Cts-Clinical and Translational Science最新文献

The authors thank Dr. Sellers for his commentary [1] on the drug–drug interaction (DDI) Tutorial [2]. The authors agree, in general, that evaluation of pharmacokinetic DDIs does not explain all DDIs, but pharmacokinetic DDIs significantly improve dosing recommendations for new drugs (Table 1). The authors also agree that standard population studies do not always reflect the true risk or nature of DDIs in specific subgroups. Pharmacodynamic DDIs, which can be synergistic, additive, or antagonistic, are extremely important [3-5], typically specific for a therapeutic area (e.g., antiviral, pain, oncology) and can lead to unpredictable clinical outcomes. DDIs for biologics are due to the mechanism of action or modulation of disease pathophysiology [4]. While pharmacodynamic and disease-specific interactions remain important, the approach to pharmacokinetic DDIs outlined in the Tutorial offers a robust scientific rationale, reflecting advances in our understanding of metabolizing enzymes and transporters. This information enables the development of sound dosing recommendations for DDIs across therapeutic areas (Table 1). Additionally, scientists should consider DDIs related to the underlying disease or therapeutic area. Dr. Sellers notes that patients are the true “victims” and doctors are the true “perpetrators” of DDIs; the authors agree that clinicians need additional training on the management of complex DDIs to decrease adverse events. The authors believe that the terms “victims” and “perpetrators” are clearly and informatively defined in the DDI tutorial. In cases of interaction between DRUG A (investigational drug) and DRUG B (concomitant medication), it is essential for clinicians to determine whether the dose adjustment is needed for DRUG A as the victim or for DRUG B as the victim when DRUG A acts as the perpetrator. Grouping these together obscures which drug is affected, whereas the “victim” and “perpetrator” terminology provides clarity. The DDI tutorial also gives recommendations about what information should be included in a label and how it should be provided. In addition, there should be collaboration among societies to explain complex DDIs to clinicians and trainees (focusing on audience) to improve prescribers' understanding of managing complex DDIs.

Most importantly, the understanding of DDIs and the methods for detecting DDIs will improve by including the interplay between drug metabolizing enzymes and transporters using physiologically-based pharmacokinetic modeling, clinical studies in the patient population, real-world data that may be optimized through machine learning, and developing tools for individualized DDI risk assessment.

The authors have nothing to report.

The authors declare no conflicts of interest.

This article is linked to Sellers papers. To view this article, visit https://doi.org/10.1111/cts.70458.

Vancomycin is commonly used to treat Staphylococcus aureus infections. In critically ill patients receiving continuous renal replacement therapy (CRRT), adsorptive membranes like oXiris may alter drug pharmacokinetics. This retrospective study developed a population pharmacokinetics (PopPK) model using MonolixSuite software, incorporating adsorptive membrane use as a covariate. The final one-compartment model estimated that the population volume of distribution of vancomycin was 94.97 L (RSE 10.5%) and population clearance was 2.82 L/h (RSE 19.0%). Adsorptive membrane use was a significant covariate, slightly increasing vancomycin clearance, while aging was associated with reduced clearance. Monte Carlo simulations indicated that a regimen of 2 g loading dose followed by 1 g every 24 h achieved an AUC_0–24/MIC ≥ 400 mg h/L in more than 90% of patients. Individualized vancomycin dosing in this population should consider membrane type, along with patient-specific factors such as age, to optimize therapeutic outcomes.

Collectively, pediatric rare diseases affect millions of children worldwide. Yet, treatment options are limited. Dose selection presents unique challenges in pediatric rare disease drug development. Traditional dose-finding approaches are impractical for these populations, and conventional pediatric dosing methods like exposure matching face limitations when insufficient adult data exists. Herein, we analyzed dosing strategies and study design characteristics used for new molecular entities (NMEs) for orphan indications approved between 2013 and 2022 that included a pediatric indication at initial approval. Among 63 evaluable products included in this analysis, initial pediatric dose selection was supported by adult data in the same indication (37%), adult healthy volunteer data (33%), nonclinical data only (14%), adult data from different indications (10%), and pediatric data from different indications (5%). The use of modeling and simulation to support initial dose selection was explicitly mentioned for 21% of products. Nearly half (48%) utilized multiple data sources for dose selection. Study design characteristics included multiple dose level evaluation (49%), intra-patient dose escalation (33%), interim pharmacokinetic evaluation (10%), pharmacokinetic/biomarker-driven dosing (5%), and age group staggering (5%). Multiple design features were incorporated in 17% of drugs. This analysis reveals diverse approaches to pediatric dose selection in rare diseases and the use of adaptive study design elements suggests recognition of the need for flexible approaches in these challenging populations. Utility of modeling and simulation, ability to leverage all available data sources, and increased implementation of adaptive trial designs could improve dose selection and optimization in pediatric rare disease drug development.

