3,643 research outputs found
The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions
Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe
Drug repurposing using biological networks
Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning
Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments
HIV reverse transcriptase: Structural interpretation of drug resistant genetic variants from India
The reverse transcriptase (RT) enzyme is the prime target of nucleoside/ nucleotide (NRTI) and non-nucleoside (NNRTI) reverse transcriptase
inhibitors. Here we investigate the structural basis of effects of drug-resistance mutations in clade C RT using three-dimensional structural
modeling. Apropos the expectation was for unique mechanisms in clade C based on interactions with amino acids of p66 subunit in RT molecule.
3-D structures of RT with mutations found in sequences from 2 treatment naïve, 8 failed and one reference clade C have been modeled and
analyzed. Models were generated by computational mutation of available crystal structures of drug bound homologous RT. Energy minimization
of the models and the structural analyses were carried out using standard methods. Mutations at positions 75,101,118,190,230,238 and 318
known to confer drug resistance were investigated. Different mutations produced different effects such as alteration of geometry of the drugbinding
pocket, structural changes at the site of entry of the drug (into the active site), repositioning the template bases or by discriminating the
inhibitors from their natural substrates. For the mutations analyzed, NRTI resistance was mediated mainly by the ability to discriminate between
inhibitors and natural substrate, whereas, NNRTI resistance affected either the drug entry or the geometry of the active site. Our analysis
suggests that different mutations result in different structural effects affecting the ability of a given drug to bind to the RT. Our studies will help
in the development of newer drugs taking into account the presence of these mutations and the structural basis of drug resistance
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