Anti-Cancer Peptides: Status and Future Prospects

Biological Activity of Natural and Synthetic Peptides as Anticancer Agents

Cancer is one of the leading causes of morbidity and death worldwide, making it a serious global health concern. Chemotherapy, radiotherapy, and surgical treatment are the most used conventional therapeutic approaches, although they show several side effects that limit their effectiveness. For these reasons, the discovery of new effective alternative therapies still represents an enormous challenge for the treatment of tumour diseases. Recently, anticancer peptides (ACPs) have gained attention for cancer diagnosis and treatment. ACPs are small bioactive molecules which selectively induce cancer cell death through a variety of mechanisms such as apoptosis, membrane disruption, DNA damage, immunomodulation, as well as inhibition of angiogenesis, cell survival, and proliferation pathways. ACPs can also be employed for the targeted delivery of drugs into cancer cells. With over 1000 clinical trials using ACPs, their potential for application in cancer therapy seems promising. Peptides can also be utilized in conjunction with imaging agents and molecular imaging methods, such as MRI, PET, CT, and NIR, improving the detection and the classification of cancer, and monitoring the treatment response. In this review we will provide an overview of the biological activity of some natural and synthetic peptides for the treatment of the most common and malignant tumours affecting people around the world.

Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.

Characterization, Antioxidant Capacity and Protective Effect of Peptides from Cordyceps militaris Cultivated with Tussah Pupa on Oxidative Injured HepG2 Cells

The antioxidant capacity and protective effect of peptides from protein hydrolysate of Cordyceps militaris cultivated with tussah pupa (ECPs) on H2O2-injured HepG2 cells were studied. Results indicated ECP1 (<3 kDa) presented the strongest antioxidant activity compared with other molecular weight peptides. Pretreated with ECPs observably enhanced survival rates and reduced apoptosis rates of HepG2 cells. ECPs treatment decreased the ROS level, MDA content and increased CAT and GSH-Px activities of HepG2 cells. Besides, the morphologies of natural peptides from C. militaris cultivated with tussah pupa (NCP1) and ECP1 were observed by scanning electron microscopy (SEM). Characterization results suggested the structure of NCP1 was changed by enzymatic hydrolysis treatment. Most of hydrophobic and acidic amino acids contents (ACC) in ECP1 were also observably improved by enzymatic hydrolysis. In conclusion, low molecular weight peptides had potential value in the development of cosmetics and health food.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

Physical-Chemical Approach to Designing Drugs with Multiple Targets

Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.

Lactoferricin B Combined with Antibiotics Exhibits Leukemic Selectivity and Antimicrobial Activity

The fusion of penetrating peptides (PPs), e.g., cell penetration peptides (CPPs) or antimicrobial peptides (AMPs), together with antimicrobial agents is an expanding research field. Specific AMPs, such as lactoferricin B (LfcinB), have demonstrated strong antibacterial, antifungal, and antiparasitic activity, as well as valuable anticancer activity, proving beneficial in the development of anticancer conjugates. The resulting conjugates offer potential dual functionality, acting as both an anticancer and an antimicrobial agent. This is especially necessary in cancer treatment, where microbial infections pose a critical risk. Leukemic cells frequently exhibit altered outer lipid membranes compared to healthy cells, making them more sensitive to compounds that interfere with their membrane. In this study, we revisited and reanalyzed our earlier research on LfcinB and its conjugates. Furthermore, we carried out new experiments with a specific focus on cell proliferation, changes in membrane asymmetric phosphatidylserine location, intracellular reactive oxygen species (ROS) generation, mitochondrial functions, and in vitro bacterial topoisomerase inhibition.

Anticancer activities of natural antimicrobial peptides from animals

Cancer is the most common cause of human death worldwide, posing a serious threat to human health and having a negative impact on the economy. In the past few decades, significant progress has been made in anticancer therapies, but traditional anticancer therapies, including radiation therapy, surgery, chemotherapy, molecular targeted therapy, immunotherapy and antibody-drug conjugates (ADCs), have serious side effects, low specificity, and the emergence of drug resistance. Therefore, there is an urgent need to develop new treatment methods to improve efficacy and reduce side effects. Antimicrobial peptides (AMPs) exist in the innate immune system of various organisms. As the most promising alternatives to traditional drugs for treating cancers, some AMPs also have been proven to possess anticancer activities, which are defined as anticancer peptides (ACPs). These peptides have the advantages of being able to specifically target cancer cells and have less toxicity to normal tissues. More and more studies have found that marine and terrestrial animals contain a large amount of ACPs. In this article, we introduced the animal derived AMPs with anti-cancer activity, and summarized the types of tumor cells inhibited by ACPs, the mechanisms by which they exert anti-tumor effects and clinical applications of ACPs.

