Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting
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eLife assessment
This study presents a valuable finding on an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. The evidence supporting the claims of the authors is solid, although a proper validation of the methodology could strengthen the paper. The work will be of interest to scientists working in the field of drug discovery, particularly in the field of kinase inhibitors.
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Abstract
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.
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Author Response
Reviewer #3 (Public Review):
Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.
From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for …
Author Response
Reviewer #3 (Public Review):
Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.
From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for multiple targets. The MMS method is a promising solution to reduce off-target effects and could be applicable to other inhibitor-target interactions. My suggestion is that a comparative analysis of MMS with other similar methods can be conducted to highlight the advantage of MMS over others.
We thank the reviewer for this excellent summary and their suggestions. We agree that comparing new methods to prior ones is an important step in benchmarking new approaches and methods. However, to our knowledge, no other method exists for calculating selective combinations of kinase inhibitors. We compare our JSD selectivity scoring metric to other representative target-specific and non target-specific selectivity metrics (Figure 2 Figure Supplement 2).
The paper is not well organized and not easily readable. For example, first, the captions of the figures are two long; some of these texts could be moved to methods or results sections. Second, the concept of "penalty distribution" or "penalty prior" is vital to understand the MMS method, thus, at least a brief definition and introduction should be put in the main text rather than supporting method, as well as the rationale to use it. Third, the method section can be divided into several subsections with clear organizations and connections. Fourth, what is the difference between "a less selective inhibitor profile" and "an even less selective inhibitor profile" in Figure 3? Overall, the details of the paper are difficult to understand in the current version. I suggest rewriting the paper in a more concise and logical style.
We appreciate these suggestions and have significantly edited and revised our manuscript in order to facilitate clear communication. Specifically:
We have added an additional description of the penalty distribution to the description of the MMS method in the main Results section of the manuscript as opposed to solely in the Materials and Methods section.
We have provided a high-level concise summary of the MMS method in the results section in order to help orient a reader to the method. This description follows the same order (1 to 5) as the associated Figure 2, we hope this helps more clearly communicate the method.
We have moved descriptive figure captions to the methods section and, in general, substantially reduce the size of figure captions.
We have subdivided the Materials and Methods section as suggested.
We now describe in our main text how the simulated profiles were generated by smoothing the PKIS2645-like profile with two restraints; non-zero activity for LS inhibitors, and similar on-target probability for PKIS2-645-like, RS, and LS inhibitors to facilitate direct comparisons. We provide a new figure to quantify the selectivity of these simulated inhibitors and their similarity with true compounds (Figure 3 Figure Supplement 1).
We have removed content from the introduction and results sections that was less important to communicate to a general audience in order to make the manuscript more concise. We have also removed or condensed extraneous supplemental figures that were not required to communicate the central results and findings of experiments (ex: supplemental figures for Figure 3 and Figure 4 from the prior submission).
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eLife assessment
This study presents a valuable finding on an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. The evidence supporting the claims of the authors is solid, although a proper validation of the methodology could strengthen the paper. The work will be of interest to scientists working in the field of drug discovery, particularly in the field of kinase inhibitors.
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Reviewer #1 (Public Review):
Identifying compounds that can selectively inhibit protein kinases is of significant importance. Here, the authors describe a computational method to use existing kinome-wide profiling data to identify sets of compounds that, when combined, are more selective than any of the compounds on their own.
The authors explain the methodology well and the methodology is well-supported. The outcome of the methodology is assessed using an assay orthogonal to the original profiling assays. It is hard to assess whether the methodology works when a different assay is used.
The discussion of using this method for polypharmacology is naively discussed and under-supported.
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Reviewer #2 (Public Review):
There currently are several hundreds of kinase inhibitors described and available for purchase. However, most of the target the ATP binding site of the protein kinase domain and, since it is pretty well conserved across the whole protein family, it means that the inhibitors are rarely selective, and most are able to simultaneously inhibit several kinases with, sometimes, different binding affinities. In this m/s, the authors present a strategy to combine kinase inhibitors with the aim of reducing off-target effects while preserving the inhibition potency in the intended target. To develop the methodology, the authors have used a set of publicly available data (protein kinase inhibitor set-2, or PKIS-2) containing affinity data on 406 kinases and 645 inhibitors. The authors run a series of simulations …
Reviewer #2 (Public Review):
There currently are several hundreds of kinase inhibitors described and available for purchase. However, most of the target the ATP binding site of the protein kinase domain and, since it is pretty well conserved across the whole protein family, it means that the inhibitors are rarely selective, and most are able to simultaneously inhibit several kinases with, sometimes, different binding affinities. In this m/s, the authors present a strategy to combine kinase inhibitors with the aim of reducing off-target effects while preserving the inhibition potency in the intended target. To develop the methodology, the authors have used a set of publicly available data (protein kinase inhibitor set-2, or PKIS-2) containing affinity data on 406 kinases and 645 inhibitors. The authors run a series of simulations suggesting that, in a few cases, the identified combination of inhibitors is superior to the most specific single kinase inhibitor (i.e. show fewer off-target effects while maintaining the inhibition of the on-target). Finally, they test one of these examples in cells using nanoBRET.
The manuscript tackles an interesting problem (i.e. poor selectivity of kinase inhibitors) that, in some cases, has important clinical bearings. The approach is novel, interesting, and well-executed. However, unfortunately, I am not convinced that the strategy presents a real advantage over the most selective inhibitor.
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Reviewer #3 (Public Review):
Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.
From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for multiple targets. The MMS …
Reviewer #3 (Public Review):
Seeking a selective inhibitor that precisely inhibits on-target activities and avoids side effects is a major challenge in the field of drug discovery and therapeutics. The authors proposed an alternative method that combines multiple inhibitors to maximize on-target inhibition and minimize off-target inhibition. Focusing on the kinase-inhibitor interaction dataset, the authors developed a quantitative way to measure the selectivity for mixtures of inhibitors by using the Jenson-Sahannon distance metric. The method sounds technical.
From their computation and assays, the multi-compound-multitarget scoring (MMS) method framework was validated to be able to select a combination of inhibitors that is more selective than a single highly selective inhibitor for one kinase target, or for multiple targets. The MMS method is a promising solution to reduce off-target effects and could be applicable to other inhibitor-target interactions. My suggestion is that a comparative analysis of MMS with other similar methods can be conducted to highlight the advantage of MMS over others.
The paper is not well organized and not easily readable. For example, first, the captions of the figures are two long; some of these texts could be moved to methods or results sections. Second, the concept of "penalty distribution" or "penalty prior" is vital to understand the MMS method, thus, at least a brief definition and introduction should be put in the main text rather than supporting method, as well as the rationale to use it. Third, the method section can be divided into several subsections with clear organizations and connections. Fourth, what is the difference between "a less selective inhibitor profile" and "an even less selective inhibitor profile" in Figure 3? Overall, the details of the paper are difficult to understand in the current version. I suggest rewriting
the paper in a more concise and logical style. -
