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Discovering small-molecule senolytics with deep neural networks

Nature Aging (2023)Cite this article

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Abstract

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

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Fig. 1: Graph neural networks predict senolytic activity.
Fig. 2: Identification of structurally diverse compounds with senolytic activity.
Fig. 3: Validation of identified compounds in a model of therapy-induced senescence.
Fig. 4: Validation of identified compounds in a model of replicative senescence.
Fig. 5: Molecular docking- and TR-FRET-based identification of Bcl-2 as a potential binding target.
Fig. 6: In vivo efficacy of BRD-K56819078 in an aged mouse model.

Data availability

Data generated from chemical screens, machine learning models and computational analyses are available as Supplementary Data 1–4. For molecular docking studies, protein structures from accession codes 6qgh (Bcl-2), 3wiz (Bcl-XL), 1osf (Hsp90), 4hg7 (MDM2) and 4f1s (PI3K) were obtained from the PDB at https://www.rcsb.org/. All other data are available from the corresponding author upon request. Source Data are provided with this paper.

Code availability

Chemprop is publicly available at https://github.com/chemprop/chemprop. A detailed code platform, including a Jupyter notebook and the Chemprop checkpoints for the different models developed in this work, is publicly available at https://github.com/felixjwong/senolyticsai.

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Acknowledgements

F.W. was supported by the James S. McDonnell Foundation. J.J.C. was supported by the Broad Institute of MIT and Harvard. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We apologize to those colleagues whose relevant works we have been unable to mention, in attempting to survey such a broad area within a limited amount of space.

Author information

Author notes

  1. These authors contributed equally: Felix Wong, Satotaka Omori.

Authors and Affiliations

  1. Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Felix Wong, Nina M. Donghia & James J. Collins

  2. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Felix Wong, Satotaka Omori, Erica J. Zheng & James J. Collins

  3. Integrated Biosciences, Inc, San Carlos, CA, USA

    Felix Wong & Satotaka Omori

  4. Division of Cancer Cell Biology, Institute of Medical Science, The University of Tokyo, Minato-Ku, Tokyo, Japan

    Satotaka Omori

  5. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Nina M. Donghia & James J. Collins

  6. Program in Chemical Biology, Harvard University, Cambridge, MA, USA

    Erica J. Zheng

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Contributions

F.W. conceived research, designed models and experiments, performed experiments and analysis, wrote the paper and supervised research. S.O. and N.M.D. designed and performed experiments and analysis. E.J.Z. assisted with experiments. J.J.C. supervised research. All authors assisted with manuscript editing.

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Correspondence to James J. Collins.

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J.J.C. is an academic co-founder and board member of Cellarity, and the founding Scientific Advisory Board chair of Integrated Biosciences. F.W. is a co-founder of Integrated Biosciences. The remaining authors declare no competing interests.

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Nature Aging thanks Dyrba Martin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Timeline of etoposide-induced senescence.

Control (DMSO-treated) and senescent (etoposide-treated) cells were treated with test compounds and assayed for cellular viability at the indicated times for compound screening and dose-response experiments.

Extended Data Fig. 2 Screening of 2,352 compounds for senolytic activity and validation of four active compounds.

