A Technology Platform for Next Generation VNAR Based Oncology Medicines
A Technology Platform for Next Generation VNAR Based Oncology Medicines
批准号:
104292
负责人:
金额:
$140.32万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --
中文摘要
尽管癌症患者的预后有了明显改善,但仍有35%的人死于癌症。一线化疗通常依赖于快速生长的癌细胞的敏感性,它们比“正常”的“健康”细胞更容易受到有毒物质的影响。这种治疗的副作用令人不快,再加上预后不佳,这意味着迫切需要新的靶向治疗方法来改善患者的健康。迄今为止的进展包括将药物靶向递送到癌细胞,以限制对健康组织的毒副作用。基于单克隆抗体的靶向治疗已经取得了一些成功。然而,它们的“杀伤力”对较大的实体瘤可能无效。为了提高它们的效力,肿瘤靶向抗体已经与高效的细胞毒性药物结合,并且抗体已经被设计成同时靶向两种不同的癌症蛋白,以提高肿瘤的选择性。虽然这些抗体药物偶联物(adc)和双特异性抗体方法显示出希望,但抗体的大尺寸和复杂性限制了它们穿透肿瘤的能力和设计治疗改进的能力。此外,制造成本和脱靶副作用仍然是这些药物的重大问题。因此,使用“更小”的蛋白质疗法的下一代方法具有更好的肿瘤穿透性、更高的稳定性和更低的毒性,具有很大的前景。为了应对这一挑战,该项目的目标是建立一个平台技术,以加速开发和优化基于鲨鱼中发现的抗体样单域受体(VNARs)的新型肿瘤药物。鲨鱼VNAR结构域是动物王国中最小的天然抗原结合结构域,其特性对癌症治疗具有潜在的优势。在Almac Discovery和Elasmogen的初步合作中,我们已经证明了高亲和力的vnas可以生成肿瘤靶点,并且这些vnas可以使用Almac Discovery开发的生物偶联技术进行位点特异性修饰。目的是通过结合Elasmogen在VNAR蛋白生成和筛选方面的专业知识,以及Almac Discovery在蛋白质工程和肿瘤药物发现方面的专业知识,在这一初步概念验证研究的基础上进一步发展。其理念是开发一个模块化的VNAR“即插即用”平台,以加速创新VNAR抗癌药物的开发。成功开发一种平台技术,可以克服全长抗体的局限性,并快速提供VNAR治疗候选物,提高疗效,将使这些下一代方法的全部潜力得以实现,并显著改善人类健康。”
英文摘要
"Despite marked improvements in outcomes for cancer patients, 35% will succumb to the disease. First line chemotherapy treatments generally rely on the sensitivity of fast-growing cancers being more susceptible to toxic agents than ""normal"" healthy cells. The unpleasant side-effects of this treatment, together with a poor prognosis means there is an urgent need for new targeted therapeutics to improve patient health. Developments to-date have included the targeted delivery of drugs to cancer cells as a way of limiting toxic side effects on healthy tissues. Targeted therapies based on monoclonal antibodies have been used with some success. However, their ""killing power"" can be ineffective against larger solid tumours.To ramp-up their potency, tumour targeting antibodies have been conjugated with highly potent cytotoxic drugs, and antibodies have been engineered that simultaneously target two different cancer proteins in order to improve tumour selectivity. Whilst these antibody drug conjugates (ADCs) and bi-specific antibody approaches show promise, the large size and complexity of antibodies limits their tumour penetrating ability and the ability to engineer therapeutic improvements. Furthermore, the cost of manufacture and off-target side effects are still significant problems for these agents. Consequently, next generation approaches using 'smaller' protein therapeutics with improved tumour penetration, increased stability and less toxicity hold great promise.To meet this challenge, the aim of this project is to build a platform technology for the accelerated development and optimisation of novel oncology medicines based on antibody-like single domain receptors found in sharks (VNARs).Shark VNAR domains are the smallest naturally occurring antigen binding domain in the animal kingdom and their properties are potentially advantageous for cancer therapy. In an initial collaboration between Almac Discovery and Elasmogen, we have shown that high affinity VNARs can be generated to an oncology target and that these VNARs can be site-specifically modified using a bio-conjugation technology developed by Almac Discovery.The aim is to build on this initial proof-of-concept research by combining Elasmogen's expertise in the generation and screening of VNAR proteins, with Almac Discovery's expertise in protein engineering and oncology drug discovery. The idea is to develop a modular VNAR ""plug-and-play"" platform for the accelerated development of innovative VNAR cancer drugs.Successful development of a platform technology that can overcome the limitations of full-length antibodies, and rapidly deliver VNAR therapeutic candidates with improved efficacy, will enable the full potential of these next-generation approaches to be realised and significantly improve human health."
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海外基金
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
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批准号:--
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项目类别:外国青年学者研究基金项目
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资助金额:--
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批准年份:2024
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负责人:江洋子
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依托单位: