Drug-Drug Interactions in Patients with Breast Cancer

The research paper investigates the intricate landscape of drug-drug interactions (DDIs) within the context of breast cancer treatment, with a particular focus on the elderly population and the use of complementary and alternative medicine (CAM). The study underscores the heightened susceptibility of elderly patients to DDIs due to the prevalence of polypharmacy and the widespread utilization of CAM among breast cancer patients. The potential ramifications of DDIs, encompassing adverse drug events and diminished treatment efficacy, are elucidated. The paper accentuates the imperative for healthcare providers to comprehensively understand both conventional and CAM therapies, enabling them to provide patients with informed guidance regarding safe and efficacious treatment options, culminating in enhanced patient outcomes.

Optimizing hepatitis C virus treatment through pharmacist interventions: Identification and management of drug-drug interactions

To quantify drug-drug-interactions (DDIs) encountered in patients prescribed hepatitis C virus (HCV) treatment, the interventions made, and the time spent in this process.METHODSAs standard of care, a clinical pharmacist screened for DDIs in patients prescribed direct acting antiviral (DAA) HCV treatment between November 2013 and July 2015 at the University of Colorado Hepatology Clinic. HCV regimens prescribed included ledipasvir/sofosbuvir (LDV/SOF), paritaprevir/ritonavir/ombitasvir/dasabuvir (OBV/PTV/r + DSV), simeprevir/sofosbuvir (SIM/SOF), and sofosbuvir/ribavirin (SOF/RBV). This retrospective analysis reviewed the work completed by the clinical pharmacist in order to measure the aims identified for the study. The number and type of DDIs identified were summarized with descriptive statistics.RESULTSSix hundred and sixty four patients (83.4% Caucasian, 57% male, average 56.7 years old) were identified; 369 for LDV/SOF, 48 for OBV/PTV/r + DSV, 114 for SIM/SOF, and 133 for SOF/RBV. Fifty-one point five per cent of patients were cirrhotic. Overall, 5217 medications were reviewed (7.86 medications per patient) and 781 interactions identified (1.18 interactions per patient). The number of interactions were fewest for SOF/RBV (0.17 interactions per patient) and highest for OBV/PTV/r + DSV (2.48 interactions per patient). LDV/SOF and SIM/SOF had similar number of interactions (1.28 and 1.48 interactions per patient, respectively). Gastric acid modifiers and vitamin/herbal supplements commonly caused interactions with LDV/SOF. Hypertensive agents, analgesics, and psychiatric medications frequently caused interactions with OBV/PTV/r + DSV and SIM/SOF. To manage these interactions, the pharmacists most often recommended discontinuing the medication (28.9%), increasing monitoring for toxicities (24.1%), or separating administration times (18.2%). The pharmacist chart review for each patient usually took approximately 30 min, with additional time for more complex patients.CONCLUSIONDDIs are common with HCV medications and management can require medication adjustments and increased monitoring. An interdisciplinary team including a clinical pharmacist can optimize patient care.

Drug-drug cocrystals:Opportunities and challenges

Recently,drug-drug cocrystal attracts more and more attention.It offers a low risk,low-cost but high reward route to new and better medicines and could improve the physiochemical and biopharmaceutical properties of a medicine by addition of a suitable therapeutically effective component without any chemical modification.Having so many advantages,to date,the reported drug-drug cocrystals are rare.Here we review the drug-drug cocrystals that reported in last decade and shed light on the opportunities and challenges for the development of drug-drug cocrystals.

