Advances in pharmacology最新文献_第3页

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

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

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

Evolution of D-amino acid oxidase inhibitors: From concept to clinic. D- 氨基酸氧化酶抑制剂的演变：从概念到临床

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2024-11-26 DOI: 10.1016/bs.apha.2024.10.016

Ayush Bajaj, Takashi Tsukamoto

引用次数: 0

Neutral sphingomyelinase 2: A promising drug target for CNS disease. 中性鞘磷脂酶2：一个治疗中枢神经系统疾病的有希望的药物靶点。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2024-10-28 DOI: 10.1016/bs.apha.2024.10.015

Meixiang Huang, Matthew Stremlau, Jason Zavras, Cristina Zivko, Ajit G Thomas, Peter Pietri, Vasiliki Machairaki, Barbara S Slusher

{"title":"Neutral sphingomyelinase 2: A promising drug target for CNS disease.","authors":"Meixiang Huang, Matthew Stremlau, Jason Zavras, Cristina Zivko, Ajit G Thomas, Peter Pietri, Vasiliki Machairaki, Barbara S Slusher","doi":"10.1016/bs.apha.2024.10.015","DOIUrl":"10.1016/bs.apha.2024.10.015","url":null,"abstract":"Neutral sphingomyelinase 2 (nSMase2), encoded by the SMPD3 gene, is a pivotal enzyme in sphingolipid metabolism, hydrolyzing sphingomyelin to produce ceramide, a bioactive lipid involved in apoptosis, inflammation, membrane structure, and extracellular vesicle (EV) biogenesis. nSMase2 is abundantly expressed in the central nervous system (CNS), particularly in neurons, and its dysregulation is implicated in pathologies such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), prion diseases, and neuroviral diseases. In this review, we discuss the critical role of nSMase2 in the CNS and its involvement in neurological as well as non-neurological diseases. We explore the enzyme's functions in sphingolipid metabolism, its regulatory mechanisms, and the implications of its dysregulation in disease pathogenesis. The chapter highlights the therapeutic potential of pharmacologically targeting nSMase2 with small molecule inhibitors and emphasizes the need for further research to optimize inhibitor specificity and efficacy for clinical applications. By understanding the multifaceted roles of nSMase2, we aim to provide insights into novel therapeutic strategies for treating complex diseases associated with its dysregulation.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"102 ","pages":"65-101"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389627","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

Inhibition of brain glutamate carboxypeptidase II (GCPII) to enhance cognitive function. 抑制脑谷氨酸羧肽酶II （GCPII）增强认知功能。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2024-11-05 DOI: 10.1016/bs.apha.2024.10.018

Robyn Wiseman, Kristin L Bigos, Amy F T Arnsten, Barbara S Slusher

{"title":"Inhibition of brain glutamate carboxypeptidase II (GCPII) to enhance cognitive function.","authors":"Robyn Wiseman, Kristin L Bigos, Amy F T Arnsten, Barbara S Slusher","doi":"10.1016/bs.apha.2024.10.018","DOIUrl":"10.1016/bs.apha.2024.10.018","url":null,"abstract":"Cognitive deficits are a class of symptoms present in a broad range of disorders that go largely unaddressed by current medications. Disruptions in executive function and memory can be detrimental to patient quality of life, so there is a large unmet medical need for novel therapies to improve cognitive performance. Recent research has highlighted the importance of the type II metabotropic glutamate receptor 3 (mGluR3) in patterns of persistent neuronal firing in the dorsolateral prefrontal cortex of primates, a region critical for higher order cognitive processes. The selective, endogenous agonist of the mGlu3 receptor is N-acetylaspartyl glutamate (NAAG). NAAG is hydrolyzed by the enzyme glutamate carboxypeptidase II (GCPII) which is highly upregulated in neuroinflammatory conditions. Inhibition, GCPII has been investigated as a promising therapeutic avenue in a range of preclinical models and the relationship between NAAG and cognitive function has been studied in multiple clinical populations. The following chapter summarizes the body of preclinical and clinical work supporting the inhibition of GCPII to improve cognitive deficits and the drug discovery approaches that have been utilized to improve pharmacokinetics and brain penetration for future clinical translation of GCPII inhibitor.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"102 ","pages":"27-63"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389609","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

