Clinical Pharmacology & Therapeutics最新文献

Iptacopan, a first-in-class complement factor B inhibitor acting proximally in the alternative complement pathway, has been shown to be safe and effective for patients with complement-mediated diseases. Iptacopan selectively binds with high affinity to factor B, a soluble, plasma-based, hepatically produced protein. Factor B is abundant in the circulation but can be saturated at the iptacopan clinical dose of 200 mg twice daily. Iptacopan pharmacokinetics (PK) are influenced by target binding. This target-mediated drug disposition (TMDD) behavior makes PK data useful for understanding target occupancy and motivates modeling of drug-target binding to connect exposure with pharmacological effect. A phase I hepatic impairment (HI) PK study measuring both total and unbound iptacopan PK profiles provided an opportunity to characterize the effect of variation in target concentration (due to varying hepatic function) on iptacopan PK. HI caused no change in total iptacopan exposure but increased unbound iptacopan exposure 1.38- to 3.72-fold in participants with mild, moderate, or severe HI relative to demographically matched participants with normal hepatic function, with the largest increases in severe HI. A two-site competitive binding model was developed to elucidate the relationship between iptacopan PK and factor B occupancy to characterize exposure thresholds for maximal target engagement. The model was used to assess alternative dose regimens to provide insight into how to approach dose recommendations for patients with severe HI. This study provides an example of small-molecule TMDD, a behavior typically associated with targeted biologics; its importance is too often underappreciated in small-molecule drug development.

Insights into the time needed for evaluation of risk minimization measures' (RMMs) effectiveness might identify areas for improvement. We assessed the duration of time intervals between regulatory milestones for RMM effectiveness studies assessed by the Pharmacovigilance Risk Assessment Committee (PRAC) of the European Medicines Agency (EMA). We included completed RMM effectiveness post-authorization safety studies (PASSs) assessed by PRAC between 2016 and 2022. Regulatory documents submitted by marketing authorization holders and assessment reports were extracted from non-public EMA databases. To calculate the duration of time intervals, we collected the dates of study request, protocol assessment start, protocol approval, study start, final study report assessment start, and final study report PRAC outcome. We identified 98 PASSs. The median duration from study request to final study report PRAC outcome was 52 months (Q1–Q3: 40–70). The median duration from study request to study start was 21 months (Q1–Q3: 15–30; n = 95) and from study start to final study report assessment start was 21 months (Q1–Q3: 13–36; n = 95). The final study report assessment often comprised <6 months (median: 4; Q1–Q3: 1–6). For PASSs with a PRAC-approved protocol (n = 80, 81.6%), the median duration of protocol assessment was 7 months (Q1–Q3: 4–10). Concluding, the median duration from study request to RMM effectiveness PASS completion exceeded 4 years. Next to the study conduct duration, the period from study request until study start was the most time-consuming. The duration of this period might be minimized by improved guidance on RMM effectiveness PASSs and encouraging timely protocol submission.

The liver is crucial for metabolizing the anticancer drug palbociclib, but limited information is available on the impact of hepatic impairment on its toxicity and efficacy, with no real-world data available. This study aims to evaluate how hepatic impairment affects hematological toxicity and progression-free survival (PFS) of palbociclib in advanced hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer, using the National Cancer Institute scoring system, in a large real-world dataset. This multicenter retrospective observational study included female patients treated with palbociclib between August 2017 and February 2024. Regression analysis was used to compare the risk of developing grade 3/4 hematological toxicity and PFS between patients with normal and mild impaired liver function. In total, 478 female patients were included. Patients with mild hepatic impairment (n = 205) did not have an increased risk of developing grade 3/4 neutropenia compared with patients with normal hepatic function (n = 273) (hazard ratio (HR) = 1.11; 95% CI 0.83–1.47). In addition, the PFS was not significantly different between both groups (HR = 1.15; 95% CI 0.93–1.42). In real-world settings, patients with mild hepatic impairment do not have a higher risk of developing palbociclib-induced neutropenia or disease progression than patients with normal hepatic function. These findings can guide clinicians when treating breast cancer patients with mild hepatic impairment.

Drug development is a lengthy process with considerable uncertainty at each milestone. Several trials are needed to progress to confirmatory evaluation and establish a positive benefit–risk balance. One of the critical milestones is the decision to progress to phase III based on phase II trial results. Use of probability of success is becoming standard in pharmaceutical companies to support this decision. However, the lack of consistency in terminology makes it difficult to assess the comparative value of different approaches. By leveraging the availability of high-quality external data (e.g., real-world data, historical clinical trial data, etc.), probability of success-based procedures may further improve decision-making. We performed a scoping review of approaches to calculate the probability of success of a phase III trial depending on the available data sources and the availability of specific endpoints. Calculation of probability of success is relatively straightforward if data for the primary endpoint of the phase III trial are also available in phase II trials. Often, phase II trials are based on biomarker or surrogate outcomes, due to challenges associated with study duration and required sample size. Probability of success-based procedures as reviewed can incorporate external data sources, for example, from clinical trials testing the same or similar drug or real-world data on the targeted population—optimizing the calculation of probability of trial success and the projected drug candidate value. We conclude the paper by reflecting on alternative approaches and ideas for uses within pharmaceutical companies and academia.

