Frequent Drug Combinations Research Articles

Research on a data mining algorithm based on BERTopic for medication rules in Traditional Chinese Medicine prescriptions

AbstractBackgroundA data mining algorithm is proposed based on BERTopic to provide new insights into the analysis of medication rules in Traditional Chinese Medicine (TCM) prescriptions.MethodsUsing the BERTopic algorithm, collected TCM prescriptions for corneal diseases are converted to embeddings through a transformer based on the Bidirectional Encoder Representations from Transformers pre‐trained model. Then, Uniform Manifold Approximation and Projection is applied to perform dimensionality reduction in prescription embeddings. Subsequently, Hierarchical Density‐Based Spatial Clustering of Applications with Noise is used for clustering. Finally, class‐based term frequency–inverse document frequency is used to generate several main drug combinations from the clustered results.ResultsThe highest frequency of drugs used included Buddleja officinalis, Bidens pilosa, Angelica sinensis, Eriocaulon buergerianum, and Raw Rehmannia glutinosa. The most frequent drug combinations were “Eriocaulon buergerianum, Raw Rehmannia glutinosa, Prunella vulgaris, Notopterygium incisum” “Lycii Fructus, Bidens pilosa, Buddleja officinalis” and “Kochiae Fructus, Cortex Dictamni.”ConclusionsThe proposed data mining algorithm based on BERTopic demonstrated promising outcomes in the analysis of TCM prescription medication rules. This method exhibited simplicity and efficiency, thereby offering a novel avenue for analysis.

Ethanol inhibits dopamine uptake via organic cation transporter 3: Implications for ethanol and cocaine co-abuse

Concurrent cocaine and alcohol use is among the most frequent drug combination, and among the most dangerous in terms of deleterious outcomes. Cocaine increases extracellular monoamines by blocking dopamine (DA), norepinephrine (NE) and serotonin (5-HT) transporters (DAT, NET and SERT, respectively). Likewise, ethanol also increases extracellular monoamines, however evidence suggests that ethanol does so independently of DAT, NET and SERT. Organic cation transporter 3 (OCT3) is an emergent key player in the regulation of monoamine signaling. Using a battery of in vitro, in vivo electrochemical, and behavioral approaches, as well as wild-type and constitutive OCT3 knockout mice, we show that ethanol’s actions to inhibit monoamine uptake are dependent on OCT3. These findings provide a novel mechanistic basis whereby ethanol enhances the neurochemical and behavioral effects of cocaine and encourage further research into OCT3 as a target for therapeutic intervention in the treatment of ethanol and ethanol/cocaine use disorders.

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case-time-control design.

Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication-induced delirium in older adults is well understood, limited population-level evidence is available, particularly on combinations of medications associated with delirium in older adults. We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case-time-control design. We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005-2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti-inflammatory medications, were considered drugs of interest. The first-time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case-time-control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l-day with a 35-day washout period) that are statistically associated with delirium, and the association was tested by computing a time-trend adjusted matched odds-ratio (MOR) and its 95% confidence interval (CI). We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case-time-control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR=6.17; 95%CI=[2.05-18.54]), haloperidol (MOR=4.81; 95%CI=[3.16-6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR=3.94; 95%CI=[3.03-5.10]), and fentanyl exposure (MOR=3.46; 95%CI [2.05-9.21]). The association rule method applied to a case-time-control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.

Polypharmacy, potentially serious clinically relevant drug-drug interactions, and inappropriate medicines in elderly people with type 2 diabetes and their impact on quality of life.

