Peptide-Based Therapeutics for Oncology

Discovery of anticancer peptides from natural and generated sequences using deep learning

Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However, many predictive methods lack sufficient wet lab validation, thereby constraining the progress of models and impeding the discovery of novel ACPs. This study proposes a comprehensive research strategy by introducing CNBT-ACPred, an ACP prediction model based on a three-channel deep learning architecture, supported by extensive in vitro and in vivo experiments. CNBT-ACPred achieved an accuracy of 0.9554 and a Matthews Correlation Coefficient (MCC) of 0.8602. Compared to existing excellent models, CNBT-ACPred increased accuracy by at least 5 % and improved MCC by 15 %. Predictions were conducted on over 3.8 million sequences from Uniprot, along with 100,000 sequences generated by a deep generative model, ultimately identifying 37 out of 41 candidate peptides from >30 species that exhibited effective in vitro tumor inhibitory activity. Among these, tPep14 demonstrated significant anticancer effects in two mouse xenograft models without detectable toxicity. Finally, the study revealed correlations between the amino acid composition, structure, and function of the identified ACP candidates.

Phage display screening in breast cancer: From peptide discovery to clinical applications

Breast cancer is known as the most common type of cancer found in women and a leading cause of cancer death in women, with the global incidence only increasing. Breast cancer in Malaysia is also unfortunately the most prevalent in Malaysian women. Many treatment options are available for breast cancer, but there is increasing resistance developed against treatment and increased recurrence risk, emphasizing the need for new treatment options. This review will focus on the applications of phage display screening in the context of breast cancer. Phage display screening can facilitate the drug discovery process by providing rapid screening and isolation of peptides that bind to targets of interest with high specificity. Peptides derived from phage display target various types of proteins involved in breast cancer, including HER2, C5AR1, p53 and PRDM14, either for therapeutic or diagnostic purposes. Different approaches were employed as well to produce potential peptides using radiolabelling and conjugation techniques. Promising results were reported for in vitro and in vivo studies utilizing peptides derived from phage display screening. Further optimization of the protocols and factors to consider are required to mitigate the challenges involved with phage display screening of peptides for breast cancer diagnosis and treatment.

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

The use of peptides for immunodiagnosis of human Chagas disease

Chagas disease, caused by the protozoa Trypanosoma cruzi, continues to be a serious public health problem in Latin America, worsened by the limitations in its detection. Given the importance of developing new diagnostic methods for this disease, the present review aimed to verify the number of publications dedicated to research on peptides that demonstrate their usefulness in serodiagnosis. To this end, a bibliographic survey was conducted on the PubMed platform using the keyword "peptide" or "epitope" combined with "Chagas disease" or "Trypanosoma cruzi"; "diagno*" or "serodiagnosis" or "immunodiagnosis", without period restriction. An increasing number of publications on studies employing peptides in ELISA and rapid tests assays was verified, which confirms the expansion of research in this field. It is possible to observe that many of the peptides tested so far originate from proteins widely used in the diagnosis of Chagas, and many of them are part of commercial tests developed. In this sense, as expected, promising results were obtained for several peptides when tested in ELISA, as many of them exhibited sensitivity and specificity values above 90%. Furthermore, some peptides have been tested in several studies, confirming their diagnostic potential. Despite the promising results observed, it is possible to emphasize the need for extensive testing of peptides, using different serological panels, in order to confirm their potential. The importance of producing an effective assay capable of detecting the clinical stages of the disease, as well as new immunogenic antigens that enable new serological diagnostic tools for Chagas disease, is evident.