a, Cellular viability of vehicle- and etoposide-treated cells after a 3-day course of test compound treatment (10 μM). Values are from two biological replicates, and viability measurements are normalized by the interquartile mean of each cell plate. Active compounds (red points) are those for which the relative control cell viability is >0.5, the relative Snc viability is <0.7, and the ratio of Snc to control cell viability is <0.7. All other compounds are inactive (blue points). The Pearson’s correlation coefficient, R, and two-sided p-value are shown. b, Dose-response curves of control and etoposide-treated IMR-90 cells, treated with each compound shown. Zero μM (1% DMSO vehicle) treatment was included. Values are normalized by the average of two untreated samples from the same phenotype: here, a cellular viability of 1 indicates that of either untreated control cells or Sncs. Black curves indicate control (vehicle-treated) cells, and blue curves indicate (etoposide-treated) Sncs. Measurements are shown for two biological replicates in each treatment group (open points), and mean viability values (closed points) were fitted to calculate IC50 values. The therapeutic index (TI) is the ratio of IC50 values for vehicle- and etoposide-treated cells. The chemical structure of each compound is shown at the bottom of each plot. Note that, with our criteria for activity, ABT-263 has borderline activity at 1 µM and was inactive in our screen at 1 µM (panel (c)), due to marginal decreases in Snc viability. c, Senolytic screening results for 2,352 compounds at a final concentration of 1 µM. Values indicate the mean of two biological replicates, and viability measurements are normalized by the interquartile mean of each cell plate. Active and inactive compounds (red and blue points, respectively) are designated as in (a). Two known senolytics, ABT-737 and A-1331852, are inactive and highlighted with large blue points, and two active compounds, sulfisoxazole and imipramine hydrochloride, are highlighted with large red points. Sncs were induced with etoposide, and control cells were treated with vehicle (0.5% DMSO). d, Similar to (a), but for the screen shown in (c).

Extended Data Fig. 3 Comparison of machine learning models.

Shown are precision-recall curves for the two-best random forest models, trained and tested on the data shown in Fig. 1d. The black dashed curves represent the baseline fraction of active compounds in the training set (1.9%). Blue curves and the 95% confidence interval (CI) indicate the variation generated by bootstrapping. AUC, area under the precision-recall curve. The model hyperparameters used were: a, max depth, 5; number of estimators, 80; max features, 20; b, max depth, 5; number of estimators, 40; max features, 40.

Extended Data Fig. 4 Chemical filters for favorable medicinal chemistry properties and structural novelty.

The numbers of compounds after each chemical filtering step are shown, for both the Broad Institute Drug Repurposing Hub and the extended Broad Institute library. Numbers of curated compounds are indicated at bottom.

Extended Data Fig. 5 Screening of 216 compounds with high predicted senolytic activity, 50 compounds with low predicted senolytic activity, and validation of six additional active compounds.

a, Relative viability of vehicle- and etoposide-treated cells after a 3-day course of test compound treatment (10 μM). Values are from two biological replicates, and viability measurements are normalized by the interquartile mean of each cell plate. Active compounds (red points) are those for which relative control cell viability is >0.5, relative Snc viability is <0.7, and the ratio of Snc to control cell viability is <0.7. All other compounds are inactive (blue points). The Pearson’s correlation coefficient, R, and two-sided p-value are shown. b, Dose-response curves of control and etoposide-treated IMR-90 cells, treated with each compound shown. Compounds were serially diluted twofold starting from a final concentration of 50 μM, and 0 μM (1% DMSO vehicle) treatment was included. Cells were treated for 3 days. Cellular viability was determined by the metabolic reduction of resazurin into fluorescent resorufin, and values are normalized by the fluorescence intensities of the average of two untreated samples from the same phenotype: here, a cellular viability of 1 indicates that of either untreated control cells or Sncs. Vehicle treatment may result in cellular viability values <1 due to minor effects of DMSO on cellular viability. Black curves indicate control (vehicle-treated) cells, and blue curves indicate (etoposide-treated) Sncs. Measurements are shown for two biological replicates in each treatment group (open points), and mean viability values (closed points) were fitted to calculate IC50 values. The therapeutic index (TI) is the ratio of IC50 values for vehicle- and etoposide-treated cells. The chemical structure of each compound is shown at the bottom of each plot.

Extended Data Fig. 6 Structural comparisons of identified compounds.

Shown are the compounds in the training dataset with highest structural similarity to each of BRD-K20733377, BRD-K56819078, and BRD-K44839765, as measured by the Tanimoto similarity.