The aim of the study is to investigate the patterns of polypharmacy, clinical‐relevant drug‐drug interactions (DDIs), and potentially inappropriate medicines (PIMs), and whether polypharmacy, potential serious clinically‐relevant DDIs, or PIMs can be associated with low quality of life (QoL) index scores of older adults with type 2 diabetes (T2D). A cross‐sectional study was conducted using data of 670 elderly T2D sub‐cohort from a nationwide pharmacy‐based intensive monitoring study of inception cohort of T2D in Portugal. 72.09% were found on polypharmacy (≥5 medicines). Participants on polypharmacy were mostly females (P = .0115); more obese (P = .0131); have more comorbid conditions (P < .0001); more diabetes complications (P < .0001); and use more of glucose lowering drugs (P = .0326); insulin (P < .0001); chronic medicines (P < .0001); and have higher diabetes duration (P = .0088) than those without polypharmacy. 10.59% of the participants were found to have potential serious clinically relevant DDIs. The most frequent drug‐combinations were angiotensin‐converting enzyme (ACE) inhibitors with angiotensin‐receptor blockers (ARBs), aspirin with Selective serotonin reuptake inhibitors (SSRIs), and clopidogrel with calcium channel blockers. PIMs are found in 36.11% of the participants. The most common PIMs were benzodiazepines, long‐acting sulfonylureas, and iron overdose. The adjusted multivariate models show that Polypharmacy, PIMs, and potential serious clinically relevant DDIs were associated with lower QoL index scores (OR 1.80 95% CI 1.15‐2.82), (OR 1.57 95% CI 1.07‐2.28), and (OR 1.34 95% CI 0.73‐2.48) respectively. The study shows that polypharmacy, potential serious clinical‐relevant DDIs, and PIMs may correlate with risk of reduced health related QoL outcome of older adults with T2D.

Analysis of depressive episodes, their recurrence and pharmacologic treatment in primary care patients: A retrospective descriptive study.

BackgroundDepression is one of the most prevalent health problems, frequently being a medium- and long-term condition, with a high comorbidity rate and with frequent relapses and recurrences. Although numerous studies have compared the effectiveness of specific antidepressant therapy drugs and have assessed relapses, scientific evidence on the relationship between pharmacologic treatments and recurrence is scarce. The objective of this study is to describe depressive episodes in a primary care patient cohort, the percentage of depression recurrences and the administered pharmacologic treatment, from a naturalistic perspective.MethodsRetrospective descriptive study. 957 subjects were included. The dependent variable was a depression diagnosis and independent variables were: gender, age at time of data collection; age of onset, first-episode treatment, number of recurrences, age at recurrences, treatment prescribed for recurrences using therapeutic groups categorization.ResultsRecurrences are frequent, affecting more than 40% of the population. In the first episode, 13.69% of the patients were not prescribed pharmacological treatment, but this percentage decreased over the following depression episodes. 80.9% of the patients who did not receive drug treatment in the first depression episode did not experience subsequent episodes. Monotherapy, and specifically, SSRIs were the most frequently prescribed treatment option for all depressive episodes. Regards the combined pharmacologic treatment, the most frequent drug combinations were SSRIs and benzodiazepines.LimitationsIn order to increase the power of results, the statistical analysis was performed using therapeutic groups categorization, not individually analyzing each drug and dose.ConclusionsDepressive episode recurrence is frequent in primary care patients. Further studies having a prospective design are needed in order to expand on this issue.

Identifying drug combinations associated with acute kidney injury using association rules method

Older adults are at an increased risk of acute kidney injury (AKI) because of aging, multiple comorbidities, and polypharmacy. The aim of this case-crossover study was to apply association rule (AR) analysis to ascertain drug combinations contributing to the risk of AKI in adults aged 65 years and older. We sourced a nationwide representative sample of New Zealanders aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005-2015) of drugs of interest were sourced from New Zealand pharmaceutical collections (Pharms). We classified medication exposure, as a binary variable, at individual drug level belonging to medication classes including antimicrobials, antihistamines, diuretics, opioids, nonsteroidal anti-inflammatory medications. Several studies have associated the drugs of interest from these medication classes with AKI in older adults. We extracted the first-time coded diagnosis of AKI from the National Minimal Data Set. A unique patient identifier linked the prescription data set to the event data set, to set up a case-crossover cohort, indexed at the first AKI event. ARs were then applied to identify frequent drug combinations in the case and the control periods (l-day observation with a 35-day washout period), and the association of AKI with each frequent drug combination was tested by computing a matched odds ratio (MOR) and its 95% confidence interval (CI). We identified 55 747 individuals (mean age 82.14) from 2005 to 2014 with incident AKI and exposed to at least one of the drugs of interest. ARs identified several medication classes including antimicrobials, nonsteroidal anti-inflammatory drugs, and opioids are associated with AKI. The frequently used medicines associated with AKI are trimethoprim (MOR = 1.68; 95% CI = [1.54-1.80]), ondansetron (MOR = 1.43; 95% CI = [1.25-1.64]), codeine phosphate plus metoclopramide (MOR = 1.37; 95% CI = [1.11-1.63]), and norfloxacin (MOR = 1.24; 95% CI [1.05-1.42]). We applied ARs, a novel methodology, to big data to ascertain drug combinations associated with AKI. ARs uncovered previously implicated medication classes that increase the risk of AKI in older adults. The finding that ondansetron increases the risk of AKI requires further investigation.