Immunization rates of maternal influenza vaccination during pregnancy remain suboptimal, with concerns about potential harm to the mothers and their offspring. We conducted a population-based cohort study, using mother–child linked database in Korea: (a) maternal cohort between December 2019, and March 2022; (b) neonatal cohort between September 2020, and June 2021. Exposure was defined as influenza vaccination during pregnancy. Study outcomes included gestational outcomes, vaccine-related adverse events, and other health outcomes in mothers and childbirth and immune-related health outcomes in children. After 1-to-1 propensity score matching using diverse potential confounders, effect estimates with 95% confidence intervals were estimated using the log-binomial model for cumulative outcomes and the Cox proportional model for time-to-event outcomes. After 1-to-1 propensity score matching, we identified 174,008 and 53,344 pairs for the maternal and neonatal cohorts, respectively. In the maternal cohort, influenza vaccination during pregnancy was not associated with preeclampsia, antenatal bleeding, and various adverse outcomes, including neurological, vascular, blood, and lymphatic system disorders, except for marginally elevated risks of gestational diabetes mellitus (effect estimate 1.06, 95% confidence interval 1.05 to 1.08) and postpartum hemorrhage (1.05, 1.01 to 1.08). In the neonatal cohort, maternal influenza vaccination did not increase risks of childbirth (e.g., preterm/low birth weight, congenital malformations, mortality) and immune-related outcomes, except for a slightly increased risk of lower respiratory tract infection (1.06, 1.007 to 1.12). In this population-based cohort study, influenza vaccination during pregnancy was not associated with an increased risk of a range of adverse outcomes in mothers and their offspring.

Tuberculosis (TB) is a major health burden in Africa. Although TB is treatable, anti-TB drugs are associated with adverse drug reactions (ADRs), which are partly attributed to pharmacogenetic variation. The distribution of star alleles (haplotypes) influencing anti-TB drug metabolism is unknown in many African populations. This presents challenges in implementing genotype-guided therapy in Africa to decrease the occurrence of ADRs and enhance the efficacy of anti-TB drugs. In this study, we used StellarPGx to call variants and star alleles in NAT1, NAT2, GSTM1, GSTT1, GSTP1, and CYP2E1, from 1079 high-depth African whole genomes. We present the distribution of common, rare, and potential novel star alleles across various Sub-Saharan African (SSA) populations, in comparison with other global populations. NAT1*10 (53.6%), GSTT1*0 (65%), GSTM1*0 (48%), and NAT2*5 (17.5%) were among the predominant functionally relevant star alleles. Additionally, we predicted varying phenotype distributions for NAT1 and NAT2 (acetylation) and the glutathione-S-transferase (GST) enzymes (detoxification activity) between SSA and other global populations. Forty-seven potentially novel haplotypes were identified computationally across the genes. This study provides insight into the distribution of key variants and star alleles potentially relevant to anti-TB drug metabolism and other drugs prescribed across various African populations. The high number of potentially novel star alleles exemplifies the need for pharmacogenomics studies in the African context. Overall, our study provides a foundation for functional pharmacogenetic studies and potential implementation of pharmacogenetic testing in Africa to reduce the risk of ADRs related to treatment of TB and other diseases.

Comparisons of maximum drug concentration (C_max) and total area under the concentration vs. time curve (AUC) may be inadequate for bioavailability (BA)/bioequivalence (BE) assessments in cases where the shape of the pharmacokinetic (PK) profile of a drug impacts the clinical performance. In such cases, partial area under the concentration vs. time curve (pAUC) has been recommended by regulatory agencies to support BA or BE assessments as a measure of drug exposure over specified time intervals of clinical relevance. This white paper serves as an update to the previously published white paper by Fang et al. at the US Food and Drug Administration (FDA), which introduced the general framework to provide pAUC recommendations. Since August 2020, 18 product-specific guidances (PSGs) have been published or revised using the general framework to provide consistent, science- and risk-based pAUC recommendations. Notable regulatory examples of pAUC applications discussed include loxapine inhalation powder, leuprolide long-acting injectables (LAIs), and goserelin LAIs. This paper discusses recent applications of pAUC in the United States, highlights key examples of pAUC recommendations for regulatory applications, and provides insights about areas for global harmonization of pAUC recommendations.

Biomarkers play a pivotal role in the selection and enrollment of trial participants. Particularly, predictive biomarkers help tailor medical care to individual patients; however, also prognostic biomarkers require consideration at the design stage. At the time of initiating a clinical trial, there may be uncertainty about whether a biomarker is predictive or prognostic, and the trial design may need to account for this. Relevant discussions between drug developers and regulators on the role of a biomarker in a specific drug development program are expected to take place during Scientific Advice (SA) procedures. SA procedures at the European Medicines Agency from January 1, 2018, to December 31, 2020, were systematically searched for methodological discussions around the use of predictive or prognostic biomarkers. The final analysis included 45 SA procedures for which key characteristics were summarized quantitatively. Selected methodological issues such as the cutoff selection of continuous biomarkers or study design considerations were elaborated in a qualitative summary. Our results identify commonly encountered points for discussion between drug developers and the European Medicines Agency for biomarker-informed patient selection and enrollment. Identified topics addressed during SA procedures include cutoff selection, study design, multiplicity control, and data-driven subgroup selection. The majority of the identified 45 SA procedures concern development programs in oncology. Addressing these issues upfront will allow for an improved dialogue between drug developers and regulators and support the drug development program and ultimately patient-centered access to medicines.