Prominent members of the solute carrier (SLC) superfamily of membrane transporters are established targets of central nervous system (CNS) drugs, including the blockbuster neurotransmitter reuptake inhibitors. Numerous other SLC transporters exhibit dysregulated expression in the CNS relative to other tissues, many of which have been genetically or functionally linked to neurological disorders and may therefore represent tractable therapeutic targets. As key regulators of metabolite and ion fluxes, SLCs shape developmental programmes and cellular states associated with epilepsy, neurodegeneration and autism spectrum disorders. Recent advances in assigning biochemical and cellular functions to previously uncharacterized SLCs, together with emerging chemical strategies to increase or decrease transporter abundance, are expanding the therapeutic landscape and positioning SLC biology at the forefront of next-generation CNS drug discovery.
As Takeda’s oveporexton (Orzeyful) becomes a first-in-class treatment for narcolepsy type 1, industry eyes broader indications for orexin receptor agonists.
Artificial intelligence (AI) in drug discovery has attracted increasing interest over the past decade. It is now time for a critical review of progress in the field: where did we advance - and where are we yet to see impact - when it comes to what matters in drug discovery, which is to deliver safer and more efficacious medicines to patients faster? Although a wide variety of AI methods have been developed, applied and benchmarked, evidence of their clinically relevant impact is, so far, disappointingly limited. In this Perspective we discuss potential reasons, including an insufficient focus on clinical translation during model development, difficulties with applying AI algorithms on conditional life science data, and insufficient problem definitions and the resulting underspecification of computational models for real-world use cases. 'Technology push' compared with 'science pull' is also likely to be an underlying factor, as well as the substantial time required to operationalize technical capabilities into systems that are sufficiently scaled and accessible for users. We provide recommendations for the development of AI in drug discovery with the aim of increasing its translational relevance. For example, benchmarking studies of AI tools in drug discovery need to move on from model validation and instead focus on their ability to improve decision making.
T follicular helper (TFH) cells support B cell function by promoting memory B cell differentiation and sustaining long-lasting antibody responses, thereby enabling immunity that protects the host from subsequent infections. TFH cells are essential for orchestrating the antibody-mediated immunity achieved via vaccination, which has had a profound global impact in reducing the morbidity and mortality associated with infectious diseases. TFH cells provide help to B cells both within and outside germinal centres and deliver key signals that drive immunoglobulin class switching and affinity maturation. The formation and function of TFH cells is dynamic and adaptable, with significant cellular plasticity to ensure that robust antibody production accompanies most immune responses. However, when antibodies are directed against self or non-pathogenic antigens, they can contribute to the development of autoimmune diseases, allergic reactions and transplant rejection. Beyond supporting antibody responses, TFH cells have been implicated in cancer, diabetes and atherosclerosis. Given the multifaceted roles of TFH cells in health and disease, and the changes to their biology during normal ageing, the development of targeted strategies to modulate TFH cell activity is an attractive approach to promote health across the lifespan.
