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.

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.

<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.

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.

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.

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 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.

Evidence-based expert consensus on clinical management of safety of Bruton’s tyrosine kinase inhibitors(2024)

Bruton’s tyrosine kinase inhibitors(BTKis)have revolutionized the treatment of B-cell lymphomas.However,safety issues related to the use of BTKis may hinder treatment continuity and further affect clinical efficacy.A comprehensive and systematic expert consensus from a pharmacological perspective is lacking for safety issues associated with BTKi treatment.A multidisciplinary consensus working group was established,comprising 35 members from the fields of hematology,cardiovascular disease,cardio-oncology,clinical pharmacy,and evidencebased medicine.This evidence-based expert consensus was formulated using an evidence-based approach and the Delphi method.The Joanna Briggs Institute Critical Appraisal(JBI)tool and Grading of Recommendations Assessment,Development,and Evaluation(GRADE)approach were used to rate the quality of evidence and grade the strength of recommendations,respectively.This consensus provides practical recommendations for BTKis medication based on nine aspects within three domains,including the management of common adverse drug events such as bleeding,cardiovascular events,and hematological toxicity,as well as the management of drug-drug interactions and guidance for special populations.This multidisciplinary expert consensus could contribute to promoting a multi-dimensional,comprehensive and standardized management of BTKis.

Tuberculosis-diabetes comorbidities: Mechanistic insights for clinical considerations and treatment challenges

Tuberculosis(TB)remains a leading cause of death among infectious diseases,particularly in poor countries.Viral infections,multidrug-resistant and ex-tensively drug-resistant TB strains,as well as the coexistence of chronic illnesses such as diabetes mellitus(DM)greatly aggravate TB morbidity and mortality.DM[particularly type 2 DM(T2DM)]and TB have converged making their control even more challenging.Two contemporary global epidemics,TB-DM behaves like a syndemic,a synergistic confluence of two highly prevalent diseases.T2DM is a risk factor for developing more severe forms of multi-drug resistant-TB and TB recurrence after preventive treatment.Since a bidirectional relationship exists between TB and DM,it is necessary to concurrently treat both,and promote recommendations for the joint management of both diseases.There are also some drug-drug interactions resulting in adverse treatment outcomes in TB-DM patients including treatment failure,and reinfection.In addition,autophagy may play a role in these comorbidities.Therefore,the TB-DM comorbidities present several health challenges,requiring a focus on multidisciplinary collaboration and integrated strategies,to effectively deal with this double burden.To effectively manage the comorbidity,further screening in affected countries,more suitable drugs,and better treatment strategies are required.

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.

Organic anion transporters also mediate the drug–drug interaction between imipenem and cilastatin

This study aimed to clarify that organic anion transporters(OATs)mediate the drug–drug interaction(DDI)between imipenem and cilastatin.After co-administration with imipenem,the plasma concentrations and the plasma concentration-time curve(AUC)of cilastatin were significantly increased,while renal clearance and cumulative urinary excretion of cilastatin were decreased.At the same time,imipenem significantly inhibited the uptake of cilastatin in rat kidney slices and in human OAT1(hOAT1)-HEK293 and human OAT3(hOAT3)-HEK293 cells.Probenecid,p-aminohippurate,and benzylpenicillin inhibited the uptake of imipenem and cilastatin in rat kidney slices and in hOAT1-and hOAT3-HEK 293 cells,respectively.The uptakes of imipenem and cilastatin in hOAT1-and hOAT3-HEK 293 cells were significantly higher than that in mock-HEK-293 cells.Moreover,the K m values of cilastatin were increased in the presence of imipenem with unchanged V max,indicating that imipenem inhibited the uptake of cilastatin in a competitive manner.When imipenem and cilastatin were co-administered,the level of imipenem was higher compared with imipenem alone both in vivo and in vitro.But,cilastatin significantly inhibited the uptake of imipenem when dehydropeptidase-1(DPEP1)was silenced by RNAi technology in hOAT1-and hOAT3-HEK 293 cells.In conclusion,imipenem and cilastatin are the substrates of OAT1 and OAT3.OAT1 and OAT3 mediate the DDI between imipenem and cilastatin.Meanwhile,cilastatin also reduces the hydrolysis of imipenem by inhibiting the uptake of imipenem mediated by OAT1 and OAT3 in the kidney as a complement.

Measurement-induced disturbance between two atoms in Tavis-Cummings model with dipole dipole interaction

Quantum correlation, measured by measurement-induced disturbance （MID）, between two two-level atoms is investi- gated in detail in Tavis-Cummings model with dipole--dipole interaction （DDI）. We find that MID can be determined only by the dipole-dipole interaction between the two atoms when the cavity and atoms are at resonance. Moreover, DDI will have different effects on MID for two different kinds of initial states.

老年患者长期用药的潜在药物相互作用及其影响因素分析

背景药物-药物相互作用(drug-drug interactions,DDIs)是导致药物不良反应的因素,识别和避免DDIs对于优化老年患者药物治疗效果十分必要。目的分析老年患者长期用药中潜在的具有临床意义的DDIs发生情况及相关影响因素,为老年药物合理使用提供依据。方法2018年12月-2020年3月、2021年5月-2022年4月,在北京三个社区≥65岁的老年人群中开展横断面研究,使用《老年人潜在的具有临床意义的DDIs国际共识清单》评估患者长期用药中的DDIs发生情况,通过logistic回归分析DDIs的危险因素。结果共纳入老年患者683例,年龄[Md(IQR):84(79~88)]岁,至少发生1种潜在DDIs的患者202例(29.6%)。其中药品使用的种类越多,发生潜在DDIs的概率越高(与2~4种相比:5~9种,OR=2.751,95%CI:1.627~4.652,P<0.001;10~14种,OR=5.006,95%CI:2.813~8.907,P<0.001;≥15种,OR=11.27,95%CI:5.140~24.711,P<0.001),衰弱状态(OR=1.757,95%CI:1.184~2.606,P=0.005)、有潜在不适当用药(OR=2.722,95%CI:1.863~3.976,P<0.001)、有心血管疾病(OR=6.759,95%CI:1.549~29.501,P=0.011)者发生潜在DDIs的概率更高。最常见的潜在DDIs为同时使用两种以上保钾药,所有与非甾体抗炎药有关的药物相互作用中,患者均使用了阿司匹林。结论社区老年人用药中潜在的具有临床意义的DDIs多见,应特别关注多药、衰弱、具有心血管疾病、有潜在不适当用药的老年患者DDIs发生情况,加强潜在DDIs的相关风险监测,以优化药物治疗效果,减少不良结局。

