Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics

Background

For treating a complex disease such as cancer, we need effective means to control the biological network that underlies the disease. However, biological networks are typically robust to external perturbations, making it difficult to beneficially alter the network dynamics by controlling a single target. In fact, multi-target therapeutics is often more effective compared to monotherapies, and combinatory drugs are commonly used these days for treating various diseases. A practical challenge in combination therapy is that the number of possible drug combinations increases exponentially, which makes the prediction of the optimal drug combination a difficult combinatorial optimization problem. Recently, a stochastic optimization algorithm called the Gur Game algorithm was proposed for drug optimization, which was shown to be very efficient in finding potent drug combinations.

Results

In this paper, we propose a novel stochastic optimization algorithm that can be used for effective optimization of combinatory drugs. The proposed algorithm analyzes how the concentration change of a specific drug affects the overall drug response, thereby making an informed guess on how the concentration should be updated to improve the drug response. We evaluated the performance of the proposed algorithm based on various drug response functions, and compared it with the Gur Game algorithm.

Conclusions

Numerical experiments clearly show that the proposed algorithm significantly outperforms the original Gur Game algorithm, in terms of reliability and efficiency. This enhanced optimization algorithm can provide an effective framework for identifying potent drug combinations that lead to optimal drug response.

     

Combined effects of aspirin and vitamin D3 on two OSCC cell lines: a preliminary study

Objectives

We evaluated the potential effects of aspirin combined with vitamin D3 on cell proliferation and apoptosis in oral cancer cells.

Results

Compared to the untreated control or individual drug, the combinations of aspirin and vitamin D3 significantly decreased the rates of cell proliferation by CCK-8 assay, and caused higher rates of cell apoptosis in both CAL-27 and SCC-15 cells by Annexin V-FITC apoptosis assay and flow cytometry. Remarkably, the combined treatment with aspirin and vitamin D3 significantly suppressed the expression of Bcl-2 protein and p-Erk1/2 protein, examined by western blot analysis.

Conclusions

Our study demonstrates that aspirin and vitamin D3 have biological activity against two human OSCC cell lines and their activity is synergistic or additive when two drugs used in combination with therapeutic concentrations. The combination of aspirin and vitamin D3 may be an effective approach for inducing cell death in OSCC.

     

Dual-drug loaded nanoneedles with targeting property for efficient cancer therapy

Background

Since the anticancer drugs have diverse inhibited mechanisms to the cancer cells, the use of two or more kinds of anticancer agents may achieve excellent therapeutic effects, especially to the drug-resistant tumors.

Results

In this study, we developed a kind of dual drug [methotrexate (MTX) and 10-hydroxycamptothecine (HCPT)] loaded nanoneedles (DDNDs) with pronounced targeting property, high drug loading and prolonged drug release. The anti-solvent precipitation of the HCPT and MTX modified PEG-b-PLGA (PEG-b-PLGA-MTX, PPMTX) leads to nucleation of nanoneedles with nanocrystalline HCPT as the core wrapped with PPMTX as steric stabilizers. In vitro cell uptake studies showed that the DDNDs revealed an obviously targeting property and entered the HeLa cells easier than the nanoneedles without MTX modification. The cytotoxicity tests illustrated that the DDNDs possessed better killing ability to HeLa cells than the individual drugs or their mixture in the same dose, indicating its good synergistic effect and targeting property. The in vivo studies further confirmed these conclusions.

Conclusions

This approach led to a promising sustained drug delivery system for cancer diagnosis and treatment.

     

Skin-impedance in Fabry Disease: A prospective,controlled, non-randomized clinical study

Background

We previously demonstrated improved sweating after enzyme replacement therapy (ERT) in Fabry disease using the thermo-regularity sweat and quantitative sudomotor axon reflex tests. Skin-impedance, a measure skin-moisture (sweating), has been used in the clinical evaluation of burns and pressure ulcers using the portable dynamic dermal impedance monitor (DDIM) system.

Methods

We compared skin impedance measurements in hemizygous patients with Fabry disease (22 post 3-years of bi-weekly ERT and 5 ERT naive) and 22 healthy controls. Force compensated skin-moisture values were used for statistical analysis. Outcome measures included 1) moisture reading of the 100^th repetitive reading, 2) rate of change, 3) average of 60–110^th reading and 4) overall average of all readings.

Results

All outcome measures showed a significant difference in skin-moisture between Fabry patients and control subjects (p < 0.0001). There was no difference between Fabry patients on ERT and patients naïve to ERT. Increased skin-impedance values for the four skin-impedance outcome measures were found in a small number of dermatome test-sites two days post-enzyme infusions.

Conclusion

The instrument portability, ease of its use, a relatively short time required for the assessment, and the fact that DDIM system was able to detect the difference in skin-moisture renders the instrument a useful clinical tool.

