Concurrent Use Of Multiple Drugs Research Articles

A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.

The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is of great significance for drug development and health. However, most recent studies have limited the drug-drug interaction prediction task to identifying interactions between substructures, overlooking molecular hierarchical information. Moreover, the extracted substructures in these methods are always restricted to have the same number of atoms as contained in the molecular graph, which does not align with real-world facts. In this study, a molecular fragment representation learning framework for drug-drug interaction prediction is introduced. Initially, a fragment extraction module is designed to acquire a series of molecular fragments. Subsequently, to capture more comprehensive features, molecular hierarchical information is effectively integrated, enabling drug-drug interaction prediction by identifying pairwise interactions between molecular fragments of each drug. Comprehensive evaluations demonstrate that the proposed method achieved state-of-the-art performance in both DrugBank and Twosides datasets, particularly achieving an improved accuracy of over 20% for unseen drugs in both two datasets. Furthermore, case studies and visual analysis confirm that the proposed method can accurately identify crucial substructures influencing the interactions, which are basically consistent with functional group structures in reality. In conclusion, this method not only enhances the performance of drug-drug interaction prediction but also offers high interpretability. Source code is freely available at https://github.com/kennysyp/MFR-DDI .

Abstract 4923: A systematic framework for predicting adverse drug reaction signals using medical data

Abstract Prescribing a combination of medications is a common strategy to enhance the effectiveness of disease treatment. While this approach aims to improve therapeutic outcomes, it may lead to unintended adverse drug reactions (ADRs) due to interactions between prescribed drugs and individual factors. Recognizing the importance of ADRs, various machine learning models have been developed. However, these models face limitations, particularly in predicting reactions induced by three or more drugs and in accounting for individual patient factors, such as age and medical history. To address these limitations, we propose a systematic framework that leverages medical data for predicting ADR signals. In the framework, the MIMIC-IV database was systematically preprocessed, and a series of machine learning models were developed that predict the ADR signals. The model predictions were validated using the eICU collaborative research database. The machine learning models process multiple inputs, including information on administered drugs, age, gender, ethnicity, and underlying medical conditions to predict ADR signals. The ADR signals are determined by classifying abnormalities in 20 specific laboratory test values, such as hematocrit, creatinine, and hemoglobin. The machine learning models developed in this study hold promise as a valuable tool for assessing potential risks, such as ADRs, associated with the concurrent use of multiple drugs. Citation Format: Junhyeok Jeon, Eujin Hong, Hyun Uk Kim. A systematic framework for predicting adverse drug reaction signals using medical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4923.

Navigating the Polypharmacy Landscape: A Bibliometric Analysis of Computational Approaches for Predicting Adverse Drug Reactions

Polypharmacy, the concurrent use of multiple drugs in a patient due to complex diseases or multiple morbidities, poses potential hazards through adverse drug reactions (ADRs). Conventional in vivo and in vitro ADR identification methods are challenging, making computational alternatives vital for minimizing patient risk. This study evaluates the scientific outputs of computational approaches to predict ADRs associated with polypharmacy through bibliometric analysis. A comprehensive literature search was conducted on Web of Science, Scopus and PubMed, which yielded 258 selected publications. Quantitative variable analysis was performed, and VosViewer was used to visualise networks and co-occurrences. The United States and China lead in publications, with ‘drug-drug interaction’ being the most frequent keyword. The Journal of Biomedical Informatics was ranked top, followed by BMC Bioinformatics and Briefings in Bioinformatics. The results indicate a growing global interest in computational methods for predicting adverse drug reactions associated with polypharmacy, primarily focusing on drug-drug interactions.

