Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targets') whole-animal activity of the clinical kinase inhibitor sorafenib in a Drosophila medullary thyroid carcinoma (MTC) model. We demonstrate that RAF—the original intended sorafenib target—and MKNK kinases function as pharmacological liabilities because of inhibitor-induced transactivation and negative feedback, respectively. Through progressive synthetic refinement, we report a new class of 'tumor calibrated inhibitors' with unique polypharmacology and strongly improved therapeutic index in fly and human MTC xenograft models. This platform provides a rational approach to creating new high-efficacy and low-toxicity drugs.

为新适应症对已批准药物进行合成定制通常很困难，因为最合适的目标可能并不明显，因此几乎没有指导化学的路线图。在这里，我们报告了一种从 FDA 批准的激酶抑制剂开始访问新靶点和化学空间的多学科方法。通过将化学和遗传修饰剂筛选与计算模型相结合，我们确定了在果蝇中强烈增强（“前靶点”）或限制（“反靶点”）临床激酶抑制剂索拉非尼的全动物活性的不同激酶甲状腺髓样癌（MTC）模型。我们证明 RAF（最初的预期索拉非尼靶点）和 MKNK 激酶分别由于抑制剂诱导的反式激活和负反馈而发挥药理作用。通过逐步的合成改进，我们报告了一类新的“肿瘤校准抑制剂”，具有独特的多药理学和显着提高的苍蝇和人类 MTC 异种移植模型的治疗指数。该平台为创造新的高效低毒药物提供了合理的途径。