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
Acinetobacter baumannii
(
A. baumannii
) stands as a critical priority pathogen, rapidly developing resistance against the currently available antibiotic treatments, including last‐resort antibiotics. This nosocomial pathogen is responsible for alarmingly high mortality rates across the world. The drug repurposing approach may aid effectively in this crucial situation as the pharmacokinetic profile and safety details of currently available drugs are already well known, thereby taking comparatively less time than the traditional drug development process. In this study, the Food and Drug Administration (FDA) sanctioned drugs with structural similarity to ampicillin were screened, followed by pharmacokinetic and antimicrobial activity evaluations via in silico analysis. Further, the binding affinity of the drugs toward penicillin‐binding protein 3 (PBP3) and its prevalent mutants was evaluated via molecular docking and simulation studies. Based on results, the antidiabetic drug canagliflozin has been found to possess good binding affinity with wild type penicillin‐binding protein 3 (PBP3
WT)
(−8.01 kcal/mol), as well as its clinically prevalent mutants, PBP3
A515V
(−7.76 kcal/mol), PBP3
T526S
(−7.26 kcal/mol). According to our results, the drug possesses stable molecular dynamics interactions with PBP3
WT
, as well as its mutants, PBP3
A515V
and PBP3
T526S
. Based on our observations, we suggest canagliflozin as a potent PBP3‐binding drug against the
A. baumannii
pathogen.
Srujal Kacha, A. Anbarasu· ChemistrySelect· 0 citations
Background Carbapenem resistant Acinetobacter baumannii (CRAB) is recognized as one of the most critical priority pathogens by the World Health Organization due to its persistence in nosocomial settings, extensive antimicrobial resistance, and increasing dissemination at the global level. Despite the escalating availability of genomic data, genotype–phenotype integrated studies exploring the genetic determinants associated with carbapenem resistance remain limited. Methods In this study, a comprehensive comparative genomics was performed using publicly available 395 clinical A. baumannii genomes, comprising of 267 CRAB and 128 carbapenem susceptible A. baumannii (CSAB). Comparative genomic analyses included sequence types (STs), virulence factors (VFs), antimicrobial resistance genes (ARGs), and mobile genetic elements (MGEs) characterization. Pangenome-wide association study (PanGWAS) was performed to test the associations between genotypes and carbapenem resistance phenotype. Results CRAB genome subset demonstrated higher abundance of ARGs (acquired carbapenemases in particular), plasmids, carbapenem resistance-associated insertion sequences, and integrons than CSAB genomes. PanGWAS identified six positively associated genes (relE, umuC, hphA, hsmA, hphR, and fecI) significantly enriched in CRAB population. Core SNP phylogeny integrated with STs and acquired carbapenemase genes exhibited heterogeneous distribution of resistance genes across lineages, indicating potential role of both clonal dissemination and horizontal gene transfer. Conclusion This study provides an overall genomic architecture of CRAB integrating comparative genomics, PanGWAS, and phylogenomics approaches. The findings underscore the complex interplay between ARGs, VFs, and MGEs in the genomic evolution of CRAB, expanding current understanding of CRAB adaptation and may contribute toward enhanced surveillance, antimicrobial stewardship, and exploration of alternative therapeutic targets.
Sara Pearl, A. Anbarasu· Frontiers in Cellular and In...· 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.
