Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction

Aswathy, Lilly; Jisha, Radhakrishnan S; Masand, Vijay H.; Gajbhiye, Jayant M.; Shibi, Indira G.

doi:10.1007/s40203-018-0049-1

Cited by 19 publications

(13 citation statements)

References 64 publications

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“…As expected, the oral bioavailability decreases with increasing molecular weight and increases with the deacetylation degree. PreADMET predictions concerning the percent of the human intestinal absorption (HIA, Supplementary Table 2) reveal a mean absorbance (20%<HIA<70%) (Aswathy et al, 2018) for the monomeric units, the highest value (60.25%) being registered for the GlcN oligomer. Chito-oligomers containing two monomeric units reflect a poor absorption (HIA<20%) and the other COs do not reflect intestinal absorption (HIA = 0).…”

Section: Resultsmentioning

confidence: 99%

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Roman

Som

et al. 2019

Front. Bioeng. Biotechnol.

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Chitosan is a natural polymer revealing an increased potential to be used in different biomedical applications, including drug delivery systems, and tissue engineering. It implies the evaluation of the organism response to the biomaterial implantation. Low-molecular degradation products, the chito-oligomers, are resulting mainly from the influence of enzymes, which are found in the organism fluids. Within this study, we have performed the computational assessment of pharmacological profiles and toxicological effects on human health of small chito-oligomers with distinct molecular weights, deacetylation degrees, and acetylation patterns. Our approach is based on the fact that regulatory agencies and researchers in the drug development field rely on the use of modeling to predict biological effects and to guide decision making. To be considered as valid for regulatory purposes, every model that is used for predictions should be associated with a defined toxicological endpoint and has appropriate robustness and predictivity. Within this context, we have used FAF-Drugs4, SwissADME, and PreADMET tools to predict the oral bioavailability of chito-oligomers and SwissADME, PreADMET, and admetSAR2.0 tools to predict their pharmacokinetic profiles. The organs and genomic toxicities have been assessed using admetSAR2.0 and PreADMET tools but specific computational facilities have been also used for predicting different toxicological endpoints: Pred-Skin for skin sensitization, CarcinoPred-EL for carcinogenicity, Pred-hERG for cardiotoxicity, ENDOCRINE DISRUPTOME for endocrine disruption potential and Toxtree for carcinogenicity and mutagenicity. Our computational assessment showed that investigated chito-oligomers reflect promising pharmacological profiles and limited toxicological effects on humans, regardless of molecular weight, deacetylation degree, and acetylation pattern. According to our results, there is a possible inhibition of the organic anion transporting peptides OATP1B1 and/or OATP1B3, a weak potential of cardiotoxicity, a minor probability of affecting the androgen receptor, and phospholipidosis. Consequently, these results may be used to guide or to complement the existing in vitro and in vivo toxicity tests, to optimize biomaterials properties and to contribute to the selection of prototypes for nanocarriers.

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Section: Resultsmentioning

confidence: 99%

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Roman

Som

et al. 2019

Front. Bioeng. Biotechnol.

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“…In the same study, ligand-based drug design led to the identification of a lead compound that has similar anti-Aβ aggregation properties as the peptide. The structural and physicochemical requirements for the potential Aβ aggregation inhibition were investigated on a set of 30 curcumin derivatives using docking, 2D-QSAR, HQSAR, and 3D QSAR [111]. The Aβ aggregation inhibitory activities of newly design molecules were predicted through inverse QSAR, and further screened using machine learning and toxicity prediction models to finally identify six lead compounds.…”

Section: Cadd For the Development Of Anti-aβ Aggregation Inhibitorsmentioning

confidence: 99%

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

Mouchlis

Melagraki

Zacharia

et al. 2020

IJMS

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Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer’s small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer’s. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

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“…Quantitative regression models suggest that the use of canvas descriptors can achieve better statistical accuracy similar to 3D field-based techniques that often require molecular alignment of diverse chemical scaffolds in one universal chemical space. Aswathy et al (2018) identified the structural and physicochemical requirements for the potential inhibition of Aβ aggregation, and molecular docking analyses of the representative inhibitors were performed to determine the binding modes of inhibitors at the active site of the protein. They used a random forest based model to test the activity of novel chemical entities and to screen the newly designed molecules.…”

Section: Machine Learning Techniques and Its Application On Alzheimermentioning

confidence: 99%

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective

Saravanan

Zhang

et al. 2020

Front. Bioeng. Biotechnol.

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Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches. In this article, we have comprehensively reviewed the developmental advances in the theoretical models for Aβ peptide folding and interactions, particularly in the context of neurological disorders. Furthermore, we have extensively reviewed the advances in molecular dynamics simulation as a tool used for studying the conversions between polymorphic amyloid forms and applications of using machine learning approaches in predicting Aβ peptides and aggregation-prone regions in proteins. We have also provided details on the theoretical advances in the study of Aβ peptides, which would enhance our understanding of these peptides at the molecular level and eventually lead to the development of targeted therapies for certain acute neurological disorders such as Alzheimer's disease in the future.

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Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction

Cited by 19 publications

References 64 publications

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Computational Assessment of the Pharmacological Profiles of Degradation Products of Chitosan

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer’s Therapeutics

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective

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