Extended Data Fig. 7 BRD-K20733377, BRD-K56819078, and BRD-K44839765 exhibit senolytic activity in a model of doxorubicin-induced senescence.

a, SA-β-gal staining of vehicle- (0.5% DMSO) and doxorubicin-treated IMR-90 cells plated at times on corresponding to the day before and day of compound addition (see also Fig. 1b and Extended Data Fig. 1). Each image is representative of two biological replicates. Scale bar, 100 μm. b, Relative mRNA expression of p16, p21, and KI67 in vehicle- (0.5% DMSO), doxorubicin-, and etoposide-treated IMR-90 cells harvested on the day of compound addition. Data for vehicle-and etoposide-treated cells are identical to those shown in Fig. 1c of the main text, and are shown here for comparison. Data from three biological replicates are shown. Error bars indicate one standard deviation. One-way, two-sided ANOVA with Tukey’s multiple comparisons: *p≤0.05,**p<0.01,***p<0.001. c, Dose-response curves of control and doxorubicin-treated IMR-90 cells, treated with each compound shown. Compounds were serially diluted twofold starting from a final concentration of 50 μM, and 0 μM (1% DMSO vehicle) treatment was included. Cells were treated for 3 days. Cellular viability was determined by the metabolic reduction of resazurin into fluorescent resorufin, and values are normalized by the fluorescence intensities of the average of two untreated samples from the same phenotype: here, a cellular viability of 1 indicates that of either untreated control cells or Sncs. Vehicle treatment may result in cellular viability values <1 due to minor effects of DMSO on cellular viability. Black curves indicate control (vehicle-treated) cells, and blue curves indicate (doxorubicin-treated) Sncs. Measurements are shown for two biological replicates in each treatment group (open points), and mean viability values (closed points) were fitted to calculate IC50 values. The therapeutic index (TI) is the ratio of IC50 values for vehicle- and doxorubicin-treated cells. The chemical structure of each compound is displayed in each inset. Data for control cells are identical to those shown in Fig. 3a–d of the main text.

Extended Data Fig. 8 Preliminary assessments of compound toxicological properties.

a, Fractional hemolysis measurements of human red blood cells treated with BRD-K20733377, BRD-K56819078, BRD-K44839765, and ABT-737 at the indicated final concentrations. Vehicle (1% DMSO) was used as a negative control, and Triton X-100 was used as a positive control. Black points indicate values from individual biological replicates, and red bars indicate average values. b, Ames test mutagenesis measurements of the fractions of revertant S. typhimurium TA100 cultures treated with BRD-K20733377, BRD-K56819078, BRD-K44839765, and ABT-737 at a final concentration of 100 µM. Vehicle (1% DMSO) was used as a negative control, and 0.25 µg/mL (~1 µM) 4-nitroquinoline 1-oxide was used as a positive control. Black points indicate values from individual biological replicates, and purple bars indicate average values. Higher fractions of revertant cultures indicate higher mutagenic potential.

Supplementary information

Supplementary Information

Supplementary Note 1, Tables 1–11, references and legends for Supplementary Data 1–4.

Reporting Summary

Supplementary Data 1

Senolytic screens of 2,352 compounds and 200 RDKit features used to augment the model. Compounds were screened at final concentrations of 10 μM and 1 μM in biological duplicate.

Supplementary Data 2

Model predictions for 804,959 compounds. Values indicate the mean of 20 Chemprop models. Compounds are from either the Broad Institute’s Drug Repurposing Hub or an extended Broad Institute library.

Supplementary Data 3

Compound filtering, curation and testing for senolytic activity. Compounds were filtered as described in Methods. Curated compounds were screened at a final concentration of 10 μM in biological duplicate.

Supplementary Data 4

Additional calculations of physicochemical properties. For comparison, values for ABT-737, ABT-263 and A-1331852 are shown. As in Table 1, Lipinski-conforming indicates that a compound violates no more than one of Lipinski’s rule of five: (a) ≤5 hydrogen bond donors, (b) ≤10 hydrogen-bond acceptors, (c) molecular weight <500 Da and (d) log P partition coefficient <5. Veber-conforming indicates that a compound violates none of Veber’s rules for oral bioavailability: (a) ≤10 rotatable bonds and (b) TPSA ≤140 Å2.

Source data

Source Data

Statistical source data.

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Wong, F., Omori, S., Donghia, N.M. et al. Discovering small-molecule senolytics with deep neural networks. Nat Aging (2023). https://doi.org/10.1038/s43587-023-00415-z

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