Current Landscape of NRF2 Biomarkers in Clinical Trials
Abstract
:1. The KEAP1-NRF2 System
2. Pharmacological Inducers of KEAP1-NRF2 Signaling
2.1. Dimethyl Fumarate (DMF, BG-12, Tecfidera)
2.2. Bardoxolone-methyl (BARD-Me; CDDO-Me)
2.3. Oltipraz
2.4. Sulforaphane (SFN)
2.5. Other Natural Product Inducers
3. Biomarker-Based Clinical Studies and NRF2 Inducers
3.1. Nrf2 Target Genes
3.2. Gene Expression and Function
3.3. Oxidative-Stress-Mediated Biomarkers
3.4. Inflammation-Mediated Biomarkers
3.5. Carcinogen Metabolites/DNA Adducts
3.6. Metabolomics
4. Integrated Assessment of Biomarker Outcomes
5. Conclusions
5.1. Critical Path for Biomarkers in NRF2 Drug Development?
5.2. Metrics of Success and Confounders
5.3. Lessons from Dose-Response
5.4. Take-Home Messages
- NRF2 Target Genes: Given the preclinical evidence that all 4 of the agents can activate Nrf2 signaling, it is comforting that all four increased activities or transcript levels of classic NRF2 target genes in clinical trials. NQO1 was most studied and showed reasonable consistency across trials. Worryingly, in most studies the induction of NQO1 transcripts exhibited a limited dynamic range (~ < 2-fold). In an oltipraz study, concordance between expression in surrogate (e.g., PBMCs) and target tissues (e.g., colonic mucosa) was reported. Limited studies suggest possible merit for PCA or clustering analyses to characterize induction “signatures” that may be more revealing than single candidate genes;
- Gene Expression/Function: Most studies here have focused on pathways affecting cancer development and progression. The most promising outcomes (with SFN) have centered on modulation of epigenetic regulators such as histone deacetylase (HDAC) and histone acetyltransferase activities. One cancer-related gene expression panel was largely unaltered in a BARD-Me study and the other two agents were not evaluated in this context;
- Oxidative Stress: Many of the workhorse biomarkers of oxidative stress have been applied to clinical studies with SFN and oltipraz, but not the other two drugs. Oxidized DNA products along with DNA strand breaks have shown protective responses in some of the interventions. Studies using the oxidation products of lipids and proteins have been more variable in their responses, although MDA looks promising. The more integrated measures of TAC, TOS, and OSI have not been revealing in limited studies. Cellular GSH levels have been measured frequently and show repeated, albeit still inconsistent, modulation by intervention;
- Inflammation: In the aggregate over 35 individual inflammation biomarkers have been measured, while barely half evoked a significant response in any study; very few have been evaluated in multiple studies. The context for selection of candidate biomarkers is rarely presented in these studies. The NRF2 target gene Il-6 [227] shows some responsiveness, while other cytokines such as IL-1, IL-8 IL-13, TNFα, and IFNγ have been null in multiple studies. Lipid mediators including PGD2, tetranor-PGEM, 11β-PGF2α, and 11-dehydro-TXB2 offer some promise. Subgroup analyses of responders only within a DMF trial exhibited significant reductions in intracellular NF-κB signaling molecules [50];
- Carcinogen Metabolism/Adducts: Monitoring detoxication metabolites following interventions in study populations provides strong links to canonical NRF2 mechanisms of action. Multiple studies in settings of unavoidable exposures to air pollution and dietary carcinogens highlight successful interventions with oltipraz and SFN. However, such studies require sophisticated mass spectrometry methodologies for metabolite, DNA adduct, and protein adduct quantification. Moreover, interception of all classes of carcinogens and toxins is not achievable. Perhaps phase 0 “microdosing” trials with small, safe amounts of heavy-isotope-labeled substrates can provide an effective means to prioritize tractable exposures [242]. As with all reviewed biomarkers, extrapolation from biomarker change to extent of risk reduction has not been realized;