     

Predicting adverse drug reactions through interpretable deep learning framework

Background

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.

Methods

In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Results

We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.

Conclusions

The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

     

A systems biology approach to identify effective cocktail drugs

Background

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

Results

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

Conclusions

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

     

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

Introduction

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

Objectives

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

Methods

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC–MS based untargeted and stable isotope assisted metabolomic techniques.

Results

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

Conclusion

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

     

IL-1 does not reverse the anti-proliferative effect of aspirin in breast cancer cells

Objectives

Interleukin- 1 (IL-1) is a multifunctional proinflammatory cytokine. There have been studies suggesting a role in affecting growth and invasiveness of malignant breast cells by either blocking or stimulating growth of cultured MCF-7 breast cancer cells. This effect may be mediated by induction of COX-2. Aspirin is an inhibitor of COX-2 and has been implicated, with other non-steroidal anti-inflammatory drugs (NSAIDS) in prevention and treatment of breast cancer. In this study the in vitro effects of IL-1 and aspirin on growth of MCF-7 human breast cancer cells was examined.

Methods

MCF-7 cells were treated with various concentrations of IL-1 and aspirin alone and in combination. Cell growth was assessed by cell number measurement.

Results

Aspirin significantly decreased growth rate in a dose-dependant manner, alone and as a combined treatment with IL-1 with a maximum reduction in growth rate at 300 mg/ml (P < 0.05). Treatment with IL-1 alone showed no significant effect on growth rate of MCF-7 cells (P > 0.05).

Conclusion

This study confirms that aspirin suppresses the proliferation rate of MCF-7 cells both as a single agent and in combination with IL-1. It also suggests that IL-1 alone does not stimulate or inhibit growth of MCF-7 cells.

     

Monitoring cancer prognosis,diagnosis and treatment efficacy using metabolomics and lipidomics

Introduction

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

Objectives

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.

Methods

A literature search of the current relevant primary research was performed.

Results

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

Conclusion

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

     

Characterization of Fabry mice treated with recombinant adeno-associated virus 2/8-mediated gene transfer

Background

Enzyme replacement therapy (ERT) with α-galactosidase A (α-Gal A) is currently the most effective therapeutic strategy for patients with Fabry disease, a lysosomal storage disease. However, ERT has limitations of a short half-life, requirement for frequent administration, and limited efficacy for patients with renal failure. Therefore, we investigated the efficacy of recombinant adeno-associated virus (rAAV) vector-mediated gene therapy for a Fabry disease mouse model and compared it with that of ERT.

Methods

A pseudotyped rAAV2/8 vector encoding α-Gal A cDNA (rAAV2/8-hAGA) was prepared and injected into 18-week-old male Fabry mice through the tail vein. The α-Gal A expression level and globotriaosylceramide (Gb3) levels in the Fabry mice were examined and compared with Fabry mice with ERT. Immunohistochemical and ultrastructural studies were conducted.

Results

Treatment of Fabry mice with rAAV2/8-hAGA resulted in the clearance of accumulated Gb3 in tissues such as liver, spleen, kidney, heart, and brain with concomitant elevation of α-Gal A enzyme activity. Enzyme activity was elevated for up to 60 weeks. In addition, expression of the α-Gal A protein was identified in the presence of rAAV2/8-hAGA at 6, 12, and 24 weeks after treatment. α-Gal A activity was significantly higher in the mice treated with rAAV2/8-hAGA than in Fabry mice that received ERT. Along with higher α-Gal A activity in the kidney of the Fabry mice treated with gene therapy, immunohistochemical studies showed more α-Gal A expression in the proximal tubules and glomerulus, and less Gb3 deposition in Fabry mice treated with this gene therapy than in mice given ERT. The α-gal A gene transfer significantly reduced the accumulation of Gb3 in the tubules and podocytes of the kidney. Electron microscopic analysis of the kidneys of Fabry mice also showed that gene therapy was more effective than ERT.

Conclusions

The rAAV2/8-hAGA mediated α-Gal A gene therapy provided improved efficiency over ERT in the Fabry disease mouse model. Furthermore, rAAV2/8-hAGA-mediated expression showed a greater effect in the kidney than ERT.

     

Kinase profiling of liposarcomas using RNAi and drug screening assays identified druggable targets

Background

Liposarcoma, the most common soft tissue tumor, is understudied cancer, and limited progress has been made in the treatment of metastatic disease. The Achilles heel of cancer often is their kinases that are excellent therapeutic targets. However, very limited knowledge exists of therapeutic critical kinase targets in liposarcoma that could be potentially used in disease management.

Methods

Large RNAi and small-molecule tyrosine kinase inhibitor screens were performed against the proliferative capacity of liposarcoma cell lines of different subtypes. Each small molecule inhibitor was either FDA approved or in a clinical trial.

