Advances in pharmacology最新文献_第4页

Risk of senescence, polypharmacy, and their outcomes in elderly cardiovascular disease patients. 老年心血管疾病患者的衰老风险、多药治疗及其结局

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-03-20 DOI: 10.1016/bs.apha.2025.02.011

Tamer Cebe, Fatih Kızılyel

{"title":"Risk of senescence, polypharmacy, and their outcomes in elderly cardiovascular disease patients.","authors":"Tamer Cebe, Fatih Kızılyel","doi":"10.1016/bs.apha.2025.02.011","DOIUrl":"10.1016/bs.apha.2025.02.011","url":null,"abstract":"Cardiovascular diseases (CVDs) are closely associated with a chronic inflammatory condition known as senescence and present a considerable challenge when managed alongside age-associated comorbidities. Due to the coexistence of three main predisposing factors (advanced age, multiple morbidity, and polypharmacotherapy), elderly patients with CVDs are prone to the occurrence of drug interactions and adverse effects of incorrect drug combinations. Polypharmacy, routine cardiovascular medications, and age-related pharmacokinetic alterations are the major challenges in cardiovascular practice. Polypharmacy might impair the post-surgical recovery process due to ADRs and side effects. Ironically, patients with CVDs may also require conventional senotherapeutic drugs such as cardiac glycosides, statins, aspirin, ACE inhibitors, and angiotensin receptor blockers for their daily routine. Considering medical necessities, polypharmacy, and drug safety of the elderly population, the management of elderly cases presents a serious challenge. We aim to present the cardiometabolic impacts of polypharmacy management in elderly patients and to design optimal senotherapeutic strategies and drug management strategies in cardiac surgical practice.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"104 ","pages":"351-392"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polypharmacy as a reason for misinterpreting laboratory results in the elderly. 多药是老年人误读化验结果的原因之一。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-05-28 DOI: 10.1016/bs.apha.2025.05.001

Said İncir, Murat Bolayirli

{"title":"Polypharmacy as a reason for misinterpreting laboratory results in the elderly.","authors":"Said İncir, Murat Bolayirli","doi":"10.1016/bs.apha.2025.05.001","DOIUrl":"10.1016/bs.apha.2025.05.001","url":null,"abstract":"Today, clinical biochemistry laboratories play increasingly significant roles in diagnosing patients, monitoring treatment responses, and making prognoses. Over the last 50 years, technological advances have greatly impacted laboratory medicine. The analytical performance of autoanalyzers has reached higher levels through continuous improvement processes. Nonetheless, the preanalytical phase, the most important source of error in laboratory processes, considerably affects clinical laboratory test results. In the preanalytical phase, both controllable and uncontrollable variables influence laboratory test outcomes. Medications are among the controllable variables. Drugs can affect laboratory test results in a dose-dependent manner. Some of these effects may be classified as expected, while others are unexpected. Additionally, laboratory test results may be more misleading due to increased drug interactions in the geriatric population. Polypharmacy is a concerning issue for the elderly. Older individuals are at a higher risk of adverse drug reactions (ADRs) because of metabolic changes and reduced drug clearance associated with aging; this risk is further heightened by the rising number of prescribed medications. The use of multiple drugs increases the potential for drug-drug interactions. These interactions can lead to significant changes in laboratory parameters. Polypharmacy affects different organ systems to varying degrees, subsequently altering laboratory values. Managing laboratory abnormalities in polypharmacy requires a systematic approach grounded in a comprehensive medication history, chronological correlation, clinical judgment, and interdisciplinary collaboration.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"104 ","pages":"515-581"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects. 先进的蛋白质分子建模：方法、突破和未来展望。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-28 DOI: 10.1016/bs.apha.2025.02.005

Vijay Kumar Nuthakki, Rakesh Barik, Sharanabassappa B Gangashetty, Gatadi Srikanth

引用次数: 0

Future prospective of AI in drug discovery. 人工智能在药物发现中的未来展望。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-06 DOI: 10.1016/bs.apha.2025.01.009

Mithun Bhowmick, Sourajyoti Goswami, Pratibha Bhowmick, Santanu Hait, Dipayan Rath, Sabina Yasmin

{"title":"Future prospective of AI in drug discovery.","authors":"Mithun Bhowmick, Sourajyoti Goswami, Pratibha Bhowmick, Santanu Hait, Dipayan Rath, Sabina Yasmin","doi":"10.1016/bs.apha.2025.01.009","DOIUrl":"10.1016/bs.apha.2025.01.009","url":null,"abstract":"Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and lower productivity in pharmaceutical industries, to discover effective drugs and their development. AI can revolutionize the history of drug discovery and development because it will solve all these problems. AI can identify some promising drug candidates, reduce costs, and increase precision. AI algorithms analyze large datasets, predict molecular interactions, and help optimize the design of clinical trials, making the process of drug discovery and biomedical research much more efficient. By combining cutting-edge computation with more conventional pharmaceutical strategy, AI aids in expediting the process of therapeutics development. This chapter is an investigation of the core reasons behind lower approval rates of new drugs, the potential scope of AI to improve the drug discovery and development scenario, and the practical applications in the field. This article will further explore future opportunities, key methodologies, and challenges in the implementation of AI in pharmaceutical research.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"429-449"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-throughput computational screening for lead discovery and development. 用于铅发现和开发的高通量计算筛选。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-06 DOI: 10.1016/bs.apha.2025.01.001

