Clinical Pharmacology & Therapeutics最新文献

The short-term efficacy of allopurinol add-on thiopurine therapy has been well documented; however, the long-term effects and dose recommendations are poorly detailed. This study aimed to elucidate the long-term implications of this combination therapy and to develop initial dose applying pharmacokinetic modeling. Forty-two pediatric patients with acute lymphoblastic leukemia, treated with 6-mercaptopurine (6-MP) and allopurinol, were enrolled in this prospective before-after pharmacokinetic study. Metabolite levels (6-thioguanine nucleotides (6-TGNs), methyl mercaptopurine nucleotides (6-MMPN), DNA-thioguanine (DNA-TG)), thiopurine methyltransferase (TPMT) activity were measured pre- and post-combination. Another retrospective cohort of 40 patients receiving 6-MP monotherapy was taken as controls. Compared with the control group, the combination therapy showed a similar myelosuppression effect ((P = 0.060, adjusted Hazard Rates, aHR = 0.94 (0.89–1.00)), while markedly reducing the cumulative hazard of severe neutropenia (P = 0.009, aHR = 0.49 (0.28–0.83)) and hepatotoxicity (P < 0.001, aHR = 0.54 (0.40–0.73)). Allopurinol combination led to a fourfold reduction in TPMT activity, the 6-MMPN:6-TGNs ratios, and 6-MMPN: DNA-TG ratios. This metabolic adjustment improved control of white blood cell (WBC) counts, neutrophil counts (ANC), and aminotransferase levels. LOESS regression estimates indicated significant fluctuations in WBC, ANC, and 6-MP/allopurinol dosage ratios following 3 months of combination therapy (P < 0.001), reflecting the need for close monitoring and frequent dose adjustments. Pharmacokinetic analysis further reinforces the benefits of allopurinol add-on 6-MP strategy, and suggested that, for patients with normal or high TPMT activity, an initial 6-MP dose of 20–30 mg/m²/day is recommended for those with BSA ≤ 1 m², and 15–20 mg/m²/day for those with BSA > 1 m², when co-administered with allopurinol (50 mg/m²/day).

Physiology Based Pharmacokinetic (PBPK) modeling is an established essential tool for predicting and/or analyzing drug–drug interactions (DDI). Uncertainty and variability associated with in vitro determined DDI-related parameters have often been considered a limitation for predicting PBPK-DDIs. Sensitivity analysis (SA) around DDI input parameters using PBPK analysis is often applied for assessing the relevance of clinical DDI predictions/prioritization/study designs. This perspective aims to explore and advocate practical approaches for precipitant (inhibitor/inducer) PBPK-DDI SA for optimal clinically relevant evaluations.

Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) are promising biomarkers for predicting survival and informing treatment decisions in metastatic colorectal cancer (mCRC); yet their clinical application remains limited. In this study, we analyzed CellSearch image archives from 446 patients with mCRC treated in the CAIRO2 study to evaluate the predictive value of CTCs and tdEVs and explore their utility in guiding personalized therapy. Using pharmacodynamic modeling, we examined longitudinal changes in manually and automatically classified CTC and tdEV counts, assessing their relationship with tumor size dynamics and overall survival. Automated tdEV counts demonstrated the strongest association with survival, followed by automated CTCs, which outperformed manually assessed counts. The combination of automated tdEVs and manual CTCs further improved predictive performance. Simulations predict transitioning high-risk patients (CTC/tdEV score >1.75) to second-line FOLFIRI at week 4 or 10 improves median survival from 15.1 months (12.8–18.4 CI) to 22.7 months (17.1–35.8 CI), and from 13.3 months (10.5–21.7 CI) to 23.3 months (17.8–31.2 CI), respectively. Additionally, biomarker monitoring demonstrated reduced cost (€3,913 vs. €7,802) and environmental burden (10 kg vs. 167 kg CO₂e per patient). These findings suggest that tdEVs, alone or in combination with CTCs, may help optimize treatment timing and outcomes in mCRC. The integration of CTCs and tdEVs into clinical practice could offer a personalized, cost-effective, and more sustainable alternative to routine imaging in managing advanced colorectal cancer.

Polypharmacy is increasingly recognized as a relevant issue in diabetes care, but its prevalence and clinical relevance in individuals with type 1 diabetes remain underexplored. This study aimed to determine the prevalence of polypharmacy and to identify associated clinical and psychological factors. Participants were recruited from a tertiary diabetes outpatient clinic between February 2020 and April 2021. Polypharmacy was defined as the concurrent use of five or more medications, including insulin. Clinical, sensor-based, and psychosocial data were collected. Logistic regression was used to identify variables independently associated with polypharmacy. A total of 484 individuals with type 1 diabetes were included (mean age 51.3 ± 15.9 years; 51.2% male; median diabetes duration 30 [IQR 16–40] years; mean HbA_1c 60.3 ± 11.6 mmol/mol). Polypharmacy was present in 175 (36.2%) participants. Individuals with polypharmacy were more often female, were older, and had longer diabetes duration, higher BMI, higher HbA_1c, more complications, and higher rates of hospital admission. They also were more likely to have impaired awareness of hypoglycemia and reported higher levels of fear of hypoglycemia with no differences in hyperglycemia-related worry or behavior or diabetes-related emotional distress. Polypharmacy affects over one-third of individuals with type 1 diabetes and is associated with poorer health status and a greater hypoglycemia-related burden. Future studies should investigate whether targeted medication review and psychological interventions may alleviate some of the burden in this high-risk group.

