Recent advances in liposomes and peptide-based therapeutics for glioblastoma treatment.

In the context of glioblastoma treatment, the penetration of drugs is drastically limited by the blood-brain-barrier (BBB). Emerging therapies have focused on the field of therapeutic peptides for their excellent BBB targeting properties that promote a deep tumor penetration. Peptide-based strategies are also renowned for their abilities of driving cargo such as liposomal system allowing an active targeting of receptors overexpressed on GBM cells. This review provides a detailed description of the internalization mechanisms of specific GBM homing and penetrating peptides as well as the latest in vitro/in vivo studies of liposomes functionalized with them. The purpose of this review is to summarize a selection of promising pre-clinical results that demonstrate the advantages of this nanosystem, including an increase of tumor cell targeting, triggering drug accumulation and thus a strong antitumor effect. Aware of the early stage of these studies, many challenges need to be overcome to promote peptide-directed liposome at clinical level. In particular, the lack of suitable production, the difficulty to characterize the nanosystem and therapeutic competition leaded by antibodies.

In Vitro Study of Tumor-Homing Peptide-Modified Magnetic Nanoparticles for Magnetic Hyperthermia.

Cancer cells have higher heat sensitivity compared to normal cells; therefore, hyperthermia is a promising approach for cancer therapy because of its ability to selectively kill cancer cells by heating them. However, the specific and rapid heating of tumor tissues remains challenging. This study investigated the potential of magnetic nanoparticles (MNPs) modified with tumor-homing peptides (THPs), specifically PL1 and PL3, for tumor-specific magnetic hyperthermia therapy. The synthesis of THP-modified MNPs involved the attachment of PL1 and PL3 peptides to the surface of the MNPs, which facilitated enhanced tumor cell binding and internalization. Cell specificity studies revealed an increased uptake of PL1- and PL3-MNPs by tumor cells compared to unmodified MNPs, indicating their potential for targeted delivery. In vitro hyperthermia experiments demonstrated the efficacy of PL3-MNPs in inducing tumor cell death when exposed to an alternating magnetic field (AMF). Even without exposure to an AMF, an additional ferroptotic pathway was suggested to be mediated by the nanoparticles. Thus, this study suggests that THP-modified MNPs, particularly PL3-MNPs, hold promise as a targeted approach for tumor-specific magnetic hyperthermia therapy.

Anticancer Activity of HER2-targeting CPP-PTEN-THP Chimeric Proteins.

BACKGROUND/AIM: Protein phosphatase and tensin homolog (PTEN) is a tumor suppressor protein with potential to be a new biotechnological drug for PTEN-deficient cancer treatment. This study aimed to develop PTEN-based chimeric proteins (CPP-PTEN-THP) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer treatment, addressing current limitations like inadequate delivery, poor tumor penetration, and low selectivity, while assessing their potential HER2-specific anticancer effects. MATERIALS AND METHODS: pCEFL-EGFP vector was used for both TAT-PTEN-LTV and KLA-PTEN-LTV construction. Non-contact co-cultures were employed using HEK-293T cells for protein expression, and HCC-1954 and MCF-7 cell lines for cytotoxicity testing. Protein detection was analyzed by western blotting and a docking prediction analysis was performed to infer the interactions. RESULTS: Endogenous and recombinant PTEN protein expression was confirmed in cell lysates. A 54-kDa signal matching the theoretical size of PTEN was detected, showing a greater level in TAT-PTEN-LTV (215.1±26.45%) and KLA-PTEN-LTV (129.2±1.44%) compared to endogenous PTEN. After the noncontact co-culture method, cytotoxic studies showed HCC-1954 preferential cell inhibition growth, with 25.95±0.9% and 12.25±1.29% inhibition by KLA-PTEN-LTV and TAT-PTEN-LTV respectively, compared to MCF-7 cells. An LTV-HER2 interaction model was proposed, inferring that LTV interactions are mainly due to the Pro, Trp, and Tyr residues that target HER2. CONCLUSION: The developed PTEN-based chimeric proteins have HER2-specific anticancer activity against HCC-1954 cells.

Placenta-targeted Treatment Strategies for Preeclampsia and Fetal Growth Restriction: An Opportunity and Major Challenge.