- Metabolomics: Targeted and nontargeted metabolomics are beginning to be applied successfully as biomarkers in clinical trials of NRF2 inducers, albeit exclusively to date with SFN. Recent studies in mice have shown the power of these tools to define the impact of modulation of Nrf2 signaling on cancer cell metabolism [243] and the maintenance of health in space flight [244]. Combinations of omics approaches are likely to provide more integrated pictures of the actions of targeted NRF2 activation on early, intermediate, and later events on the pathways of disease prevention and mitigation.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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DMF | BARD-Me | Oltipraz | SFN | TOTAL | Percent Significant Outcomes | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sig. Δ | NS | Sig. Δ | NS | Sig. Δ | NS | Sig. Δ | NS | Sig. Δ | NS | |||
Nrf2 Target Genes | Activity | |||||||||||
NQO1 | 1 | 1 | 1 | 1 | 50% | |||||||
GST | 1 | 2 | 1 | 2 | 2 | 50% | ||||||
SOD | 1 | 0 | 1 | ALL NULL | ||||||||
GPX | 1 | 0 | 1 | ALL NULL | ||||||||
Transcripts | ||||||||||||
NQO1 | 1 | 1 | 1 | 1 | 4 | 5 | 7 | 6 | 54% | |||
HMOX1 | 1 | 3 | 6 | 3 | 7 | 30% | ||||||
GCLC | 1 | 2 | 0 | 3 | ALL NULL | |||||||
GCLM | 2 | 2 | 2 | 2 | 50% | |||||||
GSTM | 1 | 1 | 2 | 1 | 3 | 25% | ||||||
GSTP | 1 | 1 | 1 | 1 | 50% | |||||||
UGT | 1 | 0 | 1 | ALL NULL | ||||||||
GPX | 1 | 1 | 0 | 2 | ALL NULL | |||||||
γGCS | 2 | 2 | 0 | 100% | ||||||||
TR1 | 1 | 1 | 0 | 100% | ||||||||
LTB4DH | 1 | 1 | 0 | 100% | ||||||||
AKR1C1 | 1 | 2 | 1 | 2 | 33% | |||||||
AKR1C2 | 1 | 1 | 0 | 100% | ||||||||
AKR1C3 | 1 | 0 | 1 | ALL NULL | ||||||||
HBG1 | 1 | 0 | 1 | ALL NULL | ||||||||
CBR1 | 1 | 1 | 1 | 1 | 50% | |||||||
SLC7A11 | 1 | 0 | 1 | ALL NULL | ||||||||
PCA cytoprotection/detox/antioxidant | 1 | 1 | 0 | 100% | ||||||||
Nrf2 related genes (aggregated transcripts) | ||||||||||||
NQO1, HMOX1, AKR1C1, HSP27, HSP70 | 1 | 1 | 0 | 100% | ||||||||
Gene Function/Expression | HDAC | 3 | 2 | 3 | 2 | 60% | ||||||
Histone acetylation | 1 | 1 | 1 | 1 | 50% | |||||||
CYP3A4 | 1 | 0 | 1 | ALL NULL | ||||||||
TGFβ pathway | 1 | 1 | 0 | 100% | ||||||||
Epidermal growth factor receptor | 1 | 1 | 0 | 100% | ||||||||
Insulin signaling | 1 | 1 | 0 | 100% | ||||||||
Cancer-related | ||||||||||||
RNA-seq of prostate cancer genes | 1 | 1 | 0 | 100% | ||||||||
p21WAF/CIP1 | 1 | 0 | 1 | ALL NULL | ||||||||
Cyclin D1 | 1 | 1 | 0 | 100% | ||||||||
STAT3 | 1 | 0 | 1 | ALL NULL | ||||||||
p-STAT3 | 1 | 0 | 1 | ALL NULL | ||||||||
p21 | 1 | 0 | 1 | ALL NULL | ||||||||
Active caspase 3 | 1 | 0 | 1 | ALL NULL | ||||||||
VEGF | 1 | 0 | 1 | ALL NULL | ||||||||
HIF1α | 1 | 0 | 1 | ALL NULL | ||||||||
Decorin | 1 | 1 | 0 | 100% | ||||||||
Insulin-like growth factor | 1 | 0 | 1 | ALL NULL | ||||||||
p16 | 1 | 1 | 0 | 100% | ||||||||
Oxidative Stress | GSH (Glutathione) levels | 2 | 3 | 2 | 1 | 4 | 4 | 50% | ||||
8-OHdG and oxidized nucleosides | 1 | 3 | 3 | 1 | 75% | |||||||
DNA strand breaks | 1 | 1 | 0 | 100% | ||||||||
PCOOH (phosphatidylcholine hydroperoxide) | 1 | 1 | 0 | 100% | ||||||||
8-isoprostane | 1 | 3 | 1 | 3 | 25% | |||||||
TBARS | 2 | 0 | 2 | ALL NULL | ||||||||
Protein carbonyls | 1 | 0 | 1 | ALL NULL | ||||||||
TAC (Total antioxidant capacity) | 1 | 2 | 1 | 2 | 33% | |||||||
