Cts-Clinical and Translational Science最新文献

NIM-1324 is an oral investigational new drug for autoimmune disease that targets the Lanthionine Synthetase C-like 2 (LANCL2) pathway. Through activation of LANCL2, NIM-1324 modulates CD4+ T cells to bias signaling and cellular metabolism toward increased immunoregulatory function while providing similar support to phagocytes. In primary human immune cells, NIM-1324 reduces type I interferon and inflammatory cytokine (IL-6, IL-8) production. Oral NIM-1324 was assessed for safety, tolerability and PK in normal healthy volunteers in a randomized, double-blind, placebo-controlled trial. Subjects (n = 57) were randomized into five single ascending dose (SAD) cohorts (250, 500, 750, 1000, 1500 mg, p.o.) and three multiple ascending dose (MAD) cohorts (250, 750, 1500 mg QD for 7 days, p.o.). NIM-1324 did not increase total AE rates in individual cohorts or pooled active groups in SAD or MAD with no SAEs in the study. Oral NIM-1324 dosing does not result in any clinically significant findings by biochemistry, coagulation, ECG, hematology, or urinalysis when compared to placebo. Plasma exposure, as measured by area under the curve from 0 to 24 h (AUC_0-24), scaled dose proportionally over 250–1000 mg. At 250 mg, NIM-1324 successfully engaged the target with an upregulation of Lancl2 and key transcriptional biomarkers in whole blood. In conclusion, NIM-1324 treatment is well-tolerated up to daily oral doses of at least 1500 mg (nominal), a ≥ six-fold margin over the anticipated therapeutic dose, and 1000 mg (maximum observed exposure), at least a four-fold margin over the anticipated therapeutic dose with no dose limiting toxicities.

Vaccine hesitancy is an attitude of indecision toward vaccination that is related to but not determinative of vaccination behaviors. Although theories of vaccine hesitancy emphasize it is often vaccine-specific, we do not know the extent to which this is true across sociodemographic groups. In this study, we asked: What latent classes of vaccine hesitancy might exist when examining parents' attitudes toward vaccines in general and COVID-19 and human papillomavirus (HPV) vaccination specifically? Which sociodemographic, health access, and health-related variables are predictive of membership in those classes? To answer those questions, we analyze online survey data from parents of pediatric patients recruited through eight clinics within the University of Arkansas for Medical Sciences Rural Research Network. Data were collected between September 16, 2022 and December 6, 2022. Latent class analysis revealed three underlying classes of vaccine hesitancy, or hesitancies: The “Selectively Hesitant,” the “COVID-Centric Hesitant,” and the “Pervasively Hesitant.” Significant predictors of class membership were age, education, health insurance status, and usual source of care. Vaccine hesitancy may be specific to certain vaccines for some parents and more generalized for others. The distinct classes of vaccine hesitancy revealed in this study suggest the need for distinct approaches to addressing vaccine hesitancy depending on the population.

Exercise increases blood and lymph flow in working muscles, potentially affecting the bioavailability and effect of subcutaneously administered drugs. The aim of this study was to assess the influence of a single exercise session on pharmacokinetics and pharmacodynamics of a single dose of subcutaneously administered unfractionated heparin. In a crossover design, 15 healthy males underwent four experimental days where 15,000 IU of unfractionated heparin was injected subcutaneously into the thigh of the non-dominant leg. Following the injection, one of four interventions was performed in randomized order on four separate occasions, each lasting 1 h: (1) no exercise, (2) double-legged exercise, (3) single-legged exercise with the non-dominant leg (where heparin was injected), and (4) single-legged exercise with the dominant leg. Blood was sampled during and after the interventions and analyzed for activated partial thromboplastin time (aPTT) and plasma heparin via an anti-factor Xa assay. The primary endpoint (maximum aPTT minus baseline aPTT) showed no statistically significant differences between interventions, nor did maximum minus baseline plasma heparin activities. However, after 1 h, change in aPTT was greater, following double-legged exercise compared with no exercise (mean difference 3.5 s, 95% CI 0.8–6.2) and greater after single-legged exercise with the non-dominant leg compared with the dominant (9.7 s, 3.9–15.5). Similar results were observed for plasma heparin activities. In conclusion, exercise does not affect the overall pharmacokinetics and pharmacodynamics of unfractionated heparin but tends to accelerate absorption and hence effect. The study thus underscores that physical exercise affects temporal uptake of subcutaneously administered therapy.

