Adv. Pharmacol. [JOURNAL]
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200 papers
Computational exploration of viral cell membrane structures for identifying novel therapeutic target.
Mahata K, Biswas M, Sengupta S … +2 more , Rani C, Bairagya HR
Adv Pharmacol · 2025 · PMID 40175045 · Publisher ↗
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 s...
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.
Identifying novel drug targets with computational precision.
Dave R, Giordano P, Roy S … +1 more , Imran H
Adv Pharmacol · 2025 · PMID 40175044 · Publisher ↗
Computational precision in drug discovery integrates algorithms and high-performance computing to analyze complex biological data with unprecedented accuracy, revolutionizing the identification of therapeutic targets. Th...
Computational precision in drug discovery integrates algorithms and high-performance computing to analyze complex biological data with unprecedented accuracy, revolutionizing the identification of therapeutic targets. This process encompasses diverse computational and experimental approaches that enhance drug discovery's speed and precision. Advanced techniques like next-generation sequencing enable rapid genetic characterization, while proteomics explores protein expression and interactions driving disease progression. In-silico methods, including molecular docking, virtual screening, and pharmacophore modeling, predict interactions between small molecules and biological targets, accelerating early drug candidate identification. Structure-based drug design and molecular dynamics simulations refine drug designs by elucidating target structures and molecular behaviors. Ligand-based methods utilize known chemical properties to anticipate new compound activities. AI and machine learning optimizes data analysis, offering novel insights and improving predictive accuracy. Systems biology and network pharmacology provide a holistic view of biological networks, identifying critical nodes as potential drug targets, which traditional methods might overlook. Computational tools synergize with experimental techniques, enhancing the treatment of complex diseases with personalized medicine by tailoring therapies to individual patients. Ethical and regulatory compliance ensures clinical applicability, bridging computational predictions to effective therapies. This multi-dimensional approach marks a paradigm shift in modern medicine, delivering safer, more effective treatments with precision. By integrating bioinformatics, genomics, and proteomics, computational drug discovery has transformed how therapeutic interventions are developed, ensuring an era of personalized, efficient healthcare.
Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects.
Nuthakki VK, Barik R, Gangashetty SB … +1 more , Srikanth G
Adv Pharmacol · 2025 · PMID 40175043 · Publisher ↗
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 me...
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.
Harnessing machine learning for rational drug design.
Chaudhary S, Rahate K, Mishra S
Adv Pharmacol · 2025 · PMID 40175042 · Publisher ↗
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 discove...
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.
High-throughput computational screening for lead discovery and development.
Shaik NS, Balya H
Adv Pharmacol · 2025 · PMID 40175041 · Publisher ↗
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,...
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.
Targeting disease: Computational approaches for drug target identification.
Puniani S, Gupta P, Singh N … +2 more , Nagpal D, Sheikh AM
Adv Pharmacol · 2025 · PMID 40175040 · Publisher ↗
With the advancing technology, the way to drug discovery has evolved. The use of AI and computational methods have revolutionized the methods to develop novel therapeutics. In previous years, the methods to discover new...
With the advancing technology, the way to drug discovery has evolved. The use of AI and computational methods have revolutionized the methods to develop novel therapeutics. In previous years, the methods to discover new drugs included high-throughput screening and bioassays which were labor-dependent, extremely expensive and had high probability to inaccurate results. The introduction of Computational studies has changed the process by introducing various methods to determine hit compounds and their methods of analysis. Methods such as molecular docking, virtual screening, and dynamics have changed the path to optimize and produce lead molecules. Similarly, network pharmacology also works on the identification of target proteins complex disease pathways with the help of protein-protein interactions and obtaining hub proteins. Various tools such as STRING database, cytoscape and metascape are employed in the study to construct a network between the proteins responsible for the disease progression and helps to obtain the vital target proteins, simplifying the process of drug-target identification. These approaches when employed together, results in obtaining results with better precision and accuracy which can be further validated experimentally, saving the resources and time. This chapter highlights the foundation of computational approaches in drug discovery and provides a detailed understanding of how these approaches are helping the researchers to produce novel solutions using artificial intelligence and machine learning.