Facial and bilateral lower extremity edema due to drug-drug interactions in a patient with hepatitis C virus infection and benign prostate hypertrophy: A case report

BACKGROUND New direct-acting antivirals(DAAs)-based anti-hepatitis C virus(HCV)therapies are highly effective in patients with HCV infection.However,safety data are lacking regarding HCV treatment with DAAs and drugs for comorbidities.CASE SUMMARY Herein,we reported a case of HCV-infection in a 46-year-old man with benign prostatic hypertrophy.The patient received sofosbuvir/velpatasvir as well as methadone maintenance therapy for drug abuse.The viral load became negative at week 1 post treatment.He developed facial and bilateral lower extremity edema 48 h after starting receiving tamsulosin.Edema disappeared 10 d after treatment with oral furosemide and spironolactone.CONCLUSION In conclusion,this is the first case of an acute edema in the course of treatment with new DAAs,methadone and tamsulosin.These agents are useful in clinical management of patients with HCV infection,particularly in men with benign prostatic hypertrophy.Clinicians should be aware of potential drug-drug interactions in this subset of patients.

Possibility of Drug-Drug Interaction in Prescription Dispensed by Community and Hospital Pharmacy

Objective: To analyze the use of all subsidized prescription drugs including their use of drug combination generally accepted as carrying a risk of severe interactions. Methodology: In a cross sectional study, we analyzed all prescriptions (n = 1014) involving two or more drugs dispensed to the population (age range 4-85 years) from all pharmacies, clinics and hospitals. Data were stratified by age and sex, and frequency of common interacting drugs. Potential drug interactions were classified according to clinical relevance as significance of severity (types A: major, B: moderate, and C: minor) and documented evidence (types 1, 2, 3, and 4). Result and Discussion: The growing use of pharmacological agents means that drug interactions are of increasing interest for public health. Monitoring of potential drug interactions may improve the quality of drug prescribing and dispensing, and it might form a basis for education focused on appropriate prescribing. To make the manifestation of adverse interaction subside, management strategies must be exercised if two interacting drugs have to be taken with each other, involving: adjusting the dose of the object drug;spacing dosing times to avoid the interaction. The pharmacist, along with the prescriber has a duty to ensure that patients are aware of the risk of side effects and a suitable course of action they should take. Conclusion: It is unrealistic to expect clinicians to memorize the thousands of drug-drug interactions and their clinical significance, especially considering the rate of introduction of novel drugs and the escalating appreciation of the importance of pharmacogenomics. Reliable regularly updated decision support systems and information technology are necessary to help avert dangerous drug combinations.

<i>In Vitro</i>and <i>in Vivo</i>(Mouse) Evaluation of Drug-Drug Interactions of Repaglinide with Anti-HIV Drugs

Repaglinide is type 2 short acting anti-diabetic drug which is primarily metabolized by CYP2C8 and CYP3A4 and is also a substrate of influx transporter OATP1B1. HIV drugs are potent inhibitors of CYP3A4 and OATP transporters. Several drug-drug interactions (DDIs) were noticed when protease inhibitors (PIs) coadministered with drugs metabolized by CYP3A4. The PIs are also potent mechanism based inhibitors, out which ritonavir is most potent. In the current study we evaluated in vitro (mouse and human liver microsomes) and in vivo DDIs of repaglinide with anti-HIV drugs. Out of the following tested drugs (Amprenavir, Indinavir, Nelfinavir, Ritonavir, Saquinavir, Delavirdine, Maraviroc, Efavirenz, Nevirapine and Ketoconazole) Amprenavir (APV), Ritonavir (RTV) and Ketoconazole (KTZ) showed inhibition of OH-repaglinide formation in human and mouse liver microsomes. The positive reversible inhibitions were further tested for irreversible inhibitions where we didn’t observe any irreversible inhibitions. In vitro inhibitions were further evaluated in the in vivo pharmacokinetics (mouse) where repaglinide pharmacokinetics was altered by RTV and KTZ. The DDIs in both studies were very strong;the dose of repaglinide is reduced to 20 fold. In conclusion, there could be possible DDIs when RTV dosed with repaglinide;we have also demonstrated that mouse could be useful preclinical tool when used in conjunction with in vitro screening models for DDIs.

Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such novelties.Drug-drug interaction(DDI)significantly contributed to drug administration and development.It continues as the main obstacle in offering inexpensive and safe healthcare.It normally happens for patients with extensive medication,leading them to take many drugs simultaneously.DDI may cause side effects,either mild or severe health problems.This reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery time.Several efforts were made to formulate new methods for DDI prediction to overcome this issue.In this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data environment.In the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for prediction.The input data is preprocessed at the primary level to improve its quality.Next,the salp swarm optimization algorithm(SSO)is used to select features.In this study,the deep belief network(DBN)model is exploited to predict the DDI accurately.The SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the work.The experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

Short-Term Drug-Drug Interaction between Sildenafil and Bosentan under Long-Term Use in Patients with Pulmonary Arterial Hypertension

Sildenafil and bosentan are often co-administered for pulmonary arterial hypertension (PAH) treatment. The plasma concentration of sildenafil can be decreased by half if co-administered with bosentan. Many patients take these agents simultaneously in the morning and the evening. The aim of this study was to examine the pharmacokinetics of sildenafil which was interfered with bosentan administration to ascertain whether these agents should be given concomitantly or separately. A two-way crossover study was conducted in 6 PAH patients with combination therapy of sildenafil and bosentan. Participants underwent the sequence of treatment phases: phase S (sildenafil administered 3 h before bosen-tan);phase B (bosentan administered 3 h before sildenafil);and phase C (administered concomitantly). Blood samples were collected on the last day of each phase. There was no significant difference in maximum plasma concentration or area under the plasma concentration-time curve (AUC0-8) between phase C and phase S (95.5 ± 24.8 vs. 72.9 ± 40.9 (p = 0.07), 209.7 ± 81.8 vs. 180.2 ± 126.4 (p = 0.24), respectively) or between phases C and B (87.8 ± 42.0 vs. 99.6 ± 33.9 (p = 0.59), 197.2 ± 88.2 vs. 240.7 ± 121.8 (p = 0.19), respectively) (ng/mL, mean ± standard deviation). Large intra-and inter-individual variability in sildenafil concentration was noted. The timing of administration of sildenafil and bosentan does not significantly influence the plasma concentration of sildenafil. Physicians do not need to be overly concerned about the timing of administration of these drugs to maximize the sildenafil concentration.

Drug-Drug Interaction Studies of Levocetirizine with Atenolol

The objective of the study was to evaluate the drug-drug interaction studies of levoceterizine with atenolol. Calibration curve studies of working standard solutions of levocetirizine and atenolol （0.01-0.1 mmol） were scanned. Maxima appeared at 231 nm for levocetirizine and 224 nm for atenolol. The calibration curve obeyed Beer Lambert＇s Law. Lone availabilities of both the drugs were studied in pH 1, pH 4, pH 7.4 and pH 9 at 37℃ on B.P. （British Pharmacopoeia） dissolution apparatus. To study the drug-drug interaction of levocetirizine （5 mg tablet） and atenolol （100 mg tablet）, both the drugs were introduced to the dissolution apparatus in simulated gastric juice （pH 1）, pH 4, pH 7.4 and pH 9 at 37℃ at zero time and measured the absorbance maxima of both the drugs at the corresponding wavelength. Graphs were plotted for availability percentage （%） of drug versus time at each set of experiment. The availability percentage （%） of levocetirizine in the buffers of pH simulated to gastric pH 4, pH 7.4 and pH 9 in the presence of atenolol was 436.78%, 376.90%, 436.78% and 436.78%, respectively, but the availability of atenolol was increased up to 214.80%, 212.96%, 214.93% and 231.51% in simulated to gastric pH and in the buffers ofpH 4, pH 7.4 and pH 9, respectively. On the basis of these studies, it is concluded that levocetirizine forms a charge-complex with atenolol; therefore, co-administration of these drugs should be avoided.