The placenta plays a crucial role in maintaining normal pregnancy. The failure of spiral artery remodeling (SAR) is a key factor leading to placental ischemia and poor perfusion which is strongly associated with obstetric diseases, including preeclampsia (PE) and fetal growth restriction (FGR). Existing interventions for PE and FGR are limited and termination of pregnancy is inevitable when the maternal or fetus condition deteriorates. Considering the safety of the mother and fetus, treatments that may penetrate the placental barrier and harm the fetus are not accepted. Developing targeted treatment strategies for these conditions is urgent and necessary. With the proven efficacy of targeted therapy in treating conditions such as endometrial cancer and trophoblastic tumors, research on placental dysfunction continues to deepen. This article reviews the studies on placenta-targeted treatment and drug delivery strategies, summarizes the characteristics proposes corresponding improvement measures in targeted treatment, provides solutions for existing problems, and makes suggestions for future studies.

The investigation of the death-inducing potency of a recombinant Adenovector expressing Mda-7-tlyp-1 on different cancer cell lines.

Aim: The potency of Adenovector expressing Mda7-tLyp1 (Ad-Mda7-tLyp1) for death induction was evaluated on the breast (MCF7), liver (HepG2), and gastric (MKN45) cancer cell lines. Background: Mda-7 could be a possible complementary to traditional cancer therapy, and tethering to tumor-homing peptides (THPs) might improve its therapeutic efficacy. Methods: After the preparation of recombinant Ad-Mda7-tLyp1 and Ad-Mda7, the expression of recombinant proteins was analyzed by ELISA. Adenovectors were transduced (MOI=2-5) into Hep-G2, MCF7, MKN45, and normal skin fibroblast, then tumor-killing effect was measured by cytopathic effect (CPE) monitoring, MTT viability test, BAX gene expression analysis, and Caspase3/7 assay. Results: ELISA assay revealed a sustained level of recombinant protein secretion following Adenovector transduction. In CPE microscopy, all cancer cell lines showed a significant reduction (≥50%) in their normal phenotype after receiving Ad-Mda7-tLyp1 and Ad-Mda7. The viability was significantly lower compared to the control, indicating an anti-proliferating effect. In parallel, the viability test showed that Ad-Mda7 and Ad-Mda7-tLyp1 have a significant killing effect (≥50%) on MCF-7, Hep-G2, and MKN45 compared to normal fibroblast (P≤0.05). BAX gene expression analysis showed that both Ad-Mda7-tLyp1 and Ad-Mda7 vectors induced >2-fold increase of apoptosis (P<0.05), particularly in MCF7. Similarly, caspase3/7 activity showed a significant increase (P<0.05) following Ad-Mda7, and Ad-Mda7-tLyp1 transduction into cancer cell lines, but not in normal fibroblasts. Conclusion: The newly constructed Ad-Mda-tlyp1 showed a suitable tumor cell killing activity and enough specificity on studied cell lines.

Albumin Nanoparticles Surface Decorated with a Tumor-Homing Peptide Help in Selective Killing of Triple-Negative Breast Cancer Cells.

In this article, we describe a method of delivery of doxorubicin using a novel tumor-homing peptide-based albumin nanoparticle system to triple-negative breast cancer cells (TNBC). The absence and reduced expression of the hormone (estrogen, progesterone) and HER2 (human epidermal growth factor 2) receptors, respectively, render TNBC patients nonsusceptible to different available targeted therapies. These peptide-modified nanoparticles could be taken up by TNBC cells more effectively than their bare counterparts. The drug-loaded peptide-modified nanoparticles achieved an optimal but crucial balance between cell killing in cancerous cells and cell survival in the noncancerous ones. This appears to be because of different routes of entry and subsequent fate of the bare and peptide-modified nanoparticles in cancerous and noncancerous cells. In a TNBC mouse model, the peptide-modified system fared better than the free drug in mounting an antitumor response while not being toxic systemically.

StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.

One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.

NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides.

