scholarly journals Systems biology–based drug repositioning identifies digoxin as a potential therapy for groups 3 and 4 medulloblastoma

2018 ◽  
Vol 10 (464) ◽  
pp. eaat0150 ◽  
Author(s):  
Lei Huang ◽  
Sarah Garrett Injac ◽  
Kemi Cui ◽  
Frank Braun ◽  
Qi Lin ◽  
...  
Keyword(s):  
Systems Biology ◽  
Cardiac Glycoside ◽  
Simulated Annealing Algorithm ◽  
Drug Repositioning ◽  
Plasma Concentrations ◽  
Drug Candidates ◽  
Growth Of Groups ◽  
New Treatments

Medulloblastoma (MB) is the most common malignant brain tumor of childhood. Although outcomes have improved in recent decades, new treatments are still needed to improve survival and reduce treatment-related complications. The MB subtypes groups 3 and 4 represent a particular challenge due to their intragroup heterogeneity, which limits the options for “rational” targeted therapies. Here, we report a systems biology approach to drug repositioning that integrates a nonparametric, bootstrapping-based simulated annealing algorithm and a 3D drug functional network to characterize dysregulated driver signaling networks, thereby identifying potential drug candidates. From more than 1300 drug candidates studied, we identified five members of the cardiac glycoside family as potentially inhibiting the growth of groups 3 and 4 MB and subsequently confirmed this in vitro. Systemic in vivo treatment of orthotopic patient-derived xenograft (PDX) models of groups 3 and 4 MB with digoxin, a member of the cardiac glycoside family approved for the treatment of heart failure, prolonged animal survival at plasma concentrations known to be tolerated in humans. These results demonstrate the power of a systematic drug repositioning method in identifying a potential treatment for MB. Our strategy could potentially be used to accelerate the repositioning of treatments for other human cancers that lack clearly defined rational targets.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2017 ◽  
Vol 3 ◽  
pp. e106 ◽  
Author(s):  
James P. McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S. Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Application Programming Interface ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Drug Candidates ◽  
Application Programming ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

scholarly journals Finding melanoma drugs through a probabilistic knowledge graph

2016 ◽  
Author(s):  
James P McCusker ◽  
Michel Dumontier ◽  
Rui Yan ◽  
Sylvia He ◽  
Jonathan S Dordick ◽  
...  
Keyword(s):  
Systems Biology ◽  
Probabilistic Methods ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Web Interface ◽  
Drug Candidates ◽  
Probabilistic Knowledge ◽  
Data Explosion

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates, however filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein, and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an API or web interface, and has generated 25 high quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Comparison of Specific Antibody, D-Phe-Pro-Arg-CH2Cl and Aprotinin for Prevention of In Vitro Effects of Recombinant Tissue-Type Plasminogen Activator on Haemostasis Parameters

1987 ◽  
Vol 58 (03) ◽  
pp. 921-926 ◽  
Author(s):  
E Seifried ◽  
P Tanswell
Keyword(s):  
Specific Antibody ◽  
Plasma Concentrations ◽  
Fibrinolytic Therapy ◽  
Tissue Type ◽  
Control Value ◽  
Type Plasminogen Activator ◽  
In Vitro Effects ◽  
Fibrinolytic Parameters

SummaryIn vitro, concentration-dependent effects of rt-PA on a range of coagulation and fibrinolytic assays in thawed plasma samples were investigated. In absence of a fibrinolytic inhibitor, 2 μg rt-PA/ml blood (3.4 μg/ml plasma) caused prolongation of clotting time assays and decreases of plasminogen (to 44% of the control value), fibrinogen (to 27%), α2-antiplasmin (to 5%), FV (to 67%), FVIII (to 41%) and FXIII (to 16%).Of three inhibitors tested, a specific polyclonal anti-rt-PA antibody prevented interferences in all fibrinolytic and most clotting assays. D-Phe-Pro-Arg-CH2Cl (PPACK) enabled correct assays of fibrinogen and fibrinolytic parameters but interfered with coagulometric assays dependent on endogenous thrombin generation. Aprotinin was suitable only for a restricted range of both assay types.Most in vitro effects were observed only with rt-PA plasma concentrations in excess of therapeutic values. Nevertheless it is concluded that for clinical application, collection of blood samples on either specific antibody or PPACK is essential for a correct assessment of in vivo effects of rt-PA on the haemostatic system in patients undergoing fibrinolytic therapy.

Flavonoids as P-gp Inhibitors: A Systematic Review of SARs

2019 ◽  
Vol 26 (25) ◽  
pp. 4799-4831 ◽  
Author(s):  
Jiahua Cui ◽  
Xiaoyang Liu ◽  
Larry M.C. Chow
Keyword(s):  
Molecular Mechanisms ◽  
Metastatic Cancer ◽  
Drug Candidates ◽  
Chemical Structures ◽  
Transporter Family ◽  
Promising Area ◽  
New Generation ◽  
Nontoxic Inhibitors

P-glycoprotein, also known as ABCB1 in the ABC transporter family, confers the simultaneous resistance of metastatic cancer cells towards various anticancer drugs with different targets and diverse chemical structures. The exploration of safe and specific inhibitors of this pump has always been the pursuit of scientists for the past four decades. Naturally occurring flavonoids as benzopyrone derivatives were recognized as a class of nontoxic inhibitors of P-gp. The recent advent of synthetic flavonoid dimer FD18, as a potent P-gp modulator in reversing multidrug resistance both in vitro and in vivo, specifically targeted the pseudodimeric structure of the drug transporter and represented a new generation of inhibitors with high transporter binding affinity and low toxicity. This review concerned the recent updates on the structure-activity relationships of flavonoids as P-gp inhibitors, the molecular mechanisms of their action and their ability to overcome P-gp-mediated MDR in preclinical studies. It had crucial implications on the discovery of new drug candidates that modulated the efflux of ABC transporters and also provided some clues for the future development in this promising area.