口服降糖药处方中代谢酶和转运体介导的药物相互作用的调查分析

目的了解我院门诊口服降糖药处方中联合用药存在的潜在药物相互作用情况。方法收集我院2018年6月1-30日门诊含口服降糖药的处方,筛选出联合用药的处方。依据国内外药品说明书和知网、维普、万方、PubMed及Medline数据库中相关文献报道,查询并记录联合用药中药物代谢酶和转运体所介导的药物相互作用处方。结果共查阅1540张处方,其中联合用药处方1332张,发现746例次潜在的药物相互作用。主要涉及的细胞色素P450(CYP)酶有CYP2C9、CYP2C8和CYP3A4,相关转运体有P-gp、OATP1B1和OATP1B3。处方中存在潜在药物相互作用例次较多的口服降糖药为瑞格列奈(327例,43.8%)、二甲双胍(264例,35.4%)和格列齐特(90例,12.1%)。结论我院门诊口服降糖药处方中代谢酶和转运体介导的药物相互作用较为常见,临床医师及药师应避免患者联合应用已有文献报道的存在药物相互作用的药物,以提高药物治疗的安全性和合理性。

中药临床研究联合用药安全性评价的思考和建议

目的探讨中药临床研究联合用药安全性评价方法,以满足临床安全、有效用药的需求。方法从中药临床研究入手,分析中药临床研究联合用药安全性存在的问题,并对其关键问题提出适当的建议。结果中药联合用药安全性评价体系需要建设和完善。结论中药联合用药的安全性评价需要从研发早期开始,贯穿临床试验至上市后评价的药品全生命周期;重视研究设计和风险-获益评估,搭建研究设计-监测评价-分析预测-风险管理的动态研究模式,建立多学科的综合评估体系。

基于药物相互作用软件评价降糖药物的相互作用

目的评价并分析不同降糖药物的药物相互作用(DDI)。方法将目前临床上常用的降糖药物(包括胰岛素和27种非胰岛素制剂)使用DDI数据库Lexi-InteractionTM分析评价其DDI并进行归纳和排序。结果 28种药物共涉及DDI数量882种,其中C级最多(69.8%),C、D、X级占总数的91.2%,其中X级为28种。平均每种药物DDI数量最多的为磺脲类药物,其次为二甲双胍,最少的为α-糖苷酶抑制剂。结论对于合并用药较多、老年患者及肝肾功能不全者应优先选用DDI数量较少的降糖药物,如α-糖苷酶抑制剂、胰高血糖素样肽-1(GLP-1)类似物、钠-葡萄糖共转运蛋白2抑制剂(SGLT2i)等。

基于药物互作网络的协同与拮抗预测研究

药物的协同与拮抗关系预测,有助于药物的使用安全及组合用药的发展。该文从药物互作网络(DDINet)出发,基于网络拓扑结构构造分类特征,提出一种预测药物协同和拮抗关系的方法。从特征选择结果可知,根据药物与其公共邻居节点关系构造的特征表现出了明显的正负样本分布差距,能有效地反映出药物的协同或拮抗关系。在使用不同特征分类器的分类结果中,最优AUC和分类精度值分别达到了0.9687和0.9187。而在协同与拮抗关系预测结果中,其预测精度值达到了0.45和0.75以上。这说明基于网络拓扑结构的方法能有效对药物协同和拮抗关系进行分类和预测。与传统基于药物功能、结构、靶基因等相似性特征的方法相比,该方法计算简单高效,将会有效促进组合用药的发展。

结构域相互作用数据库的产生、发展与应用

结构域是进化上的保守序列单元,是蛋白质的结构和功能的标准组件.典型的两个蛋白质间的相互作用涉及特殊结构域间的结合,而且识别相互作用结构域对于在结构域水平上彻底理解蛋白质的功能与进化、构建蛋白质相互作用网络、分析生物学通路等十分重要.目前,依赖于对实验数据的进一步挖掘和对各种不同输入数据的计算预测,已识别出了一些相互作用/功能连锁结构域对,并由此构建了内容丰富、日益更新的结构域相互作用数据库.综述了产生结构域相互作用的8种计算预测方法.介绍了5个结构域相互作用公共数据库3DID、iPfam、InterDom、DIMA和DOMINE的有关信息和最新动态.实例概述了结构域相互作用在蛋白质相互作用计算预测、可信度评估,蛋白质结构域注释,以及在生物学通路分析中的应用.

基于结构域的芥子油苷合成相关蛋白质相互作用模型构建和预测

从蛋白质一级序列抽取蛋白质结构域信息建立结构域特征向量,使用结构域特征向量训练基于高斯径向基核的支撑向量机,用5折交叉验证对支撑向量机参数网格寻优,然后用最优化参数训练得到蛋白质相互作用预测器。结果表明:预测器的交叉验证ROC曲线下面积达到了85.49%,可以有效预测拟南芥相关的蛋白质相互作用。