topiramate (USL255 / Qudexy XR)

Topiramate-induced hair loss in a patient using valproate and levetiracetam.

Unler Mehmet M, Alacan Omer Faruk OF

Prediction of migraine treatment response with clinical and genetic data: A machine learning analysis of the HUNT study.

Abildsnes Håkon Kvisle HK, Danelakis Antonios A, Giri Samita S, Giles Dominic D et al.

Migraine is highly heritable, yet no machine learning models for predicting treatment responses have incorporated genetic data. We aimed to develop machine learning models including both genetic and clinical information to predict treatment responses of migraine preventives and treatment retention of triptans. This was a cross-sectional population-based machine learning analysis of the Trøndelag Health Studies and the national prescription registry. Clinical predictors included demographics, serum metabolic measures, comorbidities, mental health and lifestyle factors. Genetic predictors included 108 migraine risk loci. Preventive response was defined as ≥50% reduction in triptan dispensations after first preventive dispensation, and triptan retention as ≥3 dispensations. The dataset was split into training (70%), validation (10%) and test set (20%). A series of standard and causal machine learning architectures were trained and optimized on the training and validation set, and the best model was evaluated on the held-out test data. Shapley Additive exPlanation values identified key predictors. In total, 475 participants tried a preventive (amitriptyline, beta-blocker, candesartan or topiramate) and 565 a triptan (371 sumatriptan) during the project period. Using only clinical features, the best models achieved area under curve of 0.56-0.69 for the preventives, and 0.56 for triptans. When combining clinical and genetic data, the area under curve was unchanged or reduced in all cases except for amitriptyline (0.80) and sumatriptan (0.55). The most important predictors were hormone-related features, lipid levels and hemodynamic parameters. We developed models that predicted treatment responses to amitriptyline, beta-blockers, candesartan and topiramate with modest accuracy. Incorporating genetic data did not enhance performance, although the limited sample size and the uncertain accuracy of cohort definition and treatment response assessment prevent firm conclusions.

Antiseizure medication dosing and monitoring in the intensive care unit: a practical narrative review.

Webb Andrew J AJ, Barlow Brooke B, Seto Stephanie L SL, Maciel Carolina B CB et al.

Antiseizure medications (ASMs) are commonly used in the intensive care unit (ICU) and often exhibit pharmacokinetics that differ substantially from those in healthy volunteers or in the outpatient setting. Organ dysfunction, polypharmacy, exogenous devices, and greater severity of illness all influence ASM pharmacokinetics, dosing decisions, and monitoring parameters. It is essential for critical care clinicians to familiarize themselves with the pharmacokinetics, dosing considerations, effects of exogenous devices, and therapeutic drug monitoring (TDM)-all covered in this narrative review-to incorporate in their approach to ASMs in the critical care setting. We identified relevant literature from MEDLINE from inception to March 2026 related to ASMs in the ICU. Data on pharmacokinetics, dosing in the ICU, the effect of renal replacement therapy, extracorporeal membrane oxygenation (ECMO), and plasmapheresis (PLEX) on ASM exposure, and the role of TDM in the ICU were collected and summarized. Considerations for 15 ASMs (brivaracetam, cannabidiol, carbamazepine, cenobamate, clobazam, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, perampanel, phenobarbital, phenytoin, topiramate, valproate, and zonisamide) were included in the review. Dosing considerations for TDM, including indications and target reference ranges, and specific settings such as organ dysfunction, illness severity, renal replacement therapy, ECMO, and PLEX are discussed. The use of ASMs in the ICU require a distinct approach from outpatient settings. Severity of illness, organ dysfunction and replacement devices, and frequent drug-drug interactions warrant tailored agent selection, dosing, and monitoring.

Cancer risk of glucagon-like peptide-1 receptor agonists for obesity: comparison with bariatric surgery and other weight-loss drugs.

Bitar Elio R ER, Besir Karim K, Cummins Kaelyn C KC, Sears Olivia O et al.

Obesity is associated with an increased risk of several cancers, including colorectal cancer (CRC), pancreatic cancer, and hepatocellular carcinoma (HCC). Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have been linked to a lower risk of certain obesity-associated cancers, but most studies have focused on patients with diabetes. Their impact on nondiabetic patients using GLP-1 RAs for weight loss remains unclear. Using the TriNetX Research network, we conducted a retrospective cohort study of nondiabetic adults with obesity who received semaglutide or tirzepatide, underwent bariatric surgery, or used other weight-loss medications (orlistat, phentermine/topiramate, or naltrexone/bupropion). Patients were stratified by GLP-1 RA dose (any vs therapeutic) and propensity-score matched. Cancer incidence was assessed using Kaplan-Meier analysis and Cox proportional hazards models. We identified 662,013 GLP-1 RA users, 272,343 bariatric surgery patients, and 158,446 users of other weight-loss medications. Compared with bariatric surgery, therapeutic-dose GLP-1 RA use was associated with a lower incidence of CRC (hazard ratio [HR], 0.72; P =.038) and pancreatic cancer (HR, 0.63; P =.041), but not HCC. Compared with other weight-loss medications, therapeutic-dose GLP-1 RAs were associated with a lower CRC risk (HR, 0.68; P =.008), without significant differences in pancreatic or liver cancer. The use of any dose was not associated with significant differences. Therapeutic-dose GLP-1 RA use was associated with a lower incidence of CRC and pancreatic cancer compared with bariatric surgery and with a lower incidence of CRC compared with other weight-loss medications. These findings suggest that GLP-1 RA use is not associated with an increased cancer risk and raise the possibility that therapeutic-dose treatment may be associated with a lower cancer incidence.