High-Throughput Compound Quality Assessment with High-Mass-Resolution Acoustic Ejection Mass Spectrometry: An Automatic Data Processing Toolkit.

Pharmacological screening heavily relies on the reliability of compound libraries. To ensure the accuracy of screening results, fast and reliable quality control (QC) of these libraries is essential. While liquid chromatography (LC) with ultraviolet (UV) or mass spectrometry (MS) detection has been employed for molecule QC on small sample sets, the analytical throughput becomes a bottleneck when dealing with large libraries. Acoustic ejection mass spectrometry (AEMS) is a high-throughput analytical platform that covers a broad range of chemical structural space. In this study, we present the utilization of an AEMS system equipped with a high-resolution MS analyzer for high-throughput compound QC. To facilitate efficient data processing, which is a key challenge for such a high-throughput application, we introduce an automatic data processing toolkit that allows for the high-throughput assessment of the sample standards' quantitative and qualitative characteristics, including purity calculation with the background processing option. Moreover, the toolkit includes a module for quantitatively comparing spectral similarity with the reference library. Integrating the described high-resolution AEMS system with the data processing toolkit effectively eliminates the analytical bottleneck, enabling a rapid and reliable compound quality assessment of large-scale compound libraries.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Effects of 2,3-DPG knockout on SCD phenotype in Townes SCD model mice.

Sickle cell disease (SCD) is a severe, multisystemic hematological disorder that impacts nearly every major organ in adults. The current approved treatments for SCD directly target mutant hemoglobin or address downstream disease pathology. Several compounds targeting reduction of 2,3-DPG by activation of Pyruvate Kinase-R are currently being evaluated in SCD patients. In this study, we genetically engineered a mouse lacking 2,3-DPG on the Townes SCD mouse model background and evaluated the effects of 2,3-DPG loss on disease pathology. Animals lacking 2,3-DPG showed improvements in hematological markers and reductions in RBC sickling relative to native Townes mice, however, minimal difference in organ damage was observed in 2,3-DPG deficient mice compared to native Townes animals. When animals lacking 2,3-DPG were dosed with a compound designed to increase hemoglobin oxygen affinity, oxygen delivery related toxicity was observed.

Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries.

Over the past 20 years, the toolbox for discovering small-molecule therapeutic starting points has expanded considerably. Pharmaceutical researchers can now choose from technologies that, in addition to traditional high-throughput knowledge-based and diversity screening, now include the screening of fragment and fragment-like libraries, affinity selection mass spectrometry, and selection against DNA-encoded libraries (DELs). Each of these techniques has its own unique combination of advantages and limitations that makes them more, or less, suitable for different target classes or discovery objectives, such as desired mechanism of action. Layered on top of this are the constraints of the drug-hunters themselves, including budgets, timelines, and available platform capacity; each of these can play a part in dictating the hit identification strategy for a discovery program. In this article, we discuss some of the factors that we use to govern our building of a hit identification roadmap for a program and describe the increasing role that DELs are playing in our discovery strategy. Furthermore, we share our learning during our initial exploration of DEL and highlight the approaches we have evolved to maximize the value returned from DEL selections. Topics addressed include the optimization of library design and production, reagent validation, data analysis, and hit confirmation. We describe how our thinking in these areas has led us to build a DEL platform that has begun to deliver tractable matter to our global discovery portfolio.

Comparison of Species and Cell-Type Differences in Fraction Unbound of Liver Tissues, Hepatocytes, and Cell Lines.

Fraction unbound (fu) of liver tissue, hepatocytes, and other cell types is an essential parameter used to estimate unbound liver drug concentration and intracellular free drug concentration. fu,liver and fu,cell are frequently measured in multiple species and cell types in drug discovery and development for various applications. A comparison study of 12 matrices for fu,liver and fu,cell of hepatocytes in five different species (mouse, rat, dog, monkey, and human), as well as fu,cell of Huh7 and human embryonic kidney 293 cell lines, was conducted for 22 structurally diverse compounds with the equilibrium dialysis method. Using an average bioequivalence approach, our results show that the average difference in binding to liver tissue, hepatocytes, or different cell types was within 2-fold of that of the rat fu,liver Therefore, we recommend using rat fu,liver as a surrogate for liver binding in other species and cell types in drug discovery. This strategy offers the potential to simplify binding studies and reduce cost, thereby enabling a more effective and practical determination of fu for liver tissues, hepatocytes, and other cell types. In addition, fu under hepatocyte stability incubation conditions should not be confused with fu,cell, as one is a diluted fu and the other is an undiluted fu Cell density also plays a critical role in the accurate measurement of fu,cell.

