Drug Discovery Today最新文献

Gene therapies are innovative treatments that could revolutionize healthcare. Given their unique mechanisms of action, methods of manufacturing and administration, and the potential for long-lasting impact, gene therapies require long-term follow-up (LTFU) of safety and effectiveness post authorization, often using real-world data (RWD). With over 2000 gene therapies in development, there is a need for knowledge-building regarding LTFU studies. Here, we review regulatory, scientific, and operational requirements and considerations for LTFU studies using RWD from a global perspective. The insights in this review support post-authorization LTFU studies for gene therapies, thereby contributing to their safe and effective use.

Microtubules act as molecular 'tracks' for the intracellular transport of accessory proteins, enabling them to assemble into various larger structures, such as spindle fibres formed during the cell cycle. Microtubules provide an organisational framework for the healthy functioning of various cellular processes that work through the process of dynamic instability, driven by the hydrolysis of GTP. In this role, tubulin proteins undergo various modifications, and in doing so modulate various healthy or pathogenic physiological processes within cells. In this review, we provide a detailed update of small molecule chemical agents that interact with tubulin, along with their implications, specifically in non-cancer disease management.

Chemotherapy remains a gold standard in cancer treatment by targeting the rapidly dividing cancer cells. However, chemoresistance is a major obstacle to successful cancer treatment, often leading to recurrence, metastasis, and high mortality. Deubiquitinases (DUBs), enzymes that remove ubiquitin and stabilize proteins, have been implicated in chemoresistance and can either promote therapeutic resistance or enhance sensitivity depending on their targets. In this review, we highlight the chemoresistance mechanisms of DUBs in various cancers, including breast, lung, liver, gastrointestinal, colorectal, ovarian, prostate, and blood cancers. Given these mechanisms, the development of DUB inhibitors has gained considerable attention in cancer therapeutics and combination therapies involving these inhibitors show potential to overcome drug resistance and improving treatment outcomes.

Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyond lifestyle changes and current medications. Existing anti-obesity drugs face limitations regarding efficacy, side effects, weight regain and high costs. Artificial intelligence (AI) is emerging as a pivotal tool in drug discovery, expediting the identification of novel drug candidates and optimizing treatment strategies. This review examines AI's potential in developing next-generation anti-obesity therapeutics, with a focus on glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and their role in discovering anti-obesity peptides. Additionally, it explores integration challenges and offers future perspectives on leveraging AI to reshape the landscape of anti-obesity drug discovery.

The endoplasmic reticulum (ER) plays a crucial part in protein synthesis, folding and quality control. Disruptions in these processes lead to ER stress (ERS) and activate the unfolded protein response (UPR) to restore cellular homeostasis. In gastrointestinal cancers, inositol-requiring enzyme 1α (IRE1α) is a key regulator of the UPR, helping cancer cells adapt to hostile conditions such as hypoxia, oxidative stress and chemotherapy. Elevated IRE1α activity supports tumor survival, progression and metastasis by mitigating ERS-induced apoptosis. However, targeting IRE1α signaling presents a promising therapeutic strategy, potentially impairing cancer cell adaptation to stress and enhancing treatment efficacy. Targeting IRE1α offers promising therapeutic opportunities for gastrointestinal cancer.

Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review discusses AI methodologies that impact clinical trial outcomes, focusing on clinical text embedding, trial multimodal learning, and prediction techniques, while addressing practical challenges and opportunities.

Ovarian cancer (OC) has poor survival statistics and increasing prevalence. One of the new options for its therapy could be overcoming platinum resistance. In this review, we have considered the idea of repositioning angiotensin-converting enzyme inhibitors (ACE-Is) as chemosensitizers. These drugs have been shown to suppress angiogenesis and OC cell migration in preclinical studies. Moreover, clinical data have shown that using ACE-Is with standard chemotherapy prolongs patient survival. Based on this rationale, we discuss the available in vitro models of OC for future studies with ACE-Is and demonstrate an in silico approach that has enabled us to select the most promising molecules: perindopril, ramipril, trandolapril and their diketopiperazine derivatives.

Over the past decade, dissolving microneedles (DMNs) have emerged as a promising approach for drug delivery to the brain. They are tiny devices designed to penetrate biological barriers, offering a painless method for localized and controlled drug delivery. They are suitable for delivering drugs that are susceptible to degradation when delivered orally. Recently, drug-loaded DMNs have been explored for treating neurodegenerative diseases, including Alzheimer's (AD) and Parkinson's disease (PD). DMNs can deliver drugs efficiently to the brain via the intranasal, transdermal, and intracranial routes. In this review, we discuss the use of DMNs for delivering drugs to the brain, recent technological advances, clinical status, and current challenges related to their translation.