CRISPR-Cas9, adapted from the bacterial Type II CRISPR adaptive immune system, functions as a programmable RNA-guided endonuclease that employs a single-guide RNA to direct Cas9 to specific genomic loci. CRISPR-Cas9 has transformed targeted genome editing by replacing complex protein engineering with programmable Watson–Crick base pairing between the guide RNA and target DNA. This review was developed following a structured literature search of major biomedical databases and clinical trial registries to synthesize current evidence on the therapeutic applications of CRISPR-Cas9 in oncology and inherited genetic disorders. Clinical studies of
ex vivo BCL11A
-enhancer editing have shown fetal hemoglobin reactivation, with most evaluable participants with sickle cell disease remaining free of severe vaso-occlusive crises for the prespecified period and most evaluable participants with transfusion-dependent β-thalassemia achieving sustained transfusion independence.
In vivo
reductions in circulating transthyretin protein levels have been achieved for transthyretin amyloidosis via lipid nanoparticle delivery, while clinically meaningful improvements in selected measures of visual function were observed in a subset of patients receiving subretinal AAV-delivered CRISPR editing for
CEP290
-associated Leber congenital amaurosis type 10. Preclinical and early clinical studies have further investigated CRISPR-engineered T cells designed to improve antitumor activity, persistence, or resistance to inhibitory signaling. Despite these advances, key translational hurdles include the risk of off-target mutations and large-scale chromosomal rearrangements. Furthermore, immune responses against bacterial Cas9 nucleases and viral delivery vectors may limit the long-term efficacy of CRISPR-based therapies, while technical barriers surrounding delivery to extrahepatic tissues, such as skeletal muscle and the central nervous system, continue to hinder broader clinical success. Ethical concerns regarding germline modifications and the high cost of individualized therapies present additional translational challenges. Consequently, emerging DSB-independent technologies, such as base editing and prime editing, may reduce selected DSB-associated liabilities, but each introduces distinct editing, delivery, and genotoxicity risks that require product-specific evaluation.
Khushi Bashir, Prathiksha Vasudev, Vikas Chhetri et al.· Frontiers in Genome Editing· 0 citations
The rapid emergence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) has significantly reduced the effectiveness of conventional therapeutic regimens necessitating the discovery of novel drug targets and inhibitors. Recent bioinformatics-driven approaches for identifying putative inhibitors targeting essential mycobacterial proteins are comprehensively reviewed. Unlike previous reviews that tend to focus either on drug resistance mechanisms or drug discovery using computational approaches separately, this review integrates both aspects by linking genetic mutations associated with drug resistance and advanced computational approaches for anti-TB drug discovery. Integrative computational strategies including subtractive genomics, molecular docking, molecular dynamics simulations and machine learning-based prioritisation are emphasised. These approaches enable the identification of pathogen specific targets with minimal homology with the host proteins. This review further highlights the importance of natural products, peptides and drug repurposing strategies in targeting MDR-TB. Computational pipelines have shown the potential to greatly speed up early-stage drug discovery while lowering related costs and time, despite the challenges. The benefits of combining multi-omics data with artificial intelligence to create strain-specific treatment approaches are further demonstrated by case-based insights. Furthermore, this review highlights the current challenges in translating computational predictions into experimental and clinical validation while providing future directions including AI/ML based drug discovery, network pharmacology, host-directed therapies, and personalized medicine. Overall, this review underscores the critical role of bioinformatics in addressing the global burden of MDR-TB and highlights its transformative role in guiding next-generation anti-TB drug development. Not applicable.
Elizabeth Annie George, Mahima Senthilkumar, Kavitha Thirupugazh et al.· Beni-Suef University Journal...· 0 citations
Pseudomonas aeruginosa
is an opportunistic pathogen that employs quorum sensing (QS) to regulate virulence and biofilm formation, leading to the emergence of multidrug resistance (MDR). This study aimed to identify phytocompounds from
Cistus munbyi
essential oil as potential inhibitors of the LasR QS receptor in
P. aeruginosa
. A library of 44 phytocompounds was screened through molecular docking studies targeting LasR and its variants (LasR‐Var1: R144I and LasR‐Var2: R180W). Cuminaldehyde and sabinyl acetate emerged as top candidates, exhibiting strong binding affinities comparable to the reference compound, N‐3‐oxo‐dodecanoyl‐L‐homoserine lactone (OdDHL). Molecular dynamics (MD) simulations over 200 ns confirmed stable interactions with key conserved residues, with cuminaldehyde demonstrating superior stability in LasR_Var2 (RMSD: 0.83 ± 0.33 nm). Density functional theory (DFT) analysis revealed favorable chemical reactivity for cuminaldehyde (energy gap: 5.071 eV) and sabinyl acetate (energy gap: 6.162 eV), supporting their potential as QS inhibitors. RMSD, RMSF, Rg, and SASA validated the structural stability of these complexes, while PCA‐based FEL analysis highlighted distinct conformational dynamics. These findings underscore the potential of cuminaldehyde and sabinyl acetate as anti‐QS agents to mitigate
P. aeruginosa
virulence and combat MDR. The study advocates for further in vitro validation to translate these in silico findings into novel phytochemical‐based therapeutics.
Arnav Padhi, E. Subudhi, P. M. Behera et al.· ChemistrySelect· 0 citations
Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.
Shruthi Suresh, A. S. Parvathy, Megha Raj et al.· Frontiers in Digital Health· 0 citations
Vc7 has been identified as a structurally stable and highly immunogenic construct, suggesting its potential as a universal multi-epitope vaccine candidate for the prevention of brucellosis.
Rhitam Biswas, Swapno Surabhi Sinha, Aditi Roy et al.· Frontiers in Bioinformatics· 0 citations
This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in Crohn's disease and enhancing precision healthcare delivery.