Neelufar Shama Shaik, Harika Balya

{"title":"High-throughput computational screening for lead discovery and development.","authors":"Neelufar Shama Shaik, Harika Balya","doi":"10.1016/bs.apha.2025.01.001","DOIUrl":"10.1016/bs.apha.2025.01.001","url":null,"abstract":"High-throughput computational screening (HTCS) has revolutionized the drug discovery process by enabling the rapid identification and optimization of potential lead compounds. Leveraging the power of advanced algorithms, machine learning, and molecular simulations, HTCS facilitates the efficient exploration of vast chemical spaces, significantly accelerating early-stage drug discovery. The time, cost, and labor in the case of traditional experimental approaches are reduced by the ability to virtually screen millions of compounds for biological activity. This paradigm shift is also facilitated by the combination of omics data, genomics, proteomics, and metabolomics in computational pipelines, allowing detailed understanding of complex biological systems and paving the way toward personalized medicine. Core methods such as molecular docking, QSAR models, and pharmacophore modeling are the foundation of HTCS, providing predictive information on molecular interactions and binding affinities. Machine learning and artificial intelligence are augmenting these tools with more precise prediction accuracy and revealing rich patterns embedded in molecular data. With the development of HTCS, more and more, computational methods are used as a powerful tool in de novo drug design, in which computational tools produce a novel chemical entity that shows optimal fit to the target. Despite its transformative potential, HTCS faces challenges related to data quality, model validation, and the need for robust regulatory frameworks. Nevertheless, as AI-driven approaches, quantum computing, and big data analytics continue to evolve, HTCS is set to become a cornerstone of modern drug discovery, reshaping the field with smarter, more personalized therapeutic strategies that address complex diseases with precision and efficiency.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"185-207"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular dynamics simulations: Insights into protein and protein ligand interactions. 分子动力学模拟：洞察蛋白质和蛋白质配体相互作用。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-06 DOI: 10.1016/bs.apha.2025.01.007

Sonam Grewal, Geeta Deswal, Ajmer Singh Grewal, Kumar Guarve

引用次数: 0

Geropharmacology and gastrointestinal surgery. 老年药理学和胃肠外科。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-04-23 DOI: 10.1016/bs.apha.2025.04.001

Anıl Orhan, Süleyman Demiryas

引用次数: 0

Pharmacological frontiers in senescence: Transforming senescence with drug repurposing. 衰老的药理学前沿：用药物再利用改造衰老。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-06-23 DOI: 10.1016/bs.apha.2025.02.010

Andleeb Shahzadi, Sibel Ozyazgan, Ufuk Çakatay

{"title":"Pharmacological frontiers in senescence: Transforming senescence with drug repurposing.","authors":"Andleeb Shahzadi, Sibel Ozyazgan, Ufuk Çakatay","doi":"10.1016/bs.apha.2025.02.010","DOIUrl":"10.1016/bs.apha.2025.02.010","url":null,"abstract":"Repurposing conventional drugs as senotherapeutics offers a pragmatic and efficient approach to targeting cellular senescence, a key driver of aging-related diseases. Instead of relying solely on novel drug development, repurposing allows for the use of existing drugs with well-characterized pharmacokinetics, safety profiles, and clinical data, thereby accelerating their translation into senescence-targeted interventions. This chapter provides a comprehensive classification of senotherapeutics into senolytics, senomorphics, senoblockers, and senoreversers, detailing their mechanisms of action, molecular targets, and therapeutic applications. By categorizing these conventional agents based on their functional roles, this chapter presents a structured framework for understanding the pharmacological landscape of senotherapeutics. Additionally, this chapter discusses tissue-specific targeting, optimizing the dosing strategy to enhance the precision and safety of repurposed senotherapeutics. This chapter offers a systematic evaluation of drug repurposing, bridges the gap between preclinical and clinical applications, addressing both opportunities and challenges in repurposing the drugs. Eventually, this approach holds the potential to extend healthspan, mitigate age-related dysfunction, and provide more accessible and effective therapeutic options for disorders associated with cellular senescence.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"104 ","pages":"121-176"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADMET tools in the digital era: Applications and limitations. ADMET工具在数字时代：应用和限制。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-12 DOI: 10.1016/bs.apha.2025.01.004

Sonali S Shinde, Prabhanjan S Giram, Pravin S Wakte, Sachin S Bhusari

引用次数: 0

Deep learning: A game changer in drug design and development. 深度学习：改变药物设计和开发的游戏规则。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-06 DOI: 10.1016/bs.apha.2025.01.008

Sushanta Kumar Das, Rahul Mishra, Amit Samanta, Dibyendu Shil, Saumendu Deb Roy

{"title":"Deep learning: A game changer in drug design and development.","authors":"Sushanta Kumar Das, Rahul Mishra, Amit Samanta, Dibyendu Shil, Saumendu Deb Roy","doi":"10.1016/bs.apha.2025.01.008","DOIUrl":"10.1016/bs.apha.2025.01.008","url":null,"abstract":"The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"101-120"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0