PROTACs in Treatment of Cancer: A review

2021 ◽  
Vol 21 ◽  
Author(s):  
Poonam Arora ◽  
Manjinder Singh ◽  
Varinder Singh ◽  
Shiveena Bhatia ◽  
Sandeep Arora
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Cancer Treatment ◽  
Enzyme System ◽  
Target Protein ◽  
Design Activity ◽  
High Potency ◽  
Drug Molecules ◽  
Viable Treatment

: Cancer treatment has become a major challenge amidst the resistance and relapse caused by the various treatments available. The PROteolysis TAargeting Chimera (PROTAC) technology involves the degradation of target protein against the inhibition by small drug molecules. The PROTACs with high potency and activity have been frequently reported; however, no PROTAC acting against cancer has reached the clinical trials so far. The concept of PROTACs involves the reduction in the disease causing protein by its degradation through ubiquitin-proteosomal enzyme system. This concept has attracted a lot of attention from both industry and academia due to its potential in drug discovery (in the form of PROTACs) which can conquer the resistance associated with current treatments of cancer. Thus, it is the need of hour to identify and synthesize more PROTACs for a viable treatment of cancer. This article reviews the design, activity and the effects produced in cancer by some recently developed PROTACs.

Sitagliptin: a potential drug for the treatment of SARS-CoV-2?

2020 ◽  
Author(s):  
Sanaa Bardaweel
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Antimalarial Drug ◽  
Drug Repurposing ◽  
Spike Protein ◽  
Diabetic Patients ◽  
Potential Drug ◽  
Chemokine Production ◽  
Drug Discovery And Development ◽  
Sequence Identity

Recently, an outbreak of fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. As the coronavirus pandemic rages, drug discovery and development become even more challenging. Drug repurposing of the antimalarial drug chloroquine and its hydroxylated form had demonstrated apparent effectiveness in the treatment of COVID-19 associated pneumonia in clinical trials. SARS-CoV-2 spike protein shares 31.9% sequence identity with the spike protein presents in the Middle East Respiratory Syndrome Corona Virus (MERS-CoV), which infects cells through the interaction of its spike protein with the DPP4 receptor found on macrophages. Sitagliptin, a DPP4 inhibitor, that is known for its antidiabetic, immunoregulatory, anti-inflammatory, and beneficial cardiometabolic effects has been shown to reverse macrophage responses in MERS-CoV infection and reduce CXCL10 chemokine production in AIDS patients. We suggest that Sitagliptin may be beneficial alternative for the treatment of COVID-19 disease especially in diabetic patients and patients with preexisting cardiovascular conditions who are already at higher risk of COVID-19 infection.

Triazol: a privileged scaffold for proteolysis targeting chimeras

2019 ◽  
Vol 11 (22) ◽  
pp. 2919-2973 ◽  
Author(s):  
Li-Wen Xia ◽  
Meng-Yu Ba ◽  
Wei Liu ◽  
Weyland Cheng ◽  
Chao-Ping Hu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Anticancer Activity ◽  
Mode Of Action ◽  
Critical Analysis ◽  
Enzyme Inhibitors ◽  
Structure Activity Relationship ◽  
Target Protein ◽  
Activity Relationship ◽  
Structure Activity ◽  
Privileged Scaffold

Current traditional drugs such as enzyme inhibitors and receptor agonists/antagonists present inherent limitations due to occupancy-driven pharmacology as the mode of action. Proteolysis targeting chimeras (PROTACs) are composed of an E3 ligand, a connecting linker and a target protein ligand, and are an attractive approach to specifically knockdown-targeted proteins utilizing an event-driven mode of action. The length, hydrophilicity and rigidity of connecting linkers play important role in creating a successful PROTAC. Some PROTACs with a triazole linker have displayed promising anticancer activity. This review provides an overview of PROTACs with a triazole scaffold and discusses its structure–activity relationship. Important milestones in the development of PROTACs are addressed and a critical analysis of this drug discovery strategy is also presented.

Preclinical investigations revealed possibilities for Salmonella tumor treatment. Bacteria can also be coupled to nanomaterials enabling drug-loading, photocatalytic and/or magnetic properties, using the bacteria's net negative charge

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Clinical Trials ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Negative Charge ◽  
Human Cancer ◽  
Drug Loading ◽  
Tumor Therapy ◽  
Preclinical Research ◽  
Immunodeficient Mice ◽  
Virulence Attenuation

Except in human clinical trials, preclinical tests showed the potential of Salmonella bacteria for tumor therapy. There are still various challenges to tackle before salmonella bacteria may be employed to treat human cancer. Due to its pathogenic nature, attenuation is essential to minimize the host's harmful effects of bacterial infection. Loss of anticancer efficacy from bacterial virulence attenuation can be compensated by giving therapeutic payloads to microorganisms. Bacteria can also be linked to micro-or nanomaterials with diverse properties, such as drug-loaded, photocatalytic and/or magnetic-sensing nanoparticles, using the net negative charge of the bacteria. Combining bacteria-mediated cancer treatment with other medicines that have been clinically shown to be helpful but have limits may provide surprising therapeutic results. Recently, this strategy has received attention and is underway. The use of live germs for cancer treatment has not yet been approved for human clinical trials. The non-invasive oral form of administration benefits from safety, making it more suitable for clinical cancer patients.Infection of live germs through systemic means, on the other hand, involves toxicity risk. Although Salmonella bacteria can be genetically manipulated with high tumor targeting, harm to normal tissues can not be excluded when medications with nonspecific toxicity are administered. It is preferred if the action of selected drugs may be restricted to the tumor site rather than healthy tissues, thereby boosting cancer therapy safety. In recent years, many regulatory mechanisms have been developed to manage pharmaceutical distribution through live bacterial vectors. Engineered salmonella can accumulate 1000 times greater than normal tissue density in the tumor. The QS-regulated mechanism, which initiates gene expression when bacterial density exceeds a particular threshold level, also promises Salmonella bacteria for targeted medication delivery. Nanovesicle structures of Salmonella bacteria can also be used as biocompatible nanocarriers to deliver functional medicinal chemicals in cancer therapy. Surface-modified nanovesicles preferably attach to tumor cells and are swallowed by receptor-mediated endocytosis before being destroyed to release packed drugs. The xenograft methodology, which comprises the implantation of cultivated tumor cell lines into immunodeficient mice, has often been used in preclinical research revealing favorable results about the anticancer effects of genetically engineered salmonella.

