Phase II trial of the antiangiogenic agent IM862 in metastatic renal cell carcinoma

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.

Spectroscopic, electronic structure, molecular docking, and molecular dynamics simulation study of 7-Trifluoromethyl-1H-indole-2-carboxylic acid as an aromatase inhibitor

The present work encompasses a combined experimental and theoretical investigation of the molecular structure, vibrational wavenumbers, electronic structure at the ground and electronic excited states, molecular electrostatic potential surface of 7-(Trifluoromethyl)-1H-indole-2-carboxylic acid (TICA) and possibility of the title molecule as an aromatase inhibitor using molecular docking and molecular dynamic simulations. A stable conformer has been obtained using potential energy scans by varying appropriate dihedral angles. The obtained minimum energy conformer was further optimized at the 6-311++G (d, p) basis set by applying the most accepted B3LYP functional. A good agreement between experimental and calculated normal modes of vibration has been observed. The hydrogen-bonded interaction between two monomeric units of TICA has been investigated using NBO,QTAIM, and NCI (noncovalent interactions) analysis. Molecular docking of TICA with human placental aromatase (PDB ID: 3S79) reveals the formation of polar hydrogen bonds as well as hydrophobic interactions between the ligand and the protein, right in the binding cavity. TICA satisfies all pharmacokinetic filters (Lipinski rule of five, the Veber rule, Ghose rule, Egan rule, as well as the Muegge rule) and has a high bioavailability score of 0.85. Dynamic stability of the ligand within the binding pocket of the target protein has been confirmed by 100 ns molecular dynamics simulation results. The present study provides an excellent starting point for additional in vivo research, and TICA may eventually serve as a significant therapeutic candidate for the treatment of breast cancer.

Characterization of tryptophan-containing dipeptides for anti-angiogenic effects

AIMS

In the pathogenesis of several diseases, neo-angiogenesis is increased (e.g. tumour growth). The peptide L-glutamyl-L-tryptophan (EW/IM862) has been claimed to exhibit inhibitory effects on tumour growth in vivo. However, the potential role of natural peptides with respect to anti-angiogenic properties is unsettled. The current study explores anti-angiogenic effects of the dipeptides WL, EW, IW and WE.

METHODS AND RESULTS

Using a bottom-up strategy, we first evaluated the effects of the peptides on VEGFR-2 signalling and quantified their effects in different angiogenesis assays. WL consistently had the strongest effects on phosphorylation of VEGFR-2 and downstream signalling. Therefore, this peptide was chosen in comparison with EW to further assess anti-angiogenic properties. However, sprout formation in three-dimensional (3D) fibrin gel bead assay was significantly inhibited by EW only. Furthermore, vessel sprouting in the mouse aortic ring assay was decreased by the presence of WL and EW compared to control. Results from a chorioallantoic membrane assay showed that under vascular endothelial growth factor (VEGF) stimulation WL and EW decreased the number of blood vessels versus control. These results were in line with those obtained in a matrigel plug assay. The VEGF-induced increase in the haemoglobin content was nearly abolished when treatment was combined with either WL or EW application. In the murine model of oxygen-induced retinopathy, WL exhibited a small albeit significant anti-angiogenic effect.

CONCLUSION

Comprehensive screening of WL suggests an anti-angiogenic effect, demonstrated in in vitro, ex vivo and in vivo models. Thus, WL is a dipeptide with potential anti-angiogenic effects and is worthy for further exploration.

Synthesis of Modified RGD-Based Peptides and Their in vitro Activity

Arg-Gly-Asp (RGD) peptides represent the most outstanding recognition motif involved in cell adhesion that binds to the α_v β₃ integrin, which has been targeted for cancer therapy. Various RGD-containing peptides and peptidomimetics have been designed and synthesized to selectively inhibit this integrin. In this study, the synthesis of RGD-based peptides through the incorporation of the short bioactive peptide Phe-Ala-Lys-Leu-Phe (FAKLF) at the C and N termini of RGD has been achieved by using a solid-phase peptide synthesis approach. The peptides were purified by means of preparative RP-HPLC and their structures were confirmed through HRMS (ESI). The MTT assay revealed that the RGD and FAKLF peptides inhibited the proliferation of human umbilical vein endothelial cells (HUVECs) in a dose-dependent manner, with IC₅₀ values of 3000 and 500 ng mL^-1 , respectively. Interestingly, a drastic improvement was observed in the antiproliferative activity of the combined structures of the FAKLFRGD and RGDFAKLF peptides, leading to IC₅₀ values of 200 and 136.7 ng mL^-1 , respectively. Meanwhile, based on apoptosis results, the potential of peptides to induce apoptosis, in accordance with their antiproliferative activity, indicated that the RGD and FAKLF peptides, and the peptides synthesized based on their combinations induced cell apoptosis in a dose-dependent manner followed by inhibition of the proliferation of endothelial cells. Moreover, the incorporation of d-leucine increased the induction of apoptosis by these peptides.

