Dyslipidemia is a major driver of atherosclerotic cardiovascular disease, with elevated low-density lipoprotein cholesterol (LDL-C) and triglycerides playing key roles in disease progression. Although conventional lipid-lowering therapies such as statins, ezetimibe, bempedoic acid, and proprotein convertase subtilisin/kexin type 9 inhibitors are effective, their impact is often limited by lifelong treatment requirements, suboptimal adherence, and inadequate achievement of lipid targets, particularly in patients with inherited disorders such as familial hypercholesterolemia. Recent advances in gene-based therapies have introduced a paradigm shift by targeting lipid metabolism at its genetic and molecular basis. Techniques including CRISPR-Cas9 gene editing, small interfering RNA, and antisense oligonucleotides enable precise modulation of key genes such as PCSK9, ANGPTL3, APOC3, and LPA, leading to sustained reductions in atherogenic lipids. These therapies act upstream at the level of gene expression or mRNA, offering improved specificity, longer duration of action, and the potential for infrequent or single-dose administration. Clinical and translational studies demonstrate significant lipid-lowering efficacy across multiple pathways, including LDL-C, triglycerides, and lipoprotein(a), addressing previously unmet therapeutic needs. Despite their promise, important challenges remain, including long-term safety concerns, off-target effects, cost, ethical considerations, and regulatory complexities. Emerging strategies such as combination gene targeting and personalized genomic therapy further expand therapeutic potential while supporting a transition toward preventive cardiology. Overall, gene-based therapies represent a transformative approach in lipid management, with the potential to overcome the limitations of conventional treatments and enable durable, precision-based cardiovascular risk reduction.
INTRODUCTION
Cardiovascular diseases remain a major cause of morbidity and mortality, and many disease-relevant RNA mechanisms remain difficult to address with conventional therapeutic modalities. Antisense oligonucleotides (ASOs) provide a sequence-defined RNA-targeting modality to modulate transcript abundance, splicing, and regulatory RNA function. In cardiovascular drug discovery, however, target complementarity is only the starting point. Translational success requires early alignment between target biology, tissue exposure, and therapeutic index.
AREAS COVERED
Based on PubMed and Web of Science searches through June 2026, this review discusses the principles that shape cardiovascular ASO candidate development, with emphasis on mechanism selection, chemical design, and exposure feasibility. Selected examples from lipoprotein-related targets and transthyretin amyloidosis are used to illustrate why target compartment and pharmacodynamic evidence are central to translational decision-making.
EXPERT OPINION
The near-term impact of cardiovascular ASO therapeutics is likely to be strongest for targets in accessible compartments, particularly liver-derived mediators with clear links to cardiovascular pathology. Applications requiring direct engagement of cardiovascular tissues, including vascular and myocardial targets, will require evidence that target engagement can be achieved in the relevant cell populations at tolerable exposure levels. Future development should therefore integrate sequence optimization with exposure-informed target qualification and therapeutic-index engineering throughout ASO candidate selection.
D. Park, A. Bühler, Christian Schöllhorn et al.· Expert Opinion on Drug Disco...· 0 citations
Background Inflammatory Bowel Diseases (IBD) are chronic conditions presenting significant diagnostic and management challenges. Current invasive methods and traditional biomarkers often lack sufficient accuracy and fail to address the disease’s heterogeneity and unpredictable therapeutic responses. This necessitates more precise, personalized clinical tools. Methods This scoping review synthesized recent findings on emerging biomarkers for IBD. We focused on technological advances in omics platforms (genomics, transcriptomics, proteomics, metabolomics, microbiomics), artificial intelligence, biosensors, and imaging techniques. Analysis identified biomarker potential for early diagnosis, disease activity monitoring, progression prognosis, and therapeutic response prediction. The review adhered to PRISMA-ScR guidelines and was registered with the Open Science Framework (OSF). The search encompassed five major databases: PubMed/MEDLINE, Scopus, Web of Science, Embase, and Google Scholar. Results Studies demonstrate vast potential in non-invasive biomarkers for refined early diagnosis, optimized disease monitoring, and treatment response prediction. Key findings include metabolomic and gut microbiota profiles, genetic and epigenetic markers, and AI integration of complex data. These approaches promise to overcome conventional indicator limitations. From 784 initial records, 27 articles were included, published between 2021 and 2025. Conclusion Emerging biomarkers are fundamental for the transition to precision medicine in IBD. Their implementation aims to enhance pathogenesis understanding, personalize therapies, and improve patient quality of life, establishing pathways for more effective, individualized management approaches.
