Trial of Lixisenatide in Early Parkinson's Disease.

BACKGROUND: Lixisenatide, a glucagon-like peptide-1 receptor agonist used for the treatment of diabetes, has shown neuroprotective properties in a mouse model of Parkinson's disease. METHODS: In this phase 2, double-blind, randomized, placebo-controlled trial, we assessed the effect of lixisenatide on the progression of motor disability in persons with Parkinson's disease. Participants in whom Parkinson's disease was diagnosed less than 3 years earlier, who were receiving a stable dose of medications to treat symptoms, and who did not have motor complications were randomly assigned in a 1:1 ratio to daily subcutaneous lixisenatide or placebo for 12 months, followed by a 2-month washout period. The primary end point was the change from baseline in scores on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III (range, 0 to 132, with higher scores indicating greater motor disability), which was assessed in patients in the on-medication state at 12 months. Secondary end points included other MDS-UPDRS subscores at 6, 12, and 14 months and doses of levodopa equivalent. RESULTS: A total of 156 persons were enrolled, with 78 assigned to each group. MDS-UPDRS part III scores at baseline were approximately 15 in both groups. At 12 months, scores on the MDS-UPDRS part III had changed by -0.04 points (indicating improvement) in the lixisenatide group and 3.04 points (indicating worsening disability) in the placebo group (difference, 3.08; 95% confidence interval, 0.86 to 5.30; P = 0.007). At 14 months, after a 2-month washout period, the mean MDS-UPDRS motor scores in the off-medication state were 17.7 (95% CI, 15.7 to 19.7) with lixisenatide and 20.6 (95% CI, 18.5 to 22.8) with placebo. Other results relative to the secondary end points did not differ substantially between the groups. Nausea occurred in 46% of participants receiving lixisenatide, and vomiting occurred in 13%. CONCLUSIONS: In participants with early Parkinson's disease, lixisenatide therapy resulted in less progression of motor disability than placebo at 12 months in a phase 2 trial but was associated with gastrointestinal side effects. Longer and larger trials are needed to determine the effects and safety of lixisenatide in persons with Parkinson's disease. (Funded by the French Ministry of Health and others; LIXIPARK ClinicalTrials.gov number, NCT03439943.).

Rusfertide, a Hepcidin Mimetic, for Control of Erythrocytosis in Polycythemia Vera.

BACKGROUND: Polycythemia vera is a chronic myeloproliferative neoplasm characterized by erythrocytosis. Rusfertide, an injectable peptide mimetic of the master iron regulatory hormone hepcidin, restricts the availability of iron for erythropoiesis. The safety and efficacy of rusfertide in patients with phlebotomy-dependent polycythemia vera are unknown. METHODS: In part 1 of the international, phase 2 REVIVE trial, we enrolled patients in a 28-week dose-finding assessment of rusfertide. Part 2 was a double-blind, randomized withdrawal period in which we assigned patients, in a 1:1 ratio, to receive rusfertide or placebo for 12 weeks. The primary efficacy end point was a response, defined by hematocrit control, absence of phlebotomy, and completion of the trial regimen during part 2. Patient-reported outcomes were assessed by means of the modified Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF) patient diary (scores range from 0 to 10, with higher scores indicating greater severity of symptoms). RESULTS: Seventy patients were enrolled in part 1 of the trial, and 59 were assigned to receive rusfertide (30 patients) or placebo (29 patients) in part 2. The estimated mean (±SD) number of phlebotomies per year was 8.7±2.9 during the 28 weeks before the first dose of rusfertide and 0.6±1.0 during part 1 (estimated difference, 8.1 phlebotomies per year). The mean maximum hematocrit was 44.5±2.2% during part 1 as compared with 50.0±5.8% during the 28 weeks before the first dose of rusfertide. During part 2, a response was observed in 60% of the patients who received rusfertide as compared with 17% of those who received placebo (P = 0.002). Between baseline and the end of part 1, rusfertide treatment was associated with a decrease in individual symptom scores on the MPN-SAF in patients with moderate or severe symptoms at baseline. During parts 1 and 2, grade 3 adverse events occurred in 13% of the patients, and none of the patients had a grade 4 or 5 event. Injection-site reactions of grade 1 or 2 in severity were common. CONCLUSIONS: In patients with polycythemia vera, rusfertide treatment was associated with a mean hematocrit of less than 45% during the 28-week dose-finding period, and the percentage of patients with a response during the 12-week randomized withdrawal period was greater with rusfertide than with placebo. (Funded by Protagonist Therapeutics; REVIVE ClinicalTrials.gov number, NCT04057040.).