Prevalence of Potential Drug-Drug Interactions in Hospitalized Surgical Patients

The objective of this study is to estimate the prevalence and describe the characteristics of pDDIs （potential drug-drug interactions） in medical prescriptions of hospitalized surgical patients. In this cross-sectional study, we analyzed 370 medical prescriptions from the surgery unit of a Mexican public teaching hospital. The identification and classification of potential drug-drug interactions were performed with the Micromedex 2.0 electronic drug information database. Results were analyzed with descriptive statistics and we estimated OR （odds ratio） to determine associated risk factors. From the study, it was found that the prevalence of potential drug-drug interactions was 45.9%. A total of 385 interactions were identified. Of these, 54.3% were classified as major and 60.5% as pharmacodynamic. Prescriptions for more than seven drugs （OR =7.33, CI （confidence interval） = 4.59-11.71） and advanced age 〉 60 years, （OR = 1.79, CI = 1.06-2.74） were positively associated with the presence of potential drug-drug interactions. We found a high prevalence of clinically relevant pDDIs in the surgery unit. In view of this outcome, the safety of drug combinations in hospitalized surgical patients should be evaluated during the prescription process in order to prevent adverse events.

Multidisciplinary Approach to Drug-Drug Interactions between Tacrolimus and Sofosbuvir/Velpatasvir and Glecaprevir/Pibrentasvir in Kidney Transplant Patients during Hepatitis C Treatment:A Case Series Report

The direct acting antivirals(DAAs)are now the standard of care for hepatitis C virus(HCV)treatment with high and effective sustained virologic responserate(SVR)and great safety profile,including solid organ transplant patients.There are increasing reports showing DAAs are effective with high SVR rates and safety profile in kidney transplant recipients.There are reports on drug-drug interaction(DDI)between tacrolimus with DAAs.However,data remain lacking on potential DDIs between tacrolimus and DAA regimens and the management process.This case series reports three kidney transplant patients on tacrolimus who were successfully treated for HCV with multidisciplinary approach,although there was DDI between tacrolimus with sofosbuvir/velpatasvir and glecaprevir/pibrentasvir,which required tacrolimus dose adjustment to maintain therapeutic level during and after DAA treatment.Such DDIs should be aware of and closely monitored by pharmacist and physicians with tacrolimus dose adjustment as needed during and right after DAA treatment in post-kidney transplant patients.

Deep learning for drug-drug interaction prediction:A comprehensive review

The prediction of drug-drug interactions(DDIs)is a crucial task for drug safety research,and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy.Traditional wet chemical experiments for DDI are cumbersome and time-consuming,and are too small in scale,limiting the efficiency of DDI predictions.Therefore,it is particularly crucial to develop improved computational methods for detecting drug interactions.With the development of deep learning,several computational models based on deep learning have been proposed for DDI prediction.In this review,we summarized the high-quality DDI prediction methods based on deep learning in recent years,and divided them into four categories:neural network-based methods,graph neural network-based methods,knowledge graph-based methods,and multimodal-based methods.Furthermore,we discuss the challenges of existing methods and future potential perspectives.This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning.Deep learning models can scale to large-scale datasets and accept multiple data types as input,thus making DDI predictions more efficient and accurate.

A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction

Identifying drug–drug interactions(DDIs)is an important aspect of drug design research,and predicting DDIs serves as a crucial guarantee for avoiding potential adverse effects.Current substructure-based prediction methods still have some limitations:(i)The process of substructure extraction does not fully exploit the graph structure information of drugs,as it only evaluates the importance of different radius substructures from a single perspective.(ii)The process of constructing drug representations has overlooked the significant impact of relation embedding on optimizing drug representations.In this work,we propose a substructure-aware graph neural network incorporating relation features(RFSA-DDI)for DDI prediction,which introduces a directed message passing neural network with substructure attention mechanism based on graph self-adaptive pooling(GSP-DMPNN)and a substructure-aware interaction module incorporating relation features(RSAM).GSP-DMPNN utilizes graph self-adaptive pooling to comprehensively consider node features and local drug information for adaptive extraction of substructures.RSAM interacts drug features with relation representations to enhance their respective features individually,highlighting substructures that significantly impact predictions.RFSA-DDI is evaluated on two real-world datasets.Compared to existing methods,RFSA-DDI demonstrates certain advantages in both transductive and inductive settings,effectively handling the task of predicting DDIs for unseen drugs and exhibiting good generalization capability.The experimental results show that RFSA-DDI can effectively capture valuable structural information of drugs more accurately for DDI prediction,and provide more reliable assistance for potential DDIs detection in drug development and treatment stages.

替雷利珠单抗药物利用评价标准的建立与应用

目的针对替雷利珠单抗建立药物利用评价(drug use evaluation,DUE)标准,对南京医科大学附属南京医院替雷利珠单抗使用情况进行评价,为临床合理用药提供参考依据。方法根据替雷利单抗药品说明书、临床相关指导原则、诊疗指南和专家共识等,建立替雷利珠单抗DUE标准,依据标准回顾性分析评价2023年1—9月南京医科大学附属南京医院使用替雷利珠单抗的住院病人用药情况。结果替雷利珠单抗DUE标准包含14个指标。纳入96例病人,共使用替雷利珠单抗687次。体力评分、器官功能和实验室指标、给药剂量、给药途径、输注时间、给药频次和疗效评估合理率100%;禁忌证、溶媒种类和用量合理率98.96%;联合用药合理率97.92%;药物相互作用合理率96.88%;给药顺序和时间合理率84.38%;适应证合理率80.21%;不良反应监测与处理合理率39.58%。结论建立并应用替雷利珠单抗DUE标准可发现用药过程中的不合理现象,为临床安全用药提供参考依据。

DeepDrug:A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction

Background:Computational approaches for accurate prediction of drug interactions,such as drug-drug interactions(DDIs)and drug-target interactions(DTIs),are highly demanded for biochemical researchers.Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively,their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure.Methods:In this paper,we develop DeepDrug,a deep learning framework for overcoming the above limitation by using residual graph convolutional networks(Res-GCNs)and convolutional networks(CNNs)to learn the comprehensive structure-and sequence-based representations of drugs and proteins.Results:DeepDrug outperforms state-of-the-art methods in a series of systematic experiments,including binary-class DDIs,multi-class/multi-label DDIs,binary-class DTIs classification and DTIs regression tasks.Furthermore,we visualize the structural features learned by DeepDrug Res-GCN module,which displays compatible and accordant patterns in chemical properties and drug categories,providing additional evidence to support the strong predictive power of DeepDrug.Ultimately,we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2,where 7 out of 10 top-ranked drugs are reported to be repurposed to potentially treat coronavirus disease 2019(COVID-19).Conclusions:To sum up,we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations.

