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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
Unveiling anticancer peptides; from the mechanisms of action to their development through artificial intelligence
Alexandra R Collins1, Vasilis Paspaliaris1, Varun Pandey2
1Paspa Pharmaceuticals Pty Ltd., Cauldfield, Victoria, 3162, Australia; Black Arrow Biotech Inc, Tallinn, Estonia.
Abstract:
Cancer is a leading cause of death worldwide and a major burden on the healthcare system. Current treatment methods are limited as they have low selectivity, unspecific targeting and increasing multidrug resistance. Therefore, newer modes of therapeutic strategies need to be developed. Anticancer peptides (ACP) are small bioactive peptides that have the ability to be selective and toxic to cancer, with a high efficiency in cell penetration and internalization and low risk of inducing multidrug resistance. ACPs have multitude of mechanisms giving them the ability to target multiple cancer types as well as both slow growing and metabolically active cancers. Recently, a large number of literature papers examine the structure, mechanisms of action, synthesis, modifications and other non-direct cancer treatment applications of ACPs. A growing aspect in all areas of research and development especially in peptide drug discovery is now Artificial Intelligence (AI). It enables large amounts of data to be processed quickly and allows for rapid predictions hence the increased discovery of new ACPs. In this review, we discuss the structure, mechanisms of action, synthesis of ACPs and the different types of AI models, their algorithms and the ACPs prediction models currently available.
Insights
Anticancer peptides (ACPs) offer a promising alternative to traditional cancer treatments due to their selectivity and low resistance risk. Artificial intelligence (AI) accelerates the discovery and development of novel ACPs for cancer therapy.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Oncology and Cancer Therapeutics
Background:
- Cancer remains a leading global cause of mortality, posing a significant healthcare burden.
- Existing cancer treatments face limitations including poor selectivity, non-specific targeting, and escalating multidrug resistance.
- Novel therapeutic strategies are imperative to overcome these challenges in cancer care.
Purpose of the Study:
- To review the fundamental aspects of anticancer peptides (ACPs), including their structure, mechanisms of action, and synthesis.
- To explore the burgeoning role of Artificial Intelligence (AI) in accelerating the discovery of novel ACPs.
- To discuss various AI models, algorithms, and existing ACP prediction tools.
Main Methods:
- Literature review focusing on anticancer peptides (ACPs) and their therapeutic potential.
- Analysis of ACPs' characteristics: selectivity, cell penetration, internalization, and resistance profiles.
- Examination of AI applications in peptide drug discovery, specifically for ACPs.
Main Results:
- Anticancer peptides (ACPs) demonstrate high selectivity, potent cytotoxicity against cancer cells, efficient internalization, and a low propensity for inducing multidrug resistance.
- ACPs exhibit diverse mechanisms of action, enabling efficacy against various cancer types and growth rates.
- Artificial Intelligence (AI) facilitates rapid processing of extensive data, enabling accelerated prediction and discovery of new ACPs.
Conclusions:
- Anticancer peptides (ACPs) represent a promising therapeutic avenue for cancer treatment, addressing limitations of current therapies.
- The integration of Artificial Intelligence (AI) significantly enhances the efficiency and scope of ACP discovery and development.
- Further research into ACPs and AI-driven prediction models holds substantial potential for advancing cancer therapy.
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