Abstract
Small molecule kinase inhibitors (SMKIs) are a class of therapeutic drugs that target protein kinases in diseases such as cancer. SMKIs are often designed to inhibit kinases involved in cell proliferation, but these drugs alter cell metabolism and the endocrine control of organismal metabolism. SMKI treatment in diabetic cancer patients reveals that certain SMKIs improve blood glucose levels and can mitigate insulin dependence or diabetic medication requirements in both type 1 diabetes (T1D) and type 2 diabetes (T2D). Certain SMKIs can preserve functional β-cell mass and increase insulin secretion or insulin sensitivity. It is not yet clear why different SMKIs can have opposing effects on insulin and blood glucose. Understanding the therapeutic effects of these drugs in T1D and T2D is complicated by overlapping off-target effects of SMKIs. The potency of inhibition of the intended protein kinase and inhibition of multiple off-target kinases may underpin conflicting reports of how certain SMKIs alter blood glucose and insulin. We summarize the effects of SMKIs on the intended and off-target kinases that can alter blood glucose and insulin, including c-Abl, c-Kit, EGFR, and VEGF. Inhibition of PDGFRβ consistently lowers blood glucose in T1D and T2D. The effects of SMKIs on the kinases that regulate immune pathways, such as BTK and RIPKs, mediate many of the diverse effects of these drugs on metabolism. We highlight that inhibition of RIPK2 by SMKIs is a central node in metabolism that influences key metabolic pathways including lipolysis, blood glucose control, insulin secretion, and insulin resistance.
Protein kinases and small molecule kinase inhibitors
More than 500 protein kinases have been confirmed in the human genome, and these kinases regulate cellular signaling cascades that control fundamental cell function, including proliferation, differentiation, survival, metabolism, and immunity (Adams 2001, Manning et al. 2002). Protein kinases regulate the biological activity of other proteins by transferring a γ-phosphate group from an ATP molecule to a specific serine/threonine residue or a tyrosine residue on the substrate protein (Dhanasekaran & Premkumar Reddy 1998, Manning et al. 2002). Given the critical role of protein kinases in regulating almost all cellular activities and functions, it follows that dysregulation of specific protein kinases is implicated in numerous disease states (Lahiry et al. 2010), and pharmacological inhibition of aberrant kinase activity is a promising drug target for disease treatment. The last 2 decades have seen a rapid and widespread use of more than 60 small molecule kinase inhibitors (SMKIs) approved for clinical use in cancerous malignancies, and many SMKIs are being investigated as potential therapeutic agents in autoimmune and inflammatory conditions, including asthma, psoriasis, inflammatory bowel disease, peritonitis, multiple sclerosis, pulmonary fibrosis, and type 1 and type 2 diabetes (T1D, T2D) (Patterson et al. 2014). Several of these agents have already been approved for use in several non-malignant conditions including dermatitis, ulcerative colitis, rheumatoid arthritis, and graft versus host disease (Roskoski 2020). SMKIs are often used as adjuvants in chemotherapy, but theyare not cytotoxic agents. In general, SMKIs are well-tolerated and widely used. SMKIs in clinical settings have yielded extensive, pre-existing safety and side-effect profiles for many of these drugs (Roskoski 2020).
Kinase inhibitors function by blocking the transfer of the γ-phosphate group from the ATP molecule, either by directly binding to the ATP-binding site (type I inhibitors) or by binding to an allosteric site of the kinase that prevents access to the ATP-binding site (type II–IV inhibitors). Due to the similar 3D structure and highly conserved ATP-binding pocket across the entirety of the mammalian kinome, specificity issues and inhibition of off-target kinases are of major concern in the development and use of SMKIs. Despite their intended selectivity and specificity, clinically approved kinase inhibitors have many off-target effects and inhibit a range of kinases (Davis et al. 2011). The dose is a critical determinant of off-target effects of SMKIs. The effective (i.e. bioavailable) dose is influenced by the metabolism of SMKIs. Most kinase inhibitors are rapidly absorbed and reach peak plasma concentration within 3–7 h after an oral dose. Food consumption only marginally increases the extent of or does not significantly affect the bioavailability of most kinase inhibitors (van Erp et al. 2009). Kinase inhibitors are primarily metabolized by cytochrome P450 (CYP) 3A4 with other CYP enzymes playing a secondary role in the metabolism of some inhibitors. Kinase inhibitors are predominantly excreted in feces, and a minor amount is also excreted in urine (van Erp et al. 2009). The purpose of this review is to highlight how SMKIs that are often designed for cancer treatment can alter blood glucose and insulin. We apologize that not all relevant literature could be included in this review. We selected specific references and focused on literature that highlighted the connection between SMKIs and blood glucose and insulin and also uncovered the mechanisms of action linking metabolism and SMKIs in humans and in pre-clinical studies.
