Metamizole but not ibuprofen reduces the plasma concentration of sertraline: Implications for the concurrent treatment of pain and depression/anxiety disorders

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study

BACKGROUND

Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques.

METHODS

Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022. Nine different algorithms were used for modeling to compare the prediction abilities on sertraline daily dose, including XGBoost, LGBM, CatBoost, GBDT, SVM, ANN, TabNet, KNN, and DT. Performance of four dose subgroups (50 mg, 100 mg, 150 mg, and 200 mg) were analyzed.

RESULTS

CatBoost was chosen to establish the individualized medication model with the best performance. Six important variables were found to be correlated with sertraline dose, including plasma concentration, PLT, MPV, GL, A/G, and LDH. The ROC curve and confusion matrix exhibited the good prediction performance of CatBoost model in four dose subgroups (the AUC of 50 mg, 100 mg, 150 mg, and 200 mg were 0.93, 0.81, 0.93, and 0.93, respectively).

CONCLUSION

The AI-based dose prediction model of sertraline in adolescents with depression had a good prediction ability, which provides guidance for clinicians to propose the optimal medication regimen.

Lower sertraline plasma concentration in patients co-medicated with clozapine-Implications for pharmacological augmentation strategies in schizophrenia

Augmentation of antipsychotic treatment with antidepressants represents a common and beneficial treatment strategy in patients suffering from schizophrenia. Combining clozapine and the selective serotonin reuptake inhibitor (SSRI) sertraline represents a clinically important strategy in patients with therapy-resistant schizophrenia, but there is limited knowledge about mutual pharmacokinetic interactions. In the present study, we assessed the impact of clozapine on sertraline plasma concentrations. Based on a therapeutic drug monitoring (TDM) database, sertraline plasma concentrations were compared between two groups: patients receiving a combined treatment with sertraline and clozapine (N = 15) and a matched control group receiving sertraline but no clozapine (N = 17). Group differences with respect to raw and dose-adjusted concentrations were assessed using nonparametric tests. Comedication with clozapine was associated with 67% lower median sertraline plasma concentrations (16 vs. 48 ng/mL; p = .022) and 28% lower median dose-adjusted plasma concentrations (C/D; 0.21 vs. 0.29 (ng/mL)/(mg/day); p = .049) as compared to the control group. Scatter plots revealed a complex relationship between the dosage of clozapine and dose-adjusted sertraline concentrations composed of an initial decrease at clozapine doses below 300 mg, an increase between 300 and 600 mg and a final decrease at 800 mg which was best modeled by a third order polynomial term. Cotreatment with clozapine may lead to reduced sertraline plasma concentrations which may be explained by clozapine-induced gastrointestinal hypo-mobility already present at low doses and cytochrome P450 3A4 inducing properties at high clozapine doses. For this drug combination, clinicians should consider TDM to confirm therapeutically effective plasma concentrations of sertraline.

Current Status of Therapeutic Drug Monitoring in Mental Health Treatment: A Review

Therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, inpatient, and outpatient services). Despite its usefulness, TDM remains underemployed in mental health. This is partly due to the need for evidence about the relationship between drug serum concentration and efficacy and tolerability, both in the general population and even more in subpopulations with atypical pharmacokinetics. This work aims at reviewing the scientific literature published after 2017, when the most recent guidelines about the use of TDM in mental health were written. We found 164 pertinent records that we included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical efficacy and safety, especially for antipsychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents also, several drugs result in having different dose-related concentration values compared to adults. These findings stress the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmacometabolic basis. Mini-invasive TDM procedures that may be easily performed at home or in a point-of-care are very promising and may represent a turning point toward an extensive real-world TDM application. Although the highlighted recent evidence, research efforts have to be carried on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and efficacy/safety.

Dose-Related Reference Range as a Tool in Therapeutic Drug Monitoring

BACKGROUND

Therapeutic drug monitoring (TDM) aims to individualize drug therapy. This systematic review provides a state-of-the-art overview of the benefits of adding the dose-related reference range (DRR) as a second reference range to the set of tools used by TDM for measurement and evaluation. It discusses alternative pharmacokinetic approaches for individualization of drug therapy.

METHODS

Literature was searched in PubMed. Textbooks provided Bateman transformations for calculating expected drug concentrations at various times after drug application in "normal patients," that is, the population of phase II clinical trials. The review compiles conditions and prerequisites for these transformations to be valid.

RESULTS

Relating a measured drug concentration to the orienting therapeutic reference range provides pharmacodynamic information for improving the benefit-to-risk ratio of desired drug effects versus adverse drug effects. The discriminating DRR considers a patient's individual pharmacokinetic situation. DRR is statistically based on the pharmacokinetic parameters total clearance, time to reach maximal concentrations, and elimination half-life. Relating the measured drug concentration to a range rather than a particular value, DRR determines if individual patients do or do not belong to the population of "normal patients." Once a patient is identified to be outside the population of "normal patients," the clinical-pharmacological TDM report elaborates the cause. It consists of the measured value, the TDM 9-field-board, the elimination pathways table, and a medication recommendation taking into account clinical information. The internet-based platform KONBEST supports editing of the clinical-pharmacological TDM report. It is personally signed and send to the therapist.

CONCLUSIONS

The DRR embedded into a clinical-pharmacological TDM report allows adjusting a patient's medication to the patient's individual needs (individualization of drug therapy).

Metamizole is a Moderate Cytochrome P450 Inducer Via the Constitutive Androstane Receptor and a Weak Inhibitor of CYP1A2

Metamizole is an analgesic and antipyretic drug used intensively in certain countries. Previous studies have shown that metamizole induces cytochrome (CYP) 2B6 and possibly CYP3A4. So far, it is unknown whether metamizole induces additional CYPs and by which mechanism. Therefore, we assessed the activity of 6 different CYPs in 12 healthy male subjects before and after treatment with 3 g of metamizole per day for 1 week using a phenotyping cocktail approach. In addition, we investigated whether metamizole induces CYPs by an interaction with the constitutive androstane receptor (CAR) or the pregnane X receptor (PXR) in HepaRG cells. In the clinical study, we confirmed a moderate induction of CYP2B6 (decrease in the efavirenz area under the plasma concentration time curve (AUC) by 79%) and 3A4 (decrease in the midazolam AUC by 68%) by metamizole. In addition, metamizole weakly induced CYP2C9 (decrease in the flurbiprofen AUC by 22%) and moderately CYP2C19 (decrease in the omeprazole AUC by 66%) but did not alter CYP2D6 activity. In addition, metamizole weakly inhibited CYP1A2 activity (1.79‐fold increase in the caffeine AUC). We confirmed these results in HepaRG cells, where 4‐MAA, the principal metabolite of metamizole, induced the mRNA expression of CYP2B6, 2C9, 2C19, and 3A4. In HepaRG cells with a stable knockout of PXR or CAR, we could demonstrate that CYP induction by 4‐MAA depends on CAR and not on PXR. In conclusion, metamizole is a broad CYP inducer by an interaction with CAR and an inhibitor of CYP1A2. Regarding the widespread use of metamizole, these findings are of substantial clinical relevance.