Advances in pharmacology最新文献

Beyond cell death and differentiation, cell senescence is profoundly influenced by the social nature of cells, which is intricately linked to cell communication as a fundamental aspect of biological systems shaping both individual and collective cellular behaviors. As demonstrated by cellular parabiosis, sophisticated communication plays a critical role in maintaining tissue health and delaying age-related diseases. It is now widely accepted that signaling crosstalk, through both direct cell-to-cell interactions and indirect mechanisms, drives cell heterogeneity and cell state transitions, and that increasing cell heterogeneity with age significantly contributes to the development of age-related diseases. Aging is also associated with increased stem cell heterogeneity, leading to functional decline and decreased regenerative capacity. Heterochronic parabiosis and stem cell transplantation studies have indicated that impaired regeneration observed in aging organisms can be reversed by a youthful systemic environment that restores balanced signaling and rejuvenates aged cells. Multiple reports on autologous and allogeneic transplantation have confirmed the rejuvenative potential of hematopoietic stem cell and various tissue-derived mesenchymal stem cell transplants, providing insights into the potential of integrating cellular parabiosis-like approaches into regenerative medicine to combat aging and its associated pathologies. Scientific advances in these areas are now progressing to clinical trials. In this chapter, we first summarize the current knowledge of cellular parabiosis as a complex physiological process and emphasize heterogeneity in senescent cells. Subsequently, it reviews therapeutic approaches for treating aging-induced stem cell dysfunction as innovative solutions for addressing this issue. Finally, the chapter discusses future directions and challenges for senotherapeutic applications, highlighting their potential to advance the field of regenerative medicine.

In the fast-expanding field of drug discovery, researchers and pharmaceutical professionals require immediate access to critical database resources. This book chapter explains essential databases used in various stages of drug development, such as target selection, chemical screening, and clinical trial management. Databases including PubChem, ChEMBL, and Drug Bank, highlight their contributions to providing detailed chemical knowledge, biological activity data, and drug interaction profiles. Using powerful computer programs like AI and machine learning to combine data from these sources improves decision-making, speeds up time-to-market, and raises the chances of finding effective medicines. This book chapter signifies the importance of key databases, their uses, and how they integrate into the current drug discovery process.

Operating on elderly patients has always been a risky task for surgeons. They are not only frail and susceptible to operative complications, but they also require meticulous preparation before their surgery to secure the optimal result. Unfortunately, most of these patients have comorbidities which increase the challenge. Even though the medication they use is helpful to control their diseases, it can change the plan of the surgery and its outcome dramatically. Postoperative medications and treatment also have a unique importance; underestimating them may lead to catastrophic results. Restarting routine medications of patients with multiple comorbidities as quickly as we can when we perform a successful surgery is also crucially important to control the associated diseases. This chapter will focus on how senility influences our surgical practices; how pharmaceutical agents might affect the survivability of elderly patients undergoing gastrointestinal surgery, and the potential roles of several senotherapeutics in gastrointestinal disorders.

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.

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.

Molecular dynamics (MD) simulations are a powerful tool for studying biomolecular systems, offering in-depth insights into the dynamic behaviors of proteins and their interactions with ligands. This chapter delves into the fundamental principles and methodologies of MD simulations, exploring how they contribute to our understanding of protein structures, conformational changes, and the mechanisms underlying protein-ligand interactions. We discuss the computational techniques, force fields, and algorithms that drive MD simulations, highlighting their applications in drug discovery and design. Through case studies and practical examples, we illustrate the capabilities and limitations of MD simulations, emphasizing their role in predicting binding affinities, elucidating binding pathways, and optimizing lead compounds. This chapter offers a thorough understanding of how MD simulations can be leveraged to advance the study of protein-ligand interactions.

The contemporary advancements in molecular modeling of proteins have significantly enhanced our comprehension of biological processes and the functional roles of proteins on a global scale. The application of advanced methodologies, including homology modeling, molecular dynamics simulations, and quantum mechanics/molecular mechanics strategies, has empowered numerous researchers to forecast the behavior of protein macromolecules, elucidate drug-protein interactions, and develop drugs with enhanced precision. This chapter elucidates the advent of deep learning algorithms such as AlphaFold, a notable advancement that has significantly improved the precision of intricate protein structure predictions. The recent advancements have significantly enhanced the precision of protein predictions and expedited drug discovery and development processes. Integrating approaches like multi-scale modeling and hybrid methods incorporating reliable experimental data is anticipated to revolutionize and offer more significant implications for precision medicine and targeted treatments.

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.

The high rate of medication failures poses a significant challenge for the pharmaceutical sector. Selecting appropriate data from experiments for ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction and applying it effectively in the context of physiological characteristics is difficult. Currently, ADMET prediction is conducted early in the drug design process to filter out molecules with weak pharmacokinetic properties. Numerous ADMET models for prediction have been designed using computational methods. Verified ADMET datasets have been determined through experiments, utilizing key classifying factors and descriptors to develop in silico approaches. This chapter discusses the relevance of ADMET evaluation in drug design, methodologies for model creation, available ADMET predictive tools, and the limitations of these predicted models.

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.