TEMPORARY REMOVAL: Acute cell stress screen with supervised machine learning predicts cytotoxicity of excipients

Single molecule studies of dynamic platelet interactions with endothelial cells

A biotechnological platform consisting of two-color 3D super-resolution readout and a microfluidic system was developed to investigate platelet interaction with a layer of perfused endothelial cells under flow conditions. Platelet activation has been confirmed via CD62P clustering on the membrane and mitochondrial morphology of ECs at the single cell level were examined using 3D two-color single-molecule localization microscopy and classified applying machine learning. To compare binding of activated platelets to intact or stressed ECs, a femtosecond laser was used to induced damage to single ECs within the perfused endothelial layer. We observed that activated platelets bound to the perfused ECs layer preferentially in the proximity to single stressed ECs. Platelets activated under flow were ∼6 times larger compared to activated ones under static conditions. The CD62P expression indicated more CD62P proteins on membrane of dynamically activated platelets, with a tendency to higher densities at the platelet/EC interface. Platelets activated under static conditions showed a less pronounced CD62P top/bottom asymmetry. The clustering of CD62P in the platelet membrane differs depending on the activation conditions. Our results confirm that nanoscopic analysis using two-color 3D super-resolution technology can be used to assess platelet interaction with a stressed endothelium under dynamic conditions.

Improved Antibacterial Activity of 1,3,4-Oxadiazole-Based Compounds That Restrict Staphylococcus aureus Growth Independent of LtaS Function

The lipoteichoic acid (LTA) biosynthesis pathway has emerged as a promising antimicrobial therapeutic target. Previous studies identified the 1,3,4 oxadiazole compound 1771 as an LTA inhibitor with activity against Gram-positive pathogens. We have succeeded in making six 1771 derivatives and, through subsequent hit validation, identified the incorporation of a pentafluorosulfanyl substituent as central in enhancing activity. Our newly described derivative, compound 13, showed a 16- to 32-fold increase in activity compared to 1771 when tested against a cohort of multidrug-resistant Staphylococcus aureus strains while simultaneously exhibiting an improved toxicity profile against mammalian cells. Molecular techniques were employed in which the assumed target, lipoteichoic acid synthase (LtaS), was both deleted and overexpressed. Neither deletion nor overexpression of LtaS altered 1771 or compound 13 susceptibility; however, overexpression of LtaS increased the MIC of Congo red, a previously identified LtaS inhibitor. These data were further supported by comparing the docking poses of 1771 and derivatives in the LtaS active site, which indicated the possibility of an additional target(s). Finally, we show that both 1771 and compound 13 have activity that is independent of LtaS, extending to cover Gram-negative species if the outer membrane is first permeabilized, challenging the classification that these compounds are strict LtaS inhibitors.

Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals

Modern toxicology's throughput has dramatically increased due to alternative models, laboratory automation, and machine learning. This has enabled comparative studies across species and assays to prioritize chemical hazard potential and to understand how different model systems might complement one another. However, such comparative studies of high-throughput data are still in their infancy, with more groundwork needed to firmly establish the approach. Therefore, this study aimed to compare the bioactivity of the NIEHS Division of Translational Toxicology's (DTT) 87-compound developmental neurotoxicant (DNT) library in zebrafish and an in vitro high-throughput cell culture system. The early life-stage zebrafish provided a whole animal approach to developmental toxicity assessment. Chemical hits for abnormalities in embryonic zebrafish morphology, mortality, and behavior (ZBEscreen™) were compared with chemicals classified as high-risk by the Cell Health Index (CHI™), which is an outcome class probability from a machine learning classifier using 12 parameters from the SYSTEMETRIC® Cell Health Screen (CHS). The CHS was developed to assess human toxicity risk using supervised machine learning to classify acute cell stress phenotypes in a human leukemia cell line (HL60 cells) following a 4-h exposure to a chemical of interest. Due to the design of the screen, the zebrafish assays were more exhaustive, yielding 86 total bioactive hits, whereas the SYSTEMETRIC® CHS focusing on acute toxicity identified 20 chemicals as potentially toxic. The zebrafish embryonic and larval photomotor response assays (EPR and LPR, respectively) detected 40 of the 47 chemicals not found by the zebrafish morphological screen and CHS. Collectively, these results illustrate the advantages of using two alternative models in tandem for rapid hazard assessment and chemical prioritization and the effectiveness of CHI™ in identifying toxicity within a single multiparametric assay.

Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles

There have been more than 70 FDA-approved drugs to target the ATP binding site of kinases, mainly in the field of oncology. These compounds are usually developed to target specific kinases, but in practice, most of these drugs are multi-kinase inhibitors that leverage the conserved nature of the ATP pocket across multiple kinases to increase their clinical efficacy. To utilize kinase inhibitors in targeted therapy and outside of oncology, a narrower kinome profile and an understanding of the toxicity profile is imperative. This is essential when considering treating chronic diseases with kinase targets, including neurodegeneration and inflammation. This will require the exploration of inhibitor chemical space and an in-depth understanding of off-target interactions. We have developed an early pipeline toxicity screening platform that uses supervised machine learning (ML) to classify test compounds’ cell stress phenotypes relative to a training set of on-market and withdrawn drugs. Here, we apply it to better understand the toxophores of some literature kinase inhibitor scaffolds, looking specifically at a series of 4-anilinoquinoline and 4-anilinoquinazoline model libraries.

Cytotoxicity and Thermal Characterization Assessment of Excipients for the Development of Innovative Lyophilized Formulations for Oncological Applications

Freeze-drying, also known as lyophilization, significantly improves the storage, stability, shelf life, and clinical translation of biopharmaceuticals. On the downside, this process faces complex challenges, i.e., the presence of freezing and drying stresses for the active compounds, the uniformity and consistency of the final products, and the efficiency and safety of the reconstituted lyophilized formulations. All these requirements can be addressed by adding specific excipients that can protect and stabilize the active ingredient during lyophilization, assisting in the formation of solid structures without interfering with the biological and/or pharmaceutical action of the reconstituted products. However, these excipients, generally considered safe and inert, could play an active role in the formulation interacting with the biological cellular machinery and promoting toxicity. Any side effects should be carefully identified and characterized to better tune any treatments in terms of concentrations and administration times. In this work, various concentrations in the range of 1 to 100 mg/mL of cellobiose, lactose, sucrose, trehalose, isoleucine, glycine, methionine, dextran, mannitol, and (2-hydroxypropyl)-β-cyclodextrin were evaluated in terms of their ability to create uniform and solid lyophilized structures. The freeze-dried products were then reconstituted in the appropriate cell culture media to assess their in vitro cytotoxicity on both a healthy cell line (B-lymphocytes) and their tumoral lymphoid counterpart (Daudi). Results showed that at 10 mg/mL, all the excipients demonstrated suitable lyophilized solid structures and high tolerability by both cell lines, while dextran was the only excipient well-tolerated also up to 100 mg/mL. An interesting result was shown for methionine, which even at 10 mg/mL, selectively affected the viability of the cancerous cell line only, opening future perspectives for antitumoral applications.

Optimization of the 4-anilinoquin(az)oline scaffold as epidermal growth factor receptor (EGFR) inhibitors for chordoma utilizing a toxicology profiling assay platform

The 4-anilinoquin(az)oline is a well-known kinase inhibitor scaffold incorporated in clinical inhibitors including gefitinib, erlotinib, afatinib, and lapatinib, all of which have previously demonstrated activity against chordoma cell lines in vitro. We screened a focused array of compounds based on the 4-anilinoquin(az)oline scaffold against both U-CH1 and the epidermal growth factor receptor (EGFR) inhibitor resistant U-CH2. To prioritize the hit compounds for further development, we screened the compound set in a multiparameter cell health toxicity assay. The de-risked compounds were then screened against a wider panel of patient derived cell lines and demonstrated low micromolar efficacy in cells. We also investigated the properties that gave rise to the toxophore markers, including the structural and electronic features, while optimizing for EGFR in-cell target engagement. These de-risked leads present a potential new therapeutic avenue for treatment of chordomas and new chemical tools and probe compound 45 (UNC-CA359) to interrogate EGFR mediated disease phenotypes.