Results

Screening assays identified several previously unrecognized targets including PTK2 and KIT in liposarcoma. We also observed that ponatinib, multi-targeted tyrosine kinase inhibitor, was the most effective drug with anti-growth effects against all cell lines. In vitro assays showed that ponatinib inhibited the clonogenic proliferation of liposarcoma, and this anti-growth effect was associated with apoptosis and cell cycle arrest at the G0/G1 phase as well as a decrease in the KIT signaling pathway. In addition, ponatinib inhibited in vivo growth of liposarcoma in a xenograft model.

Conclusions

Two large-scale kinase screenings identified novel liposarcoma targets and a FDA-approved inhibitor, ponatinib with clear anti-liposarcoma activity highlighting its potential therapy for treatment of this deadly tumor.

     

Exosomes derived from siRNA against GRP78 modified bone-marrow-derived mesenchymal stem cells suppress Sorafenib resistance in hepatocellular carcinoma

Background

Sorafenib is an effective clinical drug in therapy of hepatocellular carcinoma, having led to improved prognosis in hepatocellular carcinoma patients. However acquired resistance is still being encountered. So, it is urgently to develop alternative strategies to overcome drug resistance. Exosomes can be modified with a variety of molecules, thereby acting as a vehicle for the delivery of therapeutic agents. The GRP78 is overexpressed in Sorafenib resistant cancer cells compared to Sorafenib sensitive cancer cells and thus is able to act as a target for therapy of hepatocellular carcinoma.

Results

In this study, we modified BM-MSCs to express the exosomal siGRP78. And we show that siGRP78 modified exosomes combined with Sorafenib is able to target GRP78 in hepatocellular carcinoma cells and inhibit the growth and invasion of the cancer cells in vitro. Further, siGRP78 modified exosomes combined with Sorafenib also inhibit the growth and metastasis of the cancer cells in vivo.

Conclusions

siGRP78 modified exosomes could sensitize Sorafenib resistant cancer cells to Sorafenib and reverse the drug resistance.

     

Inhibition of integrin β3, a binding partner of kallistatin,leads to reduced viability,invasion and proliferation in NCI-H446 cells

Background

Kallistatin is a serine proteinase inhibitor and heparin-binding protein. It is considered an endogenous angiogenic inhibitor. In addition, multiple studies demonstrated that kallistatin directly inhibits cancer cell growth. However, the molecular mechanisms underlying these effects remain unclear.

Methods

Pull-down, immunoprecipitation, and immunoblotting were used for binding experiments. To elucidate the mechanisms, integrin β3 knockdown (siRNA) or blockage (antibody treatment) on the cell surface of small the cell lung cancer NCI-H446 cell line was used.

Results

Interestingly, kallistatin was capable of binding integrin β3 on the cell surface of NCI-H446 cells. Meanwhile, integrin β3 knockdown or blockage resulted in loss of antitumor activities induced by kallistatin. Furthermore, kallistatin suppressed tyrosine phosphorylation of integrin β3 and its downstream signaling pathways, including FAK/-Src, AKT and Erk/MAPK. Viability, proliferation and migration of NCI-H446 cells were inhibited by kallistatin, with Bcl-2 and Grb2 downregulation, and Bax, cleaved caspase-9 and caspase 3 upregulation.

Conclusions

These findings reveal a novel role for kallistatin in preventing small cell lung cancer growth and mobility, by direct interaction with integrin β3, leading to blockade of the related signaling pathway.

     

Analysis of PD-1 related immune transcriptional profile in different cancer types

Background

Programmed cell death 1 (PD-1) functions as an immune checkpoint in the process of anti-tumor immune response. The PD-1 blockade is now becoming a fundamental part in cancer immunotherapy. So it’s essential to elicit the PD-1 related immune process in different types of cancer.

Methods

The Cancer Genome Atlas was used to collect the RNA-seq data of 33 cancer types. The microenvironment cell populations-counter was used to analyze the immune cell infiltrates. KEGG and GO analysis were performed to investigate PD-1 associated biological process. Kaplan–Meier survival curves and Cox’s proportional hazards model were performed for prognostic value analysis.

Results

We demonstrated that PD-1 expression varied in different cancer types. The uveal melanoma had a low PD-1 expression and poor infiltrated with immune cells. But it showed the strong correlation of PD-1 with the most types of immune cells. The PD-1 demonstrated a robust relationship with other immunomodulators and showed its involvement in critical functions correlated with anti-tumor immune pathways. Survival analysis indicated the PD-1 expression suggested different prognosis in different cancer types.

Conclusions

Our investigations promote a better understanding of the PD-1 blockade and provide PD-1 related personized combined immunotherapy for different types of cancer patients.

