The computational study of immune cell trafficking and the tumour microenvironment is a rapidly advancing field, propelled forward

The gap

Evidence profile

Sourced from the future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 4 journals. Those papers have been cited 45 times in total.

Supporting evidence — 4 representative gaps

• Integrative computational–experimental discovery and translation of antifungal peptides for multidrug-resistant fungi (2026) · Frontiers in Microbiology · 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

  generalfuture work
  Keywords: fungal datasets mechanistic computational prediction models substantially advances systems translational validation important development design clinically
• THE HUMAN MICROBIOME AS A DETERMINANT OF DRUG RESPONSE AND PERSONALIZED MEDICINE (2026) · Zenodo (CERN European Organization for Nuclear Research) · 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.

  generalfuture work
  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) · Frontiers in Oncology · 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

  generalfuture work
  Keywords: cell immune traf cking omics computational advancing arti cial resolution multi predict clinical tumour microenvironment
• Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine (2025) · Pharmaceutics · cited 45× · doi

  The future of AI-powered pharmacogenomics and genome editing is poised to bring groundbreaking advancements in precision medicine, with emerging trends shaping the next decade of biomedical research and clinical applications. One of the most promising directions is the evolution of explainable AI (XAI), which aims to make AI-driven pre- dictions transparent, interpretable, and clinically actionable. Traditional deep learning models function as black-box algorithms, making understanding how AI reaches a spe- cific pharmacogenomic or gene-editing recommendation difficult. XAI seeks to bridge this gap by providing interpretable outputs, enabling physicians and researchers to val- idate AI-generated findings before making clinical decisions. In AI-CRISPR integration, explainability is crucial in ensuring that gene-editing interventions are accurately tar- geted with minimal off-target effects, enhancing the safety and reliability of CRISPR-based therapeutics [45]. Another emerging trend is the integration of AI with synthetic biology and high- throughput CRISPR screens, accelerating the discovery of new druggable targets and therapeutic interventions. AI-powered functional genomics studies uncover previously unrecognized gene-drug interactions, leading to novel drug repurposing opportunities. AI- driven multi-omics analysis enables researchers to construct highly personalized treatment Pharmaceutics 2025, 17, 555 22 of 25 strategies, incorporating genomic, transcriptomic, proteomic, and metabolomic data to tailor therapies to individual patients with unprecedented precision [3]. In oncology, the future implications of AI-enhanced genome editing extend beyond pharmacogenomics to personalized cancer treatment strategies. AI-driven CRISPR-based tumor profiling is being developed to predict tumor evolution, identify resistance mecha- nisms, and optimize combination therapies for cancer patients. AI also enables real-time monitoring of immune checkpoint inhibitor responses, improving patient selection for immunotherapy treatments. These AI-powered approaches are expected to revolution- ize personalized cancer care by ensuring that each patient receives the most effective, genetically tailored treatment regimen [46]. Beyond cancer, AI-powered genome engineering opens new possibilities for treating rare genetic disorders that currently lack effective therapies. AI-enhanced prime editing and base editing are emerging as highly precise alternatives to conventional CRISPR-Cas9 edit- ing, allowing for error-free single-nucleotide corrections without inducing double-strand breaks. These AI-driven gene-editing tools are being investigated for in vivo delivery using nanoparticle-based CRISPR systems, expanding the therapeutic potential of genome editing beyond ex vivo applications. Further, to ensure the benefits of AI-powered phar- macogenomics and genome editing would be equitably distributed. The future efforts of genome engineering must prioritize health equity and accessibility, particularly for underserved and marginalized populations. Integrating AI with frameworks that address social determinants of health could help reduce disparities in access to precision medicine treatment and innovation. Therefore, the combination of AI-guided delivery systems, precision genome editing, and pharmacogenomic profiling could lead to the development of one-time, curative gene therapies for rare monogenic disorders [47–49].

  generalfuture work
  Keywords: editing genome crispr powered gene precision driven treatment therapies cancer future emerging based personalized beyond

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