A formal model for analyzing drug combination effects and its application in TNF-α-induced NFκB pathway

Abstract ADAMTS-4 is a protease enzyme which involves in vascular remodeling and atherosclerosis. It was found to be upregulated in macrophages seen in atherosclerotic lesions. The aim of this study was to investigate the expression and regulation of ADAMTS-4 in oxLDL induced human monocytes/macrophages system. PBMCs isolated from human blood(hPBMCs), treated with oxLDL (50μg/ml) were used as the model system for the study. mRNA and protein expressions were studied by qRT-PCR, ELISA, and western blot analysis. ROS production and cell viability were determined by fluorescence imaging and MTT assay respectively. In the presence of oxLDL, monocytes get differentiated into macrophages, which were confirmed by the increased expression of CD-36, b- D glucuronidase activity and by the morphological changes. OxLDL increased the mRNA and protein expression of ADAMTS-4 and TIMP-3 in monocytes/ macrophages. A significant increase in the mRNA and protein expression of TNF-α was also observed in oxLDL treated cells compared to untreated control. In the presence of NAC, the ROS scavenger, the production of NFκB and ADAMTS-4 was decreased significantly. Our study suggests that oxLDL significantly upregulated the expression of ADAMTS-4 in the monocyte/macrophage system. OxLDL mediated upregulation of ADAMTS-4 in hPBMCs involves TNF-α and ROS- NFκB pathway.

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Modeling drug combination effects via latent tensor reconstruction

10.1101/2021.04.16.439989 ◽

2021 ◽

Author(s):

Tianduanyi Wang ◽

Sandor Szedmak ◽

Haishan Wang ◽

Tero Aittokallio ◽

Tapio Pahikkala ◽

...

Keyword(s):

Drug Combination ◽

Polynomial Regression ◽

Predictive Performance ◽

Predictive Modelling ◽

Drug Combinations ◽

Multiple Drug ◽

Interaction Patterns ◽

Combination Effects ◽

Drug Doses ◽

Cost Efficient

Motivation: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration which makes the comprehensive experimental screening infeasible in practice. Machine learning models offer time- and cost-efficient means to aid this process by prioritising the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modelling of drug combination effects. Results: We introduce comboLTR, highly time-efficient method for learning complex, nonlinear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose-response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line.

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Response envelope analysis for quantitative evaluation of drug combinations

Bioinformatics ◽

10.1093/bioinformatics/btz091 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3761-3770 ◽

Cited By ~ 1

Author(s):

Di Du ◽

Chia-Hua Chang ◽

Yumeng Wang ◽

Pan Tong ◽

Wai Kin Chan ◽

...

Keyword(s):

Drug Combination ◽

Response Surfaces ◽

Supplementary Information ◽

Inhibition Mechanism ◽

Published Data ◽

Computational Framework ◽

Envelope Analysis ◽

Combination Effects ◽

Open Source Software Package ◽

Response Envelope

Abstract Motivation The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. Results We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. Availability and implementation The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. Supplementary information Supplementary data are available at Bioinformatics online.

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Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

CPT Pharmacometrics & Systems Pharmacology ◽

10.1002/psp4.12104 ◽

2016 ◽

Vol 5 (10) ◽

pp. 544-553 ◽

Cited By ~ 12

Author(s):

MK Morris ◽

DC Clarke ◽

LC Osimiri ◽

DA Lauffenburger

Keyword(s):

Cell Signaling ◽

Drug Combination ◽

Logic Model ◽

Signaling Networks ◽

Systematic Analysis ◽

Combination Effects ◽

Model Ensembles ◽

Cell Signaling Networks

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Faculty Opinions recommendation of Systematic analysis of quantitative logic model ensembles predicts drug combination effects on cell signaling networks.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726701268.793528079 ◽

2017 ◽

Author(s):

Piet van der Graaf

Keyword(s):

Cell Signaling ◽

Drug Combination ◽

Logic Model ◽

Signaling Networks ◽

Systematic Analysis ◽

Combination Effects ◽

Model Ensembles ◽

Cell Signaling Networks

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A Bayesian nonparametric approach for inferring drug combination effects on mental health in people with HIV

Biometrics ◽

10.1111/biom.13508 ◽

2021 ◽

Author(s):

Wei Jin ◽

Yang Ni ◽

Leah H. Rubin ◽

Amanda B. Spence ◽

Yanxun Xu

Keyword(s):

Mental Health ◽

Drug Combination ◽

Bayesian Nonparametric ◽

Combination Effects ◽

Nonparametric Approach

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Thymoquinone Potentiates the Effect of Phenytoin against Electroshock-Induced Convulsions in Rats by Reducing the Hyperactivation of m-TOR Pathway and Neuroinflammation: Evidence from In Vivo, In Vitro and Computational Studies

Pharmaceuticals ◽

10.3390/ph14111132 ◽

2021 ◽

Vol 14 (11) ◽

pp. 1132

Author(s):

Faheem Hyder Pottoo ◽

Mohammed Salahuddin ◽

Firdos Alam Khan ◽

Fadhel Alomar ◽

Marwa Abdullah AL Dhamen ◽

...

Keyword(s):

Drug Combination ◽

Docking Studies ◽

Poor Responders ◽

Maximal Electroshock ◽

Combination Regimen ◽

Computational Studies ◽

Neuroprotective Effects ◽

Tnf Α

Epilepsy is a chronic neurodegenerative disease characterized by multiple seizures, hereto 35% of patients remain poor responders. Phenytoin (PHT; 20 and 40 mg/kg) and thymoquinone (THQ; 40 and 80 mg/kg) were given alone and as a low dose combination for 14 days (p.o), prior to challenge with maximal electroshock (MES; 180 mA, 220 V, 0.2 s). Apart from observing convulsions, hippocampal mTOR, IL-1β, IL-6 and TNF-α levels were measured. Hippocampal histomorphological analysis was also conducted. In vitro cell line studies and molecular docking studies were run in parallel. The results revealed the synergistic potential of the novel duo-drug combination regimen: PHT (20 mg/kg) and THQ (40 mg/kg) against MES-induced convulsions. MES amplified signaling through mTOR, and inflated the levels of proinflammatory markers (IL-1β, IL-6 and TNF-α), which was significantly averted (p < 0.001) with the said drug combination. The computational studies revealed that PHT and THQ cooperatively bind the active site on Akt (upstream target of m-TOR) and establish a good network of intermolecular interactions, which indicates the sequential inhibition of PI3K/Akt/m-TOR signaling with the combination. The combination also increased cell viability by 242.81% compared to 85.66% viability from the the toxic control. The results suggest that the PHT and THQ in combination possesses excellent anticonvulsant and neuroprotective effects.

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