Food peptidomic analysis of bovine milk fermented by Lacticaseibacillus casei LBC 237: In silico prediction of bioactive peptides and anticancer potential

Bioactive peptides, which exhibited diverse biological activities such as anti-cancer, anti-inflammatory, bactericidal, antiviral, and quorum sensing properties, were considered promising alternative therapeutic agents. Sourced from various raw materials, particularly foods, these peptides garnered significant interest. In this context, the study focused on exploring bioactive peptides derived from bovine whole milk fermentation by Lacticaseibacillus casei LBC 237. Comprehensive peptidomic analysis and in silico predictions, with a specific emphasis on anti-cancer properties, were conducted. The study categorized peptides into BP-LBC, originating from the metabolism of L. casei LBC 237 and not matching any sequence in the Bos taurus database, and BP-MILK, matching a sequence in the Bos taurus database. Among the 143 identified peptides with potential biological activity, 33.56% were attributed to BP-LBC, while 66.43% originated from BP-MILK, demonstrating the important contribution of proteins in bovine milk in the generation of bioactive peptides. Hydrophobic peptides, enriched in Leucine, Lysine, and Proline, dominated both fractions, significantly influencing their functional properties. Pearson correlation analysis revealed inverse relationships between bioactive peptides, molecular weight, and anti-tumor activity in BP-MILK. The DGKVWEESLK peptide exhibited in silico activity against 10 different cancer cell lines. Studying the bioactive properties of peptides from familiar sources enhances the connection between food science and human health. In addition, in silico studies have been crucial in deepening our understanding of the bioactive potential of these peptides and their mode of action.

Advances in Alzheimer's Disease-Associated Aβ Therapy Based on Peptide

Alzheimer's disease (AD) urgently needs innovative treatments due to the increasing aging population and lack of effective drugs and therapies. The amyloid fibrosis of AD-associated β-amyloid (Aβ) that could induce a series of cascades, such as oxidative stress and inflammation, is a critical factor in the progression of AD. Recently, peptide-based therapies for AD are expected to be great potential strategies for the high specificity to the targets, low toxicity, fast blood clearance, rapid cell and tissue permeability, and superior biochemical characteristics. Specifically, various chiral amino acids or peptide-modified interfaces draw much attention as effective manners to inhibit Aβ fibrillation. On the other hand, peptide-based inhibitors could be obtained through affinity screening such as phage display or by rational design based on the core sequence of Aβ fibrosis or by computer aided drug design based on the structure of Aβ. These peptide-based therapies can inhibit Aβ fibrillation and reduce cytotoxicity induced by Aβ aggregation and some have been shown to relieve cognition in AD model mice and reduce Aβ plaques in mice brains. This review summarizes the design method and characteristics of peptide inhibitors and their effect on the amyloid fibrosis of Aβ. We further describe some analysis methods for evaluating the inhibitory effect and point out the challenges in these areas, and possible directions for the design of AD drugs based on peptides, which lay the foundation for the development of new effective drugs in the future.

Eptifibatide, an Older Therapeutic Peptide with New Indications: From Clinical Pharmacology to Everyday Clinical Practice

Therapeutic peptides are oligomers or short polymers of amino acids used for various medical purposes. Peptide-based treatments have evolved considerably due to new technologies, stimulating new research interests. They have been shown to be beneficial in a variety of therapeutic applications, notably in the treatment of cardiovascular disorders such as acute coronary syndrome (ACS). ACS is characterized by coronary artery wall damage and consequent formation of an intraluminal thrombus obstructing one or more coronary arteries, leading to unstable angina, non-ST elevated myocardial infarction, and ST-elevated myocardial infarction. One of the promising peptide drugs in the treatment of these pathologies is eptifibatide, a synthetic heptapeptide derived from rattlesnake venom. Eptifibatide is a glycoprotein IIb/IIIa inhibitor that blocks different pathways in platelet activation and aggregation. In this narrative review, we summarized the current evidence on the mechanism of action, clinical pharmacology, and applications of eptifibatide in cardiology. Additionally, we illustrated its possible broader usage with new indications, including ischemic stroke, carotid stenting, intracranial aneurysm stenting, and septic shock. Further research is, however, required to fully evaluate the role of eptifibatide in these pathologies, independently and in comparison to other medications.

