The Intricacies of Polypharmacy and Drug Interactions in Schizophrenia Treatment.

Patients with concurrent schizophrenic and mood symptoms are often treated with antipsychotics plus antidepressant or thymoleptic drugs. The authors review the literature on treatment of two overlapping groups of patients: those with schizoaffective disorder and those with schizophrenia and concurrent mood symptoms. MEDLINE searches (from 1976 onward) were undertaken to identify treatment studies of both groups, and references in these reports were checked. Selection of studies for review was based on the use of specified diagnostic criteria and of parallel-group, double-blind design (or, where few such studies addressed a particular issue, large open studies). A total of 18 treatment studies of schizoaffective disorder and 15 of schizophrenia with mood symptoms were selected for review. For acute exacerbations of schizoaffective disorder or of schizophrenia with mood symptoms, antipsychotics appeared to be as effective as combination treatments, and there was some evidence for superior efficacy of atypical antipsychotics. There was evidence supporting adjunctive antidepressant treatment for schizophrenic and schizoaffective patients who develop a major depressive syndrome after remission of acute psychosis, but there were mixed results for treatment of subsyndromal depression. There was little evidence to support adjunctive lithium for depressive symptoms and no evidence concerning its use for manic symptoms in patients with schizophrenia. Empirical data suggest that both groups of patients are best treated by optimizing antipsychotic treatment and that atypical antipsychotics may prove to be most effective. Adjunctive antidepressants may be useful for patients with major depression who are not acutely ill. Careful longitudinal assessment is required to ensure identification of primary mood disorders.

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Background: Multimorbidity and polypharmacy increase the risk of hospitalization in older adults receiving potentially inappropriate medication (PIM). The current study compared the ability of PIM-Taiwan, PRISCUS, and Beers criteria to predict 90-day rehospitalization in older patients with and without PIM. Methods: The retrospective cohort study used Taiwan's Longitudinal Health Insurance Database to retrieve quarterly information about prescribed medication for adults aged ≥65 years hospitalized between 2001 and 2018. We analyzed the association of PIM with 90-day rehospitalization using logistic regression. Results: The study cohort included 206,058 older adults (mean age: 72.5 years). In the analysis, 133,201 (64.6%), 97,790 (47.5%), and 147,450 (71.6%), were identified as having PIM exposure in PIM-Taiwan, PRICUS, and Beers criteria, respectively. PIM-Taiwan criteria found exposure to PIM affecting the cardiovascular (adjusted OR [aOR] 1.37, 95% confidence interval [CI] = 1.32-1.41), gastrointestinal (aOR 1.26, 95% CI = 1.23-1.30), central nervous (aOR 1.11, 95% CI = 1.08-1.14), and respiratory (aOR 1.16, 95% CI = 1.12-1.20) systems significantly increased the risk of 90-day rehospitalization, after adjustment for covariates. In PRISCUS criteria, exposure to PIM affecting the respiratory (aOR 1.48, 95% CI = 1.41-1.56), central nervous (aOR 1.12, 95% CI = 1.09-1.15), and cardiovascular (aOR 1.20, 95% CI = 1.16-1.24) systems significantly increased the risk. In Beers criteria, exposure to PIM affecting the cardiovascular (aOR 1.37, 95% CI = 1.32-1.41), gastrointestinal (aOR 1.38, 95% CI = 1.35-1.42), central nervous (aOR 1.18, 95% CI = 1.15-1.21), endocrine (aOR 1.10, 95% CI = 1.06-1.15), and respiratory (aOR 1.09, 95% CI = 1.04-1.13) systems significantly increased the risk. Patients with 90-day rehospitalization had higher rates of the potentially harmful drug-drug interaction (DDI) pairs of serotonin syndrome (n = 19; 48.8%), QT prolongation (n = 4; 30.8%), extrapyramidal symptoms (EPS) (n = 102; 24.5%), and hypokalemia (n = 275; 20.1%). Conclusion: Beers criteria was more efficient in predicting 90-day rehospitalization among older adults experiencing PIM in Taiwan than either PIM-Taiwan or PRISCUS. The risk of 90-day rehospitalization was associated with the potentially harmful DDI classes of serotonin syndrome, QT prolongation, EPS, and hypokalemia.