Mining Directional Drug Interaction Effects on Myopathy Using the FAERS Database.

Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our paper provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin, and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization.

Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records.

We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.

Analysis on composition rules of suppress cough based on association rules and clustering algorithm

Objective To explore composition rules of suppress cough prescriptions. Methods The suppress cough prescriptions in New National Medicine were collected to build a database based on Traditional Chinese medicine inheritance assist system. The methods of association rules with apriori algorithm and complex system entropy cluster were used to achieve the frequency of medicines and association rules between drugs. Results The data-mining results indicated that the most frequently used drugs were Glycyrrhizae, Platycodon Semen armeniacae Ephdra vulgaris, tangerine or orange peel. The most frequent drug combinations were Radix Glycyrrhizae, Platycodon grandiflorum, Radix Glycyrrhizae, Semen armeniacae amarae, Radix Glycyrrhizae, tangerine or orange peel and so on. The drugs with a high degree confidence coefficient of association rules include Dried tangerine or orange peel, the root bark of white mulberry, Dried tangerine or orange peel, Snakegourd Seed. The new prescription that we can get composited by Fructus Aurantii was Angelica decursiva, Pericarpium Citri Reticulatae, Rhizoma Anemarrhenae, the root bark of white mulberry. Conclusion From the analysis above, we could conclude that the drugs in suppress cough prescriptions have more effects of relieving superficies, clearing heat and drying dampness, ventilating the lung and resolving phlegm, nourishing Yin and supplementing Qi. Key words: Suppressing-cough prescription; Association rules; Clustering algorithm

Analysis on medication rules of state medical master Yan Zhenghua's prescriptions that including Polygoni Multiflori Caulis based on data mining

The prescriptions including Polygoni Multiflori Caulis that built by Pro. Yan were collected to build a database based on traditional Chinese medicine (TCM) inheritance assist system. The method of association rules with apriori algorithm was used to achieve frequency of single medicine, frequency of drug combinations, association rules between drugs and core drug combinations. The datamining results indicated that in the prescriptions that including Polygoni Multiflori Caulis, the highest frequency used drugs were parched Ziziphi Spinosae Semen, Ostreae Concha, Ossis Mastodi Fossilia, Salviae Miltiorrhizae Radix Et Rhizoma, Paeoniae Rubra Radix, and so on. The most frequent drug combinations were "Polygoni Multiflori Caulis-parched Ziziphi Spinosae Semen", "Ostreae Concha-Polygoni Multiflori Caulis", and "Polygoni Multiflori Caulis-Ossis Mastodi Fossilia". The drug association rules of confidence coefficient 1 were "Ostreae Concha-->Polygoni Multiflori Caulis", "Poria-->Polygoni Multiflori Caulis", "parched Ziziphi Spinosae Semen-->Polygoni Multiflori Caulis", and "Paeoniae Alba Radix-->Polygoni Multiflori Caulis". The core drug combinations in the treatment of insomnia were Ossis Mastodi Fossilia, Polygoni Multiflori Caulis, Salviae Miltiorrhizae Radix et Rhizoma, Ostreae Concha, Polygalae Radix, Margaritifera Concha, Poria, and parched Ziziphi Spinosae Semen. And the core drug combinations in the treatment of obstruction of Qi in chest were Salviae Miltiorrhizae Radix Et Rhizoma, Polygoni Multiflori Caulis, parched Ziziphi Spinosae Semen, Trichosanthis Fructus, Allii Macrostemonis Bulbus, and Paeoniae Rubra Radix.

