Sickle cell disease (SCD) is caused by pathogenic variants in the β-globin gene (HBB), most commonly the variant responsible for hemoglobin S, and affects an estimated 515,000 newborns each year, with the highest burden occurring in sub-Saharan Africa. Gene editing and hematopoietic stem cell transplantation have changed the therapeutic landscape, but their cost, technical complexity, and procedure-related risks still limit their wider use. For this reason, pharmacological induction of fetal hemoglobin (HbF) remains an important therapeutic strategy. HbF reduces HbS polymerization and is associated with lower disease severity, morbidity, and mortality. Among the mechanisms involved in γ-globin silencing, epigenetic regulation offers several targets that can be explored using small molecules. This review discusses medicinal chemistry approaches primarily targeting HDAC1/2, LSD1, and DNMT1, with emphasis on inhibitor classes, binding mechanisms, structural features, preclinical evidence, and translational limitations. The available data show that each target presents a distinct set of challenges. HDAC-directed strategies require improved isoform and cellular selectivity; LSD1 inhibitors must reconcile strong HbF induction with the risks associated with prolonged target engagement; and DNMT1 modulation is moving from DNA-incorporating nucleoside analogs toward reversible non-nucleoside inhibitors. We also discuss emerging approaches, including multi-target epigenetic modulation and targeted protein degradation. Together, these strategies show how a better understanding of γ-globin repression may guide the development of safer and more accessible HbF-inducing agents.
Mateus Mello de Souza, A. R. Pavan, Victor Gabriel de Faria Pastre et al.· European journal of medicina...· 0 citations
Artificial Intelligence (AI) has become a fundamental driver of scientific progress, particularly in disease diagnosis, drug development, and drug delivery optimization. The intersection of AI, drug design, and nanosystems for delivery is accelerating the advancement of personalized nanomedicines and innovative diananostic and therapeutic approaches. This narrative review explores the integration of nanotechnology and AI in healthcare, with emphasis on cancer treatment, drug discovery, antimicrobials, and nanotoxicology. Based on studies published between 2020 and 2025, the analysis highlights AI applications in molecular profiling, predictive models for antimicrobial resistance, and nanomaterial safety assessments. In oncology, AI combined with high-performance computing enables detailed molecular profiling, supporting personalized cancer therapies. Machine learning models are also applied to the design of antimicrobial drugs, prediction of antibacterial efficacy, and strategies to counter resistant strains. Furthermore, AI plays a critical role in nanotoxicology, predicting adverse effects of nanomaterials and interpreting complex toxicological data. The convergence of AI and nanotechnology is revolutionizing healthcare by providing more accurate diagnostics, tailored treatments, and improved safety evaluations. AI-driven models enhance drug discovery, optimize delivery systems, and strengthen toxicological assessments. However, continued research is necessary to refine these technologies and ensure their effective translation into clinical practice.
