Determination of Unbound Partition Coefficient and in Vitro-in Vivo Extrapolation for SLC13A Transporter-Mediated Uptake.

ABSTRACT Unbound partition coefficient (K puu ) is important to an understanding of the asymmetric free drug distribution of a compound between cells and medium in vitro, as well as between tissue and plasma in vivo, especially for transporter-mediated processes. K puu was determined for a set of compounds from the SLC13A family that are inhibitors and substrates of transporters in hepatocytes and transportertransfected cell lines. Enantioselectivity was observed, with (R)-enantiomers achieving much higher K puu (>4) than the (S)-enantiomers (<1) in human hepatocytes and SLC13A5-transfected human embryonic 293 cells. The intracellular free drug concentration correlated directly with in vitro pharmacological activity rather than the nominal concentration in the assay because of the high K puu mediated by SLC13A5 transporter uptake. Delivery of the diacid PF-06649298 directly or via hydrolysis of the ethyl ester prodrug PF-06757303 resulted in quite different K puu values in human hepatocytes (K puu of 3 for diacid versus 59 for prodrug), which was successfully modeled on the basis of passive diffusion, active uptake, and conversion rate from ester to diacid using a compartmental model. K puu values changed with drug concentrations; lower values were observed at higher concentrations possibly owing to a saturation of transporters. Michaelis-Menten constant (K m ) of SLC13A5 was estimated to be 24 mM for PF-06649298 in human hepatocytes. In vitro K puu obtained from rat suspension hepatocytes supplemented with 4% fatty acid free bovine serum albumin showed good correlation with in vivo K puu of liver-to-plasma, illustrating the potential of this approach to predict in vivo K puu from in vitro systems

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Discovery of Fragment-Derived Small Molecules for in Vivo Inhibition of Ketohexokinase (KHK)

Increased fructose consumption and its subsequent metabolism have been implicated in hepatic steatosis, dyslipidemia, obesity, and insulin resistance in humans. Since ketohexokinase (KHK) is the principal enzyme responsible for fructose metabolism, identification of a selective KHK inhibitor may help to further elucidate the effect of KHK inhibition on these metabolic disorders. Until now, studies on KHK inhibition with small molecules have been limited due to the lack of viable in vivo pharmacological tools. Herein we report the discovery of <b>12</b>, a selective KHK inhibitor with potency and properties suitable for evaluating KHK inhibition in rat models. Key structural features interacting with KHK were discovered through fragment-based screening and subsequent optimization using structure-based drug design, and parallel medicinal chemistry led to the identification of pyridine <b>12</b>