British journal of clinical pharmacology最新文献

Aims: This study aims to identify predictive plasma protein biomarkers for anti-tuberculosis drug-induced liver injury (ATB-DILI) and develop machine learning models for pre-treatment risk stratification.

Methods: In this retrospective nested case-control study, proteomic profiling of pre-treatment plasma from 24 patients (12 ATB-DILI, 12 controls) identified differentially expressed proteins, which were validated by ELISA in an independent cohort (35 ATB-DILI, 37 controls). Multiple machine learning algorithms were implemented to develop clinical prediction models and evaluate the prognostic value of the identified protein biomarkers.

Results: Proteomic analysis of pre-treatment samples from the exploratory cohort identified five significantly differentially expressed proteins: antithrombin III, apolipoprotein D, carboxypeptidase B2, Chromogranin-A, and Retinol-binding protein 4. These proteins are functionally implicated in inflammatory responses, oxidative stress, and drug metabolism pathways. Validation using baseline plasma from an independent cohort confirmed consistent expression patterns for all five proteins (p < 0.01), with directional changes matching the discovery phase findings. The random forest model, built on these pre-treatment biomarkers, demonstrated robust predictive performance in the test set (AUC = 0.94, sensitivity = 90.0%, specificity = 90.0%, accuracy = 0.90). Importantly, consensus across multiple machine learning approaches (GBDT, SVM, GBM, etc.) confirmed predictive stability and generalizability of this protein signature (inter-model AUC range: 0.85-0.96).

Conclusion: This study has successfully identified five pre-treatment plasma protein signature that, when incorporated into machine learning models, may enable the prediction of ATB-DILI risk, offering potential for early intervention in tuberculosis therapy.

Aims: Paroxetine is a selective serotonin reuptake inhibitor (SSRI), approved for treatment of major depressive disorder and anxiety disorders. Some SSRIs are known to prolong the QT interval; however, clinical evidence to establish a lack of association between paroxetine and corrected QT interval (QTc) prolongation is limited. Therefore, this study aimed to characterize the relationship between paroxetine concentration and QT/QTc interval following therapeutic doses in healthy individuals.

Methods: This open-label, single-arm, dose-escalating concentration-QT study (NCT06065735) was performed in healthy adults (18-65 years) without a history of cardiac disease or pre-diagnosed mood disorder. Eligible individuals (n = 38) received paroxetine 20 to 60 mg QD for 1 week per dose level. Paroxetine plasma concentrations and electrocardiogram recordings were monitored over a 12 h period on Days 1 (baseline), 7 (20 mg), 14 (40 mg) and 21 (60 mg).

Results: Mean change from baseline in QTcF (ΔQTcF) fluctuated between -7.1 and +4.7 ms. However, diurnal variation was also observed without treatment. A linear regression model showed no clinically significant effect of paroxetine concentrations on ΔQTcF, with a weak slope of 0.0108 ms/ng/mL (90% CI: 0.01, 0.03) and maximum ΔQTcF of +0.42 ms (90% CI: -2.68, 3.52) at 60 mg QD, corresponding to a Cmax of 221.4 (95%CI: 179.6-272.8) ng/mL. Similarly, paroxetine did not affect the mean change in PR or QRS interval, or heart rate relative to baseline.

Conclusions: Paroxetine does not prolong QTc interval in healthy individuals to any clinically meaningful extent at therapeutically relevant doses. This study supports the favourable cardiac safety profile of paroxetine.

Background: Information on mitoxantrone pharmacokinetics in children is lacking and reduced dosing regimens applied to infants are supported by limited scientific rationale. The current study characterized mitoxantrone pharmacokinetics in a childhood acute myeloid leukaemia patient population and provides a data-informed assessment of dosing.

Methods: A total of 282 plasma samples from 44 patients aged 0.9-17 years, receiving intravenous mitoxantrone at doses of 12 mg/m²/day or 0.4 mg/kg/day (patients <12 months, ≤10 kg or <0.5 m²), were analysed, and a population pharmacokinetic model was developed. Individual clearance (CL) values were used to calculate mitoxantrone area under the plasma concentration-time curve (AUC) for each patient. Relationships among dosing regimen, pharmacokinetics and toxicity were assessed. Simulation of 1000 virtual patients, sampled from real covariate combinations, was used to investigate standardized patient dosing.

Results: A two-compartment model with fixed allometric scaling best described the data, with a final population estimated CL of 39.1 L/h (residual standard error 9.6%) observed for a patient weighing 27.5 kg. Infants receiving mg/kg dosing exhibited lower AUC values (192 ± 75 μg·h/L) than the mg/m² group (317 ± 184 μg·h/L). Simulations showed that a standardized 12 mg/m²/day dosing regimen would likely result in comparable AUCs across all ages. No correlation was observed between mitoxantrone AUC and incidence of severe toxicity (Common Terminology Criteria for Adverse Events [CTCAE] grade 3/4) in this cohort.