间充质细胞源性骨肉瘤中关键分子标志物鉴定及药物敏感性分析

背景:骨肉瘤发病机制复杂,预后较差,随着医疗技术的发展,其5年生存率有所改善,但仍未取得实质性进展。目的:筛选骨肉瘤中关键分子标志物,分析其与骨肉瘤治疗药物之间的关系,并从分子水平探讨骨肉瘤可能的疾病机制。方法:从基因表达谱数据库中获取GSE99671和GSE28425(miRNA),对GSE99671进行差异表达基因分析和加权基因共表达网络分析(WGCNA)。利用基因本体学(GO)和京都基因与基因组百科全书(KEGG)分别对差异表达基因和与疾病正相关性最高的模块基因进行功能富集分析。将上述模块基因与差异表达基因取交集作为关键基因,构建蛋白质相互作用网络,使用CytoScape软件对关键基因进行相关性分析,筛选枢纽基因(Hub基因)。使用GSE28425数据集对Hub基因进行外部验证,同时对Hub基因进行文本验证。使用CellMiner数据库对Hub基因进行药物敏感性分析,依据关联系数的绝对值|R|>0.3,P<0.05作为阈值进行筛选。结果与结论:(1)差异表达分析获得529个差异表达基因,其中177个表达上调,352个表达下调;WGCNA分析共得到592个与骨肉瘤相关性最高的基因;(2)GO富集结果显示骨肉瘤的发生发展可能与细胞外基质、骨细胞的分化与发育、人体的免疫调控、胶原蛋白的合成与分解相关;KEGG富集结果显示PI3K-Akt信号通路、焦点黏附信号通路、免疫应答等参与骨肉瘤疾病的发生;(3)交集结果显示,共获得59个关键基因,经蛋白质相互作用网络分析,筛选得到8个Hub基因,分别为LUM、PLOD1、PLOD2、MMP14、COL11A1、THBS2、LEPRE1、TGFB1,且均为表达上调;(4)外部验证发现调控Hub基因的miRNA明显下调,其中hsa-miR-144-3p和hsa-miR-150-5p的下调最为显著;文本验证结果显示Hub基因的表达与既往研究基本一致;(5)药物敏感性分析发现甲氨蝶呤、异环磷酰胺、帕博西尼的活性与PLOD1、PLOD2、MMP14的mRNA表达呈负相关关系;而唑来膦酸、雷帕霉素的活性与PLOD1、LUM、MMP14、PLOD2、TGFB1的mRNA表达呈正相关关系,提示唑来膦酸和雷帕霉素有望成为骨肉瘤的潜在治疗药物,但仍需进一步基础实验和临床研究加以验证。

碳青霉烯耐药铜绿假单胞菌的耐药特点和多位点序列分型

目的了解临床分离碳青霉烯(亚胺培南、美罗培南)耐药的铜绿假单胞菌(carbapenem resistant pseudomonas aeruginosa,CRPA)的耐药情况和遗传分型,为临床合理化用药和医院感染控制工作提供依据。方法收集2021年7月至2022年6月河南中医药大学第一附属医院临床分离的非重复碳青霉烯耐药的铜绿假单胞菌60株进行鉴定和药敏试验,分析临床分布和标本来源。随机选取其中35株CRPA,利用多位点序列分型(multilocus sequence typing,MLST)扩增铜绿假单胞菌的7个管家基因acsA、aroE、guaA、mutL、nuoD、ppsA和trpE,PCR扩增测序后采用DNAstar和PHYLOViZ 2.0等软件对分离的CRPA进行遗传差异性分析。结果60株CRPA主要分布在呼吸科、康复科、重症监护病房。标本类型以痰液为主(63.3%),其次为肺泡灌洗液(28.3%)。除多黏菌素、阿米卡星、庆大霉素外,对其他抗菌药物的耐药率均为40%以上,与同期分离的碳青霉烯敏感铜绿假单胞菌(carbapenem sensitive pseudomonas aeruginosa,CSPA)相比,除多黏菌素外,CRPA分离组对阿米卡星(23.3%)、头孢吡肟(58.3%)、头孢他啶(48.3%)、环丙沙星(55.0%)、哌拉西林/他唑巴坦(45.0%)、氨曲南(56.7%)、美罗培南(78.3%)等抗菌药物的耐药率显著高于CSPA组,差异有统计学意义(P<0.05)。MLST分析显示35株CRPA可分为25个ST型,其中优势ST为ST1182。该研究发现1种新的等位基因trpE316,3种新的ST型别,分别为ST3978、ST3979、ST3980。结论CRPA主要来源于呼吸道,多见于呼吸科,CRPA耐药形势严峻,应结合药敏结果和流行病学分析,有针对性地采取干预措施。MLST分析的结果显示ST型存在多样化,克隆类型多样化,提示这些菌株的遗传背景也极其复杂多变。

A new sample preparation method for the determination of acyclovir by RP-HPLC:application to a drug-drug interaction study between gefitinib and acyclovir in rats