Idiosyncratic Adverse Reactions of Most Frequent Drug Combinations Longterm Use Among Hospitalized patients with Polypharmacy

Inappropriate prescribing of a multiple therapeutic agents to patients with chronic conditions is very common in everyday practice. Adverse drug reactions (ADRs) are still considered as one of the main problems of drug therapy. We investigated idiosyncratic symptoms and signs of adverse drug reactions (ADRs) of the most frequent used combination of drugs among hospitalized patients prescribed polypharmacy. A cross sectional study (design) was performed in Pharmacies "Eufarm Edal" in Tuzla from 2010 to 2011. Polypharmacy was defined as using > or =4 drugs. The total study sample of 166 examiners was interviewed with a questionnaire about ADRs which was developed special for study. Linear regression analyses was used to evaluate predictors of idiosyncratic signs of adverse drug reactions of the most prevalent drug combinations; using length of drugs in cases polypharmacy more than 6 months as independent variable. Age, sex, index of cumulative morbidity, drug number in polypharmacy, type of drug combination related pharmacological effects, type of hospital clinics were used as possible confounders. The most common exposures to various drug combinations were: medication for high blood pressure and heart (62%), psychotropic drugs (59%), antacids (47%) and antibiotics (46%) among hospitalized patients with polypharmacy. Our results indicated that from 9.6% to 90.4% of hospitalized patients with polypharmacy had at least one suspicious long-term idiosyncratic drug combination use symptoms. The ADRs prevalence often used psychotropic drug combination was initiated suspected idiosyncratic adverse reactions: confusion, depression, anxiety, decreased libido and insomnia. Linear regression analyses also showed that it remains a very strange, and negative idiosyncratic and lacking therapeutic effects of use of antacids in conditions of polypharmacy. The toxicity of some drug combinations may sometimes be synergistic and be greater than the sum of the risks of toxicity of either agent used alone. In order to recognize and to prevent ADRs (including drug interactions), good communication between pharmacist and patient and/or physicians and patient is crucial, and prescribers should develop an effective therapeutic partnership with the patient and with fellow health professionals.

P03-75 - Psychopharmacotherapy characteristics of alcoholics treated on psychiatry department tuzla from 01.01.2005. to 31.12.2009. with brain comuterized tomography

IntroductionTreatment of alcoholics is complex and difficult, very often it demands complex psychopharmacotherapy.AimTo investigate characteristics of psychopharmacotherapy of inpatients alcoholics with brain computerized tomography (CT) during hospitalization on Psychiatry Department in Tuzla.MethodologyWe investigated 86 record files of inpatients that were treated from 01.01.2005 to 31.12.2009 year on Psychiatry Department, who had dismissed diagnosis Alcohol dependency F 10.2 according ICD-10, to whom computerized tomography of brain has been done durin hospitalization. Targeted variables were: age, marital status, working status, involvement in war fighting's, CT diagnosis, psychological findings, laboratory findings and characteristics of psychopharmacotherapy.ResultsThe mean age of observed patients was 50.1 ± 6.6 years. Age of inpatients was in positive correlation with duration of work, presence of silent ischemic stroke and brain cortex atrophy. Presence of PTSD was in positive correlation with involvement in the combatants, with cognitive disturbances, with depressiveness and somatic complains. Brain cortex atrophy positively correlated with silent stroke and glucose blood level.There were 72 (83.7%) inpatients that treated with antidepressants, 45 (52.3%) with sedating antipsychotic drugs, 10 (11.7%) atypical antipsychotics 12 (13.0%) with typical antipsychotics, 53 (61.6%) with hypnotics, 37 (43.0%) tranquilizers, 23 (26.7%) vitamin B. There was the most frequent combinations of antidepressants SSRI with promazine in 41 (47.7%), with diazepam 25 (29.1%), with zolpidem 20 (23.3%) and nitrazepam in 17 (19.8%) patients.ConclusionSSRI antidepressants were the most frequent used psychopharmacotherapy drugs, then sedating neuroleptics and hypnotics. The most frequent drug combinations were antidepressants, sedating neuroleptic or tranquillisers and hypnotics.

