The computational study of immune cell trafficking and the tumour microenvironment is a rapidly advancing field, propelled forward by developments in artificial intelligence, single-cell omics technologi

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• THE HUMAN MICROBIOME AS A DETERMINANT OF DRUG RESPONSE AND PERSONALIZED MEDICINE (2026) · doi

  The future of microbiome-guided precision therapy will likely depend on integration rather than isolation. Microbiome data alone may rarely be sufficient for decision-making, but when combined with host genomics, metabolomics, clinical phenotype, diet, medication history and environmental exposure, they could significantly improve prediction of therapeutic response (Franzosa et al., 2015; Integrative Human Microbiome Project Research Network, 2019). Artificial intelligence and machine learning are expected to play increasingly important roles in this integration. High-dimensional microbiome data are difficult to interpret using conventional statistical methods alone, especially when nonlinear interactions exist between factors. microbes, host pathways Computational approaches may help identify predictive signatures, therapeutic subtypes and intervention targets. external and www.ejbps.com │ Vol 13, Issue 7, 2026. │ ISO 9001:2015 Certified Journal │ 45 Roopa et al. European Journal of Biomedical and Pharmaceutical Sciences However, robust models will require high-quality training datasets, external validation and transparency to avoid overfitting or false clinical confidence (Franzosa et al., 2015). Another likely development is the movement from descriptive microbiome profiles toward functional and actionable biomarkers. Instead of asking only which organisms are present, future diagnostics may focus on what metabolic functions are active, which enzymes are expressed, or which metabolites are elevated at the time a is needed. This functional orientation aligns better with clinical pharmacology and reproducible across populations may prove more (Franzosa et al., 2015; Wilson and Nicholson, 2017). therapeutic decision dietary advice, patients may Personalized nutrition is also expected to become more refined as microbiome science advances. Rather than receive generic individualized plans based on metabolic phenotype, disease risk, medication use and microbial function. Such strategies could complement drug therapy and improve long-term disease management, especially in metabolic and gastrointestinal disorders (Valdes et al., 2018; Zeevi et al., 2015). Engineered probiotics, targeted bacteriophage therapy, defined microbial and CRISPR-based consortia microbial editing represent more advanced future possibilities. These approaches could allow selective manipulation of harmful pathways while preserving beneficial components of the microbial ecosystem. Their development will require close collaboration among microbiologists, pharmacologists, clinicians, bioinformaticians, ethicists and regulators. In the long term, microbiome assessment may become part of routine precision medicine workflows in selected settings, much as pharmacogenetic testing is used today in some therapeutic areas. Before this can occur, evidence must show that microbiome-guided decisions improve be standardized and are acceptable to patients and healthcare systems. The promise is considerable, but translation must proceed with scientific rigor (ElRakaiby et al., 2014; Wilson and Nicholson, 2017). cost-effective, outcomes, can are 10. CONCLUSION The human microbiome is now recognized as an important determinant of health, disease and therapeutic response. By directly transforming drugs, regulating host metabolic pathways, modulating immune function and generating bioactive metabolites, microbial communities can pharmacokinetics, influence pharmacodynamics, efficacy and toxicity. These effects help explain interindividual variability in treatment response of role pharmacomicrobiomics within precision medicine. substantially growing support and the For clinicians, researchers and students, microbiome science offers a broader framework for understanding why therapies succeed in some patients and fail in others.

  Keywords: microbiome therapeutic microbial metabolic future precision therapy host clinical improve response franzosa pathways patients disease
• A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment (2026) · doi

  The computational study of immune cell trafficking and the tumour microenvironment is a rapidly advancing field, propelled forward by developments in artificial intelligence, single-cell omics technologies, and increasingly sophisticated computational frame- works backed by experimental validation (147, 190). Looking ahead, the field will largely focus on tackling persistent challenges in precision, resolution, and the interpretability of complex model outputs. AI-driven immune modelling is now advancing through the integration of fundamental models with large-scale multi-omics data, including transcriptomics, proteomics, metabolomics, etc. (191). All of these will be combined to predict immune trafficking at an unpredictable resolution. For instance, tools such as graph neural networks will learn to represent TME cell-to-cell communi- cations, enabling zero-shot prediction of interactions between novel chemokine receptors and cross-linking checkpoints (81). Generative AI will synthesize artificial datasets to manage problems such as limited clinical samples and overfitting in rare types of tumours (192). Some AI methods, such as SHAP and attention mechanisms, can credibly enhance the interpretation of such predictions, revealing whether trafficking is being restored or evaded (193). Digital twins of cancer, where patient-specific virtual replicas of TME are emerging as a transformative approach, will continuously integrate clinical data from imaging and multi-omics to stimulate real-time immune cell trafficking and predict individualised

  Keywords: cell immune traf cking omics computational advancing arti cial resolution multi predict clinical tumour microenvironment
• Integrative computational–experimental discovery and translation of antifungal peptides for multidrug-resistant fungi (2026) · doi

  substantially to AFP development, but advances in mechanistic under- standing of biological systems, together with translational validation, will be essential for progress. The integration of mechanistic insight and closed-loop computational design may drive a paradigm shift in the development of rational and clinically applicable AFPs, moving beyond empirical peptide screening approaches. Despite advances in AI-powered peptide engineering, AFP devel- opment continues to face several important challenges. These chal- lenges arise from limited fungal-specific datasets, a lack of mechanistic interpretability, and a persistent gap between computa- tional prediction and translational validation. The discovery of AFPs has benefited substantially from recent advances in computational frameworks. Unfortunately, AFP prediction models are often adapted from antibacterial AMP datasets rather than trained on large fungal- specific datasets. Research on antibacterial peptides has generated large and diverse datasets, while AFP-related datasets remain com- paratively limited, particularly with respect to quantitative antifungal activity, fungal subtype specificity, and in vivo pharmacokinetic and toxicity studies. These limitations may reduce model generalizability and, in turn, can affect the predictive performance of AFP design frameworks and computational screening systems (Mookherjee et al., 2020). Another major limitation is the lack of experimentally resolved fungal target structures. Recent improvements in AlphaFold and cryo- electron microscopy have substantially advanced structural biology. Nevertheless, high-resolution structural information is not yet avail- able for many fungal membrane proteins, biofilm-associated targets, and cell wall synthesis complexes. As a result, important limitations remain regarding docking precision, the interpretation and accuracy of molecular interactions, and the reliability of mechanistic modeling for many AFP–fungal systems (Jumper et al., 2021). Furthermore, contemporary molecular dynamics simulations tend to rely on simpli- fied membrane models. Consequently, accurately modeling ergosterol heterogeneity, lipid microdomains, and diffusion barriers associated with the cell wall remains important for understanding the physico- chemical complexity of fungal membranes (Rodrigues, 2018). Improvements in computational prediction models may facilitate AFP optimization; however, successful translation ultimately requires experimental and clinical validation. Translational AI models increas- ingly incorporate antifungal activity, docking affinity and interaction dynamics with target proteins, and toxicity prediction. Clinically rel- evant AFP candidates are computationally predicted to exhibit favor- able pharmacokinetics, proteolytic stability, immunocompatibility, formulation feasibility, and scalability for industrial production. This highlights the persistent gap

  Keywords: fungal datasets mechanistic computational prediction models substantially advances systems translational validation important development design clinically