An Oral Interleukin-23-Receptor Antagonist Peptide for Plaque Psoriasis.

BACKGROUND: The use of monoclonal antibodies has changed the treatment of several immune-mediated inflammatory diseases, including psoriasis. However, these large proteins must be administered by injection. JNJ-77242113 is a novel, orally administered interleukin-23-receptor antagonist peptide that selectively blocks interleukin-23 signaling and downstream cytokine production. METHODS: In this phase 2 dose-finding trial, we randomly assigned patients with moderate-to-severe plaque psoriasis to receive JNJ-77242113 at a dose of 25 mg once daily, 25 mg twice daily, 50 mg once daily, 100 mg once daily, or 100 mg twice daily or placebo for 16 weeks. The primary end point was a reduction from baseline of at least 75% in the Psoriasis Area and Severity Index (PASI) score (PASI 75 response; PASI scores range from 0 to 72, with higher scores indicating greater extent or severity of psoriasis) at week 16. RESULTS: A total of 255 patients underwent randomization. The mean PASI score at baseline was 19.1. The mean duration of psoriasis was 18.2 years, and 78% of the patients across all the trial groups had previously received systemic treatments. At week 16, the percentages of patients with a PASI 75 response were higher among those in the JNJ-77242113 groups (37%, 51%, 58%, 65%, and 79% in the 25-mg once-daily, 25-mg twice-daily, 50-mg once-daily, 100-mg once-daily, and 100-mg twice-daily groups, respectively) than among those in the placebo group (9%), a finding that showed a significant dose-response relationship (P<0.001). The most common adverse events included coronavirus disease 2019 (in 12% of the patients in the placebo group and in 11% of those across the JNJ-77242113 dose groups) and nasopharyngitis (in 5% and 7%, respectively). The percentages of patients who had at least one adverse event were similar in the combined JNJ-77242113 dose group (52%) and the placebo group (51%). There was no evidence of a dose-related increase in adverse events across the JNJ-77242113 dose groups. CONCLUSIONS: After 16 weeks of once- or twice-daily oral administration, treatment with the interleukin-23-receptor antagonist peptide JNJ-77242113 showed greater efficacy than placebo in patients with moderate-to-severe plaque psoriasis. (Funded by Janssen Research and Development; FRONTIER 1 ClinicalTrials.gov number, NCT05223868.).

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Contrastive learning for enhancing feature extraction in anticancer peptides.

Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.

Bioinspired nanogels as cell-free DNA trapping and scavenging organelles for rheumatoid arthritis treatment.

Excessive cell-free DNA (cfDNA) in the serum and synovium is considered a causative factor of rheumatoid arthritis (RA). Thus, cfDNA scavenging by using cationic polymers has been an effective therapeutic avenue, while these stratagems still suffer from systemic toxicity and unstable capture of cfDNA. Here, inspired by the biological charge-trapping effects and active degradation function of enzyme-containing organelles in vivo, we proposed a cationic peptide dendrimer nanogel with deoxyribonuclease I (DNase I) conjugation for the treatment of RA. Benefitting from their naturally derived peptide components, the resultant nanogels were highly biocompatible. More attractively, by tailoring them with a larger size and higher surface charge density, these cationic nanogels could achieve the fastest targeting capability, highest accumulation amounts, longer persistence time, and superior DNA scavenging capacity in inflamed joints. Based on these features, we have demonstrated that the organelle mimicking cationic nanogels could significantly down-regulate toll-like receptor (TLR)-9 signaling pathways and attenuate RA symptoms in collagen-induced arthritis mice. These results make the bioinspired DNase I conjugated cationic nanogels an ideal candidate for treating RA and other immune dysregulation diseases.

