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.

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.

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.

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.

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

AIM 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.METHODS As 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.RESULTS Six 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. CONCLUSION DDIs 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.

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.

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.

肝癌靶向联合免疫治疗耐药后的二线治疗方案研究进展

近年来,靶向和免疫单药及联合治疗晚期肝癌的临床研究为一线用药方案选择提供了丰富的疗效与安全性证据。然而,对于肝癌二线治疗方案的选择,目前各项临床指南尚无统一意见,原因在于现有循证医学证据局限于索拉非尼失败后的选择,而对于新的一线方案,如靶向免疫联合治疗肝癌耐药后的二线治疗方案,依然缺乏高证据等级的临床试验结论。本文回顾了目前临床试验研究结果,根据药物作用的不同机制,对靶向免疫一线治疗耐药后肝癌二线治疗方案的研究进行了归纳,并系统总结近年研究进展。对于一线靶免联合治疗耐药的肝癌患者,靶向联合治疗、免疫双抗治疗均有望提高疗效、改善生存,未来还需更多前瞻性临床研究数据,为靶免联合治疗耐药的肝癌患者提供有效、安全的治疗方案。

抗癫痫药物致住院患者药疹的临床表现及防治策略

目的 初步评价抗癫痫药物的安全性,并探讨抗癫痫药物引发药疹的防治策略。方法 回顾性分析医院神经内科或儿科2016年1月至2022年12月收治的经皮肤科会诊确诊为由抗癫痫药物致药疹的15例住院患者的临床资料,总结常见引发药疹的抗癫痫药物种类、药疹的分型、系统受累情况、药疹治疗方法的选择和预后。结果 15例患者中,最常引发药疹的药物为奥卡西平(6/15);药疹分型中以麻疹型最常见(13/15),2例Stevens-Johnson综合征(SJS)型分别由奥卡西平和苯巴比妥引起;除药疹外,患者常合并肝功能异常(8/15)、发热(7/15)和外周血嗜酸性粒细胞水平升高(6/15)。经停用/更换致敏抗癫痫药物、抗过敏等对症治疗后,药疹完全消退、系统损害恢复正常。结论 多种抗癫痫药物均可引发药疹,抗癫痫药物引发的药疹以麻疹型多见,但应警惕SJS型的出现及肝功能损害。及时停用致敏药物并更换抗癫痫药物的种类,使用糖皮质激素和(或)抗组胺药物对症治疗,对抗癫痫药物引发的药疹疗效较好。

2020—2022年自贡市第一人民医院细菌耐药性监测

目的了解自贡市第一人民医院临床分离菌对抗菌药物的耐药性,为临床合理使用抗菌药物提供依据。方法收集我院2020—2022年临床分离菌株,采用VITEK自动化鉴定及药敏系统、纸片扩散法及E-test方法进行细菌鉴定及药物敏感试验,以2022年美国临床和实验室标准化协会(CLSI)折点标准判断结果。结果共分离出临床菌株13324株,其中革兰阴性菌占69.6%,革兰阳性菌占30.4%。前五位分离菌为大肠埃希菌、肺炎克雷伯菌、金黄色葡萄球菌、铜绿假单胞菌和流感嗜血杆菌。耐甲氧西林金黄色葡萄球菌(MRSA)和耐甲氧西林凝固酶阴性葡萄球菌(MRCNS)的检出率分别为24.1%和73.0%。耐利奈唑胺屎肠球菌和粪肠球菌的检出率分别为2.1%和12.4%,分离出1株耐万古霉素的屎肠球菌。脑脊液中分离的4株肺炎链球菌均为青霉素非敏感菌株,未检出非脑脊液来源耐青霉素肺炎链球菌。耐碳青霉烯类阴沟肠杆菌的检出率为12.3%。而耐碳青霉烯类大肠埃希菌和肺炎克雷伯菌检出率低,分别为1.1%和2.9%。铜绿假单胞菌对哌拉西林/他唑巴坦、头孢吡肟和庆大霉素的耐药率逐年增加;鲍曼不动杆菌对常见抗菌药物耐药率明显高于铜绿假单胞菌,耐碳青霉烯类菌株检出率分别为41.9%和6.9%。未分离出耐头孢噻肟的流感嗜血杆菌和卡他莫拉菌。结论临床分离菌以革兰阴性菌为主,常见分离菌的耐药率呈现平稳或略有降低的特点。然而,耐利奈唑胺粪肠球菌和耐阿莫西林/克拉维酸流感嗜血杆菌检出率明显升高,应加强医院感染防控措施和抗菌药物的合理使用。

罕见病用药保障现状与挑战

近年来,国家和政府的高度重视有效推动了中国罕见病诊疗和保障工作。但全球范围内,仅5%的罕见病存在有效治疗方法。罕见病药物可及性差仍是中国罕见病领域存在的主要困境之一,是导致患者“治疗难”和“用药难”主要原因。罕见病药物保障覆盖罕见病药物研发、生产、流通、使用、支付保障、技术创新等多个环节,涉及供给、需求、政策决策等多个主体,其复杂性不同于普通药物。本文总结国内外罕见病药物保障政策,以及目前罕见病药物治疗的技术和方法,提出存在的问题和挑战,以期对未来罕见病药物保障和技术的创新和发展做出展望。

