Francisco José Toja-Camba’s research while affiliated with University of Santiago de Compostela and other places

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 (ARI) is an atypical antipsychotic which is a substrate of P-glycoprotein (P-gp), a transmembrane glycoprotein that plays a crucial role in eliminating potentially harmful compounds from the organism. ARI once-monthly (AOM) is a long-acting injectable form which improves treatment compliance. Genetic polymorphisms in ABCB1 may lead to changes in P-gp function, leading to individual differences in drug disposition. The present study aims to determine how the different variants of the three most prevalent SNPs of the ABCB1 gene affect plasma concentrations of ARI, of its active metabolite dehydroaripiprazole (DHA) and ARI/DHA ratio in patients under AOM treatment. The metabolizing state of the two main aripiprazole metabolizing enzymes (CYP2D6 and CYP3A4) were considered to specifically study the effect of P-gp on plasma concentrations of the parent compound and its active metabolite. The study found a clear relationship between the genotypes found for the different ABCB1 SNPs and the ARI/DHA ratio. Specifically, patients with GG genotype in G2677T have almost twice the ratio compared to TT genotype. Similarly, this increase is also found in C3435T with 1.4-fold and in C1236T with 1.6-fold for the same genotypes. Regarding haplotypes, significant differences were obtained between CC-GG-CC and TT-TT-TT patients, with an 87.9% higher ratio in patients with the CC-GG-CC haplotype. There was a clear trend towards lower ARI concentrations and higher DHA concentrations when the presence of mutated T alleles increases. The ABCB1 gene could be a good partner along with CYP2D6 and CYP3A4 genotyping in conjunction with monitoring ARI plasma concentrations. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-84192-8.

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.

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 pharma-cokinetics 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 con-sidered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of anti-biotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, to 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 sys-tematic search was carried out in the EMBASE, OVID and PubMed databases and the results ob-tained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in 7 and none of the studies were performed on new an-tibiotics. The most used techniques were XGBoost and neural networks. Comparison was conducted in most cases against population pharmacokinetic models. Conclusion: AI techniques offer prom-ising 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.

Background and objectives: Risperidone, a second-generation antipsychotic widely used in the treatment of schizophrenia, requires therapeutic drug monitoring due to its high interindividual variability. UHPLC-MS/MS is considered the gold standard for pharmacokinetic studies owing to its superior sensitivity and specificity, although it involves time-consuming manual sample preparation. In contrast, the Alinity C system, fully automated, simplifies sample processing, but only measures the active moiety (risperidone plus paliperidone). The aim of this study is to compare the performance of UHPLC-MS/MS and the Alinity C system for the determination of risperidone and paliperidone concentrations in plasma. Methods: A total of 115 plasma samples of 115 patients, 92 and 23 under risperidone and paliperidone long-acting treatment, respectively, were analyzed using both methods. Results: A strong correlation for the active moiety (risperidone plus 9-OH-Risperidone) (rs = 0.95) was observed. However, Bland–Altman analysis revealed a mean bias of 0.996 ng/mL, indicating that the Alinity C system slightly overestimates concentrations compared to UHPLC-MS/MS. While there was substantial agreement between methods (κ = 0.72), discrepancies were observed in 16.3% of cases, which could impact clinical decision-making. When analyzing paliperidone separately, the agreement was lower (κ = 0.63), with greater variability observed. Conclusions: These findings suggest that, while the Alinity C system is suitable for routine therapeutic monitoring, UHPLC-MS/MS remains the preferred method in clinical scenarios requiring higher precision, particularly for patients with concentrations near therapeutic thresholds.

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.