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A Flow Cytometry-Based Cell Surface Protein Binding Assay for Assessing Selectivity and Specificity of an Anticancer Aptamer
Published on: September 13, 2022
Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics
Ji Su Hwang1, Seok Gi Kim1, Tae Hwan Shin2
1Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon 16499, Korea.
Abstract:
Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.
Insights
Anticancer peptides (ACPs) offer targeted cancer cell killing with advantages like low toxicity. Artificial intelligence, including machine learning and deep learning, accelerates the identification of novel ACPs, reducing costs and time.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Cancer involves abnormal cell growth and metastasis.
- Anticancer peptides (ACPs) target unique cancer cell membrane characteristics.
- ACPs offer specificity, low toxicity, and cost-effectiveness but are slow to identify.
Purpose of the Study:
- To explore the application of artificial intelligence (AI) in identifying anticancer peptides (ACPs).
- To highlight the advantages of AI-driven approaches over traditional wet-lab methods for ACP discovery.
- To propose combination therapy involving ACPs for enhanced cancer treatment efficacy.
Main Methods:
- Utilizing machine learning (ML), deep learning (DL), and hybrid learning models.
- Leveraging advances in computational power and big data for predictive analysis.
- Analyzing unique cancer cell membrane properties for ACP targeting.
Main Results:
- AI approaches significantly reduce the time and cost associated with ACP identification.
- ML, DL, and hybrid models show promise in discovering candidate ACPs without extensive experimentation.
- ACPs demonstrate potential for targeted cancer therapy due to specific interactions with cancer cells.
Conclusions:
- AI, particularly ML and DL, revolutionizes the discovery pipeline for anticancer peptides.
- AI-driven ACP identification offers a faster, more cost-effective alternative to traditional methods.
- Combination therapy integrating ACPs with existing treatments may significantly improve cancer therapeutic outcomes.
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