In silico exploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer's disease using SMILES based attributes in Monte Carlo optimization

Correlation intensity index-index of ideality of correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment

Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer's and Parkinson's diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (-11.5 kcal mol-1) towards monoamine oxidase-B.

Development and evolution of human glutaminyl cyclase inhibitors (QCIs): an alternative promising approach for disease-modifying treatment of Alzheimer's disease

Human glutaminyl cyclase (hQC) is drawing considerable attention and emerging as a potential druggable target for Alzheimer's disease (AD) due to its close involvement in the pathology of AD via the post-translational pyroglutamate modification of amyloid-β. A recent phase 2a study has shown promising early evidence of efficacy for AD with a competitive benzimidazole-based QC inhibitor, PQ912, which also demonstrated favorable safety profiles. This finding has sparked new hope for the treatment of AD. In this review, we briefly summarize the discovery and evolution of hQC inhibitors, with a particular interest in classic Zinc binding group (ZBG)-containing chemicals reported in recent years. Additionally, we highlight several high-potency inhibitors and discuss new trends and challenges in the development of QC inhibitors as an alternative and promising disease-modifying therapy for AD.

The searching for agents for Alzheimer's disease treatment via the system of self-consistent models

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R²_v= 0.923, and RMSE =0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.