This study develops a population pharmacokinetic model for lamotrigine (LTG) in Spanish and German patients diagnosed with epilepsy. LTG steady-state plasma concentration data from therapeutic drug monitoring were collected retrospectively from 600 patients, with a total of 1699 plasma drug concentrations. The data were analyzed according to a one-compartment model using the nonlinear mixed effect modelling program. The influences of origin (Germany or Spain), sex, age, total body weight, and comedication with valproic acid (VPA), levetiracetam, and enzyme-inducing antiepileptic drugs (phenobarbital [PB], phenytoin [PHT], primidone [PRM], and carbamazepine [CBZ]) were investigated using step-wise generalized additive modelling. The final regression model for LTG clearance (CL) was as follows: CL(L/h) = 0.028*total body weight*e(-0.713*VPA)*e0.663*PHT*e0.588*(PB or PRM)*e0.467*CBZ*e0.864*IND, where IND refers to two or more inducers added to LTG treatment; this factor as well as VPA, PHT, PB, PRM, and CBZ take a value of zero or one according to their absence or presence, respectively. The administration of inducers led to a significant increase in mean LTG CL (values of 0.045-0.070 L/h/kg vs. 0.028 L/h/kg being reached in monotherapy), whereas VPA led to a significant decrease in CL (0.014 L/h/kg). Thus, comedication with these analyzed drugs can partly explain the interindividual variability in population LTG CL, which decreased from the basic model by more than 40%. The proposed model may be very useful for clinicians in establishing initial LTG dosage guidelines. However, the interindividual variability remaining in the final model (clearance coefficient of variation close to 30%) make these a priori dosage predictions imprecise and justifies the need for LTG plasma level monitoring to optimize dosage regimens. Thus, this final model allows easy implementation in clinical pharmacokinetic software and its application in dosage individualization using the Bayesian approach.

Objective The aim of the present study was to describe the demographic, clinical and immunological characteristics of patients with late-onset (≥50 years) SLE vs patients with early-onset SLE (<50 years). Methods We performed a cross-sectional retrospective study of 3619 patients from the RELESSER database (National Register of Patients with Systemic Lupus Erythematosus of the Spanish Society of Rheumatology). Results A total of 565 patients (15.6%) were classified as late-onset SLE and 3054 (84.4%) as early-onset SLE. The male-to-female ratio was 5:1. Mean (s.d.) age at diagnosis in the late-onset group was 57.4 (10.4) years. At diagnosis, patients with late-onset SLE had more comorbid conditions than patients with early-onset SLE; the most frequent was cardiovascular disease (P <0.005). Furthermore, diagnostic delay was longer in patients with late-onset SLE [45.3 (3.1) vs 28.1 (1.0); P <0.001]. Almost all patients with late-onset SLE (98.7%) were Caucasian. Compared with early-onset SLE and after adjustment for time since diagnosis, patients with late-onset SLE more frequently had serositis, major depression, thrombotic events, cardiac involvement and positive lupus anticoagulant values. They were also less frequently prescribed immunosuppressive agents. Mortality was greater in late-onset SLE (14.3% vs 4.7%; P <0.001). Conclusion Late-onset SLE is insidious, with unusual clinical manifestations that can lead to diagnostic errors. Clinical course is generally indolent. Compared with early-onset disease, activity is generally reduced and immunosuppressants are less commonly used. Long-term prospective studies are necessary to determine whether the causes of death are associated with clinical course or with age-associated comorbidities in this population.

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