Current Approaches to Drug Discovery for Chagas Disease: Methodological Advances

2019 ◽  
Vol 22 (8) ◽  
pp. 509-520
Author(s):  
Cauê B. Scarim ◽  
Chung M. Chin
Keyword(s):  
Drug Discovery ◽  
Chagas Disease ◽  
Drug Candidates ◽  
Lead Compounds ◽  
New Drug ◽  
Compound Libraries ◽  
Screening Assays ◽  
Cruzi Infection

Background: In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays. Objective: Current approaches to drug discovery for Chagas disease. Method: This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases. Results: Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease. Conclusion: There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.

Design, Synthesis, and Pharmacokinetic Evaluation of O-Carbamoyl Tizoxanide Prodrugs

2020 ◽  
Vol 16 ◽  
Author(s):  
Xi He ◽  
Wenjun Hu ◽  
Fanhua Meng ◽  
Xingzhou Li
Keyword(s):  
Plasma Concentration ◽  
Broad Spectrum ◽  
Plasma Concentrations ◽  
Antiviral Drug ◽  
Systemic Exposure ◽  
Pharmacokinetic Profile ◽  
Hydroxyl Group ◽  
First Pass

Background: The broad-spectrum antiparasitic drug nitazoxanide (N) has been repositioned as a broad-spectrum antiviral drug. Nitazoxanide’s in vivo antiviral activities are mainly attributed to its metabolitetizoxanide, the deacetylation product of nitazoxanide. In reference to the pharmacokinetic profile of nitazoxanide, we proposed the hypotheses that the low plasma concentrations and the low system exposure of tizoxanide after dosing with nitazoxanide result from significant first pass effects in the liver. It was thought that this may be due to the unstable acyloxy bond of nitazoxanide. Objective: Tizoxanide prodrugs, with the more stable formamyl substituent attached to the hydroxyl group rather than the acetyl group of nitazoxanide, were designed with the thought that they might be more stable in plasma. It was anticipated that these prodrugs might be less affected by the first pass effect, which would improve plasma concentrations and system exposure of tizoxanide. Method: These O-carbamoyl tizoxanide prodrugs were synthesized and evaluated in a mouse model for pharmacokinetic (PK) properties and in an in vitro model for plasma stabilities. Results: The results indicated that the plasma concentration and the systemic exposure of tizoxanide (T) after oral administration of O-carbamoyl tizoxanide prodrugs were much greater than that produced by equimolar dosage of nitazoxanide. It was also found that the plasma concentration and the systemic exposure of tizoxanide glucuronide (TG) were much lower than that produced by nitazoxanide. Conclusion: Further analysis showed that the suitable plasma stability of O-carbamoyl tizoxanide prodrugs is the key factor in maximizing the plasma concentration and the systemic exposure of the active ingredient tizoxanide.

scholarly journals An In Vivo Predictive Dissolution Methodology (iPD Methodology) with a BCS Class IIb Drug Can Predict the In Vivo Bioequivalence Results: Etoricoxib Products

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 507
Author(s):  
Isabel Gonzalez-Alvarez ◽  
Marival Bermejo ◽  
Yasuhiro Tsume ◽  
Alejandro Ruiz-Picazo ◽  
Marta Gonzalez-Alvarez ◽  
...  
Keyword(s):  
Plasma Concentrations ◽  
In Vitro Dissolution ◽  
Case Examples ◽  
Dissolution Studies ◽  
Weak Bases ◽  
Transit Times ◽  
Class Iib ◽  
The Impact

The purpose of this study was to predict in vivo performance of three oral products of Etoricoxib (Arcoxia® as reference and two generic formulations in development) by conducting in vivo predictive dissolution with GIS (Gastro Intestinal Simulator) and computational analysis. Those predictions were compared with the results from previous bioequivalence (BE) human studies. Product dissolution studies were performed using a computer-controlled multicompartmental dissolution device (GIS) equipped with three dissolution chambers, representing stomach, duodenum, and jejunum, with integrated transit times and secretion rates. The measured dissolved amounts were modelled in each compartment with a set of differential equations representing transit, dissolution, and precipitation processes. The observed drug concentration by in vitro dissolution studies were directly convoluted with permeability and disposition parameters from literature to generate the predicted plasma concentrations. The GIS was able to detect the dissolution differences among reference and generic formulations in the gastric chamber where the drug solubility is high (pH 2) while the USP 2 standard dissolution test at pH 2 did not show any difference. Therefore, the current study confirms the importance of multicompartmental dissolution testing for weak bases as observed for other case examples but also the impact of excipients on duodenal and jejunal in vivo behavior.

scholarly journals Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2505
Author(s):  
Raheem Remtulla ◽  
Sanjoy Kumar Das ◽  
Leonard A. Levin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
In Silico ◽  
Disulfide Bonds ◽  
Oral Absorption ◽  
In Vivo Studies ◽  
Drug Candidates ◽  
In Silico Methods

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

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