Peer review of: Quantifying CDK inhibitor selectivity in live cells (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

Wells and Vasta et al., describe new assays for quantifying in vivo target engagement on CDK-cyclin 
complexes. For these studies, they have extended their development of BRET-based assays, 
previously developed for other enzymes or kinases (178 of the 500+) to all 21 human CDKs. Unlike 
their previous publications on BRET, they have gone much more in-depth on a single protein family 
and uncovered several interesting findings. For example, they identify several new and highly 
selective CDK inhibitors towards unique isozymes, including for the lesser studied CDKs including CDK 
8 and 19. They also demonstrate an interesting comparison of compounds with similar potency for 
CDK6/2/7 using steady state analysis but marked variations in residence time based on wash-out 
protocols. Overall the findings in the manuscript are interesting and this report will likely become an 
important resource for investigators studying CDK-family members as drug targets. However, while 
the authors have produced an impressive toolkit to study CDKs, exploration into using tracers to learn 
more about basic kinase biology and pathology is missing, which would greatly increase the paper’s 
impact. Specific concerns include: 

1. Most IC50 data in the paper is reported as averages of duplicate runs. The variance in general looks 
acceptable for most interactions, however some outliers are notable. For example, there is a ~10-fold 
difference of 6 vs 63 nM for CDK16/Y and a BMS compound in supplementary table S4. Why did the 
authors not complete assays in biological triplicate and report IC50 values +/- s.d.? Triplicates and 
standard deviations should be reported for all IC50s in the main text and figures. 

2. The authors show that BRET signals are modestly potentiated for the CDK vs CDK-cyclin complexes 
in Figure S3. Do the authors have any orthogonal data to support the formation of the intended 
complexes?...co-IP for example. Is there any chance that the tracers themselves can influence for the 
formation of CDK-cyclin complexes? 

3. Determine IC50s for CDKs and probes in the presence and absence of cyclin expression 
(referencing Figure S3). Acquiring dose response curves is a better way of measuring changes that 
could be associated with cyclin binding. 

4. Can the authors quantify residence time as a half-life value (ie. t1/2) for the inhibitors shown in 
Figure 5B? 

5. Can the authors perform the reverse of the washout experiment (or other experiments) to 
determine a kon that would explain the difference of koff that give identical binding affinities in figure 
5? 

6. The sentence on line 129 of the paper is an overstatement, the authors should dial-back overly 
verbose language. 

7. Is there a reference for the sentence on line 119 of the paper? 

8. There is a type on line 287 – should be ‘DFG-out’. 

Reviewer #2 (Remarks to the Author): 

Wells et al. report about the development of cell-permeable energy transfer probes to investigate 
intracellular potency and selectivity of 46 clinically-advanced CDK inhibitors against 21 CDKs. The 

authors used a previously established technology by the same group to apply it to a broad-based CDK 
investigative study to correlate in vitro binding kinetics to live cell occupancy which is novel to the 
field. The paper is well written, the methodologies applied are highly advanced, the data interpretation 
is thorough, and the conclusions drawn are supported by the data. To my knowledge, this is the most 
comprehensive study on CDK inhibitors conducted to date. The results are convincing and will 
undoubtedly have a significant impact on the research community, e.g. rationalizing which drug 
should be used to probe specific CDKs in diseased cells, interrogate underexplored CDKs, repurpose 
CDK drugs in clinical development. 

Minor points: 
1. Line 150: “However, abemaciclib also showed collateral engagement of CDK2, -7, -9, and exhibited 
pan-TAIRE family activity (CDK14, -16-18), (Figure 2B)”. This activity is not apparent from Fig. 2, 
which shows no collateral engagement with CDK2 and CDK7. 
2. Please discuss the rationale behind the cyclin chosen to be co-expressed. Why was cyclin E1 co-
expressed with CDK1 instead of cyclin A or cyclin B. Did the data reveal or hint if different cyclins 
affect intracellular occupancy (i.e. at different stages of the cell cycle)? 
3. How do BRET data between N-terminal fusion vs C-terminal fusion proteins compare? 

Reviewer #3 (Remarks to the Author): 

The approaches and findings of Wells et al. are incredibly important to the CDK community as the lack 
of inhibitor specificity has plagued this field. I recommend this manuscript for publication with some 
revisions. 

Major points: 

1. The referee understands the authors’ point that direct measurement of target engagement is more 
precise than inferring it from measuring phosphorylation of canonical downstream substrates. 
However, it might be informative to show that target engagement increases commensurate with 
decrease in phosphorylation of downstream substrates. This would demonstrate that the 
concentrations leading to target engagement are relevant to cellular effects elicited by compounds. 
Can the authors show side-by-side cellular target engagement and substrate inhibition for a 1-2 
representative CDKs with well-defined substrates, or alternatively show concurrent effects on some 
cellular process, e.g. - cell cycle by FACS? 

2. The authors nicely demonstrate that these assays can be run in HTS mode with transient 
transfection of NanoLuc-tagged CDKs. Can the authors demonstrate, or minimally address in the 
discussion, the possibility of using NanoBRET in cell lines where the CDK has been HiBit tagged on-
genome at the endogenous locus. Presumably orthogonal expression of LgBit to reconstitute NanoLuc 
on the HiBit-tagged CDK would allow you to follow target engagement of the CDK expressed at 
endogenous levels. 

3. The referee notices that there were no compounds with confirmed activity against CDK13, and very 
few with confirmed activity against CDK12, despite there being numerous compounds known to target 
CDK12 and CDK13 (e.g. - THZ1, THZ531, dinaciclib, flavopiridol). Can the authors comment on this? 
There are no Z’ values shown for these (and other) CDKs, does this speak to the robustness of these 
particular assays? and thus the ability to identify compounds that hit these CDKs? 

4. How are EC50 values determined in Figure S2? There are several curves where the BRET signal 
would seem to be leveling off and therefore an EC50 value could be identified as the concentration 
needed to get to 50% of BRETmax signal. However, there are several instances (e.g. CDK12, CDK13, 
CDK3, etc.) where the EC50 values can’t be determined and are given as > 1 micromolar. Can the 

authors explain this. 

5. Related to question above, the caption of Figure S2 says that for some CDK assays (with EC50s > 1 
micromolar) the BRET signal could not be saturated before hitting the solubility limit of the energy 
probes. How is saturated BRET signal (or BRETmax signal) determined? 

Minor points: 

1. For those readers not familiar with how BRET signal is calculated it would be nice to include the 
equation for easy reference. 

2. Line 32-33. CDK7 is known to regulate the eurkaryotic cell cycle and should be included in list of 
CDKs that regulate this process. Numerous references could be made, including Robert Fisher’s 
bumped hole inhibitor papers. 

3. Line 139, should be “…50% at 10 uM (micromolar)”, not 10 mM