Advancements in Precision Oncology: Harnessing High-Throughput Screening and Computational Strategies for Targeted Cancer Therapies.

IF 1.7 4区医学 Q4 BIOCHEMICAL RESEARCH METHODS

Combinatorial chemistry & high throughput screening Pub Date : 2025-07-31 DOI:10.2174/0113862073397811250716154921

Akshay Thakur, Kaunava Roy Chowdhury, Vir Vikram Sharma, Kuldeep Singh, Jeetendra Kumar Gupta, Divya Jain, Mukesh Chandra Sharma

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Abstract

Recent breakthroughs in precision medicine have significantly transformed the landscape of cancer treatment, propelling the development of individualized therapies characterized by enhanced therapeutic efficacy and reduced toxicity. This review examines the integration of high-throughput screening techniques with advanced computational methodologies, including artificial intelligence (AI) and machine learning, to expedite drug discovery and optimize treatment protocols in oncology. We explore the efficacy of targeted therapeutics, CAR T-cell therapies, and immune checkpoint inhibitors, alongside the role of combination therapies and biomarker identification in refining patient-specific treatment strategies. By aggregating scientific data from key databases, we evaluate the impact of in silico modeling on drug efficacy predictions, cost reduction, and time efficiency in the development process. This review highlights the collaborative potential of computational and synthetic approaches in redefining oncological pharmacotherapy and improving patient outcomes.

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精确肿瘤学的进展：利用高通量筛选和计算策略进行靶向癌症治疗。

精准医学的最新突破极大地改变了癌症治疗的格局，推动了以提高治疗效果和降低毒性为特征的个性化治疗的发展。本文综述了高通量筛选技术与先进计算方法（包括人工智能（AI）和机器学习）的整合，以加快肿瘤药物发现和优化治疗方案。我们探索靶向治疗、CAR - t细胞治疗和免疫检查点抑制剂的疗效，以及联合治疗和生物标志物鉴定在改进患者特异性治疗策略中的作用。通过汇总来自关键数据库的科学数据，我们评估了在开发过程中，计算机建模对药物疗效预测、成本降低和时间效率的影响。这篇综述强调了计算和合成方法在重新定义肿瘤药物治疗和改善患者预后方面的合作潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Combinatorial chemistry & high throughput screening 化学-生化研究方法

CiteScore

3.10

自引率

5.60%

发文量

327

审稿时长

7.5 months

期刊介绍： Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.