International Journal of Translational Medicine

Background/Objectives: Understanding the pharmacokinetics (PK) of antiepileptic and anti-inflammatory drugs under different physiological conditions is essential for optimizing therapy. Phenytoin, a widely used antiepileptic, and indomethacin, a nonsteroidal anti-inflammatory drug, are frequently prescribed in women of reproductive age. This study aimed to evaluate the influence of age, pregnancy, and dosing regimens on the PK of both drugs, as well as to investigate potential drug–drug interactions (DDIs). Methods: PK parameters of phenytoin and indomethacin were systematically analyzed in women aged 20–45 years under non-pregnant and pregnant conditions. Different dosing regimens were compared, and coadministration studies were conducted to assess DDI. Results: Phenytoin demonstrated stable absorption and bioavailability across ages and during pregnancy. Single daily dosing (300 mg once daily) yielded slightly higher peak concentration (C_max) values, while fractionated dosing (100 mg q8h) produced significantly higher drug exposure (AUC) and absorption fraction, particularly with prolonged administration, reflecting saturable metabolism. During pregnancy, systemic exposure (C_max and AUC) was modestly reduced, while absorption and distribution remained unchanged. Indomethacin showed minimal age-related variability and linear pharmacokinetics across dosing regimens. In pregnancy, exposure was reduced (lower C_max and AUC) with delayed T_max, indicating slower absorption. Importantly, no PK DDI was observed, as indomethacin parameters remained unchanged except for T_max, which was lower in the interaction scenario compared with baseline, suggesting a faster absorption rate without affecting overall exposure or peak concentration in the presence of phenytoin. Conclusions: Phenytoin and indomethacin exhibit stable and predictable PK across ages and during pregnancy, with dose-dependent characteristics that align with their known metabolic profiles. The absence of clinically relevant DDI supports their safe concomitant use. These findings provide preliminary reassuring evidence for clinicians and contribute to a better understanding of their pharmacological behavior in diverse patient populations.

Background: Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder. This study aims to demonstrate the potential of comprehensive polygenic scores (PGSs) as clinical biomarkers for stratifying individuals with ASD and for advancing the understanding of ASD’s heterogeneous etiology. Methods: We calculated 2602 PGSs—representing all publicly available, license-cleared PGSs in the PGS Catalog—for 75 individuals with ASD by utilizing the database of the Tohoku Medical Megabank Birth and Three-generation cohort study. Results: Unsupervised clustering revealed three ASD subgroups. We identified twenty PGSs with the most significant differences among these subgroups as distinctive PGSs for each subgroup. PGS set enrichment analysis associated these distinctive PGSs with different traits in each subgroup: high-density lipoprotein cholesterol measurements, urea measurement, and body mass index. Furthermore, distinctive PGSs indicated consistent genetic predisposition directions: lower high-density lipoprotein cholesterol levels in subgroup 1, higher urea levels in subgroup 2, and lower body mass index in subgroup 3. Conclusions: Comprehensive PGSs extending beyond psychiatry-related traits represent promising clinical biomarkers for identifying ASD subgroups with different genetic predispositions. Such stratification may enhance understanding of heterogenous genetic backgrounds and targeted drug development.

G protein-coupled receptors (GPCRs) are the largest family of diverse receptors in eukaryotic organisms, playing a critical role in modulating human physiology. It therefore comes as no surprise that about 36% of all currently available drugs target this superfamily. When an agonist binds to a GPCR, it induces conformational changes in the receptor that allow it to interact with intracellular proteins. This interaction triggers downstream signaling cascades that alter the cell’s activity. GPCR signaling is complex, as GPCRs transmit signals through coupling with G proteins, arrestins, and numerous other intracellular effectors. Different ligands, receptor subtypes, and cellular environments can result in the activation of distinct signaling pathways. Biased signaling through GPCRs has emerged as a frontier area in pharmacological research efforts towards designing targeted therapeutic interventions and enhancing drug efficacy and safety. This review presents the types of bias associated with GPCRs and the mechanisms underlying biased signaling. Examples of biased ligands and their therapeutic implications will be discussed. In addition, the inherent challenges in measuring signaling bias, and especially the translational gap between in vitro and in vivo assays and clinical outcomes, will be outlined.