摘要
High-throughput screening (HTS) is a vaunted technology in drug discovery, and drug repositioning a celebrated strategy with famous examples of successful stories; however, repositioned drugs have primarily resulted from serendipitous observations, retrospective studies, and pharmacological analyses as opposed to experimental routes. This observation points to a methodological paradox, considering that academic laboratories of the post-genomic era have benefited from unprecedented technological progress, and a facilitated access to powerful resources that, historically, were a prerogative of the pharma industry. This disconnect is exacerbated by financial, practical, and regulatory complexities affecting drug repositioning; however, the pivotal significance of stringent and rigorous data is what unconditionally sits at the crossroad of go/no-go decisions concerning the therapeutic significance, or predictive validity, of selected drugs. Here, I propose a visionary approach, to which I assigned the term labsourcing, to dramatically enhance efficiency and clinical relevance of academic drug screens and, ultimately, generate contextual and reproducible data for correct interpretations and reliable selection of drug candidates. The overall concept implies intra- and intermural aggregation of expertise (e.g., assay development, cell biology, statistics, bioinformatics) to perform multiple bioassays, under multiple conditions and readouts, using a common screening collection. Advantages of high input screens can be manifold: (i) to tackle discrepancies that may arise from the screens of libraries of variable size and content and assay types and conditions too narrow in scope; (ii) the opportunity to generate massive amounts of data applicable for multiple publications and funding requests; (iii) the educational benefits for students and post-docs collegially exposed to long-term programs; and (iv) the opportunity to democratize research and recruit small labs that could not otherwise join screening programs due to costs, timelines, and risks.