Hyperuricemia (HUA) is a metabolic abnormality syndrome caused by disorders of purine metabolism. This study aimed to investigate the predictive value of the low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (LHR) for the risk of developing HUA. We extracted data from the China Health and Retirement Longitudinal Study (CHARLS) database from 2011 to 2016. Multivariable logistic regression, restricted cubic splines (RCSs) analysis, and linear correlation analysis were conducted to evaluate the association between LHR and risk of developing HUA. Subgroup analyses and interaction tests were also performed. A higher LHR was associated with an increased incidence of HUA (7.8% vs. 9.9% vs. 13.9, p < 0.001). The LHR was also higher in the HUA group compared to the non-HUA group (2.64 ± 1.07 vs. 2.40 ± 0.91, p < 0.001). When assessed as a continuous variable, LHR was independently associated with the risk of HUA (OR = 1.27, 95% CI = 1.16–1.39, p < 0.001). The risk of developing HUA was significantly higher among individuals with the highest LHR subgroup than those with the lowest LHR subgroup (OR = 1.81, 95% CI = 1.47–2.23, p < 0.001). RCS analysis revealed a significant nonlinear association between an increased LHR and a higher risk of developing HUA. The predictive abilities of LHR for HUA were 0.577. The composite variable comprising LHR and other traditional risk factors could significantly enhance the ability to predict HUA (C statistic = 0.677). In conclusion, a higher LHR was associated with an increased risk of developing HUA. Further studies on LHR could be beneficial for preventing and treating HUA.

Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using patient clinical information based on an interpretable machine learning algorithm. This single-center retrospective study included hospitalized patients aged ≥ 18 years who received the first course of cisplatin chemotherapy from January 1, 2011, to December 31, 2020, at Nagoya City University Hospital. Cis-AKI-positive patients were defined using the serum creatinine criteria of the Kidney Disease Improving Global Outcomes guideline within 14 days of the last day of cisplatin administration in the first course. Patients who received cisplatin but did not develop AKI were considered negative. The CatBoost classification model was constructed with 29 explanatory variables, including laboratory values, concomitant medications, medical history, and cisplatin administration information. In total, 1253 patients were included, of whom 119 developed Cis-AKI (9.5%). The median time of AKI onset was 7 days, and the interquartile range was 5–8 days. The mean ± standard deviation of the total cisplatin dose in the initial treatment was 77.9 ± 27.1 mg/m² in Cis-AKI-positive patients and 69.3 ± 22.6 mg/m² in Cis-AKI-negative patients. The predictive performance was an ROC-AUC of 0.78. Model interpretation using SHapley Additive exPlanations showed that concomitant use of intravenous magnesium preparations was negatively correlated with Cis-AKI, whereas loop diuretics were positively correlated. This suggests the need for magnesium preparations to prevent AKI, although the effects of diuretics may be small. Our model can predict Cis-AKI early and may be helpful for its avoidance.

Targeted therapy and immunotherapy drugs for oncology have greater efficacy and tolerability than cytotoxic chemotherapeutic drugs. However, the cutaneous adverse drug reactions associated with these newer therapies are more common and remain poorly predicted. An effective prediction model is urgently needed and essential. This retrospective study included 1052 patients, divided into train set, test set, and external validation set. As a data-driven study, a total of 76 variables were collected. Univariate logistic analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression were utilized for feature screening. Finally, nine machine-learning models were constructed and compared, and grid search was performed to adjust the parameters. Model performance was evaluated using calibration curve and the area under the receiver operating characteristic curve (AUROC). Nine risk factors were eventually identified: age, treatment modality, cancer types, history of allergies, age-corrected Charlson comorbidity index, percentage of eosinophils, absolute number of monocytes, Eastern Cooperative Oncology Group Performance Status, and C-reactive protein. Among the models, the logistic model performed best, demonstrating strong performance in test set (AUROC = 0.734) and external validation set (AUROC = 0.817). This study identified nine significant risk factors and developed a nomogram prediction model. These findings have important implications for optimizing therapeutic efficacy and maintaining the quality of life of patients from the perspective of managing cutaneous adverse drug reactions.

Trial Registration: ChiCTR2400088422

The two most extensively studied cannabinoids, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), are used for myriad conditions. THC is predominantly eliminated via the cytochromes P450 (CYPs), whereas CBD is eliminated through both CYPs and UDP-glucuronosyltransferases (UGTs). The fractional contributions of these enzymes to cannabinoid metabolism have shown conflicting results among studies. Physiologically based pharmacokinetic (PBPK) models for CBD and THC and for drug–drug interaction studies involving CBD or THC as object drugs were developed and verified to improve estimates of these contributions. First, physicochemical and pharmacokinetic parameters for CBD, THC, and their metabolites (7-OH-CBD, 11-OH-THC, and 11-COOH-THC) were obtained from the literature or optimized. Second, PBPK base models were developed for CBD and THC after intravenous administration. Third, beginning with the intravenous models, absorption models were developed for CBD after oral and oromucosal spray administration and for THC after oral, inhalation, and oromucosal spray administration. The full models well-captured the area under the concentration–time curve (AUC) and peak concentration (C_max) of CBD and THC from the verification dataset. Predicted AUC and C_max for CBD and 7-OH-CBD were within two-fold of the observed data. For THC, 11-OH-THC, and 11-COOH-THC, 100%, 100%, and 83% of the predicted AUC values were within two-fold, respectively, of the observed values; 100%, 92%, and 94% of the predicted C_max values, respectively, were within two-fold of the observed values. The verified models could be used to help address critical public health needs, including assessing potential drug interaction risks involving CBD and THC.