Socio-demographic and clinical aspects of rheumatoid arthritis

To determine the socio-demographic profiles and some clinical aspects of patients with rheumatoid arthritis (RA). Prospective, cross-sectional study. Ambulatory out- patient clinics of Kenyatta National Hospital (KNH), a public national and referral hospital. Out of 180 patients interviewed and examined, 60 met American College of Rheumatology (ACR) diagnostic criteria of RA. Of the 60 patients recruited 52 (87%) were females with male: female ratio of 1: 6.5. The mean age of patients was 41.38(+/- 16.8) years. There were two peaks of age of occurrence, 20-29 and 40-49 years. In 75% of the study patients, one or more of metacarpophalangeal joints of the hand were involved in the disease. Other frequently involved sites were--wrists, elbows, knees, ankles and glenohumeral joints of shoulders in a symmetrical manner. Serum rheumatoid factor was positive in 78.9% while rheumatoid nodules were present in 13.3% of the study patients. A large majority of patients (88%) had active disease with 18% having mild disease, 38% moderate activity and 32% having severe disease. Only 12% of patients had disease in remission. Forty six point seven per cent (46.7%) of the study patients were on at least one Disease Modifying anti Rheumatic Drugs (DMARD) from a selection of methotrexate, sulphasalazine, hydroxychloroquine and leflunamide. The most frequent drug combination was methotrexate plus prednisolone at 30% of the study population; while 66.7% were on oral prednisolone with 25% of the study patients taking only Non-Steroidal anti Inflammatory Drugs (NSAIDS). A large majority of ambulatory patients with RA had active disease. Most of them were sub-optimally treated, especially the use of DMARDS. About two thirds were on oral steroids. Sub-optimal therapy in relatively young patients, peak 20-29 and 40-49 years is likely to impact negatively on their disease control and quality of life.

Chronic urticaria: do urticaria nonexperts implement treatment guidelines? A survey of adherence to published guidelines by nonexperts

Guidelines including level of evidence and grade of recommendation were recently published for chronic urticaria (CU). To describe the therapeutic approach in patients with CU, and to depict how recent guidelines are implemented in the daily practice of management of CU. We performed a cross-sectional multicentre study through a questionnaire answered by 139 specialists. In total, 695 patients were evaluated, mean+/-SD age 42.3+/-15 years, 62.1% women. Of the patients, 168 were treated by an allergist, 473 by a dermatologist and in 54 cases the specialist was not stated. The drug prescribed was the main variable, and chi2 and Fisher's tests were utilized for the statistical analysis. Nonsedating anti-H1 antihistamines taken regularly were the most common drugs prescribed, followed by nonsedating anti-H1 antihistamines taken as needed, corticosteroids, sedating antihistamines taken regularly, sedating antihistamines taken as needed, anti-H2 antihistamines, leukotriene antagonists, ciclosporin and doxepin. Nonsedating antihistamines plus corticosteroids was the most frequent drug combination prescribed. When comparing between allergists and dermatologists we found a positive and significant correlation only between prescription of cetirizine, dexchlorfeniramine, leukotriene antagonists and anti-H2 antihistamines and being treated by an allergist. A positive correlation was found with desloratadine and being seen by a dermatologist. We did not find any difference in CU management in the rest of the treatments studied. It is surprising that a large amount of sedating antihistamines was prescribed. In many instances these were prescribed as needed. This fact could have a negative impact on urticaria control and patient satisfaction. It seems difficult for the nonexpert to differentiate between CU and any kind of physical urticaria.

