Iron and ascorbic acid (vitamin C) are essential nutrients for the normal growth and development of humans, and their deficiency can result in serious diseases. Their interaction is of nutritional, physiological, pharmacological and toxicological interest, with major implications in health and disease. Millions of people are using pharmaceutical and nutraceutical preparations of these two nutrients, including ferrous ascorbate for the treatment of iron deficiency anaemia and ascorbate combination with deferoxamine for increasing iron excretion in iron overload. The main function and use of vitamin C is its antioxidant activity against reactive oxygen species, which are implicated in many diseases of free radical pathology, including biomolecular-, cellular- and tissue damage-related diseases, as well as cancer and ageing. Ascorbic acid and its metabolites, including the ascorbate anion and oxalate, have metal binding capacity and bind iron, copper and other metals. The biological roles of ascorbate as a vitamin are affected by metal complexation, in particular following binding with iron and copper. Ascorbate forms a complex with Fe³⁺ followed by reduction to Fe²⁺, which may potentiate free radical production. The biological and clinical activities of iron, ascorbate and the ascorbate–iron complex can also be affected by many nutrients and pharmaceutical preparations. Optimal therapeutic strategies of improved efficacy and lower toxicity could be designed for the use of ascorbate, iron and the iron–ascorbate complex in different clinical conditions based on their absorption, distribution, metabolism, excretion, toxicity (ADMET), pharmacokinetic, redox and other properties. Similar strategies could also be designed in relation to their interactions with food components and pharmaceuticals, as well as in relation to other aspects concerning personalized medicine.

The common IVSI-110 (G>A) β-thalassemia mutation is a paradigm for intronic disease-causing mutations and their functional repair by non-homologous end joining-mediated disruption. Such mutation-specific repair by disruption of aberrant regulatory elements (DARE) is highly efficient, but to date, no systematic analysis has been performed to evaluate disease-causing mutations as therapeutic targets. Here, DARE was performed in highly characterized erythroid IVSI-110(G>A) transgenic cells and the disruption events were compared with published observations in primary CD34⁺ cells. DARE achieved the functional correction of β-globin expression equally through the removal of causative mutations and through the removal of context sequences, with disruption events and the restriction of indel events close to the cut site closely resembling those seen in primary cells. Correlation of DNA-, RNA-, and protein-level findings then allowed the extrapolation of findings to other mutations by in silico analyses for potential repair based on the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) 9, Cas12a, and transcription activator-like effector nuclease (TALEN) platforms. The high efficiency of DARE and unexpected freedom of target design render the approach potentially suitable for 14 known thalassemia mutations besides IVSI-110(G>A) and put it forward for several prominent mutations causing other inherited diseases. The application of DARE, therefore, has a wide scope for sustainable personalized advanced therapy medicinal product development for thalassemia and beyond.

Haemoglobinopathies, including thalassaemias and sickle-cell syndromes, are demanding, lifelong conditions that pose a significant burden to patients, families, and healthcare systems. Despite the therapeutic advances and the resulting improvements in prognosis accomplished in past decades, these patients still face important challenges, including suboptimal access to quality care in areas with developing economies, changing epidemiology due to massive migration flows, an evolving clinical spectrum due to ageing in well-treated patients, and limited access to novel high-cost therapies. We herein describe the organization of healthcare services for haemoglobinopathies in Cyprus—with particular focus on beta-thalassaemia, the most prevalent condition in this region—along with selected patient outcomes. This report aims at underscoring the fact that nationally funded and well-coordinated prevention and care programmes for chronic and complex conditions, such as haemoglobinopathies, with active involvement from patient organizations lead to effective disease control and excellent outcomes in survival, quality of life, social adaptation, and public health savings, and allow timely and effective responses to emerging crises, such as the COVID-19 pandemic. The Cyprus paradigm could therefore serve as a blueprint for the organization or adaptation of haemoglobinopathy programs in other countries since these disorders are still widely occurring.

Thousands of drugs and nutraceuticals along with their combinations can be used to select candidate therapeutics for targeting the transmission, proliferation and the fatal or severe symptoms of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in order to reduce the unacceptably high mortality rate observed in the coronavirus disease 2019 (COVID-19) pandemic and its associated negative effects on daily life worldwide

While the end of the COVID-19 pandemic was announced earlier in 2023 by WHO, the currently dominating COVID-19 virus variants, such as the omicron sub-lineages XBB

Background: Artificial intelligence (AI) is transforming pharmacology and pharmacotherapy by enabling the integration of large, heterogeneous datasets to support precision and personalised medicine. However, the reliability of AI-assisted therapeutic decision-making depends fundamentally on the selection, quality, and interpretation of pharmacological and clinical input features or parameters. Current AI models frequently overlook the multidimensional complexity of drug- and patient-specific factors, limiting their clinical applicability and generalisability. Methods: This narrative review was prepared based on 45 years of experimental work and relevant published research in drug design, development, and clinical experience in iron chelation therapy and personalised treatment approaches. Additional literature was identified through targeted searches of PubMed and Scopus using terms related to AI, machine learning, pharmacology, pharmacotherapy, and personalised medicine. Key pharmacological and clinical input features relevant to AI-assisted personalised drug selection include physicochemical drug properties, absorption, distribution, metabolism, excretion and toxicity characteristics, route of administration, drug interactions, pharmacokinetics, pharmacodynamics, multi-omics data, therapeutic efficacy, diagnostic biomarkers, statistical validation, governance, and explainable AI. A conceptual framework for AI-assisted personalised drug selection is also proposed as an example. Results: It is suggested that reliable AI-assisted pharmacotherapy requires the integration of diverse, interdependent datasets and parameters describing drug characteristics, patient variability, clinical outcomes, and real-world evidence. The incorporation of pharmacogenomics, electronic health records, diagnostic imaging and profiling, and validated computational descriptors can improve prediction of drug efficacy, toxicity, interactions, and therapeutic response. Furthermore, robust model validation, data governance, cybersecurity, and continuous monitoring are identified as essential prerequisites for safe clinical implementation. The proposed conceptual framework illustrates how clinical admissibility filtering, model-based ranking, local attribution, and clinically supervised decision support may enhance personalised drug selection while maintaining human oversight. Conclusions: Artificial intelligence has considerable potential to improve drug selection and personalised pharmacotherapy. However, its success depends on comprehensive integration of pharmacological knowledge with high-quality clinical data, rigorous validation, and responsible governance. The multidimensional framework presented here provides a foundation for the potential development of clinically interpretable, reliable, and patient-centred AI systems capable of supporting safer and more effective precision medicine.