The most notable data resources supporting the in silico-based therapeutic discovery campaigns would be the Connectivity Map (CMap) ( Lamb et al., 2006) and its recent extension called Library of Integrated Network-Based Cellular Signatures (LINCS) ( Subramanian et al., 2017).

Leveraging big data-driven approaches, drug repositioning can be conducted computationally, which has the potential to complement traditional therapeutic discovery means and further improve the cost-effectiveness of drug development ( Li et al., 2016). Within this context, exploring new indications for existing drugs (drug-centric) or identifying effective drugs for certain diseases (disease-centric) represents an appealing concept, namely ‘drug repositioning’ (or ‘drug repurposing’), which can greatly shorten the gap between preclinical drug research and clinical applications ( Ashburn and Thor, 2004 Liu et al., 2013). Moreover, it usually takes over 10 years to bring a new drug from bench to bedside, reflecting the complex challenges in this area ( Scannell et al., 2012). Editor's evaluationĭespite the major advances in drug research and development (R&D), the cost for de novo drug development remains high, ranging from $3 billion to more than $30 billion. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL 4)-induced liver fibrosis model, demonstrating the reliability of the prediction results. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery.

Investigations of potential factors influencing drug retrieval were conducted based on these standards. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery.

Bender by Gene Gant