Aim: Risperidone is the most commonly prescribed antipsychotic drug to children and adolescents worldwide, but it is associated with serious side effects, including weight gain. This study assessed the relationship of risperidone and 9-hydroxyrisperidone trough concentrations, maximum concentrations and 24-hour area under the curves (AUCs) with body mass index (BMI) z-scores in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side effects and effectiveness. Methods: Forty-two children and adolescents (32 males) aged 6-18 years were included in a 24-week prospective observational trial. Drug plasma concentrations, side effects and effectiveness were measured at several time points during followup. Relevant pharmacokinetic covariates, including medication adherence and CYP2D6, CYP3A4, CYP3A5 and P-glycoprotein (ABCB1) genotypes, were measured. Nonlinear mixed-effects modelling (NONMEM ®) was used for a population pharmacokinetic analysis with 205 risperidone and 205 9-hydroxyrisperidone concentrations. Subsequently, model-based trough concentrations, maximum concentrations and 24-hour AUCs were analysed to predict outcomes using generalized and linear mixed-effects models. Results: A risperidone two-compartment model combined with a 9-hydroxyrisperidone one-compartment model best described the measured concentrations. Of all the pharmacokinetic parameters, higher risperidone sum trough concentrations best predicted higher BMI z-scores during follow-up (P < .001). Higher sum trough concentrations also predicted more sedation (P < .

The DBS method to quantify risperidone, aripiprazole, pipamperone, and their major metabolites did not meet the acceptance criteria in the Bland-Altman analyses. Therefore, this DBS method was not clinically valid. This study shows the importance of a clinical validation study with use of Bland-Altman plots before clinical implementation.

Nonadherence in children who use long-term medication is a serious problem and assessing adherence is an important step to provide solutions to this problem. Medication adherence can be measured by several methods, including (a) self-report questionnaires or structured interviews, (b) therapeutic drug monitoring (TDM), (c) electronic devices, and (d) pick-up/refill rates. The objective of this narrative review is to provide an overview of the literature about methods for the measurement of medication adherence in chronically ill children and adolescents. Therefore, we conducted a literature search by using multiple databases. Four methods of monitoring medication adherence are presented for the most described chronic diseases: asthma, HIV/AIDS, epilepsy, diabetes mellitus and ADHD. First, 10 commonly used self-report questionnaires and structured interviews are described, including the main characteristics, (dis)advantages and their validation studies. Second, the use of TDM in pediatric trials for medication adherence measurement is discussed. New sampling methods (e.g. dried blood spot) and sampling matrices (e.g. hair, saliva and urine) have shown their benefits for TDM in children. Third, electronic devices to measure medication adherence in children are presented, being developed for several drug administration routes. Fourth, the analyses, advantages and disadvantages of pharmacy data are discussed. The usage of this data requires specific calculations and interpretations to assess adherence. As presented in this review, every adherence method has specific (dis)advantages. When deciding which adherence method is applicable, validity and generalizability should be taken into account. Combining multiple methods seems to offer the best solution in the daily clinical practice.