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.

Thioredoxin-1 and its mimetic peptide improve systolic cardiac function and remodeling after myocardial infarction.

Myocardial infarction (MI) is characterized by a significant loss of cardiomyocytes (CMs), and it is suggested that reactive oxygen species (ROS) are involved in cell cycle arrest, leading to impaired CM renewal. Thioredoxin-1 (Trx-1) scavenges ROS and may play a role in restoring CM renewal. However, the truncated form of Trx-1, Trx-80, can compromise its efficacy by exerting antagonistic effects. Therefore, a Trx-1 mimetic peptide called CB3 was tested as an alternative way to restore CMs. This study aimed to investigate the effects of Trx-1, Trx-80, and CB3 on mice with experimental MI and study the underlying mechanism of CB3 on CMs. Mouse cardiac parameters were quantified by echocardiography, and infarction size and fibrosis determined using Trichrome and Picro-Sirius Red staining. The study found that Trx-1 and CB3 improved mouse cardiac function, reduced the size of cardiac infarct and fibrosis, and decreased the expression of cardiac inflammatory markers. Furthermore, CB3 polarized macrophages into M2 phenotype, reduced apoptosis and oxidative stress after MI, and increased CM proliferation in cell culture and in vivo. CB3 effectively protected against myocardial infarction and could represent a new class of compounds for treating MI.

Cathepsin B Responsive Peptide-Purpurin Conjugates Assembly-Initiated in Situ Self-Aggregation for Cancer Sonotheranostics.

Sonodynamic therapy (SDT) was hampered by the sonosensitizers with low bioavailability, tumor accumulation, and therapeutic efficiency. In situ responsive sonosensitizer self-assembly strategy may provide a promising route for cancer sonotheranositics. Herein, an intelligent sonotheranostic peptide-purpurin conjugate (P18-P) is developed that can self-assemble into supramolecular structures via self-aggregation triggered by rich enzyme cathepsin B (CTSB). After intravenous injection, the versatile probe could achieve deep tissue penetration because of the penetration sequence of P18-P. More importantly, CTSB-triggered self-assembly strongly prolonged retention time, amplified photoacoustic imaging signal for sensitive CTSB detection, and boosted reactive oxygen species for advanced SDT, evoking specific CTSB responsive sonotheranostics. This peptide-purpurin conjugate may serve as an efficient sonotheranostic platform for the early diagnosis of CTSB activity and effective cancer therapy.

Orally Bioavailable Macrocyclic Peptide That Inhibits Binding of PCSK9 to the Low Density Lipoprotein Receptor.

BACKGROUND: Inhibition of PCSK9 (proprotein convertase subtilisin/kexin type 9)-low density lipoprotein receptor interaction with injectable monoclonal antibodies or small interfering RNA lowers plasma low density lipoprotein-cholesterol, but despite nearly 2 decades of effort, an oral inhibitor of PCSK9 is not available. Macrocyclic peptides represent a novel approach to target proteins traditionally considered intractable to small-molecule drug design. METHODS: Novel mRNA display screening technology was used to identify lead chemical matter, which was then optimized by applying structure-based drug design enabled by novel synthetic chemistry to identify macrocyclic peptide (MK-0616) with exquisite potency and selectivity for PCSK9. Following completion of nonclinical safety studies, MK-0616 was administered to healthy adult participants in a single rising-dose Phase 1 clinical trial designed to evaluate its safety, pharmacokinetics, and pharmacodynamics. In a multiple-dose trial in participants taking statins, MK-0616 was administered once daily for 14 days to characterize the safety, pharmacokinetics, and pharmacodynamics (change in low density lipoprotein cholesterol). RESULTS: MK-0616 displayed high affinity (Ki = 5pM) for PCSK9 in vitro and sufficient safety and oral bioavailability preclinically to enable advancement into the clinic. In Phase 1 clinical studies in healthy adults, single oral doses of MK-0616 were associated with >93% geometric mean reduction (95% CI, 84-103) of free, unbound plasma PCSK9; in participants on statin therapy, multiple-oral-dose regimens provided a maximum 61% geometric mean reduction (95% CI, 43-85) in low density lipoprotein cholesterol from baseline after 14 days of once-daily dosing of 20 mg MK-0616. CONCLUSIONS: This work validates the use of mRNA display technology for identification of novel oral therapeutic agents, exemplified by the identification of an oral PCSK9 inhibitor, which has the potential to be a highly effective cholesterol lowering therapy for patients in need.

