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Wolfinger, R.

Publications and source records attributed to Wolfinger, R..

3 recordsLinked to original sources

CHEMOMETRIC APPROACHES FOR DEVELOPING INFRARED NANOSENSORS TO IMAGE ANTHRACYCLINES

Generation, identification, and validation of optical probes to image molecular targets in a biological milieu remains a challenge. Synthetic molecular recognition approaches leveraging the intrinsic near-infrared fluorescence of single-walled carbon nanotubes is a promising approach for chronic biochemical imaging in tissues. However, generation of nanosensors for selective imaging of molecular targets requires a heuristic approach. Here, we present a chemometric platform for rapidly screening libraries of candidate single-walled carbon nanotube nanosensors against biochemical analytes to quantify fluorescence response to small molecules including vitamins, neurotransmitters, and chemotherapeutics. We further show this approach can be leveraged to identify biochemical analytes that selectively modulate the intrinsic near-infrared fluorescence of candidate nanosensors. Chemometric analysis thus enables identification of nanosensor-analyte hits and also nanosensor fluorescence signaling modalities such as wavelength-shifts that are optimal for translation to biological imaging. Through this approach, we identify and characterize a nanosensor for the chemotherapeutic anthracycline doxorubicin, which provides an up to 17 nm fluorescence red-shift and exhibits an 8 {micro}M limit of detection, compatible with peak circulatory concentrations of doxorubicin common in therapeutic administration. We demonstrate selectivity of this nanosensor over dacarbazine, a chemotherapeutic commonly co-injected with DOX. Lastly, we demonstrate nanosensor tissue compatibility for imaging of doxorubicin in muscle tissue by incorporating nanosensors into the mouse hindlimb and measuring nanosensor response to exogenous DOX administration. Our results motivate chemometric approaches to nanosensor discovery for chronic imaging of drug partitioning into tissues and towards real-time monitoring of drug accumulation.

biochemistry

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patients tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

bioinformatics

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience