Discovery of non-boronic acid Arginase 1 inhibitors through virtual screening and biophysical methods.

Inhibiting Arginase 1 (ARG1), a metalloenzyme that hydrolyzes l-arginine in the urea cycle, has been demonstrated as a promising therapeutic avenue in immuno-oncology through the restoration of suppressed immune response in several types of cancers. Most of the currently reported small molecule inhibitors are boronic acid based. Herein, we report the discovery of non-boronic acid ARG1 inhibitors through virtual screening. Biophysical and biochemical methods were used to experimentally profile the hits while X-ray crystallography confirmed a class of trisubstituted pyrrolidine derivatives as optimizable alternatives for the development of novel classes of immuno-oncology agents targeting this enzyme.

High-Throughput Screening to Identify Small Molecules That Selectively Inhibit APOL1 Protein Level in Podocytes.

High-throughput phenotypic screening is a key driver for the identification of novel chemical matter in drug discovery for challenging targets, especially for those with an unclear mechanism of pathology. For toxic or gain-of-function proteins, small-molecule suppressors are a targeting/therapeutic strategy that has been successfully applied. As with other high-throughput screens, the screening strategy and proper assays are critical for successfully identifying selective suppressors of the target of interest. We executed a small-molecule suppressor screen to identify compounds that specifically reduce apolipoprotein L1 (APOL1) protein levels, a genetically validated target associated with increased risk of chronic kidney disease. To enable this study, we developed homogeneous time-resolved fluorescence (HTRF) assays to measure intracellular APOL1 and apolipoprotein L2 (APOL2) protein levels and miniaturized them to 1536-well format. The APOL1 HTRF assay served as the primary assay, and the APOL2 and a commercially available p53 HTRF assay were applied as counterscreens. Cell viability was also measured with CellTiter-Glo to assess the cytotoxicity of compounds. From a 310,000-compound screening library, we identified 1490 confirmed primary hits with 12 different profiles. One hundred fifty-three hits selectively reduced APOL1 in 786-O, a renal cell adenocarcinoma cell line. Thirty-one of these selective suppressors also reduced APOL1 levels in conditionally immortalized human podocytes. The activity and specificity of seven resynthesized compounds were validated in both 786-O and podocytes.

Publisher Correction: A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

Can Galactose Be Converted to Glucose in HepG2 Cells? Improving the in Vitro Mitochondrial Toxicity Assay for the Assessment of Drug Induced Liver Injury.

Human hepatocellular carcinoma cells, HepG2, are often used for drug mediated mitochondrial toxicity assessments. Glucose in HepG2 culture media is replaced by galactose to reveal drug-induced mitochondrial toxicity as a marked shift of drug IC50 values for the reduction of cellular ATP. It has been postulated that galactose sensitizes HepG2 mitochondria by the additional ATP consumption demand in the Leloir pathway. However, our NMR metabolomics analysis of HepG2 cells and culture media showed very limited galactose metabolism. To clarify the role of galactose in HepG2 cellular metabolism, U-13C6-galactose or U-13C6-glucose was added to HepG2 culture media to help specifically track the metabolism of those two sugars. Conversion to U-13C3-lactate was hardly detected when HepG2 cells were incubated with U-13C6-galactose, while an abundance of U-13C3-lactate was produced when HepG2 cells were incubated with U-13C6-glucose. In the absence of glucose, HepG2 cells increased glutamine consumption as a bioenergetics source. The requirement of additional glutamine almost matched the amount of glucose needed to maintain a similar level of cellular ATP in HepG2 cells. This improved understanding of galactose and glutamine metabolism in HepG2 cells helped optimize the ATP-based mitochondrial toxicity assay. The modified assay showed 96% sensitivity and 97% specificity in correctly discriminating compounds known to cause mitochondrial toxicity from those with prior evidence of not being mitochondrial toxicants. The greatest significance of the modified assay was its improved sensitivity in detecting the inhibition of mitochondrial fatty acid ß-oxidation (FAO) when glutamine was withheld. Use of this improved assay for an empirical prediction of the likely contribution of mitochondrial toxicity to human DILI (drug induced liver injury) was attempted. According to testing of 65 DILI positive compounds representing numerous mechanisms of DILI together with 55 DILI negative compounds, the overall prediction of mitochondrial mechanism-related DILI showed 25% sensitivity and 95% specificity.

Evaluation of cynomolgus monkeys for the identification of endogenous biomarkers for hepatic transporter inhibition and as a translatable model to predict pharmacokinetic interactions with statins in humans.