Jelleine, a Family of Peptides Isolated from the Royal Jelly of the Honey Bees (Apis mellifera), as a Promising Prototype for New Medicines: A Narrative Review

The jelleine family is a group of four peptides (jelleines I-IV) originally isolated from the royal jelly of honey bee (Apis mellifera), but later detected in some honey samples. These oligopeptides are composed of 8-9 amino acid residues, positively charged (+2 to +3 at pH 7.2), including 38-50% of hydrophobic residues and a carboxamide C-terminus. Jelleines, generated by processing of the C-terminal region of major royal jelly proteins 1 (MRJP-1), play an important biological role in royal jelly conservation as well as in protecting bee larvae from potential pathogens. Therefore, these molecules present numerous benefits for human health, including therapeutic purposes as shown in preclinical studies. In this review, we aimed to evaluate the biological effects of jelleines in addition to characterising their toxicities and stabilities. Jelleines I-III have promising antimicrobial activity and low toxicity (LD50 > 1000 mg/Kg). However, jelleine-IV has not shown relevant biological potential. Jelleine-I, but not the other analogues, also has antiparasitic, healing, and pro-coagulant activities in addition to indirectly modulating tumor cell growth and controlling the inflammatory process. Although it is sensitive to hydrolysis by proteases, the addition of halogens increases the chemical stability of these molecules. Thus, these results suggest that jelleines, especially jelleine-I, are a potential target for the development of new, effective and safe therapeutic molecules for clinical use.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Natural Anticancer Peptides from Marine Animal Species: Evidence from In Vitro Cell Model Systems

Anticancer peptides are short and structurally heterogeneous aminoacidic chains, which display selective cytotoxicity mostly against tumor cells, but not healthy cells, based on their different cell surface properties. Their anti-tumoral activity is carried out through interference with intracellular homeostasis, such as plasmalemma integrity, cell cycle control, enzymatic activities and mitochondrial functions, ultimately acting as angiogenesis-, drug resistance- and metastasis-inhibiting agents, immune stimulators, differentiation inducers and necrosis or extrinsic/intrinsic apoptosis promoters. The marine environment features an ever-growing level of biodiversity, and seas and oceans are poorly exploited mines in terms of natural products of biomedical interest. Adaptation processes to extreme and competitive environmental conditions led marine species to produce unique metabolites as a chemical strategy to allow inter-individual signalization and ensure survival against predators, infectious agents or UV radiation. These natural metabolites have found broad use in various applications in healthcare management, due to their anticancer, anti-angiogenic, anti-inflammatory and regeneration abilities. The aim of this review is to pick selected studies that report on the isolation of marine animal-derived peptides and the identification of their anticancer activity in in vitro cultures of cancer cells, and list them with respect to the taxonomical hierarchy of the source organism.

New Perspective for Using Antimicrobial and Cell-Penetrating Peptides to Increase Efficacy of Antineoplastic 5-FU in Cancer Cells

This study explores the effectiveness of the antineoplastic agent 5-FU in cancer cells by leveraging the unique properties of cationic antimicrobial peptides (CAMPs) and cell-penetrating peptides (CPPs). Traditional anticancer therapies face substantial limitations, including unfavorable pharmacokinetic profiles and inadequate specificity for tumor sites. These drawbacks often necessitate higher therapeutic agent doses, leading to severe toxicity in normal cells and adverse side effects. Peptides have emerged as promising carriers for targeted drug delivery, with their ability to selectively deliver therapeutics to cells expressing specific receptors. This enhances intracellular drug delivery, minimizes drug resistance, and reduces toxicity. In this research, we comprehensively evaluate the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of various AMPs and CPPs to gain insights into their potential as anticancer agents. The peptide synthesis involved a solid-phase synthesis using a Liberty Microwave Peptide Synthesizer. The peptide purity was confirmed via LC-MS and HPLC methods. For the ADMET screening, computational tools were employed, assessing parameters like absorption, distribution, metabolism, excretion, and toxicity. The cell lines A549 and UM-UC-5 were cultured and treated with 5-FU, CAMPs, and CPPs. The cell viability was measured using the MTT assay. The physicochemical properties analysis revealed favorable drug-likeness attributes. The peptides exhibited potential inhibitory activity against CYP3A4. The ADMET predictions indicated variable absorption and distribution characteristics. Furthermore, we assessed the effectiveness of these peptides alone and in combination with 5-FU, a widely used antineoplastic agent, in two distinct cancer cell lines, UM-UC-5 and A549. Our findings indicate that CAMPs can significantly reduce the cell viability in A549 cells, while CPPs exhibit promising results in UM-UC-5 cells. Understanding these multifaceted effects could open new avenues for antiviral and anticancer research. Further, experimental validation is necessary to confirm the mechanism of action of these peptides, especially in combination with 5-FU.