Molecular dynamics simulations: Insights into protein and protein ligand interactions.
Grewal S, Deswal G, Grewal AS … +1 more , Guarve K
Adv Pharmacol · 2025 · PMID 40175039 · Publisher ↗
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 t...
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.
Molecular and structure-based drug design: From theory to practice.
Saini M, Mehra N, Kumar G … +2 more , Paul R, Kovács B
Adv Pharmacol · 2025 · PMID 40175038 · Publisher ↗
Structure-based drug design (SBDD) and molecular docking have revolutionized drug discovery by providing effective strategies for identifying and optimizing therapeutic agents. This review highlights the principles and m...
Structure-based drug design (SBDD) and molecular docking have revolutionized drug discovery by providing effective strategies for identifying and optimizing therapeutic agents. This review highlights the principles and methodologies of SBDD, which uses high-resolution structural data of biological targets to design drugs with enhanced selectivity and efficacy. Techniques like nuclear magnetic resonance (NMR) spectroscopy, cryo-electron microscopy (cryo-EM), and X-ray crystallography are key in providing the structural information necessary for SBDD. Molecular docking, a crucial component of modern drug discovery, simulates interactions between drug candidates and biological targets. By predicting how a ligand fits into a receptor's binding site, researchers can assess the strength and nature of these interactions, guiding compound selection. Advances in molecular docking have incorporated machine learning to improve scoring functions and prediction accuracy. Docking studies include search algorithms, scoring functions, and binding site identification to predict the optimal orientation of a ligand when bound to a protein. Despite its widespread use, molecular docking has limitations, such as challenges in achieving high prediction accuracy, modeling protein flexibility, and accounting for solvation effects. Improvements in computational power and the integration of machine learning techniques are addressing these issues. This review emphasizes the importance of ongoing innovation and interdisciplinary collaboration in enhancing molecular docking and its role in discovering novel therapies.
Deep learning: A game changer in drug design and development.
Das SK, Mishra R, Samanta A … +2 more , Shil D, Roy SD
Adv Pharmacol · 2025 · PMID 40175037 · Publisher ↗
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 lif...
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.
Innovative computational approaches in drug discovery and design.
Das IJ, Bhatta K, Sarangi I … +1 more , Samal HB
Adv Pharmacol · 2025 · PMID 40175036 · Publisher ↗
In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algori...
In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.
Preface.
Tsukamoto T
Adv Pharmacol · 2025 · PMID 39929586 · Publisher ↗
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Neutral sphingomyelinase 2: A promising drug target for CNS disease.
Huang M, Stremlau M, Zavras J … +5 more , Zivko C, Thomas AG, Pietri P, Machairaki V, Slusher BS
Adv Pharmacol · 2025 · PMID 39929585 · Full text
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, membr...
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.
Evolution of D-amino acid oxidase inhibitors: From concept to clinic.
Bajaj A, Tsukamoto T
Adv Pharmacol · 2025 · PMID 39929584 · Publisher ↗
D-amino acid oxidase (DAAO) is a flavin-dependent peroxisomal monooxygenase with a substrate preference for glycine and certain small hydrophobic D-amino acids. Although the biochemical properties of the enzyme have been...
D-amino acid oxidase (DAAO) is a flavin-dependent peroxisomal monooxygenase with a substrate preference for glycine and certain small hydrophobic D-amino acids. Although the biochemical properties of the enzyme have been extensively studied since 1930s, the therapeutic interest in targeting the enzyme emerged more recently after the physiological significance of endogenous D-serine, a substrate for DAAO, was recognized in 1990s. This triggered a new wave of efforts by many researchers to develop more potent and drug-like DAAO inhibitors with greater translational potential. This chapter recounts the evolution of DAAO inhibitors since then driven by new molecular design strategies guided by structural biology. Some of these inhibitors were investigated in a range of preclinical in vivo studies to assess pharmacokinetics, pharmacodynamics, and behavioral pharmacology. Most importantly, these efforts culminated with the discovery of TAK-831 (luvadaxistat), an orally available brain-penetrant DAAO inhibitor currently under clinical development, representing a true bench-to-bedside success in this field.