Sequence-Dependent Interfacial Adsorption and Permeation of Dipeptides across Phospholipid Membranes

We investigate permeation of three blocked dipeptides with different side chain polarity across a phospholipid membrane and their behavior at the water-membrane interface by way of molecular dynamics simulations. Hydrophilic serine-serine dipeptide is found to desorb from the interface to aqueous phase, whereas hydrophobic phenylalanine-leucine and amphiphilic serine-leucine tend to accumulate at the interface with a free energy minimum of -3 kcal/mol. All three dipeptides exhibit free energy barriers to permeation across the membrane located at the center of the bilayer. The height of the barrier is strongly sequence dependent and increases with the dipeptide polarity. It is equal to 3.5, 6.4, and 10.0 kcal/mol for phenylalanine-leucine, serine-leucine, and serine-serine, respectively. The corresponding permeability coefficients are equal to 4.6 × 10^-3, 4.5 × 10^-5, and 8.7 × 10^-8 cm/s. The apparent insensitivity of membrane permeability to hydrophobicity of dipeptides, found in some experiments, is attributed to neglecting corrections for unstirred water layers near membrane surface, which are significant for hydrophobic species. Different hydrophobicity of the dipeptides also influences their conformations and orientations, both at the interface and inside the membrane. In particular, penetration of hydrophilic serine-serine dipeptide causes the formation of water-filled defects in the bilayer. These results are relevant to the delivery of peptide-based therapeutic agents.

MLACP: machine-learning-based prediction of anticancer peptides

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

In Silico Design of Anticancer Peptides

In the past few years, small peptides having anticancer properties have emerged as a potential avenue for cancer therapy. Compared to current anti-cancer chemotherapeutic drugs (or small molecules), anticancer peptides (ACPs) have numerous advantageous properties, such as high specificity, low production cost, high tumor penetration, ease of synthesis and modification. However, in wet lab setups, identification and characterization of novel ACPs is a time-consuming and labor-intensive process. Therefore, in silico designing of anticancer peptides is beneficial, prior to their synthesis and characterization. This approach is less time consuming and more cost-effective. In this chapter, we discuss a web-based tool, AntiCP (http://crdd.osdd.net/raghava/anticp/), for designing ACPs.

In silico models for designing and discovering novel anticancer peptides

Use of therapeutic peptides in cancer therapy has been receiving considerable attention in the recent years. Present study describes the development of computational models for predicting and discovering novel anticancer peptides. Preliminary analysis revealed that Cys, Gly, Ile, Lys, and Trp are dominated at various positions in anticancer peptides. Support vector machine models were developed using amino acid composition and binary profiles as input features on main dataset that contains experimentally validated anticancer peptides and random peptides derived from SwissProt database. In addition, models were developed on alternate dataset that contains antimicrobial peptides instead of random peptides. Binary profiles-based model achieved maximum accuracy 91.44% with MCC 0.83. We have developed a webserver, which would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides (http://crdd.osdd.net/raghava/anticp/).

Cancer treatment using peptides: current therapies and future prospects

This paper discusses the role of peptides in cancer therapy with special emphasis on peptide drugs which are already approved and those in clinical trials. The potential of peptides in cancer treatment is evident from a variety of different strategies that are available to address the progression of tumor growth and propagation of the disease. Use of peptides that can directly target cancer cells without affecting normal cells (targeted therapy) is evolving as an alternate strategy to conventional chemotherapy. Peptide can be utilized directly as a cytotoxic agent through various mechanisms or can act as a carrier of cytotoxic agents and radioisotopes by specifically targeting cancer cells. Peptide-based hormonal therapy has been extensively studied and utilized for the treatment of breast and prostate cancers. Tremendous amount of clinical data is currently available attesting to the efficiency of peptide-based cancer vaccines. Combination therapy is emerging as an important strategy to achieve synergistic effects in fighting cancer as a single method alone may not be efficient enough to yield positive results. Combining immunotherapy with conventional therapies such as radiation and chemotherapy or combining an anticancer peptide with a nonpeptidic cytotoxic drug is an example of this emerging field.

Anti-angiogenic peptides for cancer therapeutics

Peptides have emerged as important therapeutics that are being rigorously tested in angiogenesis-dependent diseases due to their low toxicity and high specificity. Since the discovery of endogenous proteins and protein fragments that inhibit microvessel formation (thrombospondin, endostatin) several peptides have shown promise in pre-clinical and clinical studies for cancer. Peptides have been derived from thrombospondin, collagens, chemokines, coagulation cascade proteins, growth factors, and other classes of proteins and target different receptors. Here we survey recent developments for anti-angiogenic peptides with length not exceeding 50 amino acid residues that have shown activity in pre-clinical models of cancer or have been tested in clinical trials; some of the peptides have been modified and optimized, e.g., through L-to-D and non-natural amino acid substitutions. We highlight technological advances in peptide discovery and optimization including computational and bioinformatics tools and novel experimental techniques.

Asp-Tyr-Leu-Lys tetrapeptide inhibits airway inflammation in toluene-2,4-diisocyanate-induced asthma mice

BACKGROUND

Airway inflammation and remodelling contribute to chronic airway obstruction of asthma. Currently, no medication effectively controls airway remodelling and related vascular changes. Therefore, new strategies need to be developed. The kringle 5 domain has anti-angiogenic activity resulting from the tetrapeptide Lys-Leu-Tyr-Asp (KLYD).

