Over the last two decades, pharmacogenetics and pharmacokinetics have been increasingly used in clinical practice in Psychiatry due to the high variability regarding response and side effects of antipsychotic drugs. Specifically, long-acting injectable (LAI) antipsychotics have different pharmacokinetic profile than oral formulations due to their sustained release characteristics. In addition, most of these drugs are metabolized by CYP2D6, whose interindividual genetic variability results in different metabolizer status and, consequently, into different plasma concentrations of the drugs. In this context, there is consistent evidence which supports the use of therapeutic drug monitoring (TDM) along with pharmacogenetic tests to improve safety and efficacy of antipsychotic pharmacotherapy. This comprehensive review aims to compile all the available pharmacokinetic and pharmacogenetic data regarding the three major LAI atypical antipsychotics: risperidone, paliperidone and aripiprazole. On the one hand, CYP2D6 metabolizer status influences the pharmacokinetics of LAI aripiprazole, but this relation remains a matter of debate for LAI risperidone and LAI paliperidone. On the other hand, developed population pharmacokinetic (popPK) models showed the influence of body weight or administration site on the pharmacokinetics of these LAI antipsychotics. The combination of pharmacogenetics and pharmacokinetics (including popPK models) leads to a personalized antipsychotic therapy. In this sense, the optimization of these treatments improves the benefit–risk balance and, consequently, patients’ quality of life.

Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time, and allows more factors to be considered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of antibiotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, for dose prediction and analyze their results in comparison with the results obtained by classical methods. Furthermore, in the review, the techniques employed and the metrics to evaluate the precision are described to improve the compression of the results. Methods: A systematic search was carried out in the EMBASE, OVID, and PubMed databases and the results obtained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in seven and none of the studies were performed on new antibiotics. The most used techniques were XGBoost and neural networks. Comparisons were conducted in most cases against population pharmacokinetic models. Conclusions: AI techniques offer promising results. However, the diversity in terms of the statistical metrics used and the low power of some of the articles make the overall assessment difficult. For now, AI-based ML techniques should be used in addition to classical population pharmacokinetic models in clinical practice.

The introduction of point-of-care (POC) assays into clinical practice in patients with inflammatory disease enables on-demand therapeutic decision making. The aim of this study was to compare the POC test Quantum blue (Bühlmann Laboratories) for infliximab (IFX), adalimumab (ADL), and its anti-drug antibodies with the traditional ELISA assay (Promonitor). A total of 200 serum samples were analyzed. Samples were classified into the following three different groups; sub-therapeutic range (IFX < 3 μg/mL and ADL < 5 μg/mL); therapeutic range (IFX: 3–7 μg/mL and ADL: 5–12 μg/mL) and supra-therapeutic range (IFX levels > 7 μg/mL and ADL levels > 12 μg/mL). Significant higher values were measured using the POC test (p < 0.001) for IFX results but no differences in ADL trough levels were observed (p = 0.3101). Spearman’s correlation indicated a good correlation between the two assays (rs = 0.88 for ADL and rs = 0.93 for IFX), and McNemar’s test revealed significant differences (p = 0.016) when classifying IFX samples between therapeutic and supra-therapeutic ranges but no significant differences were found among the other ranges for either IFX or ADL. These results show that we should be cautious when using these rapid measurement methods, and new targets should probably be defined for IFX when using this new analytical method.

