Ivacaftor

在全球，约16.2万人正与囊性纤维化（CF）这一严重的遗传性疾病抗争。CF是一种常染色体隐性遗传病，会严重缩短患者寿命。它由CF跨膜传导调节因子（CFTR）基因的功能缺失突变引发，这些突变影响CFTR阴离子通道的合成、折叠、运输和门控特性，进而干扰氯离子和碳酸氢根离子的运输。尽管调制药物在治疗CF方面取得了一定进展，但对于携带特定突变的患者，如G542X突变患者，现有调制药物却无能为力。近期，发表于iScience的一项研究Adenine base editing with engineered virus-like particles rescues the CFTR mutation G542X in patient-derived intestinal organoids带来了新希望。研究聚焦于G542X突变，它是CF的第二常见致病突变，由c.1624G>T突变产生，会导致提前终止密码子（PTC）的出现，触发无义介导的mRNA衰变（NMD），使得携带该突变的CFTR蛋白无法正常发挥功能，且对现有调制药物无反应。此前的CFTR基因编辑研究虽有成果，但各有局限。而碱基编辑技术因能实现精确的单核苷酸编辑，且不涉及双链断裂形成，在众多编辑策略中备受关注。在这项研究中，研究团队利用工程病毒样颗粒（BE-eVLPs）将NG-ABE8e/sgRNA核糖核蛋白（RNPs）递送至患者来源的肠道类器官中，尝试将G542X突变编辑为非致病的G542R突变，以恢复CFTR功能。研究结果令人振奋。首先，NG-ABE8e成功将G542X编辑为G542R，且编辑效率平均达到2%。在对两位捐赠者来源的肠道类器官进行检测时发现，转导了携带NG-ABE8e和G542X特异性sgRNA的BE-eVLPs（G542X-BE-eVLPs）的类器官细胞中，出现了G542X到G542R的编辑，而转导无sgRNA的BE-eVLPs的细胞则未发生DNA编辑。其次，该编辑过程具有高度精确性。在20-nt长的间隔结合区域内，未检测到潜在旁观者位点的编辑高于背景水平，这表明编辑设计的精准度极高，有效避免了不必要的突变。同时，通过CRISPResso2分析，也未检测到插入缺失（indel）的形成。在脱靶效应方面，研究人员利用CRISPOR进行计算机分析，找出了8个外显子脱靶位点，对其中最有可能发生脱靶编辑的ADAMTS6位点进行扩增和测序后，发现编辑效率未高于背景水平，其他位点因在种子区域存在错配，脱靶编辑的可能性较低。图 1：NG-ABE8e在患者来源的肠道类器官中将G542X编辑为G542R，无旁观者效应和脱靶效应再者，G542R的出现有效恢复了CFTR的活性。在对转导后的肠道类器官进行功能检测时发现，G542X-BE-eVLPs成功挽救了CFTR的活性。通过短路电流（DIsc）测量，2%的G542X到G542R编辑恢复了6.4%的野生型CFTR活性，且两位捐赠者来源的类器官之间变异性可忽略不计。在3D培养的类器官中进行的福司可林诱导肿胀（FIS）实验也得到了类似结果，转导G542X-BE-eVLPs的类器官对FIS有明显响应，最高可达总未分选类器官的16%，且这些类器官的面积在刺激后显著增加。此外，研究还发现，添加调制药物组合（Elexacaftor、Tezacaftor和Ivacaftor，即ETI）对G542R功能没有额外影响。图 2：G542R在患者来源的肠道类器官中恢复CFTR活性图 3：G542X-BE-eVLPs在三维培养的患者来源肠道类器官中恢复FIS总的来说，这项研究利用腺嘌呤碱基编辑技术，成功在患者来源的肠道类器官中拯救了G542X突变，为囊性纤维化的治疗开辟了新途径。尽管目前还处于原理验证阶段，但研究结果充分展示了碱基编辑技术在纠正致病突变方面的巨大潜力，为未来体内应用奠定了坚实基础。相信在科研人员的不断努力下，基因编辑技术将为更多疑难病症的治疗带来新的曙光。参考文献：Nicosia L, Pranke I, Latorre RV, et al. Adenine base editing with engineered virus-like particles rescues the CFTR mutation G542X in patient-derived intestinal organoids. iScience. 2025;28(3):111979. Published 2025 Feb 21. doi:10.1016/j.isci.2025.111979本文仅用于学术分享，转载请注明出处。若有侵权，请联系微信：bioonSir 删除或修改！点击下方「阅读原文」，前往生物谷官网查询更多生物相关资讯~

shiota/ iStock Researchers in pharma and beyond have historically glommed onto a limited number of disease targets, limiting innovation. AI could change that. Proteins that fight tumor growth, gene exons that can be modified, and receptors on immune cells that control their cancer-killing powers. These are the types of mechanisms and bodily processes that could be key to curing or treating disease, and which form the targets of modern drug development. While the number of such targets is nearly infinite, a relatively small number have emerged as the most popular among pharmaceutical companies. In a phenomenon termed “herding,” numerous pharmaceutical firms, research organizations and even academic scientists are targeting not just the same narrow band of conditions, but the same limited set of drug targets. In 2020, 68% of targets pursued by the top 10 pharmaceutical firms were the basis of five or more R&D programs. Some firms were dedicating 80% or more of their research time and dollars to those popular targets. Chief among these targets are those associated with cancer; in 2022, there were nine assets per actively investigated oncology target among the top pharma firms, up from 1.8 in 2000, with most of the research dedicated to breast, hematological and brain cancer. While there are understandable reasons for herding and even advantages in certain situations, the overall effect of this “me-too” style of innovation is to set limits in drug development. But how can we break away from the crowd and forge our own path? As vice president of business development at Evogene, which offers an AI-driven computational chemistry platform, I believe artificial intelligence is the answer. AI has the power to transform drug development by identifying novel targets and compounds that can interact with them. The Drawbacks of Herding Well-known examples of “me-too” drugs include many of the selective serotonin reuptake inhibitors (SSRIs) used in treating depression—such as Prozac, Zoloft and Paxil—and various statins—think Lipitor and Crestor. One of the most comprehensive studies of drug targets curated 1,578 FDA-approved drugs acting on 893 unique molecular targets, meaning nearly 50% of FDA-approved drugs share common targets. Similarly, about 60% of drugs on the World Health Organization’s list of essential treatments fit into this “me-too” category, and more than 50% of the 50 novel drugs approved by the FDA in 2024 relied on pre-existing targets. In many ways, herding makes sense, and it has benefitted some patients and pharma companies. Many of the targets that experience herding, especially in the oncology space, are highly efficacious, and pharma firms want to follow what works and, ideally, create best-in-class treatments in these drug classes. Of course, there is a great demand—and a large need—for these improvements. In addition, when it comes to single-gene diseases such as thalassemia, Duchenne muscular dystrophy and Huntington’s, herding has emerged alongside progress in developing gene therapies. For example, instead of focusing solely on the mutant gene, companies