Cts-Clinical and Translational Science最新文献

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techniques to identify cognitive progression in individuals with and without PD. Using data from the Parkinson's Progression Marker Initiative, shallow Markov, deep recurrent (long short-term memory [LSTM]), and nonrecurrent (temporal fusion transformer [TFT]) models were compared to predict cognitive status over time. Cognitive status was categorized into normal cognition (NC), mild cognitive impairment (MCI), and dementia. Predictions were made annually for up to 3 years using clinical data, including demographics, cognitive assessments, PD severity, and medical history. Each approach was evaluated using inverse probability weighted (IPW-) F1 scores. An ensemble method combined outputs from the Markov, LSTM, and TFT models. The dataset included 917 individuals (53% PD; 30% at risk for PD; 17% Healthy Controls). The TFT model outperformed others across all annual periods (IPW-F1 = 0.468) compared to the Markov (IPW-F1 = 0.349) and LSTM (IPW-F1 = 0.414) models, with improved performance using an ensemble approach (IPW-F1 = 0.502). For MCI and dementia predictions, which were rarer occurrences compared to NC status (ratios: 50:8:1), the TFT model consistently outperformed competing models, achieving IPW-F1 scores of 0.496 and 0.533 for MCI and dementia, respectively. In conclusion, sequential deep learning models like TFT, which mitigate long-term memory loss and can interpret complex, high-dimensional data, perform best overall in predicting clinically important cognitive transitions. These methods should be further explored for predicting degenerative conditions.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) increases plasma low-density lipoprotein-cholesterol (LDL-C) by decreasing the expression of the LDL-receptor on hepatic cells. Ongericimab (JS002) is a novel PCSK9 monoclonal antibody that exhibits a long-acting LDL-C lowering effect by exclusively inhibiting PCSK9 in pre-clinical studies. Two randomized, double-blind, placebo-controlled trials were conducted to evaluate the safety, tolerability, efficacy, immunogenicity, pharmacokinetic, and pharmacodynamic profiles of ongericimab in healthy subjects and patients with hypercholesterolemia. Eighty-four healthy subjects in the phase Ia study received a single dose of placebo or ongericimab (15–450 mg). Ninety patients with hypercholesterolemia in the phase Ib/II study received placebo or ongericimab 150 mg Q2W, 300 mg Q4W, or 450 mg Q4W for 12 weeks. Ongericimab exhibited non-linear kinetics. The apparent clearance decreased as the dosage increased, with terminal elimination half-life (t_1/2) values of 4.5–6.5 days. Overall, ongericimab was well tolerated in both studies. A single dose of ongericimab reduced LDL-C levels by 30%–73% in healthy subjects, and repeated doses of ongericimab reduced LDL-C levels by 67%–80% in patients with hypercholesterolemia. At the end of the dosing interval in the phase Ib/II study, over 70% of patients' LDL-C levels decreased by more than 50% from baseline. The results showed that ongericimab had a significant long-acting LDL-C lowering effect with good safety and potential for clinical application.

Therapeutic agents targeting the tumor necrosis factor (TNF) superfamily cytokines B-cell activating factor (BAFF, BLyS) and/or A PRoliferation Inducing Ligand (APRIL) have demonstrated clinical effectiveness in multiple autoimmune diseases, such as systemic lupus erythematosus, lupus nephritis, and immunoglobulin A nephropathy (IgAN). However, their clinical utility can often be limited by incomplete and/or prolonged times to clinical response and inconvenient dosing regimens, which may be improved by more potent dual inhibition of both cytokines. Povetacicept (ALPN-303; TACI vTD-Fc) is a crystallizable fragment (Fc) fusion protein of an engineered transmembrane activator and CAML interactor (TACI) domain which mediates more potent inhibitory activity than wild-type TACI-Fc or BAFF- or APRIL-specific antibodies and demonstrates superior pharmacokinetic and pharmacodynamic activity in multiple preclinical disease models. In this first-in-human study in healthy adults, povetacicept was well-tolerated as single ascending doses of up to 960 mg administered intravenously or subcutaneously. Dose-dependent pharmacokinetics were observed. Coverage of BAFF and APRIL was observed for 2–3 weeks and ≥4 weeks after doses of 80 mg and ≥240 mg, respectively. Maximal pharmacodynamic effects were observed at dose levels ≥80 mg for a single dose, associated with on-target reductions in antibody-secreting cells as well as in all circulating immunoglobulin isotypes, including the IgAN disease-related biomarker galactose-deficient-immunoglobulin A1 (Gd-IgA1), and were superior to results reported for wild-type TACI-Fc. These data strongly support further development of povetacicept for the treatment of B-cell-mediated automimmune diseases.