Patient-derived xenografts or organoids in the discovery of traditional and self-assembled drug for tumor immunotherapy

In addition to the rapid development of immune checkpoint inhibitors, there has also been a surge in the development of self-assembly immunotherapy drugs. Based on the immune target, traditional tumor immunotherapy drugs are classified into five categories, namely immune checkpoint inhibitors, direct immune modulators, adoptive cell therapy, oncolytic viruses, and cancer vaccines. Additionally, the emergence of self-assembled drugs with improved precision and environmental sensitivity offers a promising innovation approach to tumor immunotherapy. Despite rapid advances in tumor immunotherapy drug development, all candidate drugs require preclinical evaluation for safety and efficacy, and conventional evaluations are primarily conducted using two-dimensional cell lines and animal models, an approach that may be unsuitable for immunotherapy drugs. The patient-derived xenograft and organoids models, however, maintain the heterogeneity and immunity of the pathological tumor heterogeneity.

ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .

A Thermodynamic Study on the Interaction between RH-23 Peptide and DMPC-Based Biomembrane Models

Investigation of the interaction between drugs and biomembrane models, as a strategy to study and eventually improve drug/substrate interactions, is a crucial factor in preliminary screening. Synthesized peptides represent a source of potential anticancer and theragnostic drugs. In this study, we investigated the interaction of a novel synthesized peptide, called RH-23, with a simplified dimyristoylphosphatidylcholine (DMPC) model of the cellular membrane. The interaction of RH-23 with DMPC, organized either in multilamellar vesicles (MLVs) and in Langmuir-Blodgett (LB) monolayers, was assessed using thermodynamic techniques, namely differential scanning calorimetry (DSC) and LB. The calorimetric evaluations showed that RH-23 inserted into MLVs, causing a stabilization of the phospholipid gel phase that increased with the molar fraction of RH-23. Interplay with LB monolayers revealed that RH-23 interacted with DMPC molecules. This work represents the first experimental thermodynamic study on the interaction between RH-23 and a simplified model of the lipid membrane, thus providing a basis for further evaluations of the effect of RH-23 on biological membranes and its therapeutic/diagnostic potential.

Peptide therapeutics in the management of metastatic cancers

Cancer remains a leading health concern threatening lives of millions of patients worldwide. Peptide-based drugs provide a valuable alternative to chemotherapeutics as they are highly specific, cheap, less toxic and easier to synthesize compared to other drugs. In this review, we have discussed various modes in which peptides are being used to curb cancer. Our review highlights specially the various anti-metastatic peptide-based agents developed by targeting a plethora of cellular factors. Herein we have given a special focus on integrins as targets for peptide drugs, as these molecules play key roles in metastatic progression. The review also discusses use of peptides as anti-cancer vaccines and their efficiency as drug-delivery tools. We hope this work will give the reader a clear idea of the mechanisms of peptide-based anti-cancer therapeutics and encourage the development of superior drugs in the future.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

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.

Therapeutic peptides: current applications and future directions

Peptide drug development has made great progress in the last decade thanks to new production, modification, and analytic technologies. Peptides have been produced and modified using both chemical and biological methods, together with novel design and delivery strategies, which have helped to overcome the inherent drawbacks of peptides and have allowed the continued advancement of this field. A wide variety of natural and modified peptides have been obtained and studied, covering multiple therapeutic areas. This review summarizes the efforts and achievements in peptide drug discovery, production, and modification, and their current applications. We also discuss the value and challenges associated with future developments in therapeutic peptides.

Neuroprotective effect and potential of cellular prion protein and its cleavage products for treatment of neurodegenerative disorders part II: strategies for therapeutics development

Introduction: The cellular prion protein (PrP^C), some of its derivatives (especially PrP N-terminal N1 peptide and shed PrP), and PrP^C-containing exosomes have strong neuroprotective activities, which have been reviewed in the companion article (Part I) and are briefly summarized here.Areas covered: We propose that elevating the extracellular levels of a protective PrP form using gene therapy and other approaches is a very promising novel avenue for prophylactic and therapeutic treatments against prion disease, Alzheimer's disease, and several other neurodegenerative diseases. We will dissect the pros and cons of various potential PrP-based treatment options and propose a few strategies that are more likely to succeed. The cited references were obtained from extensive PubMed searches of recent literature, including peer-reviewed original articles and review articles.Expert opinion: Concurrent knockdown of celllular PrP expression and elevation of the extracellular levels of a neuroprotective PrP N-terminal peptide via optimized gene therapy vectors is a highly promising broad-spectrum prophylactic and therapeutic strategy against several neurodegenerative diseases, including prion diseases, Alzheimer's disease and Parkinson's disease.