Clinical PGx practice guidelines (PGx guidelines) may have limited generalizability for “marginalized” groups. We proposed the five-step Real-World Data for Genome-Guided Prescribing (ReGGRx) framework and, using All of Us research program (AoU) data, examined its ability to estimate disparities in concordance with and benefit from PGx guidelines for CYP2C19 testing when choosing antiplatelet and antidepressant drugs. The selected measures were intended to identify disparities in avoiding drug failure independent of following PGx guidelines, the odds of avoiding drug failure with PGx concordant treatment, and the degree to which “marginalized” groups (i.e., groups underrepresented in biomedical research [UBR] and with indeterminate CYP2C19 phenotypes) benefit from PGx concordant treatment, when compared with “non-marginalized” groups (i.e., non-UBR and known CYP2C19 phenotypes). Our findings identified disparities in the antidepressant cohort with UBRs (32% of cohort) having a lower odds of avoiding drug failure. For both cohorts, a lower probability of avoiding drug failure was observed in the indeterminate phenotype group (1% of cohorts) than in the known phenotype group, indicating a need to better characterize rare or ancestry-specific risk alleles. With PGx concordant treatment, negative equal opportunity difference values suggested that the UBR group was less likely to avoid drug failure than the non-UBR group. Overall, our findings illustrate the promise of the ReGGRx framework to assess PGx guideline generalizability and produce evidence for use in drug policy decisions.

Denosumab is a monoclonal antibody targeting the receptor activator of nuclear factor kappa-b ligand widely used for the prevention of skeletal-related events in patients with bone metastases. This Phase 1 randomized, double-blind, two-arm, parallel-group study assessed the equivalence in pharmacokinetics (PK) and compared the pharmacodynamics (PD), safety, and immunogenicity of the proposed biosimilar RGB-14-X and reference denosumab in healthy males. Participants were randomized 1:1 to a single subcutaneous 60 mg dose of RGB-14-X or reference denosumab, with 252 days of follow-up. Primary PK endpoints were maximum observed serum concentration (C_max) and area under the concentration-time curve from time 0 to last quantifiable concentration (AUC_0-last) and extrapolated to infinity (AUC_0-inf). Secondary objectives were to compare additional PK parameters, safety and tolerability, PD and immunogenicity between groups. Of 165 participants randomized, 162 (98.2%) completed the study. The geometric mean ratios and corresponding 90% confidence intervals of RGB-14-X versus reference denosumab for C_max, AUC_0-last, and AUC_0-inf were within the pre-specified range of 0.80–1.25, demonstrating equivalence. No notable differences were observed in secondary PK or PD parameters between groups; maximum reduction in concentration of the bone resorption marker serum C-terminal telopeptide of type I collagen (CTX) and the extent and duration of reduction in CTX levels over time were similar. RGB-14-X was well tolerated with a similar safety profile to reference denosumab. No anti-drug or neutralizing antibodies were detected in either group. RGB-14-X demonstrated biosimilarity to reference denosumab, with equivalent PK and similar PD, safety, and immunogenicity outcomes in healthy males.