Tumor homing peptides (THPs) play a crucial role in recognizing and specifically binding to cancer cells. Although experimental approaches can facilitate the precise identification of THPs, they are usually time-consuming, labor-intensive, and not cost-effective. However, computational approaches can identify THPs by utilizing sequence information alone, thus highlighting their great potential for large-scale identification of THPs. Herein, we propose NEPTUNE, a novel computational approach for the accurate and large-scale identification of THPs from sequence information. Specifically, we constructed variant baseline models from multiple feature encoding schemes coupled with six popular machine learning algorithms. Subsequently, we comprehensively assessed and investigated the effects of these baseline models on THP prediction. Finally, the probabilistic information generated by the optimal baseline models is fed into a support vector machine-based classifier to construct the final meta-predictor (NEPTUNE). Cross-validation and independent tests demonstrated that NEPTUNE achieved superior performance for THP prediction compared with its constituent baseline models and the existing methods. Moreover, we employed the powerful SHapley additive exPlanations method to improve the interpretation of NEPTUNE and elucidate the most important features for identifying THPs. Finally, we implemented an online web server using NEPTUNE, which is available at http://pmlabstack.pythonanywhere.com/NEPTUNE. NEPTUNE could be beneficial for the large-scale identification of unknown THP candidates for follow-up experimental validation.

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials.

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894-0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

A Dual-Reporter Platform for Screening Tumor-Targeted Extracellular Vesicles.

Extracellular vesicle (EV)-mediated transfer of biomolecules plays an essential role in intercellular communication and may improve targeted drug delivery. In the past decade, various approaches to EV surface modification for targeting specific cells or tissues have been proposed, including genetic engineering of parental cells or postproduction EV engineering. However, due to technical limitations, targeting moieties of engineered EVs have not been thoroughly characterized. Here, we report the bioluminescence resonance energy transfer (BRET) EV reporter, PalmReNL-based dual-reporter platform for characterizing the cellular uptake of tumor-homing peptide (THP)-engineered EVs, targeting PDL1, uPAR, or EGFR proteins expressed in MDA-MB-231 breast cancer cells, simultaneously by bioluminescence measurement and fluorescence microscopy. Bioluminescence analysis of cellular EV uptake revealed the highest binding efficiency of uPAR-targeted EVs, whereas PDL1-targeted EVs showed slower cellular uptake. EVs engineered with two known EGFR-binding peptides via lipid nanoprobes did not increase cellular uptake, indicating that designs of EGFR-binding peptide conjugation to the EV surface are critical for functional EV engineering. Fluorescence analysis of cellular EV uptake allowed us to track individual PalmReNL-EVs bearing THPs in recipient cells. These results demonstrate that the PalmReNL-based EV assay platform can be a foundation for high-throughput screening of tumor-targeted EVs.

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.

Molecular hybridization combining tumor homing and penetrating peptide domains for cellular targeting.

A complete peptide-based drug delivery unit has been designed with a tumor homing domain chemically linked to a syndiotactic cell-penetrating domain. The designed peptides were synthesized, characterized, and tested in vitro for cellular uptake and cytotoxicity evaluation. The differential uptake, cellular internalization, negligible hemotoxicity, selective toxicity to MDA-MB-231 breast cancer cells, and the superior penetration in three-dimensional MDA-MB-231 tumorospheres confirm their utility as a promising delivery vector.

PL1 Peptide Engages Acidic Surfaces on Tumor-Associated Fibronectin and Tenascin Isoforms to Trigger Cellular Uptake.

Tumor extracellular matrix (ECM) is a high-capacity target for the precision delivery of affinity ligand-guided drugs and imaging agents. Recently, we developed a PL1 peptide (sequence: PPRRGLIKLKTS) for systemic targeting of malignant ECM. Here, we map the dynamics of PL1 binding to its receptors Fibronectin Extra Domain B (FN-EDB) and Tenascin C C-isoform (TNC-C) by computational modeling and cell-free binding studies on mutated receptor proteins, and study cellular binding and internalization of PL1 nanoparticles in cultured cells. Molecular dynamics simulation and docking analysis suggested that the engagement of PL1 peptide with both receptors is primarily driven by electrostatic interactions. Substituting acidic amino acid residues with neutral amino acids at predicted PL1 binding sites in FN-EDB (D52N-D49N-D12N) and TNC-C (D39N-D45N) resulted in the loss of binding of PL1 nanoparticles. Remarkably, PL1-functionalized nanoparticles (NPs) were not only deposited on the target ECM but bound the cells and initiated a robust cellular uptake via a pathway resembling macropinocytosis. Our studies establish the mode of engagement of the PL1 peptide with its receptors and suggest applications for intracellular delivery of nanoscale payloads. The outcomes of this work can be used for the development of PL1-derived peptides with improved stability, affinity, and specificity for precision targeting of the tumor ECM and malignant cells.