Luetrakool P, Taesotikul S, Susantitapong K, Suthisisang C, Morakul S, Sutherasan Y, Tangsujaritvijit V, Dilokpattanamongkol P. Implementing pain, agitation, delirium, and sleep deprivation protocol in critically ill patients: A pilot study on pharmacological interventions. Clin Transl Sci. 2024 Mar;17(3):e13739. doi: 10.1111/cts.13739. PMID: 38421247; PMCID: PMC10903435.

On page 8, under the section titled “FUNDING INFORMATION”, the sentence “The authors confirm that this research received no external funding. The study was conducted without financial support, and the authors have no financial interests or affiliations that could be perceived as a conflict of interest in connection with this work.” were incorrect. This should have read: “This research project is supported by Mahidol University. The authors have no financial interests or affiliations that could be perceived as a conflict of interest in connection with this work.”

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The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance. The process for DHT implementation starts with identifying a clinical concept of interest followed by a digital measure selection, defining active or passive data capture and their sources, the number of sensors with respective body location, plus the duration and frequency of use in the context of perceived participant burden. Roundtable discussions among patient groups, physicians, and technology providers prior to protocol development can be very impactful for optimizing trial design. While diversity and inclusion are essential for any clinical trial, patient populations should be considered carefully in the context of trial-specific aims, requirements, and anticipated patient burden. Minimizing site burden includes assessment of training, research engagement, and logistical burden which needs to be triaged differently for early and late-stage clinical trials. Additional considerations include sharing trial results with study participants and leveraging publicly available data for compliance modeling. To the best of our knowledge, this report provides holistic considerations for sensor-based DHT implementation that may optimize participant compliance.

Tofacitinib is a targeted JAK inhibitor used to treat rheumatoid arthritis. Despite some recent safety concerns, it is considered effective and safe with appropriate patient selection. Between May 2015 and May 2024, data were retrospectively analyzed from 112 patients with a diagnosis of RA in a tertiary care hospital who had received tofacitinib for at least 1 month, with or without prior biologic DMARDs. The mean disease duration was 12 years, and the median duration of tofacitinib use was 32.5 months. The p-value for all disease activity parameters evaluated for effectiveness between the 1st- and 3rd-month visits was <0.001, except CRP (p = 0.097). Adverse events occurred in 15 (13.4%) patients, with an incidence rate of 4.54 per 100 patient-years. Observed were one myocardial infarction (0.3/100 patient-years), two pulmonary embolisms (0.6/100 patient-years), three herpes zoster (HZ) (0.9/100 patient-years), and one basal cell carcinoma (BCC) (0.3/100 patient-years). Median drug-free survival was 68 (95% CI: 54.8-81.2) months. The drug was discontinued in 28 (25%) patients due to ineffectiveness and in 13 (11.6%) due to side effects. A significant difference in drug survival rates was observed between patients who had not previously used bDMARDs and those who had received at least one bDMARD before tofacitinib (p < 0.001). Drug survival was 46.35 months in the prior bDMARD group and 71.09 months in the bDMARD-naive group. This study found significant reductions in disease activity indices at 3 and 6 months after starting tofacitinib, with sustained effectiveness. Although adverse event rates were somewhat higher than reported in the literature, tofacitinib can be used effectively and safely in appropriate patient populations for RA treatment.

Controversies regarding the benefits of statin treatment on clinical outcomes in coronary artery spasm (CAS) without obstructive coronary artery disease (CAD) persist due to limited data. In this retrospective nationwide population-based cohort study from the Taiwan National Health Insurance Research Database during the period 2000-2012, the matched cohorts consisted of 12,000 patients with CAS. After propensity score matching with 1:1 ratio, 2216 patients were eligible for outcome analysis in either statin or nonstatin group, with the mean follow-up duration of 4.8 and 4.6 years, respectively. Statin users versus nonusers had a significantly reduced risk of major adverse cardiovascular events (MACEs) (6.7% vs. 9.5%, hazard ratio [HR] 0.68; 95% confidence interval [CI] 0.55-0.84) and all-cause mortality (6.0% vs. 7.6%; HR 0.77; 95% CI 0.61-0.96). While the results of MACEs were mainly contributed by cardiovascular death (1.9% vs. 3.2%; HR 0.56; 95% CI 0.38-0.83) and ischemic stroke (3.8% vs. 5.4%; subdistribution HR 0.69; 95% CI 0.52-0.91), they were primarily driven by reductions in ischemic but not hemorrhagic stroke. The benefit of statins was significantly pronounced in patients with hypertension and diabetes. Nevertheless, the effect on MACEs was consistent irrespective of age, sex, dyslipidemia, and mental disorder. Statins significantly reduced the risk of MACEs and all-cause mortality in CAS patients. The benefit of statin therapy in reducing MACEs appeared to be linear, with greater risk reduction with higher doses and longer duration without upper threshold, reflecting the dose-dependent relationship of statins with MACEs in CAS patients.