Knowledge of the glomerular filtration rate (GFR) is mandatory when dosing renally eliminated drugs such as vancomycin. In clinical practice, different biomarkers and various equations are used to estimate GFR (eGFR), resulting in varying estimates. These variations may be explained by nonrenal factors, such as muscle status or glucocorticoid administration. This study aimed to evaluate the performance of different eGFR equations in terms of accuracy and precision compared to renal vancomycin clearance, including subgroup analyses for nonrenal confounders. We retrospectively analyzed data from 121 adult allogeneic hematopoietic stem cell transplant (allo-HSCT) patients. All patients received vancomycin treatment including trough concentration therapeutic drug monitoring. The eGFR was calculated using eight equations and compared to the renal vancomycin clearance that was calculated using a pharmacokinetic model and served as the reference. Individual muscle status was determined by computed tomography scans. Median renal vancomycin clearance was 49 mL/minute/1.73 m² (range 24–96). All eight eGFR equations overestimated renal vancomycin clearance. The six (partially) creatinine-based equations were significantly less accurate (bias: 24.0–62.8 mL/minute/1.73 m²) than both cystatin C-based equations (bias: 6.3–9.5 mL/minute/1.73 m²). This decreased accuracy for creatinine-based eGFR was more pronounced in patients with reduced muscle status or glucocorticoid medication. All CKD-EPI equations and the Hoek equation were more precise with an IQR of the difference to renal vancomycin clearance ≤22.5 mL/minute/1.73 m² compared to ≥35.5 mL/minute/1.73 m² (Cockcroft-Gault, MDRD). In conclusion, cystatin C-based eGFR equations are preferable to creatinine-based approaches for vancomycin dosing in allo-HSCT patients.

Accurately labeling outcomes in real-world data for machine learning is challenging due to data sparsity and imbalances. This study developed and evaluated a pharmacokinetic–pharmacodynamic (PKPD)-informed labeling strategy to enhance the risk prediction of docetaxel-induced neutropenia. Machine learning models were trained on real-world data from 4,248 patients using two approaches for comparison. The “naive” labeling method used only neutrophil observations, while the “PKPD-informed” method used simulations from a semi-mechanistic model to determine the neutrophil nadir for each treatment cycle. Three machine learning models (logistic regression, XGBoost, TabPFN) were trained with baseline laboratory data to predict severe neutropenia (neutrophil count <0.1 cells × 10⁹/L) prior to the first docetaxel dose. The PKPD labeling approach enabled the labeling of 3.4 times more patient instances (7,719 vs. 2,283) than the naive method. Across all machine learning architectures, models trained with PKPD-informed labels demonstrated significantly superior predictive performance (AUC-ROC and AUC-PR) compared to those trained with naive labels. This advantage was maintained even when training set sizes were matched. PKPD-informed labeling overcomes limitations of sparse real-world data, increasing both the quantity and apparent quality of labels for machine learning model training. This methodology enhances the performance of machine learning models for predicting severe neutropenia and represents a robust, generalizable framework for improving clinical outcome prediction.

A clinical drug–drug interaction (DDI) study was designed to evaluate the effect of single and multiple oral doses of encorafenib on the single oral dose pharmacokinetics (PK) of the cytochrome P450 (CYP) enzyme probe substrates, losartan (CYP2C9), midazolam (CYP3A4), caffeine (CYP1A2), omeprazole (CYP2C19), and dextromethorphan (CYP2D6) administered as a cocktail (Inje). This study was conducted, post-approval, in patients with BRAF V600-mutant advanced solid tumors, and aimed to address the remaining uncertainty in the DDI potential of encorafenib as a perpetrator of these CYP enzymes. Study participants received the cocktail on Days −7, 1, and 14 and continuous doses of encorafenib (450 mg q.d.) and binimetinib (45 mg b.i.d.) starting on Day 1. PK sampling and urine collection were conducted from 0 to 8 hours on cocktail administration days. PK parameters were calculated for each participant using noncompartmental analysis of concentration–time data or amount excreted for urine parameters. At steady-state encorafenib plasma concentrations, midazolam plasma C_max and AUC_last decreased by 74% and 82%, respectively. No clinically significant DDIs were observed at encorafenib steady-state concentrations with the other probe substrates of interest. The results from this clinical study indicate that encorafenib is a strong inducer of CYP3A (≥ 80% decrease in midazolam area under the curve (AUC)) at steady state. Based on these results regarding co-administration with encorafenib, sensitive substrates of CYP3A should be avoided or dose adjusted based on the recommendations of their approved product labeling. This information has been included in the updated prescribing information for encorafenib.

Potential adverse drug events can be signaled in Clinical Decision Support Systems (CDSSs). This study validated a Swedish CDSS (Janusmed Risk Profile) by investigating associations between calculated risk classifications of drugs with QT-prolonging potential and registered related clinical outcomes. Subjects living in Kalmar County, Sweden, between 2011 and 2020 exposed to risk drugs (risk level I: somewhat increased risk, II: moderate increased risk, III: significant increased risk) were extracted from regional electronic health records and matched to controls (risk level 0: no known increased risk) by age, sex, and index date. Ventricular arrhythmia (VA), Torsade de Pointes, cardiac arrest and death were outcomes followed for one year. Logistic regression analysis was performed adjusted for age, sex, number of drugs, days in hospital and previous diagnosis. Among the 188,453 subjects, a higher proportion of those classified by the CDSS as having a risk of QT prolongation experienced VA compared to controls (risk level I = 0.26%, II = 0.34%, III = 0.71% vs risk level 0 = 0.17%). When adjusting for other risk factors, the association decreased, but risk level III remained significant with OR 2.1 (95% CI 1.6–2.9) compared to controls. Similar results were seen for the other outcomes. Although there was an association between CDSS risk classifications and clinical outcomes, only a few subjects are affected, and other factors, such as previous diagnosis, play an important role. The need for multifactorial CDSS algorithms is thus crucial to better guide prescribers in finding high-risk patients.