Design and 22-step synthesis of highly potent D-ring modified and linker-equipped analogs of spongistatin 1.

Spongistatin 1 is among the most potent anti-proliferative agents ever discovered rendering it an attractive candidate for development as a payload for antibody-drug conjugates and other targeted delivery approaches. Unfortunately, it is unavailable from natural sources and its size and complex stereostructure render chemical synthesis highly time- and resource-intensive. As a result, the design and synthesis of more acid-stable and linker functional group-equipped analogs that retain the low picomolar potency of the parent natural product requires more efficient and step-economical synthetic access. Using uniquely enabling direct complex fragment coupling crotyl- and alkallylsilylation reactions, we report a 22-step synthesis of a rationally designed D-ring modified analog of spongistatin 1 that is characterized by GI50 values in the low picomolar range, and a proof-of-concept result that the C(15) acetate may be replaced with linker functional group-bearing esters with only minimal reductions in potency.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.