Drug-drug interactions (DDIs) pose significant challenges in pharmacotherapy, affecting drug efficacy and safety. Traditional in vitro and in vivo models often fail to accurately predict clinically relevant DDIs, necessitating the development of advanced testing platforms. This review explores cutting-edge in vitro and in vivo systems, including chimeric mice with humanized livers, clustered regularly interspaced short palindromic repeats-CRISPR-associated animal models, liver microphysiological systems, and 3-dimensional spheroids and organoids that enhance the assessment of DDIs. These models enable the precise evaluation of drug metabolism, enzyme induction/inhibition, and transporter-mediated interactions under physiologically relevant conditions. In addition, we discuss the latest advancements in predictive modeling techniques for DDIs, focusing on physiologically based pharmacokinetic models and machine learning approaches. Physiologically based pharmacokinetic models integrate drug-specific and system-specific parameters to simulate DDIs dynamically, bridging the gap between preclinical and clinical findings. Machine learning-based predictive tools use vast datasets to identify complex interaction patterns, improving DDI risk assessment in early drug development. By integrating these novel experimental and computational approaches, researchers can achieve more accurate, quantitative DDI predictions, facilitating safer drug design and regulatory decision-making. The review highlights these emerging methodologies, emphasizing the need for continued refinement to enhance their predictive power and translational relevance. Future research should focus on optimizing hybrid strategies that combine mechanistic and data-driven models to achieve robust, clinically meaningful DDI assessments. SIGNIFICANCE STATEMENT: This review showcases advanced experimental and computational tools to improve drug-drug interaction prediction. These innovations enhance drug-drug interaction accuracy, support safer drug development, aid regulatory and clinical decisions, reduce adverse reactions, and optimize patient care.

Iloperidone is a recently approved antipsychotic agent indicated for the acute treatment of schizophrenia in adults. Iloperidone is characterized as a serotonin 5-HT(2A) and dopamine D(2) receptor antagonist, which makes its core mechanism of action similar to other second-generation antipsychotic agents. The affinity (or lack thereof) of iloperidone for other receptors (e.g., histamine, muscarinic, α(1)-adrenoceptors, serotonin) results in a unique side effect and perhaps response profile that may make it an additional option for patients who have previously not tolerated or adequately responded to other available agents. Iloperidone has been studied in over 3,200 patients throughout its development. Its efficacy appears to be similar to haloperidol, risperidone and ziprasidone. It appears to be safe with minimal extrapyramidal side effects, weight gain and prolactin elevation. A cautious dosing and titration schedule is recommended at the initiation of therapy due to the potential for orthostatic hypotension and dizziness. Drug interactions through the CYP3A4 and CYP2D6 enzymes, along with the potential for QT prolongation, may influence its use in certain patients. Genetic studies conducted during drug development may facilitate the clinical use of pharmacogenomic tests to aid clinicians in optimizing the risk-benefit ratio of iloperidone. The purpose of this review is to summarize the chemistry, pharmacology and clinical aspects of iloperidone, with the goals of identifying key scientific and clinical issues for its use, as well as assessing the potential utility of iloperidone for the treatment of schizophrenia.

The impact of drug-related QT prolongation on FDA regulatory decisions

All currently available antipsychotic drugs are the dopamine D2 receptor antagonists and are capable of producing extrapyramidal side-effects (EPS). Anticholinergic drugs are primarily used to treat EPS or prevent EPS induced by antipsychotics in the treatment of psychosis and schizophrenia. However, they can cause a variety of distressing peripheral side-effects (e.g. dry mouth, urinary disturbances, and constipation) and central adverse effects (e.g. cognitive impairment, worsening of tardive dyskinesia, and delirium). Disturbances in cognitive abilities are cardinal features of schizophrenia from its earliest phases and account for much of the functional disability associated with the illness. It is likely that long-term concomitant administration of anticholinergics exacerbates the underlying cognitive impairment in patients with schizophrenia and subsequently affects patients' quality of life. Thus, current treatment guidelines for schizophrenia generally do not recommend the prophylactic and long-term use of anticholinergics. However, the high use of long-term anticholinergic drugs with antipsychotics has been identified as an important issue in the treatment of schizophrenia in several countries. To assess the benefits and limits of anticholinergic use in psychosis and schizophrenia, this article will provide a brief review of the pharmacology and clinical profiles of anticholinergic drugs and will focus on their effects on cognitive function in schizophrenia, particularly during the course of the early phase of the illness. In addition, we will address the effects of discontinuation of anticholinergics on cognitive function in patients with schizophrenia and provide a strategy for adjunctive anticholinergic use in patients treated with long-acting injectable antipsychotics.