Prevalence and Severity of Drug Interactions in a Referral Pediatric Hospital: An Observational, Retrospective Study

Background: Hospitalized infants and children are usually treated with many medications in the hospital. Concurrent use of multiple drugs, known as polypharmacy, is inevitable in critically ill patients. This study aims to investigate the possible interactions as well as their type and number, and their effect on the treatment process plus the duration of hospital stay of patients.
 Methods: In this descriptive study, the medical records of 189 patients admitted to the Intensive care unit (ICU) ward of Mofid Children's Educational Hospital in Tehran were prospectively studied over six months from August 2018 to March 2019. The collected data included disease diagnosis, patient medication information, age and gender, and treatment interventions. Interactions between drugs were identified using Up-to-date database, with the results analyzed by SPSS software.
 Results: The results revealed that hospitalization increased with an increasing number of drugs. The findings also indicated a direct relationship between the number of drug interactions and the duration of hospital stay. After examining the relationship between ICU outcome and the number of drug items as well as the number of drug interactions, it was found that there is a direct relationship between the two. There was also a direct relationship between Class D plus X interactions and mortality rate along with duration of stay.
 Conclusion: This study showed a direct relationship between drug interactions and the duration of hospitalization. In other words, as drug interactions increased, so did the duration of hospital stay.

SmileGNN: Drug-Drug Interaction Prediction Based on the SMILES and Graph Neural Network.

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug–drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug–drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug–drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug–drug interactions. Five out of the top ten predicted new drug–drug interactions are verified from the latest database, which proves the credibility of SmileGNN.

Polypharmacy in Zoological Medicine.

Polypharmacy is a term that describes the inappropriate, concurrent use of multiple drugs in an individual patient. Zoological medicine practitioners must take approved agents (veterinary or human) and extrapolate their use to non-approved species often with little species-specific pharmacological evidence to support their decisions. When considering polypharmacy, even less information exists concerning multi-drug pharmacokinetics, pharmacodynamics, or potential drug-drug interactions in non-domestic species. Unfortunately, captive, zoological species are susceptible, just like their domestic counterparts, to chronic diseases and co-morbidities that may lead to the usage of multiple drugs. Polypharmacy is a recognized and important issue in human medicine, as well as an emerging issue for veterinarians; thus, this paper will discuss the novel, potential risks of polypharmacy in zoological medicine. Hopefully, this discussion will help bring the attention of veterinarians to this issue and serve as an interesting discussion topic for pharmacologists in general.

Synergistic GABA-Enhancing Therapy against Seizures in a Mouse Model of Dravet Syndrome

Seizures remain uncontrolled in 30% of patients with epilepsy, even with concurrent use of multiple drugs, and uncontrolled seizures result in increased morbidity and mortality. An extreme example is Dravet syndrome (DS), an infantile-onset severe epilepsy caused by heterozygous loss of function mutations in SCN1A, the gene encoding the brain type-I voltage-gated sodium channel NaV1.1. Studies in Scn1a heterozygous knockout mice demonstrate reduced excitability of GABAergic interneurons, suggesting that enhancement of GABA signaling may improve seizure control and comorbidities. We studied the efficacy of two GABA-enhancing drugs, clonazepam and tiagabine, alone and in combination, against thermally evoked myoclonic and generalized tonic-clonic seizures. Clonazepam, a positive allosteric modulator of GABA-A receptors, protected against myoclonic and generalized tonic-clonic seizures. Tiagabine, a presynaptic GABA reuptake inhibitor, was protective against generalized tonic-clonic seizures but only minimally protective against myoclonic seizures and enhanced myoclonic seizure susceptibility at high doses. Combined therapy with clonazepam and tiagabine was synergistic against generalized tonic-clonic seizures but was additive against myoclonic seizures. Toxicity determined by rotorod testing was additive for combination therapy. The synergistic actions of clonazepam and tiagabine gave enhanced seizure protection and reduced toxicity, suggesting that combination therapy may be well tolerated and effective for seizures in DS.