Inhibition of brain glutamate carboxypeptidase II (GCPII) to enhance cognitive function.
Wiseman R, Bigos KL, Arnsten AFT … +1 more , Slusher BS
Adv Pharmacol · 2025 · PMID 39929583 · Full text
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 l...
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.
Drug discovery strategy for TAK-418, a specific inhibitor of LSD1 enzyme activity, as a novel therapy for autism.
Matsuda S, Hattori Y, Kimura H
Adv Pharmacol · 2025 · PMID 39929582 · Publisher ↗
The pathophysiology of neurodevelopmental disorders is associated with multiple genetic and environmental risk factors. Epigenetics, owing to its potential to recover global gene expression changes associated with diseas...
The pathophysiology of neurodevelopmental disorders is associated with multiple genetic and environmental risk factors. Epigenetics, owing to its potential to recover global gene expression changes associated with disease conditions, is a crucial target to address neurodevelopmental disorders influenced by genetic and environmental factors. Here, we discuss the discovery of selective inhibitors of lysine-specific demethylase 1 (LSD1) enzyme activity and their therapeutic potential for neurodevelopmental disorders through epigenetic regulation in the brain. Conventional LSD1 inhibitors not only inhibit LSD1 enzymatic activity but also interfere with LSD1-cofactor complex formation, thus leading to hematological side effects. Notably, investigations on the structure-activity relationship have revealed (aminocyclopropyl)benzamide and (aminocyclopropyl)thiophene carboxamide derivatives as novel series of LSD1 inhibitors with fewer hematological side effects. Subsequently, we discovered T-448 and TAK-418 (clinical candidate) that selectively and potently inhibit LSD1 enzymatic activity without disrupting the LSD1-cofactor complex, resulting in potent epigenetic modulation without significant hematological toxicity risks in rodents. T-448 and TAK-418, at doses that achieved almost complete LSD1 occupancy in the brain, improved behavioral abnormalities in multiple rodent models of neurodevelopmental disorders. Furthermore, comprehensive RNA expression analyses revealed that, although gene expression abnormalities exhibited limited commonality across disease models, TAK-418 normalized each aberrant gene expression pattern in these rodent models. A positron emission tomography tracer was discovered to potentially measure the occupancy of TAK-418 at the LSD1 active site in the brain to improve the translatability of its preclinical efficacy to therapeutic effects in humans. TAK-418-type LSD1 inhibitors may offer novel treatment options for neurodevelopmental disorders.
Soluble epoxide hydrolase: Mechanisms and therapeutic potential in psychiatric and neurological disorders.
Zhao MM, Yang JJ, Hashimoto K
Adv Pharmacol · 2025 · PMID 39929581 · Publisher ↗
Soluble epoxide hydrolase (sEH), encoded by the EPHX2 gene, is a critical enzyme involved in the metabolism of polyunsaturated fatty acids, specifically anti-inflammatory epoxy fatty acids (EpFAs). By converting EpFAs in...
Soluble epoxide hydrolase (sEH), encoded by the EPHX2 gene, is a critical enzyme involved in the metabolism of polyunsaturated fatty acids, specifically anti-inflammatory epoxy fatty acids (EpFAs). By converting EpFAs into less active forms, sEH promotes inflammation. Preclinical data using knock-out and overexpression of the Ephx2 gene have demonstrated its key role in the development and progression of symptoms in various disease models. Inhibition of sEH increases EpFAs, thereby enhancing their anti-inflammatory effects and reducing the levels of pro-inflammatory mediators. Numerous preclinical studies suggest that sEH inhibitors show promise in reducing inflammation and its related symptoms across various diseases, highlighting their therapeutic potential. This chapter reviews the role of sEH in the development and progression of various disorders including psychiatric disorders (depression, schizophrenia, autism spectrum disorder), neurological disorders (Alzheimer's disease, Parkinson's disease, brain injury), and pain.