Granulocyte colony-stimulating factor (G-CSF) mobilizes peripheral blood (PB) progenitor cells from bone marrow (BM) into circulation for PB stem cell transplantation (PBSCT). This study aimed to develop a population pharmacokinetic–pharmacodynamic (PK-PD) model of filgrastim in healthy subjects to optimize PB CD34⁺ cell collection. Plasma filgrastim concentrations and CD34⁺ cell count data were obtained from a clinical study involving healthy Korean subjects. A total of 1378 plasma concentration measurements and 982 CD34⁺ cell count data collected from 53 subjects were used in the PK-PD model. Filgrastim PKs were adequately described by a one-compartment linear disposition model with an additional transit compartment for absorption. Log-transformed body weight was the only significant covariate affecting the volume of distribution and clearance. CD34⁺ cell mobilization was best captured by a modified Friberg model, assuming continual entry of proliferating BM stem cells into PB via a single transit compartment. Simulation results suggested that the 5 μg/kg twice-daily dosing regimen may yield higher CD34⁺ cell counts compared to the 10 μg/kg once-daily regimen for achieving target CD34⁺ cell counts of 20/μL and 50/μL. We successfully developed a robust PK-PD model of G-CSF that optimizes the yield of CD34⁺ cells during allogeneic PBSCT. This model can guide the efficient determination of optimal G-CSF dosing regimens and CD34⁺ cell harvesting strategies.

Celiac disease is a chronic, immune-mediated enteropathy with symptoms triggered by exposure to dietary gluten in genetically predisposed individuals. The only available management option is lifelong adherence to a gluten-free diet. This randomized, double-blind, placebo-controlled, parallel-group, single-center study tested the effects of the cathepsin S inhibitor RO5459072 on the immune response to a 13-day gluten challenge in 19 participants with celiac disease (ClinicalTrials.gov: NCT02679014). Nine participants in the RO5459072 arm received 100 mg study drug b.i.d. (200 mg daily); 10 received a placebo. The primary end point was the number of responders to the gluten challenge (defined as individuals with an increase in the number of gliadin-specific, IFNγ-secreting T cells detected using an ELISPOT assay). However, there was a weak response to the gluten challenge across both arms. Few participants had an increase in gliadin-specific, IFNγ-secreting T cells, and the antigen-specific responses (anti-tTG and anti-DGP antibodies) were weaker than expected in both arms. Therefore, the primary end point was not met, although the study was underpowered to detect a treatment effect under these circumstances. Pharmacodynamic findings suggested that RO5459072 had some beneficial effects. Fewer participants in the RO5459072 arm exhibited gliadin-specific IFNγ-secreting T cells after 6 days' gluten intake. Participants in the RO5459072 arm also showed decreased intestinal permeability, and a decrease in the number of circulating B cells, CD4+ and CD8+ T cells compared to baseline. Nevertheless, the absence of clear effects on the response to a gluten challenge indicates that inhibition of cathepsin S may not be an effective treatment strategy for celiac disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common global cause of chronic liver disease and remains under-recognized within healthcare systems. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to recognize and stratify at-risk patients accurately. Our work aims to rapidly identify NAFLD patients within large electronic health record (EHR) databases for automated stratification and targeted intervention based on clinically relevant phenotypes. We present a rule-based phenotyping algorithm for efficient identification of NAFLD patients developed using EHRs from 6.4 million patients at Columbia University Irving Medical Center (CUIMC) and validated at two independent healthcare centers. The algorithm uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model and queries structured and unstructured data elements, including diagnosis codes, laboratory measurements, and radiology and pathology modalities. Our approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) previously unidentifiable by NAFLD diagnosis codes. Fibrosis scoring on patients without histology identified 943 subjects with scores indicative of advanced fibrosis (FIB-4, APRI, NAFLD–FS). The algorithm was validated at two independent healthcare systems, University of Pennsylvania Health System (UPHS) and Vanderbilt Medical Center (VUMC), where 20,779 and 19,575 NAFLD patients were identified, respectively. Clinical chart review identified a high positive predictive value (PPV) across all healthcare systems: 91% at CUIMC, 75% at UPHS, and 85% at VUMC, and a sensitivity of 79.6%. Our rule-based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub-phenotyping NAFLD patients within a large EHR system.