Complex questions command complex analyses: comparative effectiveness of drug treatment strategies in atrial fibrillation

Among the randomized clinical trials (RCTs) comparing rhythm- with ratecontrol trials conducted in the past 10 years [3–7], the AFFIRM trial is regarded as the most important, owing to its large sample size (4060 participants) and long follow-up (up to 6 years) [7]. Overall, the AFFIRM study showed that the strategy of restoring and maintaining sinus rhythm had no clear advantage in reducing overall mortality over the strategy of controlling the ventricular rate and allowing the AF to persist [7]. This conclusion held after combining the results of AFFIRM [7], and four other smaller trials [3–6], in a single meta-ana lysis estimate (OR: 0.87; 95% CI: 0.74–1.02 vs 1.12; 95% CI: 0.64–1.94 in the pooled ana lysis that included/excluded the AFFIRM trial) [8]. However, many clinicians feel uncomfortable with the rate-control strategy as the main therapeutic approach for AF. For some, the trial results do not mean that the rhythm control is not beneficial, but merely highlight the limitations of the current treatments to achieve and maintain sinus rhythm [9], while others believe the trial results are not generalizable to the full population of AF patients [8]. Such a conundrum demands more evidence through comparative effectiveness studies. To our knowledge, our group has published the first observational comparative effectiveness study of rhythm- versus rate-control drug strategies, published in July 2012 [10]. In this study, we used population-based administrative databases from Quebec (Canada) from 1999 to 2007 to select patients aged ≥66 years, hospitalized with an AF diagnosis who did not have AF-related drug prescriptions in the year before the admission, but received a prescription within 7 days of discharge. The study included 30,664 hospitalized AF patients who were followed-up for up to 8 years and, similar to the trials [7,8], found no difference in mortality between the rhythm- and rate-control drug treatment strategies in the first 3 - years post-treatment initiation. However, our long-term results differed from those of the clinical trials [7,8]; while the AFFIRM trial found no mortality difference at 5 years between the rhythm- and rate-control strategies [7], we found a significant 12% mortality reduction in the rhythm-control group found after 5 years of follow-up and a significant 23% reduc tion after 8 years. In the face of contradictory results between observational studies and RCTs trials, the observational design is oftentimes dismissed as being flawed due to poor control of confounding. We argue here, as others have done before us [11,12], that RCTs and observational studies are complementary designs and that much can be learned by examining results from different study designs and analyses [13,14]. Bias, due to unobserved confounding, is the Achilles’ heel of observational studies of drug effects [15–20], with lack of randomization being a particular concern in studies

Acute drug-related mortality of people recently released from prisons

The rate of acute drug-related mortality, or overdose deaths, among prisoners in the immediate post-release period is unacceptably high. Such incidents result from many factors, including decreased tolerance after a period of relative abstinence during imprisonment and the concurrent use of multiple drugs which, with every additional illicit drug consumed in combination with opioids, nearly doubles the risk of death from opioids. Other important factors are the lack of pre-release counselling, post-release follow-up and failure to identify those at risk. Substance dependence is a chronic disorder with high relapse rates and often requires long-term continuous treatment. The deaths are preventable and a number of interventions including opioid substitution treatment reduces the risk of overdose among opioid users after release.

