Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents

Speck‐Planche, Alejandro; Kleandrova, Valeria V.; Luan, Feng; Cordeiro, M. Natália D. S.

doi:10.1016/j.ejps.2012.04.012

Cited by 73 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…47 Thus, in numerous fields of research in drug discovery, various works have reported the development of advanced chemoinformatic models inspired by the idea regarding the calculation of Box-Jenkins moving averages. [33][34][35][36][48][49][50][51][52][53][54][55][56][57][58] In the first step, the following equation is employed: In Eq. 3, n(c r ) represents the number of peptides assayed by considering the same element of the experimental condition (ontology) c r , which have also been annotated as positive.…”

Section: Box-jenkins Moving Averages and Generation Of The Mtk-computmentioning

confidence: 99%

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

et al. 2016

Self Cite

View full text Add to dashboard Cite

Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

show abstract

Section: Box-jenkins Moving Averages and Generation Of The Mtk-computmentioning

confidence: 99%

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our group and other authors [69][70][71][72][73][74] have used LDA models alone or combined with Box-Jenkins Operators to predict properties of complex systems [75][76][77][78] , these models may include or not perturbation theory considerations 79 . It can be concluded that the classification results, obtained with this new PT-LFER equation, are promising and confirm the potential of the present methodology.…”

Section: Pt-nlfer Model For a Fa Distribution Networkmentioning

confidence: 99%

Experimental and computational studies of fatty acid distribution networks

et al. 2015

View full text Add to dashboard Cite

Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products (e.g., meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables (Vk) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of ω-6/ω-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions (cj) like c1 = acid and/or base methylation treatments, c2 = with/without fermentation, c3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters (V(k)), the corresponding Box-Jenkins (ΔV(kj)) and PT operators (ΔΔV(kj)) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy > 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erdős-Rényi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.

show abstract

“…Starting a decade ago, computer-assisted methods for prediction of cytotoxicity of chemical compounds against human cell lines based on analysis of structure-activity relationships were offered: Cancer Drug [12], COMPARE [13] and mt-QSAR discriminant model [14]. Existing methods for prediction of the anti-tumour activity of compounds have some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Virtual screening of chemical compounds active against breast cancer cell lines based on cell cycle modelling, prediction of cytotoxicity and interaction with targets

Konova

Lagunin

Pogodin

et al. 2015

SAR and QSAR in Environmental Research

View full text Add to dashboard Cite

Bio- and chemoinformatics methods are widely used for the detection of mechanisms of cancer, to search for potential drug targets and their ligands. Regulatory network analysis based on signalling pathways, and cell cycle regulation provides better understanding of diseases with multiple mechanisms of pathogenesis. We developed an approach for in silico prediction of the cytotoxic effect of chemical compounds in non-transformed and breast cancer cell lines. This approach combines the prediction of the interaction between chemical compounds and human proteins, cytotoxicity and regulatory network modelling taking into account gene expression. Application of our approach to virtual screening of libraries of commercially available compounds allowed selection of dozens of promising hits. These molecules are predicted to interact with the identified targets and exhibit cytotoxicity against breast cancer cell lines but not non-tumour human cell lines. Experimental testing of 49 selected compounds against MDA-MB-231 and MCF7 breast cancer cell lines confirmed the activity of eight compounds with IC50 values ranged from 0.8 to 50 μM. Thus, the developed approach may be applied for virtual screening for cytotoxic compounds against tumour cell lines.

show abstract

Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents

Cited by 73 publications

References 33 publications

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Experimental and computational studies of fatty acid distribution networks

Virtual screening of chemical compounds active against breast cancer cell lines based on cell cycle modelling, prediction of cytotoxicity and interaction with targets

Contact Info

Product

Resources

About