Advances in pharmacology最新文献_第2页

Emerging horizons of AI in pharmaceutical research.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-16 DOI: 10.1016/bs.apha.2025.01.016

Sourav Bachhar, Suryasarathi Kumar, Basudeb Dutta, Somnath Das

{"title":"Emerging horizons of AI in pharmaceutical research.","authors":"Sourav Bachhar, Suryasarathi Kumar, Basudeb Dutta, Somnath Das","doi":"10.1016/bs.apha.2025.01.016","DOIUrl":"10.1016/bs.apha.2025.01.016","url":null,"abstract":"Artificial Intelligence (AI) has revolutionized drug discovery by enhancing data collection, integration, and predictive modeling across various critical stages. It aggregates vast biological and chemical data, including genomic information, protein structures, and chemical interactions with biological targets. Machine learning techniques and QSAR models are applied by AI to predict compound behaviors and predict potential drug candidates. Docking simulations predict drug-protein interactions, while virtual screening eliminates large chemical databases through efficient sifting. Similarly, AI supports de novo drug design by generating novel molecules, optimized against a particular biological target, using generative models such as generative adversarial network (GAN), always finding lead compounds with the most desirable pharmacological properties. AI used in clinical trials improves efficiency by pinpointing responsive patient cohorts leveraging genetic profiles and biomarkers and maintaining propriety such as dataset diversity and compliance with regulations. This chapter aimed to summarize and analyze the mechanism of AI to accelerate drug discovery by streamlining different processes that enable informed decisions and bring potential life-saving therapies to market faster, amounting to a breakthrough in pharmaceutical research and development.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"325-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771232","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

Real-world application of molecular docking in drug discovery.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-09 DOI: 10.1016/bs.apha.2025.01.013

Somenath Dutta, Indrani Biswas, Subhabrata Goswami, Ananya Verma

{"title":"Real-world application of molecular docking in drug discovery.","authors":"Somenath Dutta, Indrani Biswas, Subhabrata Goswami, Ananya Verma","doi":"10.1016/bs.apha.2025.01.013","DOIUrl":"10.1016/bs.apha.2025.01.013","url":null,"abstract":"Computational drug designing comprising mainly Molecular Docking has surged in popularity due to its efficiency and precision in identifying potential therapeutic candidates, often collectively referred to as virtual screening. This method enables researchers to screen large compound libraries virtually, significantly speeding up the initial stages of drug development. The significance of molecular docking is particularly evident in the fight against rapidly evolving pathogens like SARS-CoV-2. Lately, the emergence of new COVID-19 variants, such as the highly transmissible XBB.1.5, is incessantly posing challenges. Conventional drug development approaches aimed on a single strain, outgazing the importance of virus' evolution which is well-facilitated by molecular docking that provides better assessment of therapeutic efficacy against multiple variants of this virus. In the present study, molecular docking was executed to screen potential phytochemicals against the spike protein XBB.1.5 variant, known for its critical mutations that enhance infectivity. As part of the entire screening protocol, other tools like Schrödinger's suite, SwissADME, and ProTox-II were utilized to identify the top leads. These computational facilitators assisted in estimation of binding affinity, pharmacokinetics and toxicity profiles. Estimation of these factors led to identification of promising lead compounds that depicted strong binding interactions against the mutated spike protein, suggesting their potential as broad-spectrum antiviral agents. The present study emphasizes the importance of computational tools and techniques like molecular docking in addressing the variants generated against continuous evolution of SARS-COV2. The methodologies adapted can be deployed against other disease towards development of targeted therapeutics, ensuring a proactive approach to global health threats.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"393-413"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770979","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

Innovations in vaccine design: Computational tools and techniques.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-07 DOI: 10.1016/bs.apha.2025.01.015

