Maria Tsakiroglou | Search PubMed

PubMed2026

UGT1A1 genotype testing for irinotecan: A guideline developed by the UK Centre of Excellence in Regulatory Science and Innovation in Pharmacogenomics (CERSI-PGx).

Irinotecan, a topoisomerase I inhibitor, is available as both non-pegylated and pegylated formulations. The non-pegylated formulation is licensed for use in advanced colorectal cancer either in combination with other agents or as monotherapy. However, it is also used off-label across a range of gastrointestinal malignancies and in rare malignancies such as glioblastoma and sarcomas. The pegylated formulation is licensed for use as combination therapy in adult patients with metastatic pancreatic adenocarcinoma. Irinotecan is hydrolysed to its active metabolite, SN-38, which is predominantly inactivated by the enzyme uridine diphosphate glucuronosyltransferase UGT1A1. UGT1A1 is encoded by the gene UGT1A1, which is polymorphically expressed, with allele frequencies varying across populations. Poor metabolizers carry two variants that reduce UGT1A1 enzyme expression or activity, leading to increased risk of irinotecan toxicity. Any patient who is about to be prescribed irinotecan for an epithelial malignancy should have pharmacogenetic testing, to identify clinically relevant UGT1A1 variants, where testing is available. Irinotecan dose should be reduced by 30% at Cycle 1 treatment in poor metabolizers for all indications, with doses titrated thereafter based on tolerability and neutrophil counts. The lack of evidence precludes us from making any recommendation for rare malignancies such as sarcomas. Our guideline is consistent with other international pharmacogenetics prescribing guidelines. This guideline is grounded in the latest evidence but cannot account for all individual factors relevant to patient care. Therefore, prescribers must conduct a thorough assessment of each patient's risk-benefit profile, ensuring that therapy is optimized to maximize benefits while minimizing potential harms.

Humans↗

PubMed2026

Beyond data and technology: the need for new thinking to enable the era of precision prevention.

BACKGROUND: Global flagship initiatives increasingly advocate for proactive health maintenance to alleviate the growing burden on reactive, disease-focused healthcare systems. Precision prevention is conceived as the targeted modulation of causal pathways across the disease continuum, from latent risk and pre-disease states to clinical manifestation, surpassing conventional public health prevention strategies that prioritise managing population-level risk factors. Traditional discovery and implementation models, however, remain poorly aligned with the pace and breadth of scientific and technological advances. This review outlines key barriers to scaling precision prevention and argues for the integration of conceptual, methodological, and policy perspectives into a single implementation‑oriented framework. MAIN: Individualised risk stratification lies at the core of precision prevention. Genomics serves as a stable substrate for lifetime susceptibility assessment, while meaningful prediction in multifactorial chronic disease requires additional risk monitoring using dynamic intermediate molecular markers and high-resolution exposomic data. Machine learning and other artificial intelligence (AI) methods are increasingly helpful tools for integrating large, heterogeneous and temporally structured real-world data to generate personalised predictions of health trajectories. Trustworthy AI-enabled risk prediction or decision-support systems are expected to provide transparency about model logic, assumptions and performance. In discovery, existing diagnostic classifications and conventional case-control designs can obscure mechanistic heterogeneity. Shifting toward precision phenotyping and biologically grounded disease redefinition could reveal a new layer of molecular understanding. Evidence generation strategies that reflect the temporal change of disease, including high‑risk enrichment, surrogate endpoints, and adaptive, trajectory-based monitoring, are particularly important for common conditions with prolonged latency periods (e.g., cancer, cardiovascular disease). Features often dismissed as "noise", such as stochastic molecular variation and minimal exposures, may in fact encode meaningful individual-level signals and thus merit investigation. CONCLUSION: To shift healthcare from reactive treatment toward proactive health maintenance requires coordinated action from stakeholders to reshape the pillars of discovery, reform outcome assessments and modernise implementation strategies.

Humans↗