Acute cell stress screen with supervised machine learning predicts cytotoxicity of excipients.

Excipients serve as vehicles, preservatives, solubilizers, and colorants for drugs, food, and cosmetics. They are considered to be inert at biological targets; however, several reports suggest that some could interact with human targets and cause unwanted effects. We investigated 40 commonly used drug excipients for cellular stress in the AsedaSciences® SYSTEMETRIC® Cell Health Screen, which was developed to estimate toxicity risk of small molecular entities (SMEs). The screen uses supervised machine learning (ML) to classify test compound cell stress phenotypes relative to a training set of on-market and withdrawn drugs. While 80% (n = 32) of the excipients did not show elevated risk in a broad, but pharmacologically relevant, concentration range (5 nM to 100 µM), we identified 20% (n = 8) with elevated risk. This group included two mercury containing preservatives, propyl gallate, methylene blue, benzethonium chloride, and cetylpyridinium chloride, all known for previously reported safety issues. All compounds were tested in parallel in an in vitro assay panel regularly used to investigate off-target effects of drug candidates. Target engagement in this assay panel confirmed risk-indicative biological activity for the same excipients, except propyl gallate, which may have a separate, interesting mechanism. We conclude that the SYSTEMETRIC Cell Health Screen, in conjunction with in vitro pharmacological profiling, can provide a fast and cost effective methodology for first line testing of SMEs, including excipients, to avoid cellular damage, particularly in the GI, where they are represented in high concentrations.

A screening pattern recognition method finds new and divergent targets for drugs and natural products.

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 µM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

State-dependent compound inhibition of Nav1.2 sodium channels using the FLIPR Vm dye: on-target and off-target effects of diverse pharmacological agents.

Voltage-gated sodium channels (NaChs) are relevant targets for pain, epilepsy, and a variety of neurological and cardiac disorders. Traditionally, it has been difficult to develop structure-activity relationships for NaCh inhibitors due to rapid channel kinetics and state-dependent compound interactions. Membrane potential (Vm) dyes in conjunction with a high-throughput fluorescence imaging plate reader (FLIPR) offer a satisfactory 1st-tier solution. Thus, the authors have developed a FLIPR Vm assay of rat Nav1.2 NaCh. Channels were opened by addition of veratridine, and Vm dye responses were measured. The IC50 values from various structural classes of compounds were compared to the resting state binding constant (Kr)and inactivated state binding constant (Ki)obtained using patch-clamp electrophysiology (EP). The FLIPR values correlated with Ki but not Kr. FLIPRIC50 values fell within 0.1-to 1.5-fold of EP Ki values, indicating that the assay generally reports use-dependent inhibition rather than resting state block. The Library of Pharmacologically Active Compounds (LOPAC, Sigma) was screened. Confirmed hits arose from diverse classes such as dopamine receptor antagonists, serotonin transport inhibitors, and kinase inhibitors. These data suggest that NaCh inhibition is inherent in a diverse set of biologically active molecules and may warrant counterscreening NaChs to avoid unwanted secondary pharmacology.

Expression genomics of cervical cancer: molecular classification and prediction of radiotherapy response by DNA microarray.

PURPOSE: The incidence and mortality rates of cervical cancer are declining in the United States; however, worldwide, cervical cancer is still one of the leading causes of death in women, second only to breast cancer. This disparity is at least partially explained by the absence of or comparatively ineffective screening programs in the developing world. Recent advances in expression genomics have enabled the use of DNA microarray to profile gene expression of various cancers. These expression profiles may be suitable for molecular classification and prediction of disease outcome and treatment response. We envision that expression genomics applied in cervical cancer may provide a more rational approach to the classification and treatment of the disease. EXPERIMENTAL DESIGN: In this report, we examined the expression profiles of cervical cancer compared with normal cervical tissues in DNA microarrays that contained approximately 11,000 features that correspond to either human transcripts with known function or anonymous expressed sequence tags. RESULTS: Our results showed that normal cervical tissues were completely segregated from the cancer samples using about 40 genes whose expressions were significantly different between these specimens. In addition, clinical stage IB and stage IIB tumors could also be classified based on their signature expression patterns. Most importantly, some of the tumor samples were further stratified into two major groups based on their response to radiotherapy, and we were able to predict the response of these patients to radiotherapy from their expression profiles. CONCLUSIONS: Gene expression profiling by DNA microarray may be used for further molecular classification of disease stages and prediction of treatment response in cervical cancer.