Pegcetacoplan versus Eculizumab in Paroxysmal Nocturnal Hemoglobinuria.

BACKGROUND: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired disease characterized by chronic complement-mediated hemolysis. C5 inhibition controls intravascular hemolysis in untreated PNH but cannot address extravascular hemolysis. Pegcetacoplan, a pegylated peptide targeting proximal complement protein C3, potentially inhibits both intravascular and extravascular hemolysis. METHODS: We conducted a phase 3 open-label, controlled trial to assess the efficacy and safety of pegcetacoplan as compared with eculizumab in adults with PNH and hemoglobin levels lower than 10.5 g per deciliter despite eculizumab therapy. After a 4-week run-in phase in which all patients received pegcetacoplan plus eculizumab, we randomly assigned patients to subcutaneous pegcetacoplan monotherapy (41 patients) or intravenous eculizumab (39 patients). The primary end point was the mean change in hemoglobin level from baseline to week 16. Additional clinical and hematologic markers of hemolysis and safety were assessed. RESULTS: Pegcetacoplan was superior to eculizumab with respect to the change in hemoglobin level from baseline to week 16, with an adjusted (least squares) mean difference of 3.84 g per deciliter (P<0.001). A total of 35 patients (85%) receiving pegcetacoplan as compared with 6 patients (15%) receiving eculizumab no longer required transfusions. Noninferiority of pegcetacoplan to eculizumab was shown for the change in absolute reticulocyte count but not for the change in lactate dehydrogenase level. Functional Assessment of Chronic Illness Therapy-Fatigue scores improved from baseline in the pegcetacoplan group. The most common adverse events that occurred during treatment in the pegcetacoplan and eculizumab groups were injection site reactions (37% vs. 3%), diarrhea (22% vs. 3%), breakthrough hemolysis (10% vs. 23%), headache (7% vs. 23%), and fatigue (5% vs. 15%). There were no cases of meningitis in either group. CONCLUSIONS: Pegcetacoplan was superior to eculizumab in improving hemoglobin and clinical and hematologic outcomes in patients with PNH by providing broad hemolysis control, including control of intravascular and extravascular hemolysis. (Funded by Apellis Pharmaceuticals; PEGASUS ClinicalTrials.gov, NCT03500549.).

First-in-human, double-blind, randomized phase 1b study of peptide immunotherapy IMCY-0098 in new-onset type 1 diabetes: an exploratory analysis of immune biomarkers.