Conclusion: This study provides novel insights into the pharmacokinetics of mitoxantrone in children. Infant patients receiving body weight-based dosing regimens may be at risk of suboptimal drug exposure, and many of these patients may tolerate higher mitoxantrone doses in line with older children. This trial was registered with the EU Clinical Trials Register (EudraCT number 2014-005066-30).

Aims: Clozapine is the first-line treatment for resistant schizophrenia. However, clozapine concentrations should be monitored, especially in the case of drug-drug interactions. The current work aimed to assess the clozapine interaction with pantoprazole.

Methods: This was a randomized open-label crossover study involving 12 healthy volunteers. The participants received a single dose of clozapine 12.5 mg in the two phases of the study alone or following five daily doses of pantoprazole 40 mg to be started 4 days before the clozapine dose, separated by a minimum of 2-week washout period. 144 samples were collected at 30 min, 1, 2, 3, 5 and 8 h following a single dose of clozapine 12.5 mg. The clozapine and norclozapine concentrations were determined using a validated HPLC-UV protocol.

Results: The pantoprazole treatment group had 8.7% lower clozapine bioavailability and lower maximum concentration (C_max) and area under the curve (AUC_(0-8)) by 8.9% (95% confidence intervals [95% CI], 6.7%-11.0%) and 9% (95% CI, 7.2-10.8%), respectively, as well as significantly lower norclozapine C_max and AUC_(0-8) by 8.2% (95% CI, 6.3%-10.2%) and 8.5% (95% CI, 7.3%-9.7%), respectively. However, no significant difference was found in the norclozapine AUC_(0-8) to clozapine AUC_(0-8) ratio between the two treatment groups.

Conclusions: The concurrent intake of pantoprazole decreased the clozapine and norclozapine exposure, which was explained by the pantoprazole's impact on gastric acidity and clozapine absorption rather than on metabolizing enzymes. The interaction was not clinically relevant at low doses; however, clinicians should consider concomitant acid-reducing agents when interpreting clozapine therapeutic drug monitoring results.

Aim: Tacrolimus dosing in the early post-kidney transplant period is challenging due to a narrow therapeutic index and substantial interindividual pharmacokinetic (PK) variability. This study aimed to develop and validate mechanism-informed machine learning (ML) models to support individualized tacrolimus dosing during this critical period.

Methods: A total of 4311 tacrolimus trough concentrations (C_trough) within 7 days post-transplant were obtained from 1624 kidney transplant recipients. Two ML models, Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost), were developed to predict C_trough and recommend doses to achieve target levels. Both models incorporated PK principles based on linear pharmacokinetics. Model performance was compared to a traditional Bayesian population PK (PopPK) model and purely data-driven ML models via internal cross-validation and external validation.

Results: The mechanism-informed GRU model outperformed the Bayesian PopPK model in both internal validation (MSE = 7.81 vs. 9.27 ng²/mL², R² = 0.537 vs. 0.450) and external validation (MSE = 6.09 vs. 8.96 ng²/mL², R² = 0.397 vs. 0.211). The mechanism-informed XGBoost model performed comparably to the GRU model. The incorporation of PK principles enhanced model interpretability and generalizability without reducing accuracy. When clinically administered doses, determined by conventional therapeutic drug monitoring, fell within the GRU model's recommended range, subsequent C_trough reached the therapeutic target (8-12 ng/mL) in 51.3% of cases, compared to 37.0% overall (p < 0.01).

Conclusion: Mechanism-informed ML models offer a robust and interpretable approach for individualized tacrolimus dosing, with the potential to improve therapeutic target attainment by enabling accurate dose adjustments in the early post-transplant period.

This study investigated the attitudes and beliefs of older adults towards deprescribing statins in Australia, the United Kingdom and the United States, using an online, vignette-based study. Presented with a hypothetical scenario in which a general practitioner advised stopping simvastatin, participants rated their level of agreement and explained their rationale. Analysis was conducted incorporating the Patient Deprescribing Typology (PDT), which asks participants to share medication-related learning style, beliefs about importance, decision-making preferences and attitudes towards deprescribing. The findings correlated with participants' personal experiences with statins and their willingness to deprescribe in the scenario. The results highlight the importance of adapting deprescribing decisions to patients' beliefs and backgrounds to support shared decision-making. Future research is needed to assess whether typology-based screening tools can improve patient-centred deprescribing conversations in clinical practice.

Aim: Hyponatremia is a common yet potentially serious adverse event associated with antidepressants. Identifying the antidepressant class with the least risk of hyponatremia would improve patient safety.

