scholarly journals Coherent Raman Scattering Microscopy in Oncology Pharmacokinetic Research

2021 ◽  
Vol 12 ◽  
Author(s):  
Junjie Zeng ◽  
Wenying Zhao ◽  
Shuhua Yue
Keyword(s):  
Raman Scattering ◽  
High Speed ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
High Speed Imaging ◽  
Coherent Raman Scattering ◽  
Anti Cancer ◽  
Coherent Raman

The high attrition rates of anti-cancer drugs during clinical development remains a bottleneck problem in pharmaceutical industry. This is partially due to the lack of quantitative, selective, and rapid readouts of anti-cancer drug activity in situ with high resolution. Although fluorescence microscopy has been commonly used in oncology pharmacological research, fluorescent labels are often too large in size for small drug molecules, and thus may disturb the function or metabolism of these molecules. Such challenge can be overcome by coherent Raman scattering microscopy, which is capable of chemically selective, highly sensitive, high spatial resolution, and high-speed imaging, without the need of any labeling. Coherent Raman scattering microscopy has tremendously improved the understanding of pharmaceutical materials in the solid state, pharmacokinetics of anti-cancer drugs and nanocarriers in vitro and in vivo. This review focuses on the latest applications of coherent Raman scattering microscopy as a new emerging platform to facilitate oncology pharmacokinetic research.

Bioinformatics Approaches for Anti-cancer Drug Discovery

2019 ◽  
Vol 21 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Kening Li ◽  
Yuxin Du ◽  
Lu Li ◽  
Dong-Qing Wei
Keyword(s):  
Drug Discovery ◽  
Cancer Therapy ◽  
Biological Network ◽  
Computational Prediction ◽  
Cancer Drug ◽  
Cancer Drugs ◽  
Cancer Drug Discovery ◽  
Anti Cancer

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers’ identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Chalcone-Coumarin Derivatives as Potential Anti-Cancer Drugs: An in vitro and in vivo Investigation

2013 ◽  
Vol 14 (7) ◽  
pp. 963-974 ◽  
Author(s):  
Vincent Jamier ◽  
Wioleta Marut ◽  
Sergio Valente ◽  
Christiane Chereau ◽  
Sandrine Chouzenoux ◽  
...  
Keyword(s):  
Coumarin Derivatives ◽  
Cancer Drugs ◽  
Anti Cancer

Prevascularized, Co-Culture Model for Breast Cancer Drug Development

2012 ◽  
Author(s):  
Lauren Marshall ◽  
Isabel Löwstedt ◽  
Paul Gatenholm ◽  
Joel Berry
Keyword(s):  
Breast Cancer ◽  
Drug Development ◽  
Three Dimensional ◽  
Human Tumor ◽  
3D Models ◽  
Cancer Drug ◽  
Cancer Therapies ◽  
Anti Cancer

The objective of this study was to create 3D engineered tissue models to accelerate identification of safe and efficacious breast cancer drug therapies. It is expected that this platform will dramatically reduce the time and costs associated with development and regulatory approval of anti-cancer therapies, currently a multi-billion dollar endeavor [1]. Existing two-dimensional (2D) in vitro and in vivo animal studies required for identification of effective cancer therapies account for much of the high costs of anti-cancer medications and health insurance premiums borne by patients, many of whom cannot afford it. An emerging paradigm in pharmaceutical drug development is the use of three-dimensional (3D) cell/biomaterial models that will accurately screen novel therapeutic compounds, repurpose existing compounds and terminate ineffective ones. In particular, identification of effective chemotherapies for breast cancer are anticipated to occur more quickly in 3D in vitro models than 2D in vitro environments and in vivo animal models, neither of which accurately mimic natural human tumor environments [2]. Moreover, these 3D models can be multi-cellular and designed with extracellular matrix (ECM) function and mechanical properties similar to that of natural in vivo cancer environments [3].

scholarly journals Frontiers in Anti-cancer Drug Discovery: Challenges and Perspectives of Metformin as Anti-angiogenic Add-on Therapy in Glioblastoma

2021 ◽  
Author(s):  
Laura Guarnaccia ◽  
Stefania Elena Navone ◽  
Matteo Maria Masseroli ◽  
Melissa Balsamo ◽  
Manuela Caroli ◽  
...  
Keyword(s):  
Target Therapy ◽  
Cancer Drug ◽  
Cancer Drug Discovery ◽  
Average Survival ◽  
Anti Cancer ◽  
Tumor Neovascularization ◽  
Novel Therapeutic Strategies