Modulatory and protective effects of prolyl hydroxylase domain inhibitors in the central nervous system.
Matheoudakis K, O'Connor JJ
Adv Pharmacol · 2025 · PMID 39929580 · Publisher ↗
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 o...
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.
Phosphodiesterase 4 (PDE4) and neurological disorders: A promising frontier in neuropharmacology.
Nahid S, Saeedi S, Hopkins CR
Adv Pharmacol · 2025 · PMID 39929579 · Publisher ↗
The phosphodiesterase 4 (PDE4) enzyme plays a crucial role in the central nervous system (CNS). It is extensively expressed in mammalian brain, where it regulates intracellular cyclic adenosine monophosphate (cAMP) level...
The phosphodiesterase 4 (PDE4) enzyme plays a crucial role in the central nervous system (CNS). It is extensively expressed in mammalian brain, where it regulates intracellular cyclic adenosine monophosphate (cAMP) levels. Dysregulation of PDE4 and cAMP balance is associated with various neurodegenerative diseases. By inhibiting PDE4 with drugs, cAMP levels can be stabilized, potentially improving symptoms in mental and neurological disorders such as cognition, depression, and Parkinson's disease. Mechanistically, PDE4 inhibitors exert anti-inflammatory and neuroprotective effects by increasing cAMP accumulation and activating protein kinase A (PKA). This chapter will review the relevant neurological disorders that PDE4 has been associated with and review the preclinical and clinical studies.
Biological and therapeutic significance of targeting NLRP3 inflammasome in the brain and the current efforts to develop brain-penetrant inhibitors.
Kaur B, Biby S, Namme JN … +3 more , More S, Xu Y, Zhang S
Adv Pharmacol · 2025 · PMID 39929578 · Full text
NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a pivotal regulator of the innate immune system, orchestrates inflammatory responses implicated in neurodegenerative and inflammatory diseases. Ove...
NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a pivotal regulator of the innate immune system, orchestrates inflammatory responses implicated in neurodegenerative and inflammatory diseases. Over the past 20 years, the exploration of NLRP3 activation pathways has advanced significantly. Upon NLRP3 activation, it initiates the formation of a cytosolic multiprotein complex known as the inflammasome. This complex activates caspase-1, which then processes proinflammatory cytokines IL-1β and IL-18 and leads to gasdermin-mediated cell death, pyroptosis. Structural insights into NLRP3 inflammasome assembly and caspase-1 activation have spurred development of novel small molecule inhibitors targeting this pathway, aiming to mitigate excessive inflammation without compromising immune surveillance. The initial NLRP3 inhibitor reported was glyburide, an FDA-approved antidiabetic drug of the sulfonylurea class, which was found to inhibit the release of IL-1β induced by stimuli in human monocytes and murine macrophages. Subsequently, MCC950 (also known as CRID3), a direct NLRP3 inhibitor, was discovered. While showing promising results in preclinical and clinical trials for treating diseases, higher doses of MCC950 led to elevated transaminase levels and hepatotoxicity concerns. Recent studies using MCC950 as a research tool have prompted the development of safer and more effective NLRP3 inhibitors, including a series of compounds currently undergoing clinical trials, highlighting the potential of NLRP3 inhibitors in attenuating disease progression and improving therapeutic outcomes. In this chapter, we delve into the latest progress in understanding the mechanism of NLRP3 inflammasome activation and its roles in the pathophysiology of neurological diseases. We also summarize recent development of small molecule NLRP3 inhibitors along with the associated obstacles and concerns.
Targeting PAAN/MIF nuclease activity in parthanatos-associated brain diseases.
Guttman LC, Yang L, Liu M … +2 more , Dawson VL, Dawson TM
Adv Pharmacol · 2025 · PMID 39929577 · Publisher ↗
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 of...
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.
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