Intranasal insulin is a putative neuroprotective therapy after cardiac arrest, but safety in humans at doses extrapolated from animal models is unknown. This phase I, open-label adaptive dose-escalation study explores the maximum tolerated dose of intranasal insulin in healthy human participants. Placebo or insulin at doses from 0 to 1000 units was given to healthy participants intranasally on repeated weekly visits. Serum glucose, insulin, and C-peptide levels were measured serially at 0, 15, 30, 60, 120, 180, and 240 min after administration. Twenty-four participants (12 female, median age 53, IQR 35-61) were enrolled. There was minimal change in average serum glucose after administration of intranasal insulin. Average serum insulin increased slightly in a dose-dependent manner, reaching maximum concentrations at 15 min. C-peptide decreased over time from administration in all groups. One participant had severe hypoglycemia (24 mg/dL at 45 min) and a different participant had mild hypoglycemia (51 mg/dL at 30 min), both after receiving 600 U intranasal insulin. Hypoglycemic episodes were associated with increases in serum insulin. Both participants continued in the study without hypoglycemia after additional doses. High-dose intranasal insulin up to 1000 U was generally well tolerated, with minimal measurable systemic absorption and without significant aggregate changes in mean glucose. Idiosyncratic episodic systemic absorption and hypoglycemia require further study and additional caution in potential clinical application. Further study of its target engagement and efficacy as a neuroprotective therapy after cardiac arrest at these doses is warranted.

The increase in the availability of real-world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens. To demonstrate the application of this framework, we present atezolizumab as a use case on account of its three approved alternative intravenous (IV) dosing regimens. As expected, the real-world use of atezolizumab has generally been increasing since 2016 for the 1200 mg every 3 weeks regimen and since 2019 for the 1680 mg every 4 weeks regimen. Out of the ML algorithms evaluated, XGBoost performed the best, as measured by the area under the precision-recall curve, with an emphasis on the under-sampled class given the imbalance in the data. The importance of features was measured by Shapley Additive exPlanations (SHAP) values and showed metastatic breast cancer and use of protein-bound paclitaxel as the most correlated with the use of 840 mg every 2 weeks. Although patient usage data for alternative IV dosing regimens are still maturing, these analyses provide initial insights on the use of atezolizumab and set up a framework for the re-analysis of atezolizumab (at a future data cut) as well as application to other molecules with approved alternative dosing regimens.

Most cancers and neoplastic progenitor cells have elevated telomerase activity and preservation of telomeres that promote cellular immortality, making telomerase a rational target for the treatment of cancer. Imetelstat is a first-in-class, 13-mer oligonucleotide that binds with high affinity to the template region of the RNA component of human telomerase and acts as a competitive inhibitor of human telomerase enzymatic activity. Pharmacokinetics, pharmacodynamics, exposure-response analyses, efficacy, and safety of imetelstat have been evaluated in vitro, in vivo, and clinically in solid tumor and hematologic malignancies, including lower-risk myelodysplastic syndromes (LR-MDS) and myeloproliferative neoplasms. Imetelstat was approved in the United States in June 2024 for the treatment of adult patients with LR-MDS with transfusion-dependent anemia requiring four or more red blood cell units over 8 weeks who have not responded to or have lost response to or are ineligible for erythropoiesis-stimulating agents, with a recommended dosing regimen of 7.1 mg/kg administered via 2-h intravenous infusion every 4 weeks. In the pivotal trial, significantly more patients treated with imetelstat versus placebo achieved ≥8-week and ≥24-week red blood cell-transfusion independence, and imetelstat was associated with a manageable safety profile characterized primarily by short-lived and manageable neutropenia and thrombocytopenia. This mini-review summarizes the mechanism of action, pharmacokinetic and pharmacodynamic characteristics, clinical development, and clinical efficacy and safety data of imetelstat.