recommendations for clinicians regarding our current knowledge of haloperidol PK DDIs, which will need to be updated as new information becomes available. Moderate to strong inducers (carbamazepine, phenobarbital, phenytoin, or rifampin) should be avoided since they required dose-correction factors of 2-5. Smoking appeared to be a weak inducer (dose-correction factor 1.2). Fluvoxamine, promethazine, and combinations of CYP3A4 and CYP2D6 inhibitors should be avoided. There are no long-term studies on fluoxetine to provide a dose correction factor. Limited information suggests that valproate may be an inhibitor (dose-correction factor 0.6). In most patients, haloperidol may not have clinically relevant effects as a perpetrator, but in vitro and clinical studies suggest it is a weak CYP2D6 inhibitor.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

OBJECTIVE

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2 years of updates to all six drug interaction database programs.

METHODS

The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

RESULTS

Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair.

CONCLUSIONS

Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.

Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders

Introduction

The aim of this study was to describe the number and type of drugs used to treat depressive disorders in inpatient psychiatry and to analyse the determinants of potential drug-drug interactions (pDDI) and potentially inappropriate medication (PIM).

Methods

Our study was part of a larger pharmacovigilance project funded by the German Innovation Funds. It included all inpatients with a main diagnosis in the group of depressive episodes (F32, ICD-10) or recurrent depressive disorders (F33) discharged from eight psychiatric hospitals in Germany between 1 October 2017 and 30 September 2018 or between 1 January and 31 December 2019.

Results

The study included 14,418 inpatient cases. The mean number of drugs per day was 3.7 (psychotropic drugs = 1.7; others = 2.0). Thirty-one percent of cases received at least five drugs simultaneously (polypharmacy). Almost one half of all cases received a combination of multiple antidepressant drugs (24.8%, 95% CI 24.1%–25.5%) or a treatment with antidepressant drugs augmented by antipsychotic drugs (21.9%, 95% CI 21.3%–22.6%). The most frequently used antidepressants were selective serotonin reuptake inhibitors, followed by serotonin and norepinephrine reuptake inhibitors and tetracyclic antidepressants. In multivariate analyses, cases with recurrent depressive disorders and cases with severe depression were more likely to receive a combination of multiple antidepressant drugs (Odds ratio recurrent depressive disorder: 1.56, 95% CI 1.41–1.70, severe depression 1.33, 95% CI 1.18–1.48). The risk of any pDDI and PIM in elderly patients increased substantially with each additional drug (Odds Ratio: pDDI 1.32, 95% CI: 1.27–1.38, PIM 1.18, 95% CI: 1.14–1.22) and severity of disease (Odds Ratio per point on CGI-Scale: pDDI 1.29, 95% CI: 1.11–1.46, PIM 1.27, 95% CI: 1.11–1.44), respectively.

Conclusion

This study identified potential sources and determinants of safety risks in pharmacotherapy of depressive disorders and provided additional data which were previously unavailable. Most inpatients with depressive disorders receive multiple psychotropic and non-psychotropic drugs and pDDI and PIM are relatively frequent. Patients with a high number of different drugs must be intensively monitored in the management of their individual drug-related risk-benefit profiles.

Clozapine affects the pharmacokinetics of risperidone and inhibits its metabolism and P-glycoprotein-mediated transport in vivo and in vitro: A safety attention to antipsychotic polypharmacy with clozapine and risperidone

Antipsychotic polypharmacy (APP), as one maintenance treatment strategy in patients with schizophrenia, has gained popularity in real-world clinical settings. Risperidone (RIS) and clozapine (CLZ) are the most commonly prescribed second-generation antipsychotics, and they are often used in combination as APP. In this study, the pharmacokinetics of RIS and CLZ in rats were examined after co-administration to explore the reliability and rationality of co-medication with RIS and CLZ. In addition, the effects of CLZ on RIS metabolism and transport in vitro were investigated. The results illustrated that in the 7-day continuous administration test in rats, when co-administered with CLZ, the area under curve and peak concentrations of RIS were increased by 2.2- and 3.1-fold at the first dose, respectively, increased by 3.4- and 6.2-fold at the last dose, respectively. The metabolite-to-parent ratio of RIS was approximately 22% and 33% lower than those of RIS alone group at the first and last doses, respectively. Moreover, CLZ significantly increased RIS concentrations in the brain (3.0-4.8 folds) and cerebrospinal fluid (2.1-3.5 folds) in rats, which was slightly lower than the impact of verapamil on RIS after co-medication. Experiments in vitro indicated that CLZ competitively inhibited the conversion of RIS to 9-hydroxy-RIS with the inhibition constants of 1.36 and 3.0 μM in rat and human liver microsomes, respectively. Furthermore, the efflux ratio of RIS in Caco-2 monolayers was significantly reduced by CLZ at 1 μM. Hence, CLZ may affect the exposure of RIS by inhibiting its metabolism and P-glycoprotein-mediated transport. These findings highlighted that APP with RIS and CLZ might increase the plasma concentrations of RIS and 9-hydroxy-RIS beyond the safety ranges and cause toxic side effects.