     

Multi-parametric analysis reveals enhanced G2-phase arrest of an optimized anti-HER2 antibody to inhibit breast cancer

Objectives

To find a “me-better” antibody by epitope-specific antibody optimization and multi-parametric analysis.

Results

Using epitope-specific library based on the commercial drug, Pertuzumab/2C4, we screened a novel human anti-HER2 antibody, MIL5, which has slightly higher affinity than the drug. MIL5 and 2C4 share the same epitope to bind HER2; however, MIL5 bound to HER2 His235–His245 more tightly than 2C4, which could be the main reason of its enhanced affinity. In vivo experiments also showed MIL5 had stronger anti-cancer activity than 2C4; however, the classical flow cytometry assays to detect cell apoptosis or cycling did not show convincing evidence of the advantages of MIL5. Thus we introduced the multi-parameter in-cell analysis method to evaluate the superiority of MIL5 to 2C4 in arresting cancer cells in G2-phase to inhibit cell growth and/or proliferation.

Conclusion

Multi-parametric method confirmed stronger arrest of G2 by MIL5 to show better anti-cancer function both in vitro and in vivo than 2C4.

     

High salt and fat intake,inflammation, and risk of cancer

Background

Inflammatory conditions are involved in the pathophysiology of cancer. Recent findings have revealed that excessive salt and fat intake is involved in the development of severe inflammatory reactions.

Methods

literature search was performed on various online databases (PubMed, Scopus, and Google Scholar) regarding the roles of high salt and fat intake in the induction of inflammatory reactions and their roles in the etiopathogenesis of cancer.

Results

The results indicate that high salt and fat intake can induce severe inflammatory conditions. However, various inflammatory conditions have been strongly linked to the development of cancer. Hence, high salt and fat intake might be involved in the pathogenesis of cancer progression via putative mechanisms related to inflammatory reactions.

Conclusion

Reducing salt and fat intake may decrease the risk of cancer.

     

Cellular effects of a turmeric root and rosemary leaf extract on canine neoplastic cell lines

Background

The use of nutraceuticals is gaining in popularity in human and canine oncology with a relatively limited understanding of the effects in the vastly different tumor types seen in canine oncology. We have previously shown that turmeric root (TE) and rosemary leaf (RE) extracts can work synergistically to reduce neoplastic cell growth, but the mechanisms are poorly understood and require further elucidation.

Results

Three different canine cell lines (C2 mastocytoma, and CMT-12 mammary carcinoma, D17 osteosarcoma) were treated with 6.3 μg mL^?1 extract individually, or 3.1 μg mL^?1 of each extract in combination based on studies showing synergy of these two extracts. Apoptosis, antioxidant effects, cellular accumulation of curcumin, and perturbation of signaling pathways were assessed. The TE?+?RE combination treatment resulted in Caspase 3/7 activation and apoptosis in all cell lines, beyond the effects of TE alone with the CMT-12 cell line being most susceptible. Both extracts had antioxidant effects with RE reducing reactive oxygen species (ROS) by 40–50% and TE reducing ROS by 80–90%. In addition RE treatment enhanced the c-jun N-terminal kinase (JNK) activity in the C2 cell line and TE?+?RE exposure increased activated JNK by 4–5 times in the CMT-12 cell line. Upon further examination, it was found that RE treatment caused a significant increase in the cellular accumulation of curcumin by approximately 30% in the C2 and D17 cell lines, and by 4.8-fold in the CMT-12 cell line. This increase in intracellular curcumin levels may play a role in the synergy exhibited when using TE and RE in combination.

Conclusions

The use of RE in combination with TE induces a synergistic response to induce apoptosis which is better than either extract alone. This appears to be related to a variable increased TE uptake in cells and activation of pathways involved in the apoptotic response.

     

The effect of family policies and public health initiatives on breastfeeding initiation among 18 high-income countries: a qualitative comparative analysis research design

Background

The objective of this study is to examine the effects of macro-level factors – welfare state policies and public health initiatives – on breastfeeding initiation among eighteen high-income countries.

Methods

This study utilizes fuzzy-set Qualitative Comparative Analysis methods to examine the combinations of conditions leading to both high and low national breastfeeding initiation rates among eighteen high-income countries.

Results

The most common pathway leading to high breastfeeding initiation is the combination of conditions including a high percentage of women in parliament, a low national cesarean section rate, and either low family spending, high rates of maternity leave, or high rates of women working part-time. The most common pathway leading to low breastfeeding initiation includes the necessary condition of low national adherence to the Baby-Friendly Hospital Initiative.

Conclusion

This research suggests that there is a connection between broad level welfare state polices, public health initiatives, and breastfeeding initiation. Compliance with the WHO/UNICEF initiatives depends on welfare regime policies and overall support for women in both productive and reproductive labor.