Inhibition of hepatic transporters such as organic anion transporting polypeptides (OATPs) 1B can cause drug-drug interactions (DDIs). Determining the impact of perpetrator drugs on the plasma exposure of endogenous substrates for OATP1B could be valuable to assess the risk for DDIs early in drug development. As OATP1B orthologs are well conserved between human and monkey, we assessed in cynomolgus monkeys the endogenous OATP1B substrates that are potentially suitable to assess DDI risk in humans. The effect of rifampin (RIF), a potent inhibitor for OATP1B, on plasma exposure of endogenous substrates of hepatic transporters was measured. From the 18 biomarkers tested, RIF (18 mg/kg, oral) caused significant elevation of plasma unconjugated and conjugated bilirubin, which may be attributed to inhibition of cOATP1B1 and cOATP1B3 based on in vitro to in vivo extrapolation analysis. To further evaluate whether cynomolgus monkeys are a suitable translational model to study OATP1B-mediated DDIs, we determined the inhibitory effect of RIF on in vitro transport and pharmacokinetics of rosuvastatin (RSV) and atorvastatin (ATV). RIF strongly inhibited the uptake of RSV and ATV by cOATP1B1 and cOATP1B3 in vitro. In agreement with clinical observations, RIF (18 mg/kg, oral) significantly decreased plasma clearance and increased the area under the plasma concentration curve (AUC) of intravenously administered RSV by 2.8- and 2.7-fold, and increased the AUC and maximum plasma concentration of orally administered RSV by 6- and 10.3-fold, respectively. In contrast to clinical findings, RIF did not significantly increase plasma exposure of either intravenous or orally administered ATV, indicating species differences in the rate-limiting elimination pathways.

Evaluation of early biomarkers of muscle anabolic response to testosterone.

BACKGROUND: Early biomarkers of skeletal muscle anabolism will facilitate the development of therapies for sarcopenia and frailty. METHODS AND RESULTS: We examined plasma type III collagen N-terminal propeptide (P3NP), skeletal muscle protein fractional synthesis rate, and gene and protein expression profiles to identify testosterone-induced changes in muscle anabolism. Two placebo-controlled studies enrolled community-dwelling men (study 1, 60-75 years; study 2, 18-40 years) with low to normal testosterone levels. Men were randomized to lower dose (study 1, 100 mg; study 2, 200 mg) or higher dose (study 1, 300 mg; study 2, 600 mg) single intramuscular testosterone or saline injection. After 1 week, testosterone acutely increased plasma P3NP levels in a dose-dependent manner and altered the expression of several skeletal muscle transcripts and proteins. Though not statistically significant, mixed muscle protein fractional synthesis rate tended to increase (1.08-fold with 100 mg testosterone, 1.12-fold with 300 mg testosterone). Testosterone exposure also increased skeletal muscle expression of the collagen type III gene that encodes P3NP. CONCLUSION: P3NP is a potentially useful early biomarker for muscle anabolic therapy. Skeletal muscle protein and RNA profiling are useful tools for the discovery of novel muscle anabolic biomarkers.

Potential biomarkers of muscle injury after eccentric exercise.

Proteomics was utilized to identify novel potential plasma biomarkers of exercise-induced muscle injury. Muscle injury was induced in nine human volunteers by eccentric upper extremity exercise. Liquid chromatography-mass spectrometry identified 30 peptides derived from nine proteins which showed significant change in abundance post-exercise. Four of these proteins, haemoglobin alpha chain, haemoglobin beta chain, alpha1-antichymotrypsin (ACT) and plasma C-1 protease inhibitor (C1 Inh), met the criterion for inclusion based on changes in at least two distinct peptides. ACT and C1 Inh peptides peaked earlier post-exercise than creatine kinase, and thus appear to provide new information on muscle response to injury.

Differential mass spectrometry of rat plasma reveals proteins that are responsive to 17beta-estradiol and a selective estrogen receptor modulator PPT.

Estrogens are a class of steroid hormones that interact with two related but distinct nuclear receptors, estrogen receptor (ER) alpha and beta. To identify potential ER biomarkers, we profiled the rat plasma glycoproteome after treatment with vehicle or 17beta-estradiol (E2) or an ERalpha-selective agonist PPT by differential mass spectrometry. Our comparative proteomic experiment identifies novel E2- and PPT-responsive proteins, such as serine protease inhibitor family members.

Boosting: an ensemble learning tool for compound classification and QSAR modeling.

A classification and regression tool, J. H. Friedman's Stochastic Gradient Boosting (SGB), is applied to predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Stochastic Gradient Boosting is a procedure for building a sequence of models, for instance regression trees (as in this paper), whose outputs are combined to form a predicted quantity, either an estimate of the biological activity, or a class label to which a molecule belongs. In particular, the SGB procedure builds a model in a stage-wise manner by fitting each tree to the gradient of a loss function: e.g., squared error for regression and binomial log-likelihood for classification. The values of the gradient are computed for each sample in the training set, but only a random sample of these gradients is used at each stage. (Friedman showed that the well-known boosting algorithm, AdaBoost of Freund and Schapire, could be considered as a particular case of SGB.) The SGB method is used to analyze 10 cheminformatics data sets, most of which are publicly available. The results show that SGB's performance is comparable to that of Random Forest, another ensemble learning method, and are generally competitive with or superior to those of other QSAR methods. The use of SGB's variable importance with partial dependence plots for model interpretation is also illustrated.