Serum Concentrations of Pregabalin in Patients With Epilepsy: The Influence of Dose, Age, and Comedication

Pregabalin (PGB) is a new antiepileptic drug (AED) approved for adjunctive therapy for partial seizures with and without generalized tonic-clonic seizures and for the treatment of peripheral neuropathic pain in adults. PGB does not bind to plasma proteins and is excreted predominantly unchanged by the kidneys. Previous studies indicated that PGB shows no relevant interactions with other AEDs. The aim of this study was to investigate the influence of PGB dose, patient age, and comedication on the serum concentration of PGB. In total, 198 samples of 167 (adult) inpatients who fulfilled the inclusion criteria (eg, trough concentration, body weight available) were investigated. A patient was considered twice only if the comedication had been changed. The PGB serum concentration (mg/L) in relation to PGB dose/body weight (mg/kg) per day (level-to-dose ratio, LDR, [(mg/L)/(mg/kg)=kg/L]) was calculated and compared for the most frequent drug combinations (n=97). Analysis of covariance (using age as covariate) carried out on the log-transformed data showed that comedication had a slight but significant (P = 0.02) effect on PGB serum concentrations. The median LDR of PGB was 0.29 for PGB + oxcarbazepine (n=16), 0.31 for PGB + carbamazepine (n=20), 0.35 for PGB + levetiracetam (n=11), 0.35 for PGB + lamotrigine (n=15), and 0.39 for PGB + valproic acid + lamotrigine (n=35). Regression analysis including all 198 samples indicated (in accordance with analysis of covariance) that PGB concentrations were lower in combination with enzyme-inducing AEDs (phenytoin, carbamazepine, oxcarbazepine) and were age-dependent (higher in older patients). The PGB dose-concentration relationship was nearly linear (r=0.68, P<0.0001). However, patients on the same PGB dosage per body weight had rather different PGB trough concentrations, which could be explained only in part by age and comedication. The increase of PGB serum concentrations in older patients is in accordance with expectations for drugs that are predominantly renally excreted. Unexpectedly and in contrast to other studies, our data indicate that comedication with enzyme-inducing antiepileptic drugs (eg, carbamazepine) can moderately decrease PGB serum concentrations (about 20% to 30%). Further studies should clarify the effect of age and interactions on PGB concentrations.

Serum concentrations of Levetiracetam in epileptic patients: the influence of dose and co-medication.

Levetiracetam (LEV) is a new antiepileptic drug approved as add-on therapy. Previous studies indicated that LEV has no relevant interactions with other antiepileptic drugs. The aim of this study was to investigate the influence of LEV dose, age, and co-medication on the serum concentration of LEV. In total, 363 samples of 297 inpatients who fulfilled the inclusion criteria (e.g., trough concentration, body weight available) were investigated. A patient was considered twice only if his co-medication had been changed. The LEV serum concentration in relation to LEV dose/body weight [level-to-dose ratio, LDR, (microgram/mL)/(mg/kg)] was calculated and compared for the most frequent drug combinations. Analysis of covariance (using age as covariate) carried out on the log-transformed data showed that co-medication had a highly significant (P < 0.001) effect on LEV serum concentrations. The median LDR of LEV was 0.32 for LEV + phenytoin, 0.32 for LEV + carbamazepine, 0.34 LEV + oxcarbazepine, 0.45 for LEV + lamotrigine, 0.46 for LEV + phenobarital, 0.52 for LEV monotherapy, 0.53 for LEV + valproic acid, and 0.54 LEV + valproic acid + lamotrigine. In co-medication with phenytoin (P < 0.001), carbamazepine (P < 0.001), and oxcarbazepine (P < 0.004), the LDR of LEV was significantly lower than it was with LEV monotherapy, whereas the LDR of LEV of patients on co-medication with valproic acid or lamotrigine did not differ significantly from the LDR of LEV of patients on LEV monotherapy (P > 0.05). Regression analysis including all 363 samples confirmed that other drugs (e.g., phenytoin, carbamazepine) lower LEV concentrations. In addition to co-medication, age had a significant effect on clearance of LEV. Children had lower LEV concentrations than adults on the same LEV dose per body weight. In contrast to other studies, our data point out that other enzyme-inducing antiepileptic drugs (e.g., phenytoin, carbamazepine) can moderately decrease LEV serum concentrations (by 20-30%). However, our observations should be confirmed by prospective pharmacokinetic studies.