A clinical phase I dose-finding design with adaptive shrinking boundaries for drug combination trials.

BACKGROUND: Combinations of drugs are becoming increasingly common in oncology treatment. In some cases, patients can benefit from the interaction between two drugs, although there is usually a higher risk of developing toxicity. Due to drug-drug interactions, multidrug combinations often exhibit different toxicity profiles than those of single drugs, leading to a complex trial scenario. Numerous methods have been proposed for the design of phase I drug combination trials. For example, the two-dimensional Bayesian optimal interval design for combination drug (BOINcomb) is simple to implement and has desirable performance. However, in scenarios where the lowest and starting dose is close to being toxic, the BOINcomb design may tend to allocate more patients to overly toxic doses, and select an overly toxic dose combination as the maximum tolerated dose combination. METHOD: To improve the performance of BOINcomb in the above extreme scenarios, we widen the range of variation of the boundaries by setting the self-shrinking dose escalation and de-escalation boundaries. We refer to the new design as adaptive shrinking Bayesian optimal interval design for combination drug (asBOINcomb). We conduct a simulation study to evaluate the performance of the proposed design using a real clinical trial example. RESULTS: Our simulation results show that asBOINcomb is more accurate and stable than BOINcomb, especially in some extreme scenarios. Specifically, in all ten scenarios, the percentage of correct selection is higher than the BOINcomb design within 30 to 60 patients. CONCLUSION: The proposed asBOINcomb design is transparent and simple to implement and can reduce the trial sample size while maintaining accuracy compared with the BOINcomb design.

Hepatobiliary calculi associated with ceftriaxone treatment: An analysis of FAERS data from 2004 to 2021.

INTRODUCTION: The role of gender, age, dose and other factors in the adverse reaction process of pseudocholelithiasis caused by ceftriaxone is controversial. In this study, we further explored potential risk factors using the FAERS database. METHODS: The reported odds ratio (ROR) and the information component (IC) of specific candidate factors were calculated by using the ROR method and the Bayesian confidence promotion neural network (BCPNN) method respectively to detect potential risk factors in adverse events(AEs) of ceftriaxone and hepatobiliary calculi(HBC). One candidate factor will be considered as a suspicious signal, or potential risk factors if its lower limit of 95% confidence interval of ROR (ROR025) is greater than 1 and its lower limit of 95% confidence interval of IC (IC025) is greater than 0. RESULTS: A total of 764 AEs of HBC were used to this analysis to evaluate candidate risk factors: Age group, Gender, Dose. Child (1-12 years): male ROR025 = 6.64, IC025 = 2.42, female ROR025 = 6.66, IC025 = 2.40. Adolescent group (12-18 years): male ROR025 = 5.47, IC025 = 2.08; elderly (≥65 years): female ROR025 = 1.25, IC025 = 0.22. CONCLUSIONS: Gender was not detected as a risk factor for HBC caused by ceftriaxone. However, Male infants, male children, female children, adolescent male, and elderly female were potential risk factors for HBC caused by ceftriaxone based on criteria ROR025 > 1 and IC025 > 0.

Nephrotoxicity of Immune Checkpoint Inhibitors: A Disproportionality Analysis from 2013 to 2020.

Nephrotoxicity occasionally occurs during treatment with immune checkpoint inhibitors (ICIs). Few related studies compare the differences between these drugs. This study aimed to systematically characterize nephrotoxicity after ICI initiation. Data were extracted from the US FDA Adverse Event Reporting System (FAERS) database. Disproportionality analysis, including information components (ICs) and reporting odds ratios (RORs), was performed to determine the potential renal toxicity of ICIs. A total of 7,204 reports of renal adverse events (AEs) were identified in the FAERS database. Renal AEs were most commonly reported for nivolumab (46.84%). Strong signals were detected in male patients combined with ICIs. In the clinical application of ICIs, attention should be paid to patients, especially male patients, with acute kidney injury, nephritis, autoimmune nephritis and other nephrotoxic AEs. The use of ICIs is likely to aggravate their condition.

Hepatotoxicity in immune checkpoint inhibitors: A pharmacovigilance study from 2014-2021.

Adverse events(AEs) related to hepatotoxicity have been reported in patients treated with immune checkpoint inhibitors (ICIs). As the number of adverse events increases, it is necessary to assess the differences in each immune checkpoint inhibitor regimen. The purpose of this study was to examine the relationship between ICIs and hepatotoxicity in a scientific and systematic manner. Data were obtained from the FDA Adverse Event Reporting System database (FAERS) and included data from the first quarter of 2014 to the fourth quarter of 2021. Disproportionality analysis assessed the association between drugs and adverse reactions based on the reporting odds ratio (ROR) and information components (IC). 9,806 liver adverse events were reported in the FAERS database. A strong signal was detected in older patients (≥65 years) associated with ICIs. hepatic adverse events were most frequently reported with Nivolumab (36.17%). Abnormal liver function, hepatitis, and autoimmune hepatitis were most frequently reported, and hepatitis and immune-mediated hepatitis signals were generated in all regimens. In clinical use, patients should be alert to these adverse effects, especially in elderly patients, who may be aggravated by the use of ICI.

The Material Basis and Mechanism of Xuefu Zhuyu Decoction in Treating Stable Angina Pectoris and Unstable Angina Pectoris.

METHODS: Firstly, we used a network proximity approach to calculate and compare the effectiveness of the formula with that of Western drugs for each type of angina, including all targets and intersecting targets, from a topological perspective. Secondly, we compared the mechanisms of action of the two angina pectoris at three levels and five aspects, including conventional and modular analysis approaches. Thirdly, based on the unique functions of each angina in the complex heterogeneous network, we designed a reverse process for finding the material basis using dynamic, static, and enriched items as well as a total item. Finally, the designed inverse process, material basis, and mechanism of action were validated. RESULTS: The target network of Xuefu Zhuyu decoction is closer to the target network of each type of angina than that of Western drugs, and the intersection targets have a closer proximity. Comparison of the mechanisms of action showed that stable angina and unstable angina had 158 common targets, while the unique targets were 34 and 1, respectively. Modularity analysis showed that the GO similarity of target modules was highly correlated with KEGG similarity. We ended up with 67 compounds upregulated for stable angina and 47 compounds upregulated for unstable angina. Our results were validated by literature mining, high-volume molecular docking, and miRNA enrichment analysis. CONCLUSIONS: For both types of angina pectoris, Xuefu Zhuyu decoction is superior to Western drugs. A comparison of various aspects led to the unique mechanisms of action, from which the material basis of each type of angina was deduced.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris.

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.