Predicting the risk of drug-drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study

OBJECTIVES

The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.

DESIGN

Retrospective, longitudinal study.

SETTING

We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany.

PARTICIPANTS

Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2).

OUTCOME MEASURES

The proportion of rightly classified hospital episodes.

METHODS

We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.

RESULTS

A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.

CONCLUSION

This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.

Clinically Relevant Interactions between Atypical Antipsychotics and Anti-Infective Agents

This is a comprehensive review of the literature on drug interactions (DIs) between atypical antipsychotics and anti-infective agents that focuses on those DIs with the potential to be clinically relevant and classifies them as pharmacokinetic (PK) or pharmacodynamic (PD) DIs. PubMed searches were conducted for each of the atypical antipsychotics and most commonly used anti-infective agents (13 atypical antipsychotics by 61 anti-infective agents/classes leading to 793 individual searches). Additional relevant articles were obtained from citations and from prior review articles written by the authors. Based on prior DI articles and our current understanding of PK and PD mechanism, we developed tables with practical recommendations for clinicians for: antibiotic DIs, antitubercular DIs, antifungal DIs, antiviral DIs, and other anti-infective DIs. Another table reflects that in clinical practice, DIs between atypical antipsychotics and anti-infective agents occur in patients also suffering an infection that may also influence the PK and PD mechanisms of both drugs (the atypical antipsychotic and the anti-infective agent(s)). These tables reflect the currently available literature and our current knowledge of the field and will need to be updated as new DI information becomes available.

Characteristics of U.S. adults taking prescription antipsychotic medications, National Health and Nutrition Examination Survey 2013-2018

BACKGROUND

Global 12-month psychosis prevalence is estimated at roughly 0.4%, although prevalence of antipsychotic use in the U.S. is estimated at roughly 1.7%. Antipsychotics are frequently prescribed for off label uses, but have also been shown to carry risk factors for certain comorbid conditions and with other prescription medications. The study aims to describe the socio-demographic and health characteristics of U.S. adults taking prescription antipsychotic medications, and to better understand the association of antipsychotic medications and comorbid chronic diseases.

METHODS

The study pools 2013-2018 data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cross-sectional survey of non-institutionalized U.S. residents (n = 17,691). Survey staff record prescription medications taken within the past 30 days for each respondent, from which typical and atypical antipsychotic medications were identified.

RESULTS

Prevalence of antipsychotic use among U.S. adults was 1.6% (n = 320). Over 90% of individuals taking antipsychotics reported having health insurance and a usual place for care, significantly more than their counterparts not taking antipsychotics. Further, those taking antipsychotics reported higher prevalence of comorbid chronic diseases and took an average of 2.3 prescription medications more than individuals not taking antipsychotics. Individuals taking antipsychotics were more likely to sleep 9 or more hours per night, be a current smoker, and have a body mass index greater than 30 kg/m².

CONCLUSIONS

U.S. adults who take antipsychotic medications report more consistent health care access and higher prevalence of comorbid chronic diseases compared to those not taking antipsychotics. The higher comorbidity prevalence and number of total prescriptions highlight the need for careful assessment and monitoring of existing comorbidities and potential drug-drug interactions among adults taking antipsychotics in the U.S.

Prevalence and sort of pharmacokinetic drug-drug interactions in hospitalized psychiatric patients

Psychiatric patients are high-risk patients for the development of pharmacokinetic drug-drug interactions (DDIs), leading to highly variable (victim) drug serum concentrations. Avoiding and targeting high-risk drug combinations could reduce preventable adverse drug reactions (ADRs). Pharmacokinetic cytochrome P450 (CYP)-mediated DDIs are often predictable and, therefore, preventable. The retrospective, longitudinal analysis used informations from a large pharmacovigilance study (Optimization of pharmacological treatment in hospitalized psychiatric patients study, study number 01VSF16009, 01/2017), conducted in 10 psychiatric hospitals in Germany. Medication data were examined for the co-prescription of clinically relevant CYP inhibitors or inducers and substrates of these enzymes (victim drugs). In total, data from 27,396 patient cases (45.6% female) with a mean (mean ± standard deviation (SD)) age of 47.3 ± 18.3 years were available for analysis. CYP inhibitors or inducers were at least once prescribed in 14.4% (n = 3946) of the cases. The most frequently prescribed CYP inhibitors were melperone (n = 2504, 28.1%) and duloxetine (n = 1324, 14.9%). Overall, 51.0% of the cases taking melperone were combined with a victim drug (n = 1288). Carbamazepine was the most frequently prescribed CYP inducer (n = 733, 88.8%). Combinations with victim drugs were detected for 58% (n = 427) of cases on medication with carbamazepine. Finally, a DDI was detected in 43.6% of the cases in which a CYP inhibitor or inducer was prescribed. The frequency of CYP-mediated DDI is considerably high in the psychiatric setting. Physicians should be aware of the CYP inhibitory and inducing potential of psychotropic and internistic drugs (especially, melperone).