Influence of Fatty Acid Modification on the Anticancer Activity of the Antimicrobial Peptide Figainin 1

Antimicrobial peptides derived from the skin secretions of amphibians have made important progress in tumor therapy due to their unique mechanism of destroying cell membranes. Figainin 1 (F1) is an 18-amino acid antimicrobial peptide from the skin secretions of Boana raniceps frogs. In a previous study, F1 was shown to inhibit cancer cell proliferation. F1 is composed entirely of natural amino acids; therefore, it is easily degraded by a variety of proteases, resulting in poor stability and a short half-life. In the present study, we used a fatty acid modification strategy to improve the stability of Figainin 1. Among the 8 peptides synthesized, A-10 showed the strongest antiproliferative activity against K562 cells and the other four tumor cell lines, and its stability against serum and proteinase K was improved compared with F1. We found that A-10 works through two mechanisms, cell membrane destruction and apoptosis, and can arrest the cell cycle in the G0/G1 phase. Moreover, A-10 exhibited self-assembly behavior. Overall, it is necessary to select a fatty acid with a suitable length for modification to improve the stability and antiproliferative activity of antimicrobial peptides. This study provides a good reference for the development of antimicrobial peptides as effective anticancer compounds.

Evaluation of anti-cancer effects of carnosine and melittin-loaded niosomes in MCF-7 and MDA-MB-231 breast cancer cells

Background: We investigated the anti-cancer effect of carnosine-loaded niosomes (Car-NIO) and melittin-loaded niosomes (Mel-NIO) with olaparib in breast cancer cell lines (MCF-7 and MDA-MB-231). Methods: The thin film method was used for preparing the niosomes and characterized in terms of morphology, size, and polydispersity index (PDI). We further evaluated the impact of these peptides on breast cancer cells viability, RT-qPCR assays, malondialdehyde (MDA) activity, and cell cycle progression, to determine if these are linked to carnosine and melittin's anti-proliferative properties. Results: Car-NIO and Mel-NIO in vitro study inhibited cancer cell viability. They have also upregulated the expression of protein 53 (P53), BCL2-Associated X Protein (Bax), caspase-9, caspase-3, programmed cell death 4 (PDCD4), and Forkhead box O3 (FOXO3), while downregulated the expression of B-cell lymphoma 2 (Bcl2), poly (ADP-ribose) polymerase (PARP 1), and MicroRNA-183 (miRNA-183). The MCF-7 cells were arrested at the G2/M phase in Car-NIO, on the other hand, the MDA-MB-231 cells were arrested at the S phase. While the Mel-NIO and olaparib arrested the MCF-7 and MDA-MB-231 cells at the G0/1 phase. Conclusion: Our study successfully declared that Mel-NIO had more anti-cancer effects than Car-NIO in both MCF-7 and MDA-MB-231 breast cancer cells.

Anticancer peptides from induced tumor-suppressing cells for inhibiting osteosarcoma cells

Osteosarcoma (OS) is the most frequent primary bone cancer, which is mainly suffered by children and young adults. While the current surgical treatment combined with chemotherapy is effective for the early stage of OS, advanced OS preferentially metastasizes to the lung and is difficult to treat. Here, we examined the efficacy of ten anti-OS peptide candidates from a trypsin-digested conditioned medium that was derived from the secretome of induced tumor-suppressing cells (iTSCs). Using OS cell lines, the antitumor capabilities of the peptide candidates were evaluated by assaying the alterations in metabolic activities, proliferation, motility, and invasion of OS cells. Among ten candidates, peptide P05 (ADDGRPFPQVIK), a fragment of aldolase A (ALDOA), presented the most potent OS-suppressing capabilities. Its efficacy was additive with standard-of-care chemotherapeutic agents such as cisplatin and doxorubicin, and it downregulated oncoproteins such as epidermal growth factor receptor (EGFR), Snail, and Src in OS cells. Interestingly, P05 did not present inhibitory effects on non-OS skeletal cells such as mesenchymal stem cells and osteoblast cells. Collectively, this study demonstrated that iTSC-derived secretomes may provide a source for identifying anticancer peptides, and P05 may warrant further evaluations for the treatment of OS.