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In the past several decades, the development of efficient and effective in vitro assays has increased the demand for novel techniques to process experimental data into knowledge to facilitate drug discovery and environmental toxicity assessment. Due to diverged objectives, the analysis of pharmaceutical and toxicological data requires different strategies and tools. This dissertation describes the development and application of cheminformatics approaches for both the identification of potential drug candidates and the evaluation of chemicals’ adverse effects, with various foci and methods in each project.
In the pharmaceutical field, cheminformatics is popularly used for in silico modeling of absorption, distribution, metabolism, and excretion properties, e.g., blood-brain barrier (BBB) permeability. In this dissertation, we applied combinatorial quantitative structure-activity relationship (QSAR) modeling workflow to develop BBB permeability models with high predictive accuracy.
In order to accelerate drug discovery, new cheminformatics approaches are needed to improve the success rate and hit novelty of high-throughput screening (HTS). To this end, we developed internally validated and externally predictive QSAR models for antimalarial activity. The QSAR-based virtual screening prioritized chemicals in HTS libraries for biological evaluation, and greatly improved hit rate of experimental testing. Resulting hits were highly structurally diverse and contained novel scaffolds.
Unlike drug discovery, environmental toxicity assessment focuses on complex and mostly unknown biological events which are time-consuming and costly. Consequently, great efforts have been made to utilize in vitro and in silico predictors to forecast potential human toxicity of chemicals. We found that by employing an “in vitro-in vivo” extrapolation, the predictive accuracy of QSAR models of in vivo toxicity was improved.
Another growing public safety concern is endocrine disrupting compounds (EDCs), which may cause severe adverse effects on the health of humans and wildlife. The use of computational predictors to pick candidates for further endocrine disruption testing could significantly reduce the experimental cost. Therefore, I combined both ligand-based and structure-based virtual screening to identify potential EDCs that act via estrogen receptor.
In summary, the cheminformatics approaches I developed and applied successfully fulfilled the different objectives of drug design and chemical toxicity evaluation, and thus facilitate drug discovery and environmental toxicity assessment.