Antipsychotic medicines are widely used for the management of psychotic symptoms regardless of the underlying diagnosis. Most atypical antipsychotics undergo extensive metabolism prior to excretion. Various factors may influence their pharmacokinetics, particularly elimination, leading to highly variable drug concentrations between individual patients following the same dosing regimen. Population pharmacokinetic approach, based on nonlinear mixed effects modeling, is a useful tool to identify covariates explaining pharmacokinetic variability, as well as to characterize and distinguish unexplained residual and between-subject (interindividual) variability. In addition, this approach allows the use of both sparsely and intensively sampled data. In this paper, we reviewed the pharmacokinetic characteristics of clozapine, olanzapine and aripiprazole, focusing on a population modeling approach. In particular, models based on a nonlinear mixed effects approach performed by NONMEM^® software in order to identify and quantify sources of pharmacokinetic variability are presented. Population models were identified through systematic searches of PubMed and sixteen studies were selected. Some of the factors identified that significantly contribute to variability in elimination among clozapine, olanzapine, and aripiprazole are demographic characteristics, body weight, genetic polymorphism, smoking and in some cases drug interactions. Scientific research based on pharmacometric modeling is useful to further characterize sources of variability and their combined effect.

Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were -6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

Optimizing the dosing of medicines for pediatric patients in routine clinical practice and determining the dose for clinical trials is still a challenging task. Children differ from adults in their response to drugs due to inherent differences in pharmacokinetics and/or pharmacodynamics, and responses may also vary among pediatric patients of different ages. However, the greatest disparities compared to adult pharmacokinetic profiles are observed in children below 2 years of age. The maturation of the liver and the kidneys, as well as the variation in body composition, are considered to be the main sources of pharmacokinetic variability. Hence, besides specific pharmacodynamic features, understanding age-related changes in drug absorption, distribution, and elimination is fundamental for optimizing drug efficacy and avoiding toxicity. This paper summarizes the pharmacokinetic changes throughout the childhood, along with the effect of developmental changes on drug dosage calculation. In clinical practice, age and body weight-based dosing regimens are usually used. In spite of dosing recommendations based on age and/or body weight, variabilities in pharmacokinetics and pharmacodynamic response remain, implying a need to monitor patients and optimize the dosing regimen according to physiological characteristics, disease characteristics and therapy.

Background: Topiramate (TPM) is a sulfamate-substituted monosaccharide that is structurally different from other antiepileptic drugs. TPM inhibits carbonic anhydrase activity, which is associated with loss of bicarbonate from the kidney and consequently metabolic acidosis or electrolyte imbalance. Objective: The objectives of the study were to investigate the influence of TPM therapy on bicarbonate and potassium levels in adult epileptic patients. Methods: Data were collected from 59 adult patients on monotherapy or co-therapy of TPM and other antiepileptic drugs. Serum bicarbonate and potassium levels were available from all patients. Steady-state TPM trough concentrations were determined in blood samples by high-performance liquid chromatography. Data analysis was performed by SPSS software (version 17, Chicago, IL). Results: Patients were divided into group A (duration of therapy shorter than or equal to 5 years) and group B (duration of therapy longer than 5 years). Significant difference (P < 0.05) in serum bicarbonate levels was observed between these 2 groups. Bicarbonate levels were linearly related to the TPM therapy duration. No correlation was found between the TPM dose or patient age and bicarbonate or potassium levels, as well as between therapy duration and potassium level. Linear regression analysis showed no significant association among 54 available TPM trough concentrations and bicarbonate or potassium levels. Conclusions: Results highlight the frequent occurrence of lower bicarbonate level associated with prolonged TPM therapy. Monitoring bicarbonate levels in patients on long-term TPM therapy might be useful.