Matheus Querino da Silva, João Daniel de Souza Menezes, José Luis Esteves Francisco et al.· PLoS ONE· 0 citations
Across April and May 2026, the nucleic acid therapeutics field saw broad regulatory momentum, continued clinical advancement, and significant manufacturing investment. Regulatory highlights included MHRA authorisation of the ligand-conjugated ASO donidalorsen for hereditary angioedema prophylaxis and US FDA acceptance of GSK's NDA for the ASO bepirovirsen in chronic hepatitis B, the latter also receiving Breakthrough Therapy Designation. Clinical milestones featured the 50th patient treated under n-Lorem Foundation's personalized ASO programme and first results from HF-REVERT, the inaugural randomized trial of microRNA inhibition in heart failure. Manufacturing expansion featured prominently, with both Agilent Technologies and WuXi AppTec announcing major capacity increases for oligonucleotide production. AI-driven drug discovery also advanced, with RareLabs launching a robotics- and AI-enabled platform for rare disease ASO and siRNA development, and Ribo Life Science partnering with Insilico Medicine to integrate AI across its siRNA pipeline.
Precision medicine unifies the latest capabilities from engineering, biotechnology, and artificial intelligence (AI) to deliver data-driven personalized healthcare. This paper summarizes recent advances in biosensor technologies, AI-derived biomarkers, and predictive frameworks used to help identify patients more accurately, monitor their progress continuously, and receive optimized therapies. Relevant peer-reviewed studies were identified in a systematic search of PubMed, Scopus, Web of Science, and Google Scholar for any articles published during the period of 1 January 2015 through 31 December 2024. Advanced biosensors provide immediate feedback regarding the molecular and physiological status of patients, allowing for the rapid definition of new biomarkers, as well as ongoing evaluations of their clinical status and response to treatments. Predictive AI models enhance the precision of patient stratification, treatment planning through adaptive therapies, and predicting individual drug responses, while also lessening the risk of adverse events from treatments. Several emerging technologies illustrate a demonstrated movement toward proactive precision medicine, such as pharmacovigilance based on smart biosensors and closed-loop delivery systems. Key barriers still exist, such as the need for interoperable standards, scientific validation of tools, legal applicability, and ethical issues; therefore, resolving these issues requires collaborative interdisciplinary teams conducting long-term clinical studies.
S. Bukke, Chandrashekar Thalluri, Mallikarjun Vasam et al.· Personalized Medicine· 0 citations
Artificial intelligence (AI) and gene editing are increasingly being applied to the design and evaluation of mRNA therapeutics. Although mRNA‐based medicines have achieved clear clinical impact in vaccination, broader applications remain limited by mRNA instability, delivery barriers, tissue selectivity, and unwanted immunogenicity. This review examines how AI and gene editing can be combined to address these constraints. AI‐based models support predictive optimization of untranslated regions, codon usage, secondary structure, and lipid nanoparticle (LNP) formulations, thereby improving the efficiency of sequence and delivery‐system design. In parallel, CRISPR‐Cas (clustered regularly interspaced short palindromic repeats‐associated proteins) systems, base editors, and emerging RNA‐editing tools provide platforms for disease modeling, target validation, and functional testing of mRNA‐based interventions. We emphasize that the value of this convergence lies in iterative workflows: gene‐editing screens generate quantitative datasets for model training, whereas AI helps prioritize editing strategies, guide sequence refinement, and improve delivery design. We also summarize representative applications, translational limitations, and prospects for closed‐loop AI‐gene editing platforms. Overall, the integration of computational prediction with programmable genome and RNA engineering may support more precise, adaptable, and clinically translatable mRNA therapeutics.
Haixing Shi, Shengbin Liu, Dan-Ling Dai et al.· MedComm – Biomaterials and A...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026