Riya Nag, Sanchita Srivastava, Saliha Rizvi, Samar Ahmed, Syed Tasleem Raza

{"title":"Innovations in vaccine design: Computational tools and techniques.","authors":"Riya Nag, Sanchita Srivastava, Saliha Rizvi, Samar Ahmed, Syed Tasleem Raza","doi":"10.1016/bs.apha.2025.01.015","DOIUrl":"10.1016/bs.apha.2025.01.015","url":null,"abstract":"The advancements in computational tools have revolutionized vaccine development by organizing and analyzing large-scale immunological data through immuno-informatics. This field combines computational and mathematical approaches to model molecular interactions during antigen presentation and processing. These tools have significantly accelerated vaccine development, making it more efficient and cost-effective. Applications such as SCWRL and SCAP help in side chain and backbone modeling to improve antibodies and forecast secondary structures. Multi-graft and multivalent scaffolds present antigens to elicit strong immune responses; antibodyomics studies the sequences of antibodies to find antibodies that can neutralize. It is another traditional way of doing vaccines where the pathogen's genome is scanned by diacide such as Vaxign to identify the likely vaccine agents. Codon optimization, as implemented with the aid of COOL and OPTIMIZER tools, enhances the output of proteins among which vaccines are needed. These tools also allow for predicting epitope structures the more accurately, or so. Prediction tools that include immunogenicity screening tests that map B-cell epitope and T-cell epitope such as ElliPro and DiscoTope aid in drug design, while the application of Fusion technologies facilitates vaccine development and kit diagnostics. The percentage of time trying to identify possible vaccine candidates is reduced alongside the costs with the application of these tools allowing the improvement in the prediction of vaccine candidates. The purpose of this chapter is to emphasize the invention of computational tools and methods that together are revolutionizing vaccine design and development and to underline the importance of tissue engineering and immunology advances.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"375-391"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770878","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

Modulatory and protective effects of prolyl hydroxylase domain inhibitors in the central nervous system. 脯氨酰羟化酶结构域抑制剂对中枢神经系统的调节和保护作用。

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2024-10-18 DOI: 10.1016/bs.apha.2024.10.006

Konstantinos Matheoudakis, John J O'Connor

{"title":"Modulatory and protective effects of prolyl hydroxylase domain inhibitors in the central nervous system.","authors":"Konstantinos Matheoudakis, John J O'Connor","doi":"10.1016/bs.apha.2024.10.006","DOIUrl":"https://doi.org/10.1016/bs.apha.2024.10.006","url":null,"abstract":"Oxygen is essential for all mammalian species, with complex organs such as the brain requiring a large and steady supply to function. During times of low or inadequate oxygen supply (hypoxia), adaptation is required in order to continue to function. Hypoxia inducible factors (HIF) are transcription factors which are activated during hypoxia and upregulate protective genes. Normally, when oxygen levels are sufficient (normoxia) HIFs are degraded by oxygen sensing prolyl hydroxylase domain proteins (PHD), but during hypoxia PHDs no longer exert influence on HIFs allowing their activation. Given that PHDs regulate the activity of HIFs, their pharmacological inhibition through PHD inhibitors (PHDIs) is believed to be the basis of their neuroprotective benefits. This review discusses some of the potential therapeutic benefits of PHDIs in a number of neurological disorders which see hypoxia as a major pathophysiological mechanism. These include stroke, Parkinson's disease, and amyotrophic lateral sclerosis. We also explore the potential neuroprotective benefits and limitations of PHDIs in a variety of disorders in the central nervous system (CNS). Additionally, the activation of HIFs by PHDIs can have modulatory effects on CNS functions such as neurotransmission and synaptic plasticity, mechanisms critical to cognitive processes such as learning and memory.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"102 ","pages":"211-235"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389611","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

Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-17 DOI: 10.1016/bs.apha.2025.01.014

