Cts-Clinical and Translational Science最新文献

Clinical research studies are becoming increasingly complex resulting in compounded work burden and longer study cycle times, each fueling runaway costs. The impact of protocol complexity often results in inadequate recruitment and insufficient sample sizes, which challenges validity and generalizability. Understanding the need to provide an alternative model to engage researchers and sponsors and bringing clinical research opportunities to the broader community, clinical research networks (CRN) have been proposed and initiated in the United States and other parts of the world. We report on the Johns Hopkins Clinical Research Network (JHCRN), established in 2009 as a multi-disease research collaboration between the academic medical centers and community hospitals/health systems. We have discussed vision, governance, infrastructure, participating hospitals' characteristics, and lessons learned in creating this partnership. Designed to leverage organized patient communities, community-based investigators, and academic researchers, the JHCRN provides expedited research across nine health systems in the mid-Atlantic region. With one IRB of record, a centralized contracting office, and a pool of dedicated network coordinators, it facilitates research partnerships to expand research collaborations among the differing sizes and types of hospitals/health systems in a region. As of August 2024, total 81 studies-clinical trials, cohort studies, and comparative effectiveness research have been conducted, with funding from the NIH, private foundations, and industry. The JHCRN experience has enhanced understanding of the complexity of participating sites and associated ambulatory practices. In conclusion, the CRN, as an academic–community partnership, provides an infrastructure for multiple disease studies, shared risk, and increased investigator and volunteer engagement.

This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients diagnosed with sepsis and intra-abdominal infection (IAI) was performed. We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine-learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified. High-dose corticosteroids receipt, the CD4⁺T/CD8⁺ T ratio, total parenteral nutrition, gastrointestinal perforation, (1,3)-β-D-glucan (BDG) positivity and broad-spectrum antibiotics receipt were independent predictors of IAC. Using the above parameters to establish a nomogram, the area under the curve (AUC) values of the nomogram in the derivation and validation cohorts were 0.822 (95% CI 0.777–0.868) and 0.808 (95% CI 0.739–0.876), respectively. The AUC in the derivation cohort was greater than the Candida score [0.822 (95% CI 0.777–0.868) vs. 0.521 (95% CI 0.478–0.563), p < 0.001]. The calibration curve showed good predictive values and observed values of the nomogram; the Decision Curve Analysis (DCA) results showed that the nomogram had high clinical value. In conclusion, we established a nomogram based on the CD4⁺/CD8⁺ T-cell ratio and clinical risk factors that can help clinical physicians quickly rule out IAC or identify patients at greater risk for IAC at the onset of infection.

Penetration of antimicrobial treatments into the cerebrospinal fluid is essential to successfully treat infections of the central nervous system. This penetration is hindered by different barriers, including the blood–brain barrier, which is the most impermeable. However, inflammation may lead to structural alterations of these barriers, modifying their permeability. The impact of blood–brain barrier disruption on linezolid and tedizolid (antibiotics that may be alternatives to treat nosocomial meningitis) penetration in cerebrospinal fluid (CSF) remains unknown. The aim of this study is to evaluate the impact of blood brain barrier disruption on CSF penetration of linezolid and tedizolid. Female C57BI/6 J mice were used. Blood–brain barrier disruption was induced by an intraperitoneal administration of lipopolysaccharide. Linezolid (40 mg/kg) or tedizolid-phosphate (20 mg/kg) were injected intraperitoneally. All the plasma and CSF samples were analyzed with a validated UPLC-MS/MS method. Pharmacokinetic parameters were calculated using a non-compartmental approach based on the free drug concentration. The penetration ratio from the plasma into the CSF was calculated by the AUC_0-8h (Area Under Curve) ratio (AUC_0-8hCSF/AUC_0-8hplasma). Linezolid penetration ratio was 46.5% in control group and 46.1% in lipopolysaccharide group. Concerning tedizolid, penetration ratio was 5.5% in control group and 15.5% in lipopolysaccharide group. In conclusion, CSF penetration of linezolid is not impacted by blood–brain barrier disruption, unlike tedizolid, whose penetration ratio increased.

In neurovascular settings, including treatment and prevention of ischemic stroke and prevention of thromboembolic complications after percutaneous neurointerventional procedures, dual antiplatelet therapy with a P2Y12 inhibitor and aspirin is the standard of care. Clopidogrel remains the most commonly prescribed P2Y12 inhibitor for neurovascular indications. However, patients carrying CYP2C19 no-function alleles have diminished capacity for inhibition of platelet reactivity due to reduced formation of clopidogrel's active metabolite. In patients with cardiovascular disease undergoing a percutaneous coronary intervention, CYP2C19 no-function allele carriers treated with clopidogrel experience a higher risk of major adverse cardiovascular outcomes, and multiple large prospective outcomes studies have shown an improvement in clinical outcomes when antiplatelet therapy selection was guided by CYP2C19 genotype. Similarly, accumulating evidence has associated CYP2C19 no-function alleles with poor clinical outcomes in clopidogrel-treated patients in neurovascular settings. However, the utility of implementing a genotype-guided antiplatelet therapy selection strategy in the setting of neurovascular disease and the clinical outcomes evidence in neurointerventional procedures remains unclear. In this review, we will (1) summarize existing evidence and guideline recommendations related to CYP2C19 genotype-guided antiplatelet therapy in the setting of neurovascular disease, (2) evaluate and synthesize the existing evidence on the relationship of clinical outcomes to CYP2C19 genotype and clopidogrel treatment in patients undergoing a percutaneous neurointerventional procedure, and (3) identify knowledge gaps and discuss future research directions.