Antipsychotic-induced extrapyramidal side effects (EPS) are still a major problem in the treatment of schizophrenia. Serotonin 2C receptors (5-HT(2C)) have regulatory effects on dopaminergic pathways in brain regions involved with EPS. Polymorphisms in the 5-HT(2C) gene (HTR2C) have been suggested to be associated with the risk of developing EPS. Our purpose was to evaluate the impact of polymorphisms in the HTR2C gene on the occurrence of EPS in male schizophrenic patients. Ninety-nine male Caucasian chronic schizophrenic patients on long-term treatment with classical antipsychotics were genotyped for the -997 G/A, -759 C/T, -697 G/C and Cys23Ser polymorphisms of HTR2C. EPS (dystonia, parkinsonism, tardive dyskinesia) were assessed by the Simpson-Angus Scale and the Abnormal Involuntary Movement Scale. Fifty-one patients had current or previous history of EPS, whereas 48 patients had no symptoms or history of EPS. To rule out a possible association between HTR2C polymorphisms and schizophrenia, 112 healthy male volunteers were also genotyped. Allele frequencies of -997A, -759T and -697C did not differ between the groups, whereas patients with EPS had a significantly (p = 0.025) higher frequency of the 23Ser allele (0.29) than did patients without EPS (0.15) or healthy volunteers (0.13). A similar trend was observed for a haplotype including the -997G, -759C, -697C and 23Ser alleles (p = 0.04). Results confirm previously reported associations between the HTR2C 23Ser allele and EPS occurrence and suggest the novel finding of an HTR2C haplotype association with EPS in male chronic schizophrenic patients.

Treatment of epilepsy is quite challenging as it requires the antiepileptic drugs to be taken for a long duration, sometimes throughout the lifetime of the patient. But such chronic drug intake results in adverse effects, drug interactions and added economic and emotional distress to the patients. It is therefore of utmost importance to analyze the pattern of prescription of antiepileptic drugs, the concurrent medications in epilepsy. The present study was conducted to collect, assess and understand the above data and also to analyze the rationality and correctness of prescription using the WHO prescription indicators. A total of 100 case records of patients diagnosed with epilepsy was analyzed and the data was recorded. We have total 34 children and 66 adult patients in our study. Generalized tonic-clonic seizures was the common type of seizure. Phenytoin was the most commonly prescribed antiepileptic drug. 1/3rd of the patients needed more than one drug for adequate seizure control and the most common add on drug was levetiracetam. Also, the average number of drugs prescribed to the patients was 6 which included drugs given for concomitant illnesses as well. A total of 561 medicines were prescribed which included the drugs prescribed for concomitant illnesses as well. Average number of medicines per prescription was 6. Prescription by generic name was seen in 42% of the cases. An injection was prescribed in 66% of the prescriptions. An antibiotic was encountered in 38% of the prescriptions. Most of the epileptic patients are subjected to polypharmacy which puts them at a higher risk of side effects, drug interactions and financial burden. Physicians should minimize the use of drugs and look into the rationality of each prescription.

Increasingly older adults are traveling to international destinations with malaria as a present risk. Surveillance systems indicate that older adults are more likely to suffer severe complications from malaria. The role of health care providers in selecting an appropriate medication for chemoprophylaxis or treatment of malaria in adults becomes more difficult as older adults undergo physiologic changes that alter the pharmacokinetic and pharmacodynamic nature of medications potentially causing increased drug interactions, adverse events and altered drug action. A comprehensive literature search from 1970 to present, with a focus on the past 10years, was conducted on drug interactions, pharmacokinetic and pharmacodynamic effects on antimalarials in adults. It was determined that due to pharmacodynamic and pharmacokinetic changes in older adults, especially renal and cardiovascular, special attention should be given to this population of travelers in order to minimize the likelihood of adverse events or altered drug efficacy. Antimalarial drug-disease interactions in older adults can occur more often due to QT prolongation, exacerbation of hypoglycemia, decreased renal elimination and decreased hepatic metabolism. Older antimalarials have well-documented drug-drug interactions. Tafenoquine, a new antimalarial, requires glucose-6-phosphate dehydrogenase screening like primaquine and monitoring of new potential drug interaction with MATE1 and OCT2 substrates. While drug-drug interactions in older travelers may occur more often as a result of polypharmacy, data did not indicate adverse reactions or decreased drug efficacy is greater compared with younger adults. Overall, with the exception of recently approved tafenoquine, much is known about antimalarial drug and disease interactions, but new drugs are always being approved, requiring travel health providers to understand the pharmacokinetics and pharmacodynamics of antimalarial drugs to predict the impact on safety and efficacy in travelers. This guide provides travel health providers with valuable insights on potential outcomes associated with drug interactions in adults and recommended monitoring or drug regimen modification.

To comprehensively review the 2 recent and large antipsychotic effectiveness trials for treatment of schizophrenia: the United Kingdom's Cost Utility of the Latest Antipsychotic Drugs in Schizophrenia Study (CUtLASS), and the National Institute of Mental Health-initiated Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial. We present a review of the rationale, methodology, and findings to date from the CUtLASS and CATIE schizophrenia trials, including all primary and secondary outcomes. The primary findings from both trials, CUtLASS and CATIE, suggest that first-generation antipsychotics (FGAs) and second-generation antipsychotics (SGAs) are equally effective in the treatment of schizophrenia. The exception is in treatment-resistant populations where clozapine exhibits superiority, compared with other SGAs. In the CATIE trial, there is a suggestion that olanzapine is superior in effectiveness, compared with other nonclozapine SGAs, although this seems to be mediated by past history of olanzapine use, and carries with it increased weight gain and metabolic adverse events. From a cost-effectiveness perspective, there is no evidence that SGAs are superior to FGAs, with findings suggesting the possibility that FGAs may be superior. Past efficacy trials have strongly supported the position that SGAs are superior to FGAs in the treatment of schizophrenia and in side effect profile. Two large independent effectiveness trials, CUtLASS and CATIE, have offered a strong challenge to these claims. Both suggest that SGAs, except clozapine in the treatment-resistant population, offer little, if any, clinical benefits, and, moreover, harbour their own significant side effects.