The assay of acyclovir in plasma seems to be a challenge because of its high hydrophily.In our present study,a reversed-phase high-performance liquid chromatography(RP-HPLC)method for the determination of acyclovir in rat plasma was described and validated in drug-drug interaction(DDI)between gefitinib and acyclovir in rats.The analytes were separated with gradient elution on C18 column(4.6 mm×250 mm,5μm),and the peaks were recorded using ultraviolet detector at a wavelength of 254 nm.Protein precipitation followed by methyl tertiary butyl ether extraction was used for sample preparation.The calibration curve was established between 0.2 and 40μg/mL(r^(2)=0.9999).The intra-and inter-day precisions were all less than 8%,and all the biases were not more than 10%.This new method was successfully applied to a DDI study between gefitinib and acyclovir in rats.Gefitinib up-regulated the absorption of acyclovir by about three times,and our findings guided the clinical co-administration of epidermal growth factor receptor-tyrosine kinase inhibitors(EGFR-TKIs)with acyclovir.

反药同方的临床应用及配伍分析:以黑顺片为例

目的:通过分析某院门诊中药饮片处方中黑顺片与半夏、贝母、瓜蒌、天花粉、白及、白蔹同方配伍情况,探讨附子的临床应用及反药配伍治病情况,旨在促进中医院中药饮片的合理应用。方法:在处方点评系统中抽取中药门诊2021年12月-2023年9月含有黑顺片的处方,筛选其中黑顺片与其反药同方配伍处方,对其使用情况进行统计分析。结果:共抽取含黑顺片的处方共2486张,其中黑顺片与其反药371张,开具反药配伍的主要科室是专家门诊,黑顺片-法半夏同方处方数最多,黑顺片与反药配伍多用于治疗脾肾两虚证、阳虚寒湿证,连续开具处方最多30天,用量均在规定范围内。结论:黑顺片反药配伍不是绝对禁忌,通过准确辨证,做好用药交代及监护,可保障临床用药的安全性。

中国西南地区艾滋病病毒感染者治疗前和获得性抗逆转录病毒耐药性研究

背景中国不同地区人类免疫缺陷病毒(HIV)感染者的治疗前耐药(PDR)和获得性耐药(ADR)的患病率及流行情况差异较大。这两种耐药对患者的抗病毒治疗效果具有不良影响,可能会加剧患者不良预后的发生。目前针对中国西南地区的HIV感染者PDR和ADR患病率及流行情况的研究较少。目的调查在中国西南地区HIV感染者PDR和ADR的患病率及流行情况。方法纳入2021-01-01—2023-06-30在贵阳市公共卫生救治中心就诊并进行耐药基因检测的HIV感染者。使用HIV-1 pol序列分析HIV-1基因型和耐药性。使用斯坦福大学HIV耐药性数据库分析逆转录酶和蛋白酶Sanger序列的主要耐药性突变。通过Logistic回归模型评估与PDR相关的风险因素。结果共1613例HIV感染者参与研究,其中824例初治,789例经治。初治患者最常见的基因型为B+C(47.0%),耐药率为18.7%(154/824),非核苷类逆转录酶抑制剂(NNRTIs)为14.9%(123/824),核苷类逆转录酶抑制剂(NRTIs)为1.7%(14/824),蛋白酶抑制剂(PIs)为2.7%(22/824),整合酶链转移抑制剂(INSTIs)为1.9%(16/824)。经治患者最常见的基因型为CRF01-AE(37.4%),耐药率为27.8%(219/789),NNRTIs、NRTIs、PIs和INSTIs的突变率分别为7.7%(61/789)、19.3%(152/789)、2.7%(21/789)和1.1%(9/789)。多因素Logistic回归分析结果显示,传播途径、CD_(4)^(+)T细胞计数、病毒载量和确诊与抗逆转录病毒疗法(ART)间隔时间是HIV感染者PDR的影响因素(P<0.05)。结论流行于中国西南地区的HIV感染者PDR和ADR发生率较高,分别为18.7%和27.8%。PDR的影响因素包括传播途径、CD_(4)^(+)T细胞计数、病毒载量和确诊与ART间隔时间。因此,为了避免耐药性发生,迫切需要常规基线基因型耐药性检测和足够的病毒载量监测间隔时间。