Lucanthone, a Potential PPT1 Inhibitor, Perturbs Stemness, Reduces Tumor Microtube Formation, and Slows the Growth of Temozolomide-Resistant Gliomas In Vivo.

Glioblastoma (GBM) is the most frequently diagnosed primary central nervous system tumor in adults. Despite the standard of care therapy, which includes surgical resection, temozolomide chemotherapy, radiation and the newly added tumor-treating fields, median survival remains only ∼20 months. Unfortunately, GBM has a ∼100% recurrence rate, but after recurrence there are no Food and Drug Administration-approved therapies to limit tumor growth and enhance patient survival, as these tumors are resistant to temozolomide (TMZ). Recently, our laboratory reported that lucanthone slows GBM by inhibiting autophagic flux through lysosome targeting and decreases the number of Olig2+ glioma stem-like cells (GSC) in vitro and in vivo. We now additionally report that lucanthone efficiently abates stemness in patient-derived GSC and reduces tumor microtube formation in GSC, an emerging hallmark of treatment resistance in GBM. In glioma tumors derived from cells with acquired resistance to TMZ, lucanthone retains the ability to perturb tumor growth, inhibits autophagy by targeting lysosomes, and reduces Olig2 positivity. We also find that lucanthone may act as an inhibitor of palmitoyl protein thioesterase 1. Our results suggest that lucanthone may function as a potential treatment option for GBM tumors that are not amenable to TMZ treatment. SIGNIFICANCE STATEMENT: We report that the antischistosome agent lucanthone impedes tumor growth in a preclinical model of temozolomide-resistant glioblastoma and reduces the numbers of stem-like glioma cells. In addition, it acts as an autophagy inhibitor, and its mechanism of action may be via inhibition of palmitoyl protein thioesterase 1. As there are no defined therapies approved for recurrent, TMZ-resistant tumor, lucanthone could emerge as a treatment for glioblastoma tumors that may not be amenable to TMZ both in the newly diagnosed and recurrent settings.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.