Therapeutic drug monitoring improves the benefit–risk balance of antipsychotic therapy. Ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) is considered the gold-standard method for measuring plasma drug concentrations; however, the Alinity C system has emerged as a promising alternative. This is the first study aimed at comparing UHPLC-MS/MS versus Alinity C in measuring plasma concentrations of aripiprazole and dehydroaripiprazole. A total of 86 plasma samples were analyzed. The active moiety of aripiprazole was measured in 60 samples using both systems and 26 samples were analyzed twice using Alinity C with an intermediate period of 6 months to assess its reproducibility. Spearman’s correlation revealed a good association between the two assays (rs = 0.96) and no significance differences were found by McNemar’s test when classifying samples between infra-, supra- and therapeutic ranges. Passing–Bablock regression showed a good correlation among methods (rs = 0.93) and a slope of 1.12 indicating a slight tendency of Alinity C to measure higher values than UHPLC-MS/MS. In addition, a good intra-method correlation across the two sequential analyses with Alinity C was obtained (rs = 0.99). Nonetheless, clinical decisions could be different in 15% of the cases depending on the chosen method. No differences were found in active moiety determination by Alinity C depending on the concentration of aripiprazole and dehydroaripiprazole of the samples.

Background/Objectives: Risperidone ISM^® is a long-acting injectable (LAI) formulation approved for monthly administration in schizophrenia treatment. It employs innovative in situ microimplant technology for biphasic drug release, achieving immediate and sustained therapeutic plasma concentrations without the need for oral supplementation or loading doses. This study evaluates the pharmacokinetic profile of Risperidone ISM^® in a real-world clinical setting, focusing on plasma concentrations of the active moiety (Risperidone + 9-OH-Risperidone). Methods: An observational study was conducted to measure plasma concentrations of patients receiving Risperidone ISM^® (75 mg or 100 mg). Samples were collected at pre-dose, 2 h, 24 h, 14 days, 21 days, and 25 days post-injection. Pharmacokinetic parameters, including Cmax, Cmin, Tmax, and AUC, were calculated and stratified by dose (75 mg and 100 mg) and injection site (gluteal vs. deltoid). Results: A total of 44 patients were included. Therapeutic plasma levels were reached within hours post-injection and sustained throughout the 28-day interval. Cmin values averaged 34.4 ng/mL and 36.1 ng/mL for the 75 mg and 100 mg doses, respectively. The median Tmax occurred at 24 h with a mean Cmax of 55.7 ng/mL and 59 ng/mL for 75 mg and 100 mg, respectively. Higher systemic exposure was observed for deltoid administration. Significant interindividual variability was noted, with 45.4% of patients exhibiting trough levels outside the therapeutic range. Conclusions: Risperidone ISM^® achieves rapid and sustained therapeutic plasma levels, offering significant benefits in schizophrenia treatment. However, the high interindividual variability observed must be thoroughly studied, and the contributing factors identified to ensure the therapy is as effective and safe as possible.

Aripiprazole once-monthly (AOM) exhibits an important interindividual pharmacokinetic variability with significant implications for its clinical use. CYP2D6 and CYP3A4 highly contributes to this variability, as they metabolize aripiprazole (ARI) into its active metabolite, dehydroaripiprazole (DHA) and the latter into inactive metabolites. This study aims to evaluate the effect of CYP2D6 and CYP3A4 polymorphisms in combination and the presence of concomitant inducers and inhibitors of this cytochromes on ARI and DHA plasma concentrations in a real clinical setting. An observational study of a cohort of 74 Caucasian patients under AOM treatment was conducted. Regarding CYP2D6, higher concentrations were found for active moiety (ARI plus DHA) (AM) (67 %), ARI (67 %) and ARI/ DHA ratio (77 %) for poor metabolizers (PMs) compared to normal metabolizers (NMs). No differences were found for DHA. Ms for both CYP2D6 and CYP3A4 showed a 58 % higher AM and 66 % higher plasma concentration for ARI compared with PMs for CYP2D6 and NMs for CYP3A4. In addition, PMs for both CYP2D6 and CYP3A4 have 45 % higher DHA concentrations than NMs for both cytochromes and 41 % more DHA than PMs for CYP2D6 and NMs for CYP3A4, suggesting a significant role of CYP3A4 in the elimination of DHA. Evaluating the effect of CYPD26 and CYP3A4 metabolizing state in combination on plasma concentrations of ARI, DHA and parent-to-metabolite ratio, considering concomitant treatments with inducers and inhibitor, could optimize therapy for patients under AOM treatment.