Development of a high-speed, multiplexed sample-delivery instrument for LC-MS/MS bioanalysis.

BACKGROUND: The number of new chemical entities and types of in vitro and in vivo samples that require bioanalysis in drug discovery is large and diverse. In addition, method development time is limited as data turnaround is the highest priority. These circumstances require that a well-defined set of bioanalysis options be available in short timeframes to triage samples for analysis. METHOD: The Apricot Designs Dual Arm (ADDA) instrument is an LC-MS/MS sample delivery system that features a flexible hardware design coupled with software automation to enhance throughput in LC-MS/MS bioanalysis drug discovery. The instrument can perform high-throughput LC-MS/MS (8-10 s/sample) for screening and in vitro bioanalysis, as well as multiplexed LC for traditional gradient or isocratic LC approaches. The instrument control software is designed to integrate with DiscoveryQuant™ software (AB Sciex) and a global database of MS/MS conditions. CONCLUSION: Development of the sample delivery platform and its application in high-throughput and gradient LC will be described.

Metabolism-guided design of short-acting calcium-sensing receptor antagonists.

As part of a strategy to deliver short-acting calcium-sensing receptor (CaSR) antagonists, the metabolically labile thiomethyl functionality was incorporated into the zwitterionic amino alcohol derivative 3 with the hope of increasing human clearance through oxidative metabolism, while delivering a pharmacologically inactive sulfoxide metabolite. The effort led to the identification of thioanisoles 22 and 23 as potent and orally active CaSR antagonists with a rapid onset of action and short pharmacokinetic half-lives, which led to a rapid and transient stimulation of parathyroid hormone in a dose-dependent fashion following oral administration to rats. On the basis of the balance between target pharmacology, safety, and human disposition profiles, 22 and 23 were advanced as clinical candidates for the treatment of osteoporosis.

The discovery of novel calcium sensing receptor negative allosteric modulators.

The design and profile of a series of zwitterionic calcium sensing receptor negative allosteric modulators is described. Evaluation of key analogues using a rat model demonstrate a robust response, significantly improved potency over ronacaleret and have the potential as an oral, anabolic treatment for osteoporosis.

Utility of MetaSite in improving metabolic stability of the neutral indomethacin amide derivative and selective cyclooxygenase-2 inhibitor 2-(1-(4-chlorobenzoyl)-5-methoxy-2-methyl-1H-indol-3-yl)-N-phenethyl-acetamide.

Prediction of the metabolic sites for new compounds, synthesized or virtual, is important in the rational design of compounds with increased resistance to metabolism. The aim of the present investigation was to use rational design together with MetaSite, an in silico tool for predicting metabolic soft spots, to synthesize compounds that retain their pharmacological effects but are metabolically more stable in the presence of cytochrome P450 (P450) enzymes. The model compound for these studies was the phenethyl amide (1) derivative of the nonsteroidal anti-inflammatory drug (NSAID) indomethacin. Unlike the parent NSAID, 1 is a potent and selective cyclooxygenase-2 (COX-2) inhibitor and nonulcerogenic anti-inflammatory agent in the rat. This pharmacological benefit is offset by the finding that 1 is very unstable in rat and human microsomes because of extensive P4503 A4/2D6-mediated metabolism on the phenethyl group, experimental observations that were accurately predicted by MetaSite. The information was used to design analogs with polar (glycinyl) and/or electron-deficient (fluorophenyl, fluoropyridinyl) amide substituents to reduce metabolism in 1. MetaSite correctly predicted the metabolic shift from oxidation on the amide substituent to O-demethylation for these compounds, whereas rat and human microsomal stability studies and pharmacokinetic assessments in the rat confirmed that the design tactics for improving pharmacokinetic attributes of 1 had worked in our favor. In addition, the fluorophenyl and pyridinyl amide derivatives retained the potent and selective COX-2 inhibition demonstrated with 1. Overall, the predictions from MetaSite gave useful information leading to the design of new compounds with improved metabolic properties.