Targeting PAAN/MIF nuclease activity in parthanatos-associated brain diseases. PAAN/MIF核酸酶活性在parthanatas相关脑疾病中的作用

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-01-15 DOI: 10.1016/bs.apha.2024.12.001

Lauren C Guttman, Liu Yang, Meilian Liu, Valina L Dawson, Ted M Dawson

{"title":"Targeting PAAN/MIF nuclease activity in parthanatos-associated brain diseases.","authors":"Lauren C Guttman, Liu Yang, Meilian Liu, Valina L Dawson, Ted M Dawson","doi":"10.1016/bs.apha.2024.12.001","DOIUrl":"10.1016/bs.apha.2024.12.001","url":null,"abstract":"Current FDA-approved drugs for neurodegenerative diseases primarily aim to reduce pathological protein aggregation or alleviate symptoms by enhancing neurotransmitter signaling. However, outcomes remain suboptimal and often fail to modify the course of neurodegenerative diseases. Acute neurologic injury that occurs in stroke and traumatic brain injury (TBI) also suffer from inadequate therapies to prevent neuronal cell death, resulting from both the acute insult and the subsequent reperfusion injury following recanalization of the occlusion in stroke. Approaches to prevent neuronal loss in neurodegenerative disease and acute neurologic injury hold significant therapeutic promise. Parthanatos is a cell death pathway that is activated and plays an integral role in these neurologic disorders. Parthanatos-associated apoptosis-inducing factor nuclease (PAAN), also known as macrophage migration inhibitory factor (MIF) nuclease, is the final executioner in the parthanatic cell death cascade. We posit that inhibiting parthanatos by blocking MIF nuclease activity offers a promising and precise strategy to prevent neuronal cell death in both chronic neurodegenerative disease and acute neurologic injury. In this chapter, we discuss the role of MIF's nuclease activity - distinct from its other enzymatic activities - in driving cell death that occurs in various neurological diseases. We also delve into the discovery, screening, structure, and function of MIF nuclease inhibitors, which have demonstrated neuroprotection in Parkinson's disease (PD) cell and mouse models. This analysis includes essential future research directions and queries that need to be considered to advance the clinical development of MIF nuclease inhibitors. Ultimately, our discussion aims to inspire drug development centered around inhibiting MIF's nuclease activity, potentially resulting in transformative, disease-modifying therapeutics.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"102 ","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389700","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

Harnessing machine learning for rational drug design. 利用机器学习进行合理的药物设计。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-03-03 DOI: 10.1016/bs.apha.2025.02.001

Sandhya Chaudhary, Kalpana Rahate, Shuchita Mishra

{"title":"Harnessing machine learning for rational drug design.","authors":"Sandhya Chaudhary, Kalpana Rahate, Shuchita Mishra","doi":"10.1016/bs.apha.2025.02.001","DOIUrl":"10.1016/bs.apha.2025.02.001","url":null,"abstract":"A crucial part of biomedical research is drug discovery, which aims to find and create innovative medical treatments for a range of illnesses. However, there are intrinsic obstacles to the traditional approach of discovering novel medications, including high prices, lengthy turnaround times, and poor clinical trial success rates. In recent times, the use of designing algorithms for machine learning has become a groundbreaking way to improve and optimise many stages of medication development. An outline of the quickly developing area of machine learning algorithms for drug discovery is given in this review, emphasising how revolutionary treatment development might be. To effectively get a novel medication into the market, modern medicinal development often involves many interconnected stages. The use of computational tools has become more and more crucial in reducing the time and cost involved in the investigation and creation of new medications. Our latest efforts to combine molecular modelling as well as machine learning to create the computational resources for designing modulators utilising a sensible design influenced by the pocket process that targets protein-protein interactions via AlphaSpace are reviewed in this Perspective. A significant shift in pharmaceutical research has occurred with the introduction of AI in drug discovery, which combines cutting-edge computer techniques with conventional scientific investigation to address enduring problems. By highlighting significant advancements and methodologies, this review paper elucidates the many applications of AI throughout several stages of drug discovery.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"209-230"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770842","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