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A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

ABSTRACT: Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targets') whole-animal activity of the clinical kinase inhibitor sorafenib in a Drosophila medullary thyroid carcinoma (MTC) model. We demonstrate that RAF-the original intended sorafenib target-and MKNK kinases function as pharmacological liabilities because of inhibitor-induced transactivation and negative feedback, respectively. Through progressive synthetic refinement, we report a new class of 'tumor calibrated inhibitors' with unique polypharmacology and strongly improved therapeutic index in fly and human MTC xenograft models. This platform provides a rational approach to creating new high-efficacy and low-toxicity drugs.

SUBMITTER: Sonoshita M

PROVIDER: S-EPMC5931369 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

Sonoshita Masahiro M Scopton Alex P AP Ung Peter M U PMU Murray Matthew A MA Silber Lisa L Maldonado Andres Y AY Real Alexander A Schlessinger Avner A Cagan Ross L RL Dar Arvin C AC

Nature chemical biology 20180122 3

Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targ ...[more]

PMID: 29355849

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Project description:AbstractStudying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals' home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution.Significance statementCharacterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms-such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for contact). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing.Supplementary informationThe online version contains supplementary material available at 10.1007/s00265-022-03222-5.

Dataset Information

A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

Publications

A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

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