Medication Dosing and Renal Insufficiency in a Pediatric Cardiac Intensive Unit

Appropriate drug dosing in renal insufficiency is an important consideration to optimize therapeutic outcomes and to minimize toxicity. In this issue of the journal, Moffett and colleagues [4] remind health care providers of the clinical benefits of pharmacist involvement on drug dosing for patients with renal insufficiency in a pediatric cardiac intensive care unit. The authors adapted a wellknown list of drugs primarily excreted by the kidneys for patients with renal impairment. They noted that patients who required medication adjustments were significantly younger compared to those who did not require medication adjustment. Moffett et al. also report that clinical pharmacist consultation for prescribing drugs in the pediatric intensive care setting would be associated with shorter length of stay, lower costs, and lower rate of medication errors. Clinical pharmacist services in the hospital setting are proven to be beneficial. Such services are labor-intensive and require collecting patient data, calculating a creatinine clearance (CrCl) level, and contacting a physician to change the order to increase the likelihood of accurate medication selection and dosing based on an estimated CrCl. A rational approach to drug dosing in patients with renal impairment begins with a thorough history and physical examination. Particularly important is the history of previous drug allergy or toxicity and the use of concurrently prescribed or nonprescription drugs. Physical assessment should include estimating the extracellular fluid volume (ECFV); the presence of edema or dehydration alters drug dose. Body weight and height need to be measured, and the ideal body weight should be calculated and drug doses estimated accordingly. The renal excretion of drugs depends on glomerular filtration and renal tubular secretion and reabsorption. The glomerular elimination of drugs also depends on the molecular size and protein binding of the agent. Although protein binding decreases the filtration of drugs, it may increase the amount that the renal tubule secretes. When glomerular filtration is impaired by renal disease, the clearance of drugs that are eliminated primarily by this mechanism will be decreased and the plasma half-life of the drugs prolonged. The secretion of drugs eliminated by active transport systems in the renal tubule also will be affected in patients with renal disease. As the rate of CrCl decreases, drugs dependent on renal tubular excretion for elimination will be excreted more slowly. Furthermore, because the proximal tubular secretion of some agents is carrier mediated and capacity limited, concurrent use of multiple drugs eliminated by renal tubular secretion may saturate the transport system. Renal insufficiency alters both the disposition of drugs in the body (pharmacokinetics) and tissue responses to drugs (pharmacodynamics). If drugs or their metabolites are excreted by the kidneys, they can accumulate in patients with renal insufficiency and cause adverse effects. When the necessity for drug therapy has been established, renal function should be measured. Because the rate of elimination of drug excreted by the kidneys is proportional to the glomerular filtration rate (GFR), the serum creatinine or CrCl should be used to estimate renal function. The Schwartz equation can be used to estimate CrCl in children and adolescents [5]. However, estimating GFR from the serum creatinine assumes that renal function is stable and that the serum creatinine measurement is constant. With changing renal function, the serum creatinine level will no F. Assadi (&) Rush University Medical Center, Chicago, IL, USA e-mail: Ra-Id_Abdulla@rush.edu

Methods for estimating the occurrence of polypharmacy by means of a prescription database.

Concurrent use of multiple drugs (polypharmacy, PP) may cause health risks such as adverse drug reactions, medication errors and poor compliance. The objective of this study, based on data from a prescription database, was to evaluate estimators of PP in the general population. Data were retrieved from Odense Pharmaco-epidemiological Database (OPED) and consisted of all prescriptions in 1994 from a 10% random sample of drug users (n = 26,977) in the county of Funen, Denmark. For each prescription, the period of consumption was calculated by setting the duration of treatment to equal the amount of drug purchased, as measured in defined daily doses (DDD), thereby assuming a daily intake of one DDD. PP was defined as overlapping periods of consumption for different drugs. A Venn diagram was used to illustrate and compare this estimator of PP with two other indicators of multiple-drug use: the number of drugs purchased in 3 months and the mean number of drugs used in 1 year. A receiver operating curve (ROC) was used to evaluate the possibility of predicting episodes of PP from the number of drugs purchased in 3 months. The proposed estimator of PP was robust towards changes in DDD. On an average day in 1994, the prevalence of PP was 9.9% and the standard deviation (SD) between days was 0.3%. Two to four drugs (minor PP) were used by 8.7% of the population (SD, 0.2%) and five or more drugs (major PP) by 1.2% (SD, 0.1%). The number of individuals displaying PP for the first time in 1994 stabilised after approximately 6 months, resulting in an incidence of major PP of 0.2% and of minor PP of 1.2% per month. For individuals exposed to PP, the median number of days of exposure was 61 and 10.5% were exposed for more than 350 days of the year. Purchase of five or more drugs in the first 3 months of 1994 predicted episodes of major PP in the same year with a positive predictive value of 80%. Epidemiological measures of multiple drug use can be estimated from data in a prescription database. From a conceptual point of view, an estimator based on the number of simultaneously used drugs (calculated from the date of purchase and the number of DDD) is preferable, but the number of drugs purchased in a 3-month period may also be a useful estimator.