Methods: Using electronic medical records from 15 hospitals standardized into Observational Medical Outcomes Partnership Common Data Model (2003-2023), we identified patients diagnosed with depression who initiated antidepressants, including selective serotonin reuptake inhibitor (SSRI), serotonin-norepinephrine reuptake inhibitor (SNRI), tricyclic antidepressants (TCA) or others (agomelatine, bupropion, mirtazapine, moclobemide and trazodone) for at least 30 days. The index date was defined as the first antidepressant prescription, and four mutually exclusive cohorts were constructed based on the antidepressant class prescribed on index date. Each cohort was compared with all other antidepressants. The primary outcome was incident hyponatremia (serum sodium <135 mmol/L) within the first 180 days. After propensity score stratification, hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression. Fixed-effect meta-analysis was used to pool the results from each site.

Results: We identified 17 895 (42.6%) patients in SSRI, 7395 (17.6%) in SNRI, 5424 (12.9%) in TCA and 11 322 (26.9%) in other group. The risk of hyponatremia increased within 180 days after SSRI initiation (HR 1.18, 95% CI 1.01-1.38) compared with all other depressants, with a higher risk in patients aged ≥60 years (1.29, 1.06-1.57). No significant association was found for SNRIs (1.05, 0.87-1.27), TCAs (1.03, 0.84-1.26) or other (0.90, 0.77-1.06).

Conclusion: Close monitoring of serum sodium levels is essential for SSRI users, especially those aged ≥60 years.

Aims: Kallikrein-kinin system (KKS) dysregulation is hypothesized to play a pathogenetic role in COVID-19-associated pulmonary oedema. To investigate the efficacy and safety of intravenous lanadelumab, a monoclonal antibody that inhibits plasma kallikrein, in COVID-19, we conducted a phase 2, open-label, randomized-controlled, proof-of-concept, interventional trial.

Methods: We recruited 40 patients hospitalized with moderate COVID-19 and randomized them 1:1 to receive either standard-of-care (SoC) treatment or SoC plus intravenous lanadelumab (300 mg on days one and four). The primary outcome consisted of repeated measurements of supplemental oxygen (litres/minute) necessary to maintain a peripheral oxygen saturation (SpO₂) ≥ 93%. Secondary outcomes included modified WHO-CPS scores, need for high-flow oxygen therapy or mechanical ventilation, admission to the intensive care unit, length of hospital stay and all-cause mortality over a 14-day period.

Results: Sufficient endpoint data for the population were available for the first five days, but not for the previsioned 14-day endpoint. Consequently, analysis of the primary endpoint was based on the first five days of treatment. Within this timeframe, lanadelumab did not significantly affect supplemental oxygen volumes. Neither treatment nor interaction between treatment and time was a significant predictor of oxygen volumes in a linear mixed model (p = 0.49 and p = 0.15, respectively). None of the secondary outcomes was significantly affected by lanadelumab. Intravenous lanadelumab was well tolerated.

Conclusions: This exploratory study was evaluated using a shortened primary endpoint period. Lanadelumab showed no indication of benefit on oxygen needs or other clinical outcomes in patients with COVID-19. Lanadelumab was well tolerated throughout the trial.

Aims: The inconsistent impact of medication review on adverse clinical outcomes suggests that stratification based solely on age and number of medications, without considering disease burden, is inadequate. The aim of this study was to investigate the associations between medication use and FI-OutRef (a frailty index based on abnormal routine blood tests) with 90-day readmission or mortality and to evaluate the utility of FI-OutRef in patient stratification for medication review.

Methods: This single-centre, observational, register-based cohort study included acutely admitted medical patients presenting to the emergency department (ED) of Copenhagen University Hospital Hvidovre, Denmark, who received routine blood tests (including 17 standard biomarkers) between November 2013 and March 2017. Patients <18 years old, missing ≥8 standard biomarkers or who died during hospitalization were excluded. Medications obtained within 4 months prior to hospitalization were categorized based on the number of unique prescriptions: 0, 1, 2-4, 5-9 or ≥10. FI-OutRef was defined as the number of biomarkers outside reference ranges and categorized according to the following cutoffs: ≤4, 5-7 or ≥8.

Results: Among 27 873 acutely admitted medical patients (52.5% female; median age 59.3 years), increasing FI-OutRef was significantly associated with increasing risk of readmission (hazard ratios [HRs] ranging from 1.36 to 2.75 across levels of medication use) and mortality (HRs 2.65 to 8.82), with the highest HRs observed in patients with lower medication use.

Conclusion: FI-OutRef strengthens the association between medication use and risk of 90-day readmission or mortality and could be an important component in patient stratification for medication review to reduce these adverse clinical outcomes.