Treatment of solid tumors using bispecific anti-PDL-1/ICOS antibody

2021 ◽  
Author(s):  
Martin Perez-Santos ◽  
Maricruz Anaya-Ruiz ◽  
Gabriela Sanchez-Esgua ◽  
Luis Villafaña-Diaz ◽  
Diana Barron-Villaverde
Keyword(s):  
Clinical Trials ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Cancer Treatment ◽  
Potential Application ◽  
Poor Prognosis ◽  
Bispecific Antibody ◽  
Control Points ◽  
Immune Control ◽  
Ct26 Model

PD-L1 and ICOS are immune control points in cancer and their presence in cancer tends to have a poor prognosis. WO2019122882 patent describes a bispecific antibody that targets PDL-1/ICOS with the potential application of cancer treatment. WO2019122882 patent describes a bispecific antibody with antitumor efficacy in CT26 model through of the depletion of TReg cells and improved ratio of CD8+ T cells: TReg in tumor microenvironment. The anti-PDL-1/ICOS antibody is new; however, only preclinical assays are shown using colon carcinoma model. So far, there are no reports of clinical trials to evaluate the safety, toxicity and efficacy, but it will be of great interest to analyze in the future if this antibody surpasses the action of the combinatorial therapy in cancer.

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

scholarly journals Enrollment and Racial Disparities in Cancer Treatment Clinical Trials in North Carolina

2016 ◽  
Vol 77 (1) ◽  
pp. 52-58 ◽  
Author(s):  
L. L. Zullig ◽  
A. G. Fortune-Britt ◽  
S. Rao ◽  
S. D. Tyree ◽  
P. A. Godley ◽  
...  
Keyword(s):  
North Carolina ◽  
Clinical Trials ◽  
Cancer Treatment ◽  
Racial Disparities

Factors Influencing Enrollment in Clinical Trials for Cancer Treatment

1999 ◽  
Vol 92 (12) ◽  
pp. 1189-1193 ◽  
Author(s):  
CARRIE N. KLABUNDE ◽  
Bethesda ◽  
BRIAN C. SPRINGER ◽  
BELINDA BUTLER ◽  
Winston-Salem ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Cancer Treatment ◽  
Factors Influencing

scholarly journals Phase I and phase II clinical trials in sarcoma: Implications for drug discovery and development

2019 ◽  
Vol 8 (2) ◽  
pp. 585-592 ◽  
Author(s):  
Daniel Y. Lee ◽  
Arthur P. Staddon ◽  
Jacob E. Shabason ◽  
Ronnie Sebro
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Phase I ◽  
Phase Ii ◽  
Drug Discovery And Development ◽  
Phase Ii Clinical Trials

scholarly journals Developing a machine learning model to identify protein–protein interaction hotspots to facilitate drug discovery

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10381
Author(s):  
Rohit Nandakumar ◽  
Valentin Dinu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Drug Discovery ◽  
Structural Information ◽  
Learning Model ◽  
Protein Protein Interaction ◽  
Drug Molecules ◽  
Machine Learning Model ◽  
Disease Associations ◽  
History Of

Throughout the history of drug discovery, an enzymatic-based approach for identifying new drug molecules has been primarily utilized. Recently, protein–protein interfaces that can be disrupted to identify small molecules that could be viable targets for certain diseases, such as cancer and the human immunodeficiency virus, have been identified. Existing studies computationally identify hotspots on these interfaces, with most models attaining accuracies of ~70%. Many studies do not effectively integrate information relating to amino acid chains and other structural information relating to the complex. Herein, (1) a machine learning model has been created and (2) its ability to integrate multiple features, such as those associated with amino-acid chains, has been evaluated to enhance the ability to predict protein–protein interface hotspots. Virtual drug screening analysis of a set of hotspots determined on the EphB2-ephrinB2 complex has also been performed. The predictive capabilities of this model offer an AUROC of 0.842, sensitivity/recall of 0.833, and specificity of 0.850. Virtual screening of a set of hotspots identified by the machine learning model developed in this study has identified potential medications to treat diseases caused by the overexpression of the EphB2-ephrinB2 complex, including prostate, gastric, colorectal and melanoma cancers which are linked to EphB2 mutations. The efficacy of this model has been demonstrated through its successful ability to predict drug-disease associations previously identified in literature, including cimetidine, idarubicin, pralatrexate for these conditions. In addition, nadolol, a beta blocker, has also been identified in this study to bind to the EphB2-ephrinB2 complex, and the possibility of this drug treating multiple cancers is still relatively unexplored.

Sign in / Sign up

Export Citation Format

Share Document