Clozapine is an atypical neuroleptic agent that has recently become available in Canada with potential clinical efficacy in the treatment of refractory schizophrenia, and in patients with schizophrenia neurologically intolerant to conventional neuroleptics. Although it causes few extra-pyramidal symptoms, the drug has a number of other adverse effects including a risk of agranulocytosis in one to two percent of all patients. Because of this, the use of the drug is permitted only if the white blood count is monitored weekly. The monitoring system, outlined in this article, requires a coordinated effort between clinical staff, pharmacy, laboratory and the Clozaril Support and Assistance Network. Clinical guidelines are proposed, detailing the indications and contraindications for treatment and the pharmacokinetics, dosing, adverse effects, and drug interactions with clozapine. In addition, the economics, government policies and implications for future research are considered. Although there are administrative and clinical difficulties associated with its use, clozapine represents an advance in therapeutic research. Patients and family members will be inquiring about the drug and may deserve a trial. This article aims to inform Canadian mental health professionals about the safe and beneficial use of clozapine.

Therapeutic drug-drug interactions (DDIs) have the potential to harm the patient by causing serious and unwanted side effects. Patients suffering from cancer are exposed to numerous drugs, such as therapeutic drugs and adjuvant treatments for the comorbidities and the side effects of chemotherapy. The concomitant use of anticancer and adjuvant drugs makes suchpatients prone to QT prolongation, which could precipitate torsades de pointes or TdP. The current review aims to summarize the prevalence of therapeutic DDIs causing QT prolongation in these patients. A thorough literature search was performed using databases such as PubMed, Google Scholar, and Research Gate. Medical Subject Headings (MeSH) and alternate search terminologies such as "QT prolongation", "Drug interactions", and "Cancer" were used to identify all the relevant articles published in English to date. The data fromall such published articles available on the day of the collection wereconsideredand fed into the meta package of R software for measuring the outcomes. The literature searchyielded seven unique and relevant articles, which wereincluded in our study. In these studies, therapeutic DDIs causing QT prolongation were found to occur in 3558 out of 8013 patients undergoing evaluation. Theirprevalence in patients with cancer was estimated to be 22% with a 95% CI between 4% and 63%. The patient characteristics, such as age, comorbid diseases, drugs for supportive care, and polypharmacy, were identified as risk factors associated with potential DDIs. Our data concluded that 22% of the patients administered anticancer drugs were exposed to concomitant drugs leading to DDIs, whichprolonged the QT interval. Thus, the implementation of vigilant measures and precautionary safety interventions becomes vital to forestall QT prolongation and any other adverse cardiac events in this group of patients.

Antipsychotic drugs are medications commonly for schizophrenia (SCZ) treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC) classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the “alimentary tract and metabolism” category while SCZ atypical drugs had significant interactions with drugs of the categories “nervous system” and “antiinfectives for systemic uses.” This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.

This study evaluates the relative effectiveness and side-effects of risperidone as compared with conventional neuroleptics in the treatment of schizophrenia, by meta-analysis of 11 double-blind, randomized controlled trials. The proportion of patients showing clinical improvement; use of medications for extrapyramidal side-effects (EPS); the treatment drop-out rates; and the changes in negative PANSS scores were measured. Compared with conventional neuroleptics, slightly more patients in the risperidone group showed clinical improvement [57 vs 52%; odds ratio 1.27, 95% confidence interval (CI): 1.04, 1.56]. The use of concomitant medications for EPS was significantly less in the risperidone group than in the conventional neuroleptic group (22.8 vs 38.4%; odds ratio 0.51, 95% CI: 0.41, 0.63). The overall drop-out rate was lower in the risperidone group than in other neuroleptic group (29.1 vs 33.9%; odds ratio 0.75, 95% CI: 0.61, 0.94). The difference in changes in negative PANSS score between the risperidone and the haloperidol group was -0.74 (95% CI: -1.50, 0.02). Weight gain and tachycardia are more common in patients treated with risperidone. Sensitivity analysis of different analytic approaches did not materially change the main estimates. It was concluded that the short-term efficacy of risperidone is comparable to other neuroleptics in the treatment of schizophrenia. It is associated with significantly fewer EPS than conventional neuroleptics (mainly haloperidol).