Repurposed Drugs, Molecular Vaccines, Immune-Modulators, and Nanotherapeutics to Treat and Prevent COVID-19 Associated with SARS-CoV-2, a Deadly Nanovector

The deadly pandemic, coronavirus disease 2019 (COVID-19), caused due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has paralyzed the world. Although significant methodological advances have been made in the field of viral detection/diagnosis with 251 in vitro diagnostic tests receiving emergency use approval by the US-FDA, little progress has been made in identifying curative or preventive therapies. This review discusses the current trends and potential future approaches for developing COVID-19 therapeutics, including repurposed drugs, vaccine candidates, immune-modulators, convalescent plasma therapy, and antiviral nanoparticles/nanovaccines/combinatorial nanotherapeutics to surmount the pandemic viral strain. Many potent therapeutic candidates emerging via drug-repurposing could significantly reduce the cost and duration of anti-COVID-19 drug development. Gene/protein-based vaccine candidates that could elicit both humoral and cell-based immunity would be on the frontlines to prevent the disease. Many emerging nanotechnology-based interventions will be critical in the fight against the deadly virus by facilitating early detection and enabling target oriented multidrug therapeutics. The therapeutic candidates discussed in this article include remdesivir, dexamethasone, hydroxychloroquine, favilavir, lopinavir/ritonavir, antibody therapeutics like gimsilumab and TJM2, anti-viral nanoparticles, and nanoparticle-based DNA and mRNA vaccines.

A Global Review on Short Peptides: Frontiers and Perspectives

Peptides are fragments of proteins that carry out biological functions. They act as signaling entities via all domains of life and interfere with protein-protein interactions, which are indispensable in bio-processes. Short peptides include fundamental molecular information for a prelude to the symphony of life. They have aroused considerable interest due to their unique features and great promise in innovative bio-therapies. This work focusing on the current state-of-the-art short peptide-based therapeutical developments is the first global review written by researchers from all continents, as a celebration of 100 years of peptide therapeutics since the commencement of insulin therapy in the 1920s. Peptide "drugs" initially played only the role of hormone analogs to balance disorders. Nowadays, they achieve numerous biomedical tasks, can cross membranes, or reach intracellular targets. The role of peptides in bio-processes can hardly be mimicked by other chemical substances. The article is divided into independent sections, which are related to either the progress in short peptide-based theranostics or the problems posing challenge to bio-medicine. In particular, the SWOT analysis of short peptides, their relevance in therapies of diverse diseases, improvements in (bio)synthesis platforms, advanced nano-supramolecular technologies, aptamers, altered peptide ligands and in silico methodologies to overcome peptide limitations, modern smart bio-functional materials, vaccines, and drug/gene-targeted delivery systems are discussed.

Physiological and Pathological Roles of Cdk5: Potential Directions for Therapeutic Targeting in Neurodegenerative Disease

Cyclin-dependent kinase 5 (Cdk5) is a proline-directed serine (ser)/threonine (Thr) kinase that has been demonstrated to be one of the most functionally diverse kinases within neurons. Cdk5 is regulated via binding with its neuron-specific regulatory subunits, p35 or p39. Cdk5-p35 activity is critical for a variety of developmental and cellular processes in the brain, including neuron migration, memory formation, microtubule regulation, and cell cycle suppression. Aberrant activation of Cdk5 via the truncated p35 byproduct, p25, is implicated in the pathogenesis of several neurodegenerative diseases. The present review highlights the importance of Cdk5 activity and function in the brain and demonstrates how deregulation of Cdk5 can contribute to the development of neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Additionally, we cover past drug discovery attempts at inhibiting Cdk5-p25 activity and discuss which types of targeting strategies may prove to be the most successful moving forward.