Progress in the Analysis of Multiple Activity Profile of Screening Data Using Computational Approaches

Anthocyanins from Black Chokeberry (Aroniamelanocarpa Elliot) Delayed Aging-Related Degenerative Changes of Brain

Aging is the greatest risk factor for most neurodegenerative diseases, which is associated with decreasing cognitive function and significantly affecting life quality in the elderly. Computational analysis suggested that 4 anthocyanins from chokeberry fruit increased Klotho (aging-suppressor) structural stability, so we hypothesized that chokeberry anthocyanins could antiaging. To explore the effects of anthocyanins treatment on brain aging, mice treated with 15 or 30 mg/kg anthocyanins by gavage and injected D-galactose accelerated aging per day. After 8 weeks, cognitive and noncognitive components of behavior were determined. Our studies showed that anthocyanins blocked age-associated cognitive decline and response capacity in senescence accelerated mice. Furthermore, mice treated with anthocyanins-supplemented showed better balance of redox systems (SOD, GSH-PX, and MDA) in all age tests. Three major monoamines were norepinephrine, dopamine, and 5-hydroxytryptamine, and their levels were significantly increased; the levels of inflammatory cytokines (COX2, TGF-β1, and IL-1) transcription and DNA damage were decreased significantly in brains of anthocyanins treated mice compared to aged models. The DNA damage signaling pathway was also regulated with anthocyanins. Our results suggested that anthocyanins was a potential approach for maintaining thinking and memory in aging mice, possibly by regulating the balance of redox system and reducing inflammation accumulation, and the most important factor was inhibiting DNA damage.

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.