OBJECTIVE

To investigate the therapeutic effect of KLYD and its inverse form Asp-Tyr-Leu-Lys (DYLK) on the inflammation and remodelling of toluene-2,4-diisocyanate (TDI)-sensitization/challenged mice.

METHODS

Cell numbers were measured in the presence of various concentrations of KLYD and DYLK using in vitro endothelial cell proliferation assay. The changes of cell number and the level of vascular endothelial growth factor (VEGF) in bronchoalveolar lavage (BAL) fluid and response to methacholine (MCh) were measured using the in vivo TDI-sensitized/challenged mice model. Muc5ac, smooth muscle actin (SMA) and proliferating cell nuclear antigen (PCNA) protein expression was analysed on trachea and intrapulmonary bronchi using immunohistochemical stain.

RESULTS

Compared with KLYD, DYLK had a greater inhibitory effect on endothelial cell proliferation (P<0.05). Pre-treatment of DYLK showed dose-dependent reduction in the response to MCh (P<0.05) and numbers of inflammatory cells in BAL fluids of TDI-sensitized/challenged mice. TDI induced increases in Muc5ac, SMA and PCNA protein expression and VEGF levels, which were also abolished by DYLK treatment.

CONCLUSIONS

Local administration of DYLK effectively inhibits the airway inflammation and airway remodelling of TDI-sensitized/challenged mice via down-regulation of VEGF. These findings suggest that anti-angiogenic peptide therapies, such as local administration of DYLK, are an effective strategy for the treatment of remodelling in asthma.

Anticancer and cytotoxic properties of the latex of Calotropis procera in a transgenic mouse model of hepatocellular carcinoma

AIM: To evaluate the anticancer property of the dried latex (DL) of Calotropis procera, a tropical medicinal plant, in the X15-myc transgenic mouse model of hepatocellular carcinoma and to elucidate its mechanism of action in cell culture.

METHODS: The young transgenic mice were orally fed with the aqueous suspension of DL (400 mg/kg for 5 d/wk) for 15 wk and their liver was examined for histopathological changes at 20 wk. Serum levels of vascular endothelial growth factor (VEGF) were also measured in these animals. To characterize the active fraction, DL was extracted with petroleum ether followed by methanol. The methanolic extract was sub-fractionated on a silica gel G column using a combination of non-polar and polar solvents and eleven fractions were obtained. Each fraction was analysed for cytotoxic effect on hepatoma (Huh7) and non-hepatoma (COS-1) cell lines and non-transformed hepatocytes (AML12) using tetrazolium (MTT) assay. Finally, the mechanism of cell death was investigated by measuring the levels of Bcl2, caspase 3 and DNA fragmentation.

RESULTS: DL treatment of mice showed a complete protection against hepatocarcinogenesis. No adverse effect was observed in these animals. The serum VEGF level was significantly lowered in the treated mice as compared to control animals. Cell culture studies revealed that the methanolic extract of DL as well as its fraction 8 induced extensive cell death in both Huh-7 and COS-1 cells while AML12 cells were spared. This was accompanied by extensive fragmentation of DNA in Huh-7 and COS-1 cells. No change in the levels of canonical markers of apoptosis such as Bcl2 and caspase 3 was observed.

CONCLUSION: DL of C. procera has the potential for anti-cancer therapy due to its differentiable targets and non-interference with regular pathway of apoptosis.

Current status and perspective of antiangiogenic therapy for cancer: urinary cancer

Angiogenesis is considered a prerequisite for solid tumor growth. Antiangiogenic therapy reduces tumor size and extends host survival in a number of preclinical animal models. However, in humans antiangiogenic therapy is a poor promoter of tumor regression and has shown minimal effect on patient survival. In urinary cancers, such as renal cell cancer, prostate cancer, and bladder cancer, advanced refractory disease is a good candidate for antiangiogenic therapy because of its resistance to ordinary chemotherapy, radiotherapy, and hormonal therapy. Unique characteristics of molecular mechanisms underlie the induction of angiogenesis in urinary cancers. In this review, we summarize these unique mechanisms and review the results of clinical trials of antiangiogenic therapy for these cancers, discussing prospects and problems relating to antiangiogenic therapy.

Combining radiotherapy and angiogenesis inhibitors: clinical trial design

Radiotherapy (RT) plays a vital role in the multimodality treatment of cancer. Recent advances in RT have primarily involved improvements in dose delivery. Future improvements in tumor control and disease outcomes will likely involve the combination of RT with targeted therapies. Preclinical evaluations of angiogenesis inhibitors in combination with RT have yielded promising results with increased tumor "cure." It remains to be seen whether these improvements in tumor control in the laboratory will translate into improved outcomes in the clinic. Multiple differences between these agents and cytotoxic chemotherapy must be taken into account when designing clinical trials evaluating their effectiveness in combination with RT. We discuss important considerations for designing clinical trials of angiogenesis inhibitors with RT.