The Covid-19 pandemic highlighted the urgent need for effective therapies against emerging pathogens. Drug repurposing, defined as the use of existing medications for new therapeutic purposes, was extensively pursued for SARS-CoV-2 but has not yielded successful treatments. This narrative review critically examines the pharmacological and methodological factors that contributed to these unsuccessful outcomes, paying particular attention to tests of azithromycin and hydroxychloroquine. There are many reasons the promise of repurposed drugs was not realized. Many repurposed compounds displayed promising in vitro antiviral activity that did not translate into clinical efficacy. Major pharmacokinetic (PK) limitations, for example, poor oral bioavailability, low concentrations in pulmonary tissue, and extensive plasma protein binding, prevented these drugs from reaching therapeutic levels in humans. Preclinical research often relied on non-human cell lines and animal models that inadequately reflected human physiology, leading to misleading experimental outcomes. Clinical trials were often undermined by methodological limitations, including endpoints with uncertain clinical significance, suboptimal comparators, and insufficient attention paid to key PK and pharmacodynamic (PD) parameters such as half maximal effective concentration (EC50) values. This narrative review emphasizes the importance of integrating comprehensive PK/PD assessments, relevant experimental models, and rigorous trial design to strengthen drug development during future health crises. The relative success of antivirals including molnupiravir, nirmatrelvir, and remdesivir, which were either novel or previously unapproved compounds, suggests the value of designing and developing targeted antivirals. We must coordinate global research, develop pharmacologically sound strategies, and use evidence-based decision-making to effectively prepare for future pandemics and quickly produce effective treatments.

Project Optimus has been reforming the dose selection and optimization paradigm in oncology. In this context, model-informed drug development (MIDD) approaches were utilized to validate the optimal dose selection of 6 mg/kg every 3 weeks (Q3W) for datopotamab deruxtecan (Dato-DXd) in patients with HR-positive/HER2-negative breast cancer (HR+/HER2− BC). A Tumor Growth Inhibition (TGI)–Progression-Free Survival (PFS) modeling framework was developed to assess the relationship between Dato-DXd PK exposure, tumor dynamics, and PFS, and support virtual trial simulations at different Dato-DXd dose levels. Simulations suggested that Dato-DXd at 6 mg/kg Q3W provide superior tumor control and improved PFS compared to a lower dose in patients with HR+/HER2− BC. This work underscores the importance of integrating advanced modeling techniques into the dose optimization paradigm.

Oxaliplatin-induced peripheral neuropathy (OIPN) causes numbness and pain in the limbs, often leading to interruption of chemotherapy and representing a significant clinical problem. Previous basic studies have suggested that the dipeptidyl peptidase-4 (DPP-4) inhibitor alogliptin may prevent OIPN. To evaluate whether concomitant use of DPP-4 inhibitors could prevent the development of OIPN in clinical practice, we retrospectively analyzed data from 1180 patients who initiated oxaliplatin treatment at Kyushu University Hospital between January 1, 2009 and December 31, 2019. The primary endpoint was the occurrence of OIPN of any grade. Kaplan–Meier analysis with cumulative doses demonstrated a significantly lower incidence of OIPN in the DPP-4 inhibitor group (p = 0.0422). After propensity score matching to adjust for patient backgrounds, the protective association remained significant (p = 0.0389). Furthermore, Cox proportional hazards analysis incorporating gender, age, regimen, and concomitant DPP-4 inhibitor use as covariates confirmed that DPP-4 inhibitor use was an independent protective factor for OIPN (HR = 0.690; 95% CI, 0.490–0.972; p = 0.034). These findings suggest that concomitant use of DPP-4 inhibitors may moderate the development of OIPN in patients receiving oxaliplatin.

Early-phase trials often struggle to detect treatment effects due to small sample sizes and substantial patient heterogeneity. While randomization is the standard for balancing treatment arms, many trials fail to account for key prognostic factors, potentially reducing statistical power and introducing bias. We surveyed 113 randomized oncology trials on ClinicalTrials.gov and found that established prognostic variables across cancer types, such as albumin, chloride, and Eastern Cooperative Oncology Group (ECOG) performance status, were frequently underutilized as randomization factors, despite being routinely collected at baseline. To address this, we evaluated a prognostic model–based randomization strategy using the Real-wOrld PROgnostic score (ROPRO), which integrates 27 baseline variables into a single continuous risk score across cancer indications. Using semi-synthetic simulations, we compared ROPRO-based randomization to ECOG randomization for detecting treatment effect across survival models, treatment effect sizes, and levels of patient heterogeneity. ROPRO consistently improved statistical power and reduced required sample sizes across treatment effect sizes (HR = 0.5, 0.6, 0.7) and survival models at different shapes. Power advantages ranged from +1 to +11 percentage points, with the greatest gains observed at moderate sample sizes. These findings support the use of prognostic model-informed randomization strategies in early-phase oncology trials, particularly in the context of FDA's Project Optimus, which emphasizes the need for finding optimal doses and regimens prior to registration trials.