Glioblastoma (GBM) is the most common primitive tumor in adult central nervous system (CNS), classified as grade IV according to WHO 2016 classification. GBM shows a poor prognosis with an average survival of approximately 15 months, representing an extreme therapeutic challenge. One of its distinctive and aggressive features is aberrant angiogenesis, which drives tumor neovascularization, representing a promising candidate for molecular target therapy. Although several pre-clinical studies and clinical trials have shown promising results, anti-angiogenic drugs have not led to a significant improvement in overall survival (OS), suggesting the necessity of identifying novel therapeutic strategies. Metformin, an anti-hyperglycemic drug of the Biguanides family, used as first line treatment in Type 2 Diabetes Mellitus (T2DM), demonstrated in vitro and in vivo antitumoral efficacy in many different tumors, including GBM. From this evidence, a process of repurposing of the drug has begun, leading to the demonstration of the inhibition of various oncopromoter mechanisms and, consequently, to the identification of the molecular pathways involved. Here, we review and discuss the potential metformin’s antitumoral effects on GBM, inspecting if it could properly act as an anti-angiogenic compound to be considered as a safely add-on therapy in the treatment and management of GBM patients.

scholarly journals pH-Sensitive Ratiometric Fluorescent Probe for Evaluation of Tumor Treatments

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1632
Author(s):  
Peisen Zhang ◽  
Junli Meng ◽  
Yingying Li ◽  
Zihua Wang ◽  
Yi Hou
Keyword(s):  
Ph Sensitive ◽  
Fluorescent Imaging ◽  
Tat Peptide ◽  
Cancer Drugs ◽  
Ph Response ◽  
In Vivo Experiments ◽  
Anti Cancer ◽  
Amidation Reaction

Determining therapeutic efficacy is critical for tumor precision theranostics. In order to monitor the efficacy of anti-cancer drugs (e.g., Paclitaxel), a pH-sensitive ratiometric fluorescent imaging probe was constructed. The pH-sensitive ratiometric fluorescent dye ANNA was covalently coupled to the N-terminal of the cell-penetrating TAT peptide through an amidation reaction (TAT-ANNA). The in vitro cellular experiments determined that the TAT-ANNA probe could penetrate the cell membrane and image the intracellular pH in real time. The in vivo experiments were then carried out, and the ratiometric pH response to the state of the tumor was recorded immediately after medication. The TAT-ANNA probe was successfully used to monitor the pharmacodynamics of anti-cancer drugs in vivo.

Potentiation of the Anti-Proliferative Effects of Anti-Cancer Drugs by Octreotide in vitro and in vivo

Digestion ◽  
1996 ◽  
Vol 57 (1) ◽  
pp. 22-28 ◽  
Author(s):  
G. Weckbecker ◽  
F. Raulf ◽  
L. Tolcsvai ◽  
C. Bruns
Keyword(s):  
Cancer Drugs ◽  
Anti Cancer

scholarly journals New Anti-Cancer Strategy to Suppress Colorectal Cancer Growth Through Inhibition of ATG4B and Lysosome Function

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1523 ◽  
Author(s):  
Yuanyuan Fu ◽  
Qianqian Gu ◽  
Li Luo ◽  
Jiecheng Xu ◽  
Yuping Luo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Therapy ◽  
Therapeutic Strategy ◽  
Screening Method ◽  
Cancer Drug ◽  
Autophagic Flux ◽  
Single Target ◽  
Anti Cancer

Autophagy inhibition has been proposed to be a potential therapeutic strategy for cancer, however, few autophagy inhibitors have been developed. Recent studies have indicated that lysosome and autophagy related 4B cysteine peptidase (ATG4B) are two promising targets in autophagy for cancer therapy. Although some inhibitors of either lysosome or ATG4B were reported, there are limitations in the use of these single target compounds. Considering multi-functional drugs have advantages, such as high efficacy and low toxicity, we first screened and validated a batch of compounds designed and synthesized in our laboratory by combining the screening method of ATG4B inhibitors and the identification method of lysosome inhibitors. ATG4B activity was effectively inhibited in vitro. Moreover, 163N inhibited autophagic flux and caused the accumulation of autolysosomes. Further studies demonstrated that 163N could not affect the autophagosome-lysosome fusion but could cause lysosome dysfunction. In addition, 163N diminished tumor cell viability and impaired the development of colorectal cancer in vivo. The current study findings indicate that the dual effect inhibitor 163N offers an attractive new anti-cancer drug and compounds having a combination of lysosome inhibition and ATG4B inhibition are a promising therapeutic strategy for colorectal cancer therapy.

Intracellular concentrations of anti cancer drugs in leukemic cells in vitro vs in vivo

1990 ◽  
Vol 25 (4) ◽  
pp. 252-256 ◽  
Author(s):  
B. Sundman-Engberg ◽  
U. Tidefelt ◽  
J. Liliemark ◽  
C. Paul
Keyword(s):  
Leukemic Cells ◽  
Cancer Drugs ◽  
Anti Cancer ◽  
Intracellular Concentrations
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