We advanced the concept of biosynthonics five years ago as a solution for resuscitating the productivity of small‐molecule drug pipelines across the pharmaceutical industry. Biosynthonics employs metagenomics and metabolic engineering to mine pharmacoactive structures that are otherwise beyond the reach of synthesis platforms that are currently utilized for drug discovery and lead optimization. Biosynthonics can be harnessed to generate unprecedented quantities of truly novel pharmacoactive structures, which, in turn, can have a game changing effect on the productivity and success rate of drug discovery and early clinical testing in the pharmaceutical industry. However, the impact of biosynthonics on the productivity and cost of product development is somewhat constrained owing to pervading inefficiencies in other parts of the drug discovery and development workflow. Herein, we update our vision for improving the productivity and diminishing the cost of drug discovery and development. Although biosynthonics remains a key piece in our strategy, we discuss how its integration with emerging paradigms in chemical engineering such as information science and materials science, as well as innovations in pharmaceutical manufacturing will drive the next generation of projects in the pharmaceutical industry. In many regards, our updated vision is more pragmatic and leverages the competencies of the pharmaceutical industry more effectively. We have already implemented this vision in our laboratory and early results have been very encouraging. Some of these examples have been detailed in the current work.

中文翻译：

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利用化学工程中的新兴范例来加速药物产品的开发

五年前，我们提出了生物合成技术的概念，作为恢复整个制药行业小分子药物管道生产力的解决方案。Biosynthonics利用宏基因组学和代谢工程技术来挖掘具有药理活性的结构，而这些结构超出了目前用于药物发现和前导优化的合成平台的范围。可以利用生物合成药来产生空前数量的真正新颖的药物活性结构，这反过来可以对药物发现和制药行业的早期临床测试的生产率和成功率产生改变游戏规则的作用。然而，由于药物发现和开发流程的其他部分普遍效率低下，生物合成药物对生产率和产品开发成本的影响在一定程度上受到了限制。在此，我们更新了提高生产力并降低药物发现和开发成本的愿景。尽管生物合成技术仍然是我们战略中的关键部分，但我们将讨论将其与化学工程学中新兴的范式（例如信息科学和材料科学）以及制药生产中的创新相结合，将如何驱动制药行业的下一代项目。在许多方面，我们更新的愿景更加务实，并更有效地利用了制药行业的能力。我们已经在实验室中实现了这一愿景，早期结果令人鼓舞。在当前的工作中已经详细说明了其中一些示例。