Homing Peptides for Cancer Therapy.

Tumor-homing peptides are widely used for improving tumor selectivity of anticancer drugs and imaging agents. The goal is to increase tumor uptake and reduce accumulation at nontarget sites. Here, we describe current approaches for tumor-homing peptide identification and validation, and provide comprehensive overview of classes of tumor-homing peptides undergoing preclinical and clinical development. We focus on unique mechanistic features and applications of a recently discovered class of tumor-homing peptides, tumor-penetrating C-end Rule (CendR) peptides, that can be used for tissue penetrative targeting of extravascular tumor tissue. Finally, we discuss unanswered questions and future directions in the field of development of peptide-guided smart drugs and imaging agents.

Design of a dendrimer with a matrix metalloproteinase-responsive fluorescence probe and a tumor-homing peptide for metastatic tumor cell imaging in the lymph node.

Fluorescence imaging is a noninvasive technique for cancer diagnosis. Dendrimers are regularly branched macromolecules with highly controllable size and structure that are a potent multifunctional nanoparticle. Anionic-terminal polyamidoamine (PAMAM) dendrimers were previously found to be accumulated in the lymph node, which is one of the main routes of tumor metastasis. In this study, we designed and synthesized a dendrimeric imaging probe for lymph node-resident tumor cell imaging. A matrix metalloproteinase-2 (MMP-2)-responsive fluorescence peptide probe and a tumor-homing peptide were conjugated to the carboxy-terminal dendrimer. The dendrimeric imaging probe treatment showed fluorescence signals inside some tumor cells (e.g., human fibrosarcoma HT-1080 and breast cancer 4T1 cells), depending on the MMP activity, but not in macrophage-like RAW264 cells.

Application of the Tumor Site Recognizable and Dual-Responsive Nanoparticles for Combinational Treatment of the Drug-Resistant Colorectal Cancer.

PURPOSE: Combination of PCI and chemotherapy represents a promising strategy for combating drug resistance of cancer. However, poor solubility of photosensitizers and unselectively released drugs at unwanted sites significantly impaired the treatment efficacy. Therefore, in the present study, we aimed to develop a nano-platform which could efficiently co-entrapping photosensitizers and chemotherapeutics for active targeting therapy of drug resistant cancers. METHODS: Two pro-drugs were respectively developed by covalently linking the Ce6 with each other via the GSH-sensitive linkage and the PTX with mPEG-PLA-COOH through the ROS sensitive-linker. The dual-responsive nanoparticles (PNP-Ce6) was developed by emulsion/solvent evaporation method and further modified with tLyp-1 peptides. Physicochemical properties of nanoparticles were determined by the TEM and DLC. Cellular uptake assay was investigated with the Ce6 acting as the fluorescent probe and cell growth was studied by the MTT experiment. In vivo tumor targeting and anti-tumor assay was investigated on the colorectal cancer-bearing mice. RESULTS: The developed tPNP-Ce6 were stable enough under the normal physiological conditions. However, free Ce6 and PTX were completely released when exposed the tPNP-Ce6 to the redox environment. Excellent tumor-targeting drug delivery was achieved by the tPNP-Ce6, which in turn resulted in satisfactory anit-tumor effect. Of great importance, super inhibition effect on tumor progress was achieved by the combination therapy when compared with the group only received with chemotherapy.. CONCLUSION: The results obtained in the present study indicated that the developed tPNP-Ce6 may have great potential in enhancing the therapeutic efficacy of drug-resistant colorectal cancer. Graphical Abstract Left: Targeting delivery of drug to tumor site by the tumor recognizable and dual-responsive nanoparticles and penetrating into tumor inner via the mediation of irradiation. Right: Nanoparticle distribution within tumor tissues with green represents the blood vessels stained with CD31, blue signal represents the cell nuclei stained with DAPI and red shows fluorescence of Ce6 as the indicator of the nanoparticles.