BACKGROUND: IMCY-0098, a synthetic peptide developed to halt disease progression via elimination of key immune cells in the autoimmune cascade, has shown a promising safety profile for the treatment of type 1 diabetes (T1D) in a recent phase 1b trial. This exploratory analysis of data from that trial aimed to identify the patient biomarkers at baseline associated with a positive response to treatment and examined the associations between immune response parameters and clinical efficacy endpoints (as surrogates for mechanism of action endpoints) using an artificial intelligence-based approach of unsupervised explainable machine learning. METHODS: We conducted an exploratory analysis of data from a phase 1b, dose-escalation, randomized, placebo-controlled study of IMCY-0098 in patients with recent-onset T1D. Here, a panel of markers of T cell activation, memory T cells, and effector T cell response were analyzed via descriptive statistics. Artificial intelligence-based analyses of associations between all variables, including immune responses and clinical responses, were performed using the Knowledge Extraction and Management (KEM®) v 3.6.2 analytical platform. RESULTS: The relationship between all available patient data was investigated using unsupervised machine learning implemented in the KEM® environment. Of 15 associations found for the dose C group (450 µg subcutaneously followed by 3 × 225 µg subcutaneously), seven involved human leukocyte antigen (HLA) type, all of which identified improvement/absence of worsening of disease parameters in DR4+ patients and worsening/absence of improvement in DR4- patients. This association with DR4+ and non-DR3 was confirmed using the endpoints normalized area under the curve C-peptide from mixed meal tolerance tests where presence of DR4 HLA haplotype was associated with an improvement in both endpoints. Exploratory immune analysis showed that IMCY-0098 dose B (150 µg subcutaneously followed by 3 × 75 µg subcutaneously) and dose C led to an increase in presumed/potentially protective antigen-specific cytolytic CD4+ T cells and a decrease in pathogenic CD8+ T cells, consistent with the expected mechanism of action of IMCY-0098. The analysis identified significant associations between immune and clinical responses to IMCY-0098. CONCLUSIONS: Promising preliminary efficacy results support the design of a phase 2 study of IMCY-0098 in patients with recent-onset T1D. TRIAL REGISTRATION: ClinicalTrials.gov NCT03272269; EudraCT: 2016-003514-27.

Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi-Sugeno-Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

MOTIVATION: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functional therapeutic peptides (MFTP) via sequence-based computational tools. RESULTS: Here, we propose a novel multi-label-based method, named ETFC, to predict 21 categories of therapeutic peptides. The method utilizes a deep learning-based model architecture, which consists of four blocks: embedding, text convolutional neural network, feed-forward network, and classification blocks. This method also adopts an imbalanced learning strategy with a novel multi-label focal dice loss function. multi-label focal dice loss is applied in the ETFC method to solve the inherent imbalance problem in the multi-label dataset and achieve competitive performance. The experimental results state that the ETFC method is significantly better than the existing methods for MFTP prediction. With the established framework, we use the teacher-student-based knowledge distillation to obtain the attention weight from the self-attention mechanism in the MFTP prediction and quantify their contributions toward each of the investigated activities. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available via: https://github.com/xialab-ahu/ETFC.

Glioblastoma Stem Cell Targeting Peptide Isolated Through Phage Display Binds Cadherin 2.

Glioblastoma stem cells (GSCs) have unique properties of self-renewal and tumor initiation that make them potential therapeutic targets. Development of effective therapeutic strategies against GSCs requires both specificity of targeting and intracranial penetration through the blood-brain barrier. We have previously demonstrated the use of in vitro and in vivo phage display biopanning strategies to isolate glioblastoma targeting peptides. Here we selected a 7-amino acid peptide, AWEFYFP, which was independently isolated in both the in vitro and in vivo screens and demonstrated that it was able to target GSCs over differentiated glioma cells and non-neoplastic brain cells. When conjugated to Cyanine 5.5 and intravenously injected into mice with intracranially xenografted glioblastoma, the peptide localized to the site of the tumor, demonstrating intracranial tumor targeting specificity. Immunoprecipitation of the peptide with GSC proteins revealed Cadherin 2 as the glioblastoma cell surface receptor targeted by the peptides. Peptide targeting of Cadherin 2 on GSCs was confirmed through ELISA and in vitro binding analysis. Interrogation of glioblastoma databases demonstrated that Cadherin 2 expression correlated with tumor grade and survival. These results confirm that phage display can be used to isolate unique tumor-targeting peptides specific for glioblastoma. Furthermore, analysis of these cell specific peptides can lead to the discovery of cell specific receptor targets that may serve as the focus of future theragnostic tumor-homing modalities for the development of precision strategies for the treatment and diagnosis of glioblastomas.

The novel peptide DR4penA attenuates the bleomycin- and paraquat-induced pulmonary fibrosis by suppressing the TGF-ß/Smad signaling pathway.

