The medicinal chemistry community has become increasingly aware of the value of tracking calculated physical properties such as molecular weight, topological polar surface area, rotatable bonds, and hydrogen bond donors and acceptors. We hypothesized that the shift to high-throughput synthetic practices over the past decade may be another factor that may predispose molecules to fail by steering discovery efforts toward achiral, aromatic compounds. We have proposed two simple and interpretable measures of the complexity of molecules prepared as potential drug candidates. The first is carbon bond saturation as defined by fraction sp(3) (Fsp(3)) where Fsp(3) = (number of sp(3) hybridized carbons/total carbon count). The second is simply whether a chiral carbon exists in the molecule. We demonstrate that both complexity (as measured by Fsp(3)) and the presence of chiral centers correlate with success as compounds transition from discovery, through clinical testing, to drugs. In an attempt to explain these observations, we further demonstrate that saturation correlates with solubility, an experimental physical property important to success in the drug discovery setting.

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As the antibody drug conjugate (ADC) community continues to shift towards site-specific conjugation technology, there is a growing need to understand how the site of conjugation impacts the biophysical and biological properties of an ADC. In order to address this need, we prepared a carefully selected series of engineered cysteine ADCs and proceeded to systematically evaluate their potency, stability, and PK exposure. The site of conjugation did not have a significant influence on the thermal stability and in vitro cytotoxicity of the ADCs. However, we demonstrate that the rate of cathepsin-mediated linker cleavage is heavily dependent upon site and is closely correlated with ADC hydrophobicity, thus confirming other recent reports of this phenomenon. Interestingly, conjugates with high rates of cathepsin-mediated linker cleavage did not exhibit decreased plasma stability. In fact, the major source of plasma instability was shown to be retro-Michael mediated deconjugation. This process is known to be impeded by succinimide hydrolysis, and thus, we undertook a series of mutational experiments demonstrating that basic residues located nearby the site of conjugation can be a significant driver of succinimide ring opening. Finally, we show that total antibody PK exposure in rat was loosely correlated with ADC hydrophobicity. It is our hope that these observations will help the ADC community to build "design rules" that will enable more efficient prosecution of next-generation ADC discovery programs.

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.