Rational polypharmacology: systematically identifying and engaging multiple drug targets to promote axon growth

ACS Chem Biol., 2015 · DOI: 10.1021/acschembio.5b00289 · Published: August 21, 2015

Simple Explanation

The study addresses the challenge of promoting axon repair in the central nervous system (CNS) after injury, which is often unsuccessful due to multiple factors contributing to regenerative failure. The researchers propose that drugs engaging multiple molecular targets (polypharmacology) may be more effective. To systematically discover such drugs, they combined target-based and phenotypic screening approaches using machine learning and information theory. This allowed them to identify kinases that promote neurite outgrowth, as well as those that should be avoided. The approach was also tested in a breast cancer cell line, successfully identifying known targets and targets recently shown to mediate drug resistance, suggesting broader applicability.

Study Duration

4 weeks (in vivo)

Participants

Adult female C57BL/6J mice (in vivo), embryonic rat hippocampal neurons, rat postnatal cortical neurons, SK-BR-3 breast cancer cells

Evidence Level

Not specified

Key Findings

1
Compounds that inhibit multiple targets (polypharmacology) promote robust neurite outgrowth in vitro.
2
One compound with exemplary polypharmacology, RO0480500-002, was found to promote axon growth in a rodent spinal cord injury model.
3
The machine learning approach successfully deconvolved known targets for a breast cancer cell line, as well as targets recently shown to mediate drug resistance.

Research Summary

The study developed a novel approach combining phenotypic screening with biochemical enzyme assays to systematically identify multiple biological targets relevant to neurite outgrowth. The strategy identified both targets and anti-targets from a neurite outgrowth phenotypic assay, including known regulators and novel targets. A compound with polypharmacology towards several target kinase groups, RO0480500-002, promoted sprouting of corticospinal axons after pyramidotomy, suggesting its potential for neuroregenerative applications.

Practical Implications

Drug Development

The approach facilitates the identification of novel compounds with favorable polypharmacology, even those structurally dissimilar to initial screening hits, which is crucial for drug development when initial hits have unsuitable chemistries.

Personalized Medicine

Kinase activity predictors can be used to accelerate the in silico identification of compounds and the repurposing of approved drugs, enabling personalized medicine approaches.

Broad Applicability

The modular nature of the approach allows its application to various drug discovery campaigns beyond neurite outgrowth and axon regeneration, including those addressing drug resistance in cancer.

Study Limitations

1
The method applies to kinases for which biochemical profiling data exists.
2
Targets that are not profiled (and have no pharmacological linkage to profiled targets), or that are not inhibited by any of the phenotypically screened compounds, remain “invisible” to the algorithm.
3
Kinase inhibitors may exert their biological effects via non-kinase targets.

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