老年人药物相关性跌倒预防与管理的证据总结

目的 :检索并总结老年人药物相关性跌倒预防与管理的相关证据,为降低老年人药物相关性跌倒发生率提供循证依据。方法 :按照证据资源“6S”模型自上而下依次系统检索国内外相关数据库和网站中关于老年人药物相关性跌倒预防与管理的临床决策、指南、专家共识、证据总结和系统评价,检索时限为从建库至2023年12月1日,对纳入文献进行质量评价和资料提取,根据证据汇总原则提取并整合证据。结果 :共纳入16篇文献,包括1篇临床决策、3篇指南、5篇专家共识、2篇证据总结和5篇系统评价。从跌倒风险评估、跌倒相关用药评估、预防策略、使用管理和健康教育5个方面形成了21条证据。结论 :本研究总结的老年人药物相关性跌倒预防与管理的证据内容可协助医护人员结合患者实际情况制定个性化干预方案,降低老年人药物相关性跌倒发生率。

我院贝伐珠单抗生物类似药与原研药相关不良反应回顾性分析

目的分析某院贝伐珠单抗生物类似药与原研药相关药物不良反应(ADR)的发生情况,为临床合理用药提供数据支持。方法对江苏省肿瘤医院2022年1-12月上报的贝伐珠单抗生物类似药和原研药相关ADR报告进行回顾性分析。结果我院使用贝伐珠单抗的患者共6818人次,上报ADR报告136份,贝伐珠单抗生物类似药的ADR发生率显著高于原研药(2.18%vs.0.71%,P=0.004)。ADR报告中,治疗方案以贝伐珠单抗与其他肿瘤治疗药物的联合治疗方案为主(129人次);痊愈和好转的患者有118人次;一般ADR报告108份,严重ADR报告28份;ADR主要累及系统/器官以心血管系统为主,贝伐珠单抗生物类似药与原研药引起的高血压/血压升高、白细胞/血小板降低、腹泻和发热发生率比较,差异均无统计学意义。结论贝伐珠单抗生物类似药的相关ADR发生率明显高于原研药,但ADR临床表现无明显差异,临床医生可以根据患者及其家属意愿选择使用。

他汀类药物仿制药与原研药的疗效和安全性比较的系统评价

目的:系统评价他汀类药物仿制药与原研药在真实世界中的疗效和安全性,为临床用药选择提供循证依据。方法:计算机检索the Cochrane Library、MEDLINE、Embase、中国知网、中国生物医学文献服务系统、维普数据库和万方数据库,并查阅所获文献所附参考文献,收集他汀类药物仿制药与原研药的观察性研究(暴露组患者使用他汀类药物仿制药或原研药换为仿制药;非暴露组患者使用他汀类药物原研药),检索时限均为数据库建库至2023年7月。对符合纳入标准的研究进行资料提取后,采用纽卡斯尔-渥太华量表进行质量评价;运用RevMan 5.4统计软件进行Meta分析,同时进行描述性分析。结果:共纳入14项研究,其中3项为历史对照研究,11项为队列研究。Meta分析结果显示,仿制药与原研药总体主要不良心血管事件(MACE)发生率的差异无统计学意义(HR=1.07,95%CI=0.98~1.18,P=0.14),但氟伐他汀亚组分析结果提示仿制药的总体MACE发生率显著高于原研药;仿制药的总体全因死亡率显著高于原研药,但随访期为12个月的亚组分析结果显示差异无统计学意义(P>0.05);两组患者脑卒中住院率、不良反应发生率的差异均无统计学意义(P>0.05)。“换药”研究设计的定性描述结果表明,2项回顾性队列研究中,原研药换为仿制药组患者的总胆固醇、三酰甘油、低密度脂蛋白胆固醇水平降低程度与未换药组的差异无统计学意义(P>0.05);3项历史对照研究中,将原研药转换为仿制药并不会降低患者的疗效,然而原研药升高高密度脂蛋白胆固醇的效果显著优于仿制药。结论:他汀类药物的仿制药与原研药在临床疗效和安全性方面未见明显差异。

两种地诺孕素治疗子宫内膜异位症的临床疗效及不良反应对比

目的:评价地诺孕素仿制药和原研药在治疗子宫内膜异位症中的临床疗效和安全性,为地诺孕素临床用药提供依据。方法:收集我院2022年8月至2023年8月地诺孕素治疗子宫内膜异位症的患者资料,分为两组,分别给予地诺孕素仿制药或原研药地诺孕素2 mg/d,口服,连续治疗6个月。分别在3个月和6个月对两组患者进行随访调查,比较仿制药和原研药治疗子宫内膜异位症相关疼痛的临床疗效和不良反应发生情况。结果:仿制药组和原研药组患者的盆腔相关疼痛均明显降低(P<0.05),仿制药组下降了(34.0±3.0)mm,原研药组下降了(34.5±3.9)mm。最常见的异常出血情况,仿制药组、原研药组患者的发生率分别为93%和90%,无统计学差异。结论:地诺孕素国产仿制药和原研药在临床疗效相似,安全性一致。