Search

AimThis phase I study investigated talazoparib pharmacokinetics (PK) and safety in patients with advanced solid tumours and varying degrees of hepatic function.MethodsPatients with advanced solid tumours and normal hepatic function or varying degrees of hepatic impairment (mild, moderate or severe, based on National Cancer Institute Organ Dysfunction Working Group classification) received talazoparib 0.5 mg once daily for 22 calendar days. Plasma and urine samples after single and multiple doses were collected and analysed for talazoparib using validated assays. Plasma PK data from all patients were analysed using the population PK method. Plasma and urine PK parameters in PK-evaluable patients were calculated using noncompartmental analysis (NCA). Safety was monitored in all enrolled patients.ResultsThirty-eight patients were enrolled; 37 had ≥1 PK concentration, among which 17 were evaluable for NCA. Population PK analysis (n = 37) indicated no significant impact of hepatic function on apparent clearance (CL/F) of talazoparib. Baseline creatinine clearance was the only significant covariate on CL/F (α = 0.05). NCA of data (n = 17) showed no clear trend for increase in exposure on day 22 with worsening hepatic function. Talazoparib protein binding was comparable in patients with varying hepatic function. Talazoparib was generally well tolerated, and the safety profile observed in this study was consistent with the known safety profile of the drug.ConclusionsHepatic impairment (mild, moderate or severe) has no impact on the PK of talazoparib. No dose modification is recommended for patients with advanced solid tumours and various degrees of hepatic impairment, and this labelling language has been approved by the US Food and Drug Administration and the European Medicines Agency.

The aims of this study were (i) to evaluate the effect of talazoparib (1 mg once daily) on cardiac repolarization in patients with advanced solid tumors by assessing corrected QT interval (QTc) and (ii) to examine the relationship between plasma talazoparib concentration and QTc. In this open-label phase 1 study, patients had continuous 12-lead ECG recordings at baseline followed by time-matched continuous ECG recordings and collection of talazoparib plasma pharmacokinetic samples predose and at 1, 2, 4, and 6 h postdose on treatment days 1 and 22 and before talazoparib administration on day 2. ECG recordings were submitted for independent central review where triplicate 10-s ECGs, extracted up to 15 min before pharmacokinetic samples, were assessed for RR, PR, QRS, and QT intervals and ECG morphology. QT interval was corrected for heart rate using Fridericia's (QTcF) and Bazett's (QTcB) formulae. Linear mixed-effects modeling was used to examine the relationship between QTc and RR interval change from baseline and plasma talazoparib concentration. Thirty-seven patients received talazoparib. Mean change in QTcF from time-matched baseline ranged from -3.5 to 6.9 ms, with the greatest change 1 h postdose on day 22. No clinically relevant changes in PR, QRS, QTcB, QTcF, or RR intervals, heart rate, or ECG morphology were observed. No concentration-dependent effect on heart rate or QTc was observed. No deaths, permanent treatment discontinuations due to adverse events were reported. Talazoparib (1 mg once daily) had no clinically relevant effects on cardiac repolarization.

The aim of this study was to assess the potential effects of palbociclib in combination with letrozole on QTc. PALOMA-2, a phase 3, randomized, double-blind, placebo-controlled trial, compared palbociclib plus letrozole with placebo plus letrozole in postmenopausal women with estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer. The study included a QTc evaluation substudy carried out as a definitive QT interval prolongation assessment for palbociclib. Time-matched triplicate ECGs were performed at 0, 2, 4, 6, and 8 h at baseline (Day 0) and on Cycle 1 Day 14. Additional ECGs were collected from all patients for safety monitoring. The QT interval was corrected for heart rate using Fridericia's correction (QTcF), Bazett's correction (QTcB), and a study-specific correction factor (QTcS). In total, 666 patients were randomized 2 : 1 to palbociclib plus letrozole or placebo plus letrozole. Of these, 125 patients were enrolled in the QTc evaluation substudy. No patients in the palbociclib plus letrozole arm of the substudy (N=77) had a maximum postbaseline QTcS or QTcF value of ≥ 480 ms, or a maximum increase from clock time-matched baseline for QTcS or QTcF values of ≥ 60 ms. The upper bounds of the one-sided 95% confidence interval for the mean change from time-matched baseline for QTcS, QTcF, and QTcB at all time points and at steady-state Cmax following repeated administration of 125 mg palbociclib were less than 10 ms. Palbociclib, when administered with letrozole at the recommended therapeutic dosing regimen, did not prolong the QT interval to a clinically relevant extent.

