Advances in pharmacology最新文献

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.

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.

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.

Quality of life is strongly influenced by brain aging, which is closely associated with neurodegeneration. With brain aging, various changes occur at the cellular, tissue, and organ levels, such as loss of proteostasis; dysregulation of nutrient sensing; abnormalities in the functions of mitochondria; and changes in neurophysiology. These changes also affect cognitive capabilities and result in mild to severe cognitive impairment. The three main mechanisms of brain aging, namely, senescence, inflammation, and oxidative stress, are being investigated in experimental models. Interventions such as caloric restriction, ketone diets, and intermittent fasting have shown the potential for slowing brain aging by modulating nutrition-sensing pathways, which improve metabolic health, decrease oxidative stress, and reduce inflammatory responses. However, noncompliance with these traditional interventions makes them inefficient. To overcome this drawback, caloric restriction mimetics (CRMs), which tend to produce greater effects than traditional methods without affecting dietary intake, are better therapeutic options. This chapter focuses on the transition of CRMs from preclinical to clinical trials in humans.

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.

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.

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.

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.

In this review, we critically evaluate the contribution of prodrugs to treating two related psychiatric disorders, attention-deficit hyperactivity disorder (ADHD) and binge-eating disorder (BED). ADHD is characterized by inattentiveness, distractibility, impulsiveness, and hyperactivity. BED is also an impulse-control disorder which leads to frequent, compulsive episodes of excessive eating (binges). Lisdexamfetamine (LDX; prodrug of d-amphetamine) is approved to treat both ADHD and BED. Serdexmethylphenidate (SDX; prodrug of d-threo-methylphenidate) is not clinically approved as monotherapy but, in a fixed-dose combination with immediate release d-threo-methylphenidate (Azstarys™), SDX is approved for managing ADHD in children/adolescents. The pharmacological actions of a stimulant mediate both its efficacy and side-effects. Therefore, daily management of ADHD or BED to maintain optimum efficacy and tolerability places highly restrictive requirements on the pharmacokinetic/pharmacodynamic (PK/PD) characteristics of stimulant medications, especially prodrugs. Prodrugs must have good bioavailability and rapid metabolism to provide therapeutic efficacy soon after morning dosing combined with providing stimulant coverage throughout the day/evening. A wide selection of dosages and linear PK for the prodrug and its active metabolite are essential requirements for treatment of these conditions. The proposed neurobiological causes of ADHD and BED are described. The chemical, pharmacological and PK/PD properties responsible for the therapeutic actions of the prodrugs, LDX and SDX, are compared and contrasted. Finally, we critically assess their contribution as ADHD and BED medications, including advantages over their respective active metabolites, d-amphetamine and d-threo-methylphenidate, and also their potential for misuse and abuse.