Pulmonary fibrosis (PF), which is caused by continuous alveolar epithelial cell injury and abnormal repair, is referred to as a difficult disease of the lung system by the World Health Organization due to its rapid progression, poor prognosis, and high mortality rate. However, there is still a lack of ideal therapeutic strategies. The peptide DR8 (DHNNPQIR-NH2 ), which is derived from rapeseed, exerted antifibrotic activity in the lung, liver, and kidney in our previous studies. By studying the structure-activity relationship and rational design, we introduced an unnatural hydrophobic amino acid (α-(4-pentenyl)-Ala) into DR8 and screened the novel peptide DR4penA (DHNα-(4-pentenyl)-APQIR-NH2 ), which had higher anti-PF activity, higher antioxidant activity and a longer half-life than DR8. Notably, DR4penA attenuated bleomycin- and paraquat-induced PF, and the anti-PF activity of DR4penA was equivalent to that of pirfenidone. Additionally, DR4penA suppressed the TGF-ß/Smad pathway in TGF-ß1-induced A549 cells and paraquat-induced rats. This study demonstrates that the novel peptide DR4penA is a potential candidate compound for PF therapy, and its antifibrotic activity in different preclinical models of PF provides a theoretical basis for further study.

Applications of peptides in nanosystems for diagnosing and managing bacterial sepsis.

Sepsis represents a critical medical condition stemming from an imbalanced host immune response to infections, which is linked to a significant burden of disease. Despite substantial efforts in laboratory and clinical research, sepsis remains a prominent contributor to mortality worldwide. Nanotechnology presents innovative opportunities for the advancement of sepsis diagnosis and treatment. Due to their unique properties, including diversity, ease of synthesis, biocompatibility, high specificity, and excellent pharmacological efficacy, peptides hold great potential as part of nanotechnology approaches against sepsis. Herein, we present a comprehensive and up-to-date review of the applications of peptides in nanosystems for combating sepsis, with the potential to expedite diagnosis and enhance management outcomes. Firstly, sepsis pathophysiology, antisepsis drug targets, current modalities in management and diagnosis with their limitations, and the potential of peptides to advance the diagnosis and management of sepsis have been adequately addressed. The applications have been organized into diagnostic or managing applications, with the last one being further sub-organized into nano-delivered bioactive peptides with antimicrobial or anti-inflammatory activity, peptides as targeting moieties on the surface of nanosystems against sepsis, and peptides as nanocarriers for antisepsis agents. The studies have been grouped thematically and discussed, emphasizing the constructed nanosystem, physicochemical properties, and peptide-imparted enhancement in diagnostic and therapeutic efficacy. The strengths, limitations, and research gaps in each section have been elaborated. Finally, current challenges and potential future paths to enhance the use of peptides in nanosystems for combating sepsis have been deliberately spotlighted. This review reaffirms peptides' potential as promising biomaterials within nanotechnology strategies aimed at improving sepsis diagnosis and management.

Integrin-αvß6 targeted peptide-toxin therapy in a novel αvß6-expressing immunocompetent model of pancreatic cancer.

Previously we reported that a novel αvß6-specific peptide-drug conjugate (SG3299) could eliminate established human pancreatic ductal adenocarcinoma (PDAC) xenografts. However the development of effective therapies for PDAC, which is an essential need, must show efficacy in relevant immunocompetent animals. Previously we reported that the KPC mouse transgenic PDAC model that closely recapitulates most stages of development of human PDAC, unlike in humans, failed to express αvß6 on their tumours or metastases. In this study we have taken the KPC-derived PDAC line TB32043 and engineered a variant line (TB32043mb6S2) that expresses mouse integrin αvß6. We report that orthotopic implantation of the αvß6 over-expressing TB32043mb6S2 cells promotes shorter overall survival and increase in metastases. Moreover, systemic treatment of mice with established TB32043mb6S2 tumours in the pancreas with SG2399 lived significantly longer (p < 0.001; mean OS 48d) compared with PBS or control SG3511 (mean OS 25.5d and 26d, respectively). Thus SG3299 is confirmed as a promising candidate therapeutic for the therapy of PDAC.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.