The performance of novel genetic combinations under untested environmental scenarios and management practices can be virtually examined using process‐based crop models. Indeed, there has been a long‐standing interest in the crop modeling community to expand the utility of process‐based models to broader germplasm panels (e.g., breeding lines or diversity panels). Yet, there is often a misalignment between data needed to parameterize process‐based crop models and data routinely collected by breeding programs. To address this gap, we leverage a dataset from a long‐term trial on advanced experimental lines and released varieties from the Louisiana rice breeding program to calibrate and evaluate the decision support for agrotechnology transfer (DSSAT) CSM‐CERES‐Rice model. Next, we use data collected by the same program on a large collection of breeding lines to generate numerous in silico genotypes and evaluate their performance across different management practices (different planting dates) and three climatic conditions (current climate and two future scenarios based on CMIP6‐SSP5‐8.5 climate projections). Our simulations indicate that shifting the current planting date (i.e., March) back by 1–2 months (to January) under moderate warming conditions (+1.3°C warmer and 41% higher CO2 level), and 2–3 months (to December) under extreme warming conditions (+4.1°C warmer and 133% higher CO2 level) could potentially offset the negative impacts of the increased future temperature. Given earlier planting, shorter duration varieties (i.e., those with shorter growing degree day requirements during the vegetative and grain filling periods) are found to be more favorable for supporting high yields. Such varieties with a shorter thermal time to anthesis are found to remain just outside of the current pool of variation for this trait. Opportunities and challenges for leveraging breeding data in process‐based modeling to derive insights into adaptation strategies for future climates are further discussed. Core Ideas: There has been a long‐standing interest in the crop modeling community to extend the utility of process‐based models to breeding.Data required for models are often misaligned with data routinely collected from applied breeding programs.Parameterization of the CERES‐Rice model was informed using historical and recent data collected from the Louisiana rice breeding program.The impact of future climate, planting dates, and novel genetic combinations on the yield and phenology was tested.The range of genetic variability for phenology in the current breeding program to adapt varieties to future conditions was assessed. [ABSTRACT FROM AUTHOR]

The spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single‐cell level, from single‐cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single‐cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single‐cell levels into clinical considerations, and enables the discovery and development of new therapies for neurological diseases. It is predicted that single‐cell and spatial multiomics will be integrated with metabolomic profiles and corresponding gene epigenetic modifications. The interactions amongst DNAs, RNAs and proteins in a cell provide details of intracellular functional regulations and new opportunities for the translation of temporospatial diversity of neural cell subtypes/states into clinical practice. The application of single‐cell multiomics with four‐dimensional genome to the human pathological brain will lead us to a new milestone of the diagnosis and treatment. [ABSTRACT FROM AUTHOR]

B‐cell maturation antigen (BCMA)‐targeting immunotherapies (e.g., chimeric antigen receptor T cells (CAR‐T) and bispecific antibodies (BsAbs)) have achieved remarkable clinical responses in patients with relapsed and/or refractory multiple myeloma (RRMM). Their use is accompanied by exaggerated immune responses related to T‐cell activation and cytokine elevations leading to cytokine release syndrome (CRS) in some patients, which can be potentially life‐threatening. However, systematic evaluation of the risk of CRS with BCMA‐targeting BsAb and CAR‐T therapies, and comparisons across different routes of BsAb administration (intravenous (i.v.) vs. subcutaneous (s.c.)) have not previously been conducted. This study utilized a meta‐analysis approach to compare the CRS profile in BCMA‐targeting CAR‐T vs. BsAb immunotherapies administered either i.v. or s.c. in patients with RRMM. A total of 36 studies including 1,560 patients with RRMM treated with BCMA‐targeting CAR‐T and BsAb therapies were included in the analysis. The current analysis suggests that compared with BsAbs, CAR‐T therapies were associated with higher CRS incidences (88% vs. 59%), higher rates of grade ≥ 3 CRS (7% vs. 2%), longer CRS duration (5 vs. 2 days), and more prevalent tocilizumab use (44% vs. 25%). The proportion of CRS grade ≥ 3 may also be lower (0% vs. 4%) for BsAb therapies administered via the s.c. (3 studies, n = 311) vs. i.v. (5 studies, n = 338) route. This meta‐analysis suggests that different types of BCMA‐targeting immunotherapies and administration routes could result in a range of CRS incidence and severity that should be considered while evaluating the benefit–risk profiles of these therapies. [ABSTRACT FROM AUTHOR]

This article, published in the Journal of Neurological Surgery, explores the use of AI chatbots in generating novel research questions in skull base surgery (SBS). The study compared the efficacy of ChatGPT-4 and Google Bard in producing systematic review ideas for different subspecialties within SBS. The results showed that both chatbots were successful in generating novel, unpublished systematic review topics in skull-base surgery. ChatGPT-4 had an overall rate of 30% for producing novel ideas, compared to Bard's rate of 15%. The study concludes that AI chatbots can be valuable tools in discovering new research questions in SBS. [Extracted from the article]

Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R² = 0.797 and RMSEP = 0.264 for N; R² = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies. [ABSTRACT FROM AUTHOR]