Self-Report for Measuring and Predicting Medication Adherence: Experts' Experience in Predicting Adherence in Stable Psychiatric Outpatients and in Pharmacokinetics

THE PROBLEM

Poor adherence to appropriately prescribed medication is a global challenge for psychiatrists.

PRIOR STUDIES

Measuring adherence is complicated. In our recent three-country naturalistic study including more than 1000 patients and their adherence to multiple medication prescriptions at the same time, patients' self-report of adherence to each specific drug was the only practical option for measuring adherence. Systematic literature reviews provide inconsistent results for sociodemographic, clinical and medication variables as predictors of adherence to psychiatric drugs. Our studies over the last 10 years in relatively stable psychiatric outpatients have shown that some self-reported health beliefs had consistent, strong effects and a better predictive role. Three dimensions of these health beliefs are characteristics of the individual: 1) attitudes toward psychiatric medication such as pharmacophobia (fear of taking drugs or medicines), 2) health locus of control (the belief patients have about who or what agent determines the state of their health), 3) psychological reactance (an emotional reaction in direct contradiction to rules or regulations that threaten or suppress certain freedoms in behavior). They can be measured by the Patient Health Beliefs Questionnaire on Psychiatric Treatment. The attitude toward each specific medication can be measured by the necessity-concern framework and summarized as the presence or absence of skepticism about that drug. After 25 years conducting pharmacokinetic studies in psychiatric drugs, particularly antipsychotics, we have limited understanding of how to use blood levels to predict the effects of non-adherence or to establish it.

EXPERT OPINION ON FUTURE STUDIES

Future studies to predict adherence should include the inpatient setting and explore insight. Studying the pharmacokinetics associated with non-adherence in each psychiatric drug is a major challenge. Medication adherence is a complex and dynamic process changing over time in the same patient. Personalizing adherence using psychological or pharmacological variables are in their initial stages.

Potential drug-drug interactions in the psychiatric hospital: Frequency analysis

Introduction: Drug-drug interactions are an important clinical problem in pharmacotherapy. This study is focused on different types of drugs used in a psychiatric hospital.

Materials and methods: The pharmacoepidemiological study included the analysis of medical records of 500 psychiatric inpatients. The patients were divided into 2 groups: under 65 and over 65 years of age. All the drug prescriptions were analyzed to identify the combinations of drugs that can induce drug-drug interactions and determine their clinical significance.

Results and discussion: Over 77% of hospitalized patients were administered drug combinations that could induce drug-drug interactions, most of which were of moderate clinical significance. A reliable association was found between the patient’s age, the clinical significance of drug-drug interactions, and the pharmacotherapy structure. The most common irrational drug combinations were identified.

Conclusion: Timely analysis of drug prescriptions for potential drug-drug interactions can enhance the safety of pharmacotherapy and decrease the risk of adverse drug reactions in the psychiatric inpatient setting.

Personalizing dosing of risperidone, paliperidone and clozapine using therapeutic drug monitoring and pharmacogenetics

By combining knowledge of pharmacogenetics, therapeutic drug monitoring (TDM) and drug-drug interactions (DDIs) the author developed a model for personalizing antipsychotic dosing, which is applied to risperidone, 9-hydroxyrisperidone or paliperidone, and clozapine. Drugs are approved using an average dose for an ideal average patient, but pharmacologists have described outliers: genetic poor metabolizers (PMs) and ultrarapid metabolizers (UMs). Environmental and personal variables can also make patients behave as PMs or UMs. Drug clearance is represented by the concentration-to-dose (C/D) ratio under steady-state and trough conditions. A very low C/D ratio indicates a UM, while a very high C/D ratio indicates a PM. Total risperidone C/D ratio for the oral formulation is around 7 ng/ml per mg/day and can be influenced by CYP2D6 polymorphism, DDIs with inducers and inhibitors, and renal function. Oral paliperidone has low availability; its C/D ratio is around 4.1 ng/ml per mg/d and can be influenced by inducers and renal impairment. Once-a-month long-acting paliperidone provides a C/D ratio around 7.7 ng/ml per mg/day at steady state, which is expected to be in the 8th month (before the 9th injection). TDM is particularly important for long-acting paliperidone formulations that may accumulate once steady state is reached (after years for the 3- and 6-month formulations). In the US, clozapine C/D ratios typically range from 0.6 (male smokers) to 1.2 (female non-smokers) ng/ml per mg/day. East Asians' clozapine C/D ratios appear to be twice as high. Inhibitors (including fluvoxamine and oral contraceptives) and inflammation can also increase clozapine C/D ratios. This article is part of the issue entitled 'Special Issue on Antipsychotics'.