Cancer-Cell-Specific Drug Delivery by a Tumor-Homing CPP-Gossypol Conjugate Employing a Tracelessly Cleavable Linker.

Tumor-targeted drug delivery is highly important for improving chemotherapy, as it reduces the dose of cytotoxic agents and minimizes the death of healthy tissues. Towards this goal, a conjugate was synthesized of gossypol and a MCF-7 cancer cell specific CPP (cell penetrating peptide), thus providing a selective drug delivery system. Utilizing the aldehyde moiety of gossypol, the tumor homing CPP RLYMRYYSPTTRRYG was attached through a semi-labile imine linker, which was cleaved in a traceless fashion under aqueous conditions and had a half-life of approximately 10 hours. The conjugate killed MCF-7 cells to a significantly greater extent than HeLa cells or healthy fibroblasts.

Encapsulation and Storage of Therapeutic Fibrin-Homing Peptides using Conducting Polymer Nanoparticles for Programmed Release by Electrical Stimulation.

Cys-Arg-Glu-Lys-Ala (CREKA) is an important fibrin-homing pentapeptide that has been extensively demonstrated for diagnoses and therapies (e.g., image diagnosis of tumors and to inhibit tumor cell migration and invasion). Although CREKA-loaded nanoparticles (NPs) have received major interest as efficient biomedical systems for cancer diagnosis and treatment, almost no control on the peptide release has been achieved yet. Herein, we report the development of conductive polymer NPs as therapeutic CREKA carriers for controlled dose administration through electric stimuli. Furthermore, the study was extended to CR(NMe)EKA, a previously engineered CREKA analogue in which Glu was replaced by N-methyl-Glu for improvement of the peptide resistance against proteolysis, which is one of the major weaknesses of therapeutic peptide delivery, and for enhancement of the tumor homing capacity by overstabilizing the bioactive conformation. Particularly, the present work is focused on understanding the interactions between the newly designed nanoengineered materials and biological fluids and the achievement of a modulated peptide release by fine-tuning the electrical stimuli. Two different types of stimuli were compared, chronoamperometry versus cyclic voltammetry, the latter being more effective.

Screening and characterization of a novel high-efficiency tumor-homing cell-penetrating peptide from the buffalo cathelicidin family.

Targeted delivery of antitumor drugs is especially important for tumor therapy. Cell-penetrating peptides (CPPs) have been shown to be very effective drug carriers for tumor therapy. However, most CPPs lack tumor cell specificity. Here, we identified a highly efficient CPP, CAT, from the newly identified buffalo-derived cathelicidin family, which exhibits a preferential binding capacity for multiple tumor cell lines and delivers carried drug molecules into cells. CAT showed an approximately threefold to sixfold higher translocation efficiency than some reported cell-penetrating antimicrobial peptides, including the well-known classical CPP TAT. Moreover, the delivery efficiency of CAT was greater in a variety of tested tumor cells than in normal cells, especially for the human hepatoma cell line SMMC-7721, for which delivery was 7 times more efficient than the normal human embryonic lung cell line MRC-5, according to fluorescent labeling experiment results. CAT was conjugated to the Momordica charantia-derived type-I ribosome-inactivating protein MAP 30, and the cytotoxicity of the MAP 30-CAT fusion protein in the tumor cell line SMMC-7721 was significantly enhanced compared with that of the unconjugated MAP 30. The IC50 value of MAP 30-CAT was approximately 83 times lower than the IC50 value of the original MAP 30. Interestingly, the IC50 value of MAP 30 alone for MRC-5 was approximately twofold higher than the value for SMMC-7721, showing a small difference. However, when MAP 30 was conjugated to CAT, the difference in IC50 values between the two cell lines was significantly increased by 38-fold. The results of the flow cytometric detection of apoptosis revealed that the increase in cytotoxicity after CAT conjugation was mainly caused by the increased induction of apoptosis by the fusion protein. These results suggest that CAT, as a novel tumor-homing CPP, has great potential in drug delivery applications in vivo and will be beneficial to the development of tumor therapeutics.

THPep: A machine learning-based approach for predicting tumor homing peptides.

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.