This study investigated the success rate of Phase 1 clinical trial entry and the factors influencing it in oncology projects involving academia–industry collaboration during the discovery and preclinical stages. A total of 344 oncology projects in the discovery stage and 360 in the preclinical stage, initiated through collaborations with universities or hospitals between 2015 and 2019, were analyzed. The Phase 1 clinical trial entry success rates for oncology collaborative projects were 9.9% from the discovery stage and 24.2% from the preclinical stage. For discovery stage contracts, strong statistical significance was observed for contract type (co-development OR 16.45, p = 0.008; licensing OR 42.43, p = 0.000) and technology (cell or gene therapy OR 3.82, p = 0.008). In contrast, for preclinical stage contracts, significant changes were noted for cancer type (blood cancer OR 2.24, p = 0.004), while the year of contract signing showed a relatively weak statistical significance (OR 1.24, p = 0.021). No significant changes were observed concerning partner firm size and the partnership territory. This study sheds light on how the characteristics of partnerships influence the success rates of early-phase research, providing valuable insights for future strategic planning in oncology drug development.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized, placebo-controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment-specific and non-specific effects. The non-specific treatment effect varies subject by subject conditional to the individual propensity to respond to placebo. This effect is not estimable at an individual level using the conventional parallel-group study design, since each subject enrolled in the trial is assigned to receive either active treatment or placebo, but not both. The objective of this study was to conduct a comparative analysis of the machine learning methodologies to estimate the individual probability of a non-specific treatment effect. The estimated probability is expected to support novel methodological approaches for better controlling effect of excessively high placebo response. At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k-nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non-specific treatment response. ANN achieved the highest overall accuracy among all methods tested. A fivefold cross-validation was used to assess performances and risks of overfitting of the ANN model. The analysis conducted without subjects with non-specific effect indicated a significant increase of signal detection with significant increase in effect size.

Monoclonal antibodies (mAbs) are critical components in the therapeutic landscape, but their dosing strategies often evolve post-approval as new data emerge. This review evaluates post-marketing label changes in dosing information for FDA-approved mAbs from January 2015 to September 2024, with a focus on both initial and extended indications. We systematically analyzed dosing modifications, categorizing them into six predefined groups: Dose increases or decreases, inclusion of new patient populations by body weight or age, shifts from body weight-based dosing to fixed regimens, and adjustments in infusion rates. Among the 86 mAbs evaluated, 21% (n = 18) exhibited changes in dosing information for the initial indication, with a median time to modification of 37.5 months (range: 5–76 months). Furthermore, for mAbs with extended indications (n = 26), 19.2% (n = 5) underwent dosing changes in their first extensions, with a median time to adjustment of 31 months (range: 8–71 months). Key drivers for these adjustments included optimizing therapeutic efficacy, addressing safety concerns, accommodating special populations, and enhancing patient convenience. We also discuss the role of model-informed drug development, real-world evidence, and pharmacogenomics in refining mAb dosing strategies. These insights underscore the importance of ongoing monitoring and data integration in the post-marketing phase, providing a foundation for future precision medicine approaches in mAb therapy.

Hepatic impairment (HI) trials are traditionally part of the clinical pharmacology development to assess the need for dose adaptation in people with impaired metabolic capacity due to their diseased liver. This review aimed at looking into the data from dedicated HI studies, cluster these data into various categories and connect the effect by HI with reported pharmacokinetics (PK) properties in order to identify patterns that may allow waiver, extrapolations, or adapted HI study designs. Based on a ratio ≥ 2 or ≤ 0.5 in AUC or Cmax between hepatically impaired participants/healthy controls these were considered “positive” or “negative”. In case of more than one HI severity stratum per compound included in the HI trial, the comparison of the AUC ratios for mild, moderate, or severe HI were used to investigate the increase across HI categories. For the in total 436 hits, relevant PK information could be retrieved for 273 compounds of which 199 were categorized negative, 69 positive ups and 5 positive downs. Fourteen out of 69 compounds demonstrated a steep increase in the AUC ratios from mild to severe HI. Compounds demonstrating a steep increase typically had a high plasma protein binding of > 95%, high volume of distribution, lower absolute bioavailability, minor elimination via the kidneys, were predominantly metabolized by CYP3A4 or CYP2D6 and the majority of these compounds were substrates of OATP1B1. While for compounds with steep increase studies in all severity strata may be warranted they may also offer the potential to estimate the appropriate doses in an HI trial. On the other hand, for compounds with slow or no increase across HI severity strata, reduced HI trials may be justified, e.g. only testing PK in moderate HI.

The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.