Heena R Bhojwani, Nikhil P Rajnani, Asawari Hare, Nalini S Kurup

{"title":"Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery.","authors":"Heena R Bhojwani, Nikhil P Rajnani, Asawari Hare, Nalini S Kurup","doi":"10.1016/bs.apha.2025.01.014","DOIUrl":"10.1016/bs.apha.2025.01.014","url":null,"abstract":"In recent years, the pharmaceutical industry has increasingly emphasized the role of lead compound identification in developing new therapeutic agents. Lead compounds show promising pharmacological activity against specific targets and are critical in drug development. Integrative computational approaches streamline this process by efficiently screening chemical libraries and designing potential drug candidates. This chapter highlights various computational techniques for lead compound discovery, including molecular modeling, cheminformatics, ligand- and structure-based drug design, molecular dynamics simulations, ADMET prediction, drug-target interaction analysis, and high-throughput screening. These methods improve drug discovery's efficiency, cost-effectiveness, and target-specific focus. Computational pharmaceutics has gained popularity due to the longer formulation development time which in turn increases the cost as well as decrease in the drug discovery production. Conventionally, formulation development relied on costly and unpredictable trial-and-error methods. However, analyzing the big data, artificial intelligence, and multi-scale modeling in computational pharmaceutics is transforming drug delivery. This chapter provides valuable insights throughout pre-formulation, formulation screening, in vivo predictions, and personalized medicine applications. Multiscale computational modeling is advancing drug delivery systems, enabling targeted treatments with multifunctional nanoparticles. Although in its early stages, this approach helps understand complex interactions between drugs, delivery systems, and patients.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"349-373"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770907","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

Preface.

Advances in pharmacology Pub Date : 2025-01-01 DOI: 10.1016/S1054-3589(25)00060-2

Chaitanay Vinayak Narayan, Swati Verma, Ajmer Singh Grewal, Neelam Singh, Hemlata Nimesh

引用次数: 0

The translational impact of bioinformatics on traditional wet lab techniques.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-26 DOI: 10.1016/bs.apha.2025.01.012

S Suveena, Akhiya Anilkumar Rekha, J R Rani, Oommen V Oommen, Reshmi Ramakrishnan

{"title":"The translational impact of bioinformatics on traditional wet lab techniques.","authors":"S Suveena, Akhiya Anilkumar Rekha, J R Rani, Oommen V Oommen, Reshmi Ramakrishnan","doi":"10.1016/bs.apha.2025.01.012","DOIUrl":"10.1016/bs.apha.2025.01.012","url":null,"abstract":"Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"287-311"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771003","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

Essential database resources for modern drug discovery.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-08 DOI: 10.1016/bs.apha.2025.01.002

Saloni Yadav, Sweta S Koka, Priya Jain, G N Darwhekar, Kuldeep Vinchurkar

引用次数: 0

Computational exploration of viral cell membrane structures for identifying novel therapeutic target.

Advances in pharmacology Pub Date : 2025-01-01 Epub Date: 2025-02-06 DOI: 10.1016/bs.apha.2025.01.005

Kirtiman Mahata, Manti Biswas, Shrestha Sengupta, Chitra Rani, Hridoy R Bairagya

{"title":"Computational exploration of viral cell membrane structures for identifying novel therapeutic target.","authors":"Kirtiman Mahata, Manti Biswas, Shrestha Sengupta, Chitra Rani, Hridoy R Bairagya","doi":"10.1016/bs.apha.2025.01.005","DOIUrl":"10.1016/bs.apha.2025.01.005","url":null,"abstract":"The membrane proteins of viruses play a critical role, and they shield viruses and takes biochemical mechanisms like sticking to the host cell membrane, merging with them, building new viruses, and breaking free. These steps make sure the virus can infect and multiply. But the membrane proteins of Nipah, Zika, SARS-CoV-2, and Hendra virus can cause special kinds of infections. Nipah and Hendra viruses use their fusion protein to join with the host cell membrane. Their glycoprotein interacts with host receptors. The matrix protein helps to build and support the virus structure. Zika virus relies on its envelope protein to attach and fuse with host cells. Its membrane protein keeps the viral envelope stable. SARS-CoV-2 uses its spike protein to enter host cells and its envelope protein helps assemble new viruses. The membrane protein gives structural stability whereas the nucleocapsid protein interacts with the RNA genome. These viral membranes contain various kinds of lipids and proteins and they make up about 30 % of the membrane area. Yet, scientists find it hard to predict their molecular structure and different biological characters. The coarse-grained molecular dynamics simulations, enhanced sampling methods, and various structural bioinformatics investigations on viral proteins provide reliable scientific data. These investigations reveal viral membrane proteins' structural features, movement patterns, and thermodynamic properties. These computer methods are vital for drug discovery because it allows researchers to find new compounds that target viral membrane proteins to prevent their functions.","PeriodicalId":7366,"journal":{"name":"Advances in pharmacology","volume":"103 ","pages":"265-285"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771225","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

Future prospective of AI in drug discovery.

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