might look at targeting cellular pathways disrupted by the mutant protein. Such approaches make up only 10%–30% of attempts to develop new therapies, while the majority still focus on the mutant gene itself. Such concentrated research efforts, exemplified by the focus on the CFTR gene in cystic fibrosis leading to therapies like Ivacaftor, can indeed accelerate progress in understanding and treating rare genetic diseases. But the focus on the gene itself and a small set of cellular pathways leaves other pathways under explored. This over-concentration diverts critical resources, exacerbating challenges in resource allocation and clinical trial design and ultimately hindering efforts to pursue novel targets and more innovative therapies. This is especially problematic for rare diseases, where the pool of trial participants is naturally low and standards limit patients to one trial at a time. If all or most of the patients are enrolled in trials testing drugs with the same target or few targets, there is little room to open a trial with a new target, further discouraging innovation. Innovation Through AI Pursuing new targets and therapies is essential to reversing the herding trend, and one of the tools for doing this is AI. AI offers the ability to navigate the vast chemical and biological landscape, uncovering hidden patterns and relationships that traditional methods overlook. By integrating data from genomics, proteomics and cheminformatics, AI can identify novel targets that have been underexplored, evaluating their potential with remarkable speed and accuracy. Over the past three years the market for AI platforms for the entire healthcare industry has tripled . As more companies— and regulators —embrace AI-based research, companies will be better equipped to break free from the constraints of herding and the redundancy of structurally similar drugs, shifting toward more innovative and diverse therapeutic strategies. McKinsey predicts that AI could increase the number of actionable drug targets by at least 30% . There are already signs of progress in de-herding as novel biological targets and molecules for drug development emerge. For example, AstraZeneca, in collaboration with BenevolentAI, has identified novel targets in chronic kidney disease (CKD) beyond the well-known renin-angiotensin system, opening pathways to treatments focused on kidney fibrosis and inflammation. AI-based research by Verve Therapeutics, meanwhile, has shifted from traditional statin targets to gene editing therapies for inherited cardiovascular conditions. And for rare diseases, including amyotrophic lateral sclerosis (ALS) and Duchenne muscular dystrophy, companies like Insilico have utilized AI-driven systems to identify specific genetic targets. In addition to finding new targets, deep learning enables the de novo design of novel ligands, the active molecules that are the basis of many potential new drugs. Generative architectures such as GANs (generative adversarial networks), VAEs (variational autoencoders) and GPTs (generative pre-trained models) allow researchers to create unique molecular structures that are specifically tailored to their targets. This capability is transformative because it avoids the redundancy of relying on existing compound libraries, which are often biased toward known targets. AI-driven de novo ligand generation explores new chemical spaces, ensuring structural diversity and reducing the risk of producing yet another version of an existing drug. Companies like Atomwise , Insilico and Recursion are using AI for structure-based drug design, and have discovered unique ligands for various indications. Evogene recently began to leverage its AI-driven computational chemistry platform, first developed for agricultural applications, to break into the world of novel ligand discovery and drug development. Despite its promise, AI-driven drug discovery faces challenges such as variable data quality and the “black box” nature of some models, which can complicate validation and regulatory approval. While the promise of AI-first drugs is yet to be fully realized—none have progressed beyond Phase II clinical trials due to long development timelines—it’s important to note that the most advanced drugs in the pipeline were created using technology that has since matured rapidly. These issues are recognized as part of an evolving field, with ongoing innovation addressing concerns like data standardization and model transparency. I have strong confidence that these hurdles will be overcome in time, ultimately enabling AI to revolutionize drug development and improve patient outcomes. AI use in the pharmaceutical industry is far from universal, but by collaborating with AI-first companies, pharmas can remain at the forefront of technological advancements, ensuring their pipelines are fueled by cutting-edge breakthroughs rather than incremental advances. As AI-driven research becomes the norm, fuelled by these strategic collaborations, we can anticipate transformative progress that will benefit pharmaceutical companies and patients alike.