TOS (Total oxidant status) | 1 | 0 | 1 | ALL NULL | ||||||||
OSI (Oxidative stress index) | 1 | 1 | 0 | 100% | ||||||||
MDA | 2 | 2 | 0 | 100% | ||||||||
Oxidized-LDL | 1 | 1 | 0 | 100% | ||||||||
Inflammation | Cytokine | |||||||||||
IL-1 | 1 | 2 | 0 | 3 | ALL NULL | |||||||
IL-4 | 1 | 0 | 1 | ALL NULL | ||||||||
IL-6 | 1 | 3 | 3 | 3 | 4 | 43% | ||||||
IL-8 | 1 | 3 | 0 | 4 | ALL NULL | |||||||
IL-10 | 1 | 0 | 1 | ALL NULL | ||||||||
IL-12 | 1 | 0 | 1 | ALL NULL | ||||||||
IL-13 | 1 | 1 | 0 | 2 | ALL NULL | |||||||
IL-17 | 1 | 0 | 1 | ALL NULL | ||||||||
TNFα | 1 | 1 | 1 | 1 | 2 | 33% | ||||||
IFNγ | 1 | 2 | 0 | 3 | ALL NULL | |||||||
Chemokines | ||||||||||||
CCL5 | 1 | 0 | 1 | ALL NULL | ||||||||
MIP-1B (CCL4) | 1 | 0 | 1 | ALL NULL | ||||||||
MCP-1 (CCL2) | 1 | 2 | 2 | 1 | 67% | |||||||
CXCL1 | 1 | 0 | 1 | ALL NULL | ||||||||
IP-10 (CXCL10) | 1 | 1 | 1 | 1 | 50% | |||||||
MIG | 1 | 1 | 0 | 100% | ||||||||
Lipid mediators | ||||||||||||
PGD2 | 1 | 1 | 0 | 100% | ||||||||
Tetranor-PGEM | 1 | 1 | 0 | 100% | ||||||||
11β-PGF2α | 1 | 1 | 0 | 100% | ||||||||
11-dehydro-TXB2 | 1 | 1 | 0 | 100% | ||||||||
NF-kB pathway | 1 | 1 | 0 | 100% | ||||||||
CRP | 2 | 3 | 2 | 3 | 40% | |||||||
Immune response | ||||||||||||
WBC counts | 1 | 1 | 0 | 100% | ||||||||
Neutrophil counts | 1 | 0 | 1 | ALL NULL | ||||||||
Monocyte counts | 1 | 0 | 1 | ALL NULL | ||||||||
Macrophage counts | 1 | 0 | 1 | ALL NULL | ||||||||
T cell counts | 1 | 0 | 1 | ALL NULL | ||||||||
NKT cells | 1 | 0 | 1 | ALL NULL | ||||||||
CD4+ and CD8+ T-lymphocytes | 1 | 1 | 1 | 1 | 50% | |||||||
Proinflammatory genes (aggregated transcripts) | 1 | 1 | 0 | 100% | ||||||||
PCA immune-response genes | 1 | 0 | 1 | ALL NULL | ||||||||
Others | ||||||||||||
MIF | 1 | 1 | 0 | 100% | ||||||||
SLPI | 1 | 1 | 0 | 100% | ||||||||
CD105+ and iNOS+ cells | 1 | 1 | 0 | 100% | ||||||||
Virus-induced granzyme B production in NK cells | 1 | 1 | 0 | 100% | ||||||||
Serum pepsinogen I and II | 1 | 1 | 1 | 1 | 50% | |||||||
Carcinogen Metabolites/Adducts | Aflatoxin-albumin adducts | 1 | 1 | 0 | 100% | |||||||
Aflatoxin-DNA adducts | 1 | 1 | 0 | 100% | ||||||||
Aflatoxin mercapturic acid | 1 | 1 | 0 | 100% | ||||||||
Polycyclic aromatic hydrocarbon-DNA adducts | 1 | 0 | 1 | ALL NULL | ||||||||
Benzo(a)pyrene-7,8-diol-9,10-epoxide adducts | 1 | 0 | 1 | ALL NULL | ||||||||
Mutagenicity (urine) | 1 | 0 | 1 | ALL NULL | ||||||||
Acrolein mercapturic acid | 2 | 2 | 0 | 100% | ||||||||
Benzene mercapturic acid | 3 | 3 | 0 | 100% | ||||||||
Crotonaldehyde mercapturic acid | 2 | 2 | 0 | 100% | ||||||||
Metabolomics | Cystine | 1 | 1 | 0 | 100% | |||||||
Plasma metabolites | 1 | 1 | 1 | 1 | 50% | |||||||
Urinary metabolites | 1 | 1 | 0 | 100% | ||||||||
Metabolites in prostate biopsies | 1 | 0 | 1 | ALL NULL |
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Share and Cite
Yagishita, Y.; Gatbonton-Schwager, T.N.; McCallum, M.L.; Kensler, T.W. Current Landscape of NRF2 Biomarkers in Clinical Trials. Antioxidants 2020, 9, 716. https://doi.org/10.3390/antiox9080716
Yagishita Y, Gatbonton-Schwager TN, McCallum ML, Kensler TW. Current Landscape of NRF2 Biomarkers in Clinical Trials. Antioxidants. 2020; 9(8):716. https://doi.org/10.3390/antiox9080716
Chicago/Turabian StyleYagishita, Yoko, Tonibelle N. Gatbonton-Schwager, Melissa L. McCallum, and Thomas W. Kensler. 2020. "Current Landscape of NRF2 Biomarkers in Clinical Trials" Antioxidants 9, no. 8: 716. https://doi.org/10.3390/antiox9080716
APA StyleYagishita, Y., Gatbonton-Schwager, T. N., McCallum, M. L., & Kensler, T. W. (2020). Current Landscape of NRF2 Biomarkers in Clinical Trials. Antioxidants, 9(8), 716. https://doi.org/10.3390/antiox9080716