SMKIs have opposing effects on glucose and insulin in type 1 and type 2 diabetes
Both T1D and T2D are diseases defined by elevated blood glucose levels. T1D is caused by autoimmune destruction of pancreatic β-cells, leading to inability to produce and secrete insulin to control blood glucose. In contrast, the early stages of prediabetes that occur before overt T2D are characterized by increased insulin secretion, insulin resistance, and close to normal blood glucose levels. Eventual decline in insulin production and continued insulin resistance equate to hyperglycemia in overt T2D. Despite the differences in the underlying pathophysiology of T1D and T2D, a significant number of clinical reports have demonstrated a decreased need for exogenous insulin or diabetic medications in both T1D and T2D cancer patients receiving SMKI therapy (Table 1). These initial clinical observations spurred interest in re-tasking clinically approved SMKIs for use in diabetes. The underlying mechanisms that mediate the glycemic effects of SMKIs have been studied in vitro and in various in vivo rodent models of T1D and T2D.
Clinical evidence of KIs altering blood glucose or insulin in diabetic cancer patients.
| Kinase inhibitor | Summary of effects on blood glucose or insulin | References |
|---|---|---|
| Brigatinib | ↑ Hyperglycemia in human patients | ARIAD Pharmaceuticals 2017 |
| Ceritinib | ↑ Hyperglycemia in human non-diabetic and T2D patients | Novartis Pharmaceuticals Corporation 2014, Khozin et al. 2015, Sakuma et al. 2019, Miyoshi et al. 2021 |
| Dasatinib | ↓ Fasting blood glucose and ↓ insulin in human T2D patients | Breccia et al. 2008, Agostino et al. 2011, Ono et al. 2012, Lundholm & Charnogursky 2020, Salaami et al. 2021 |
| Erlotinib | ↓ Fasting blood glucose and ↓ insulin in human T2D patients | Costa & Huberman 2006, Brooks 2012 |
| Imatinib | ↓ Fasting blood glucose and ↓ insulin in human T1D and T2D diabetic patients | Breccia et al. 2004, Veneri et al. 2005, Agostino et al. 2011, Salaroli et al. 2012 |
| Neratinib | ↓ Blood glucose in human T2D patient ↓ Glycemia and ↑ insulin secretion in mouse T1D model |
Ardestani et al. 2019, Angelis et al. 2022 |
| Nilotinib | ↑ Blood glucose and ↑ insulin levels in human non-diabetic patients | Saglio et al. 2010, Zdenek et al. 2014 |
| ↑ Blood glucose and ↓ insulin in human T2D patient | Ito et al. 2013 | |
| Pazopanib | ↓ Blood glucose in human T2D patient | Böhm et al. 2010 |
| Rociletinib | ↑ Blood glucose levels in human non-diabetic and diabetic patients | Sequist et al. 2015 |
| Sorafenib | ↓ Blood glucose levels in human non-diabetic and T2D patients | Agostino et al. 2011, Holstein et al. 2013, Di Costanzo et al. 2017 |
| Sunitinib | ↓ Blood glucose and ↓ insulin in human non-diabetic and T1D/T2D patients | Billemont et al. 2008, Templeton et al. 2008, Agostino et al. 2011, Oh et al. 2012, Chen et al. 2013b , Demirci et al. 2014, Huda et al. 2014, Tyrrell & Pwint 2014 |
There is no unifying theory explaining how SMKIs alter blood glucose or insulin, and this is further complicated by conflicting reports of specific SMKIs promoting hyperglycemia or hypoglycemia in different patients or experiment models. For example, there are many reports of the SMKI dasatinib lowering blood glucose in T1D, T2D, and non-diabetic patients (Breccia et al. 2008, Agostino et al. 2011, Ono et al. 2012, Yu et al. 2019, Lundholm & Charnogursky 2020, Salaami et al. 2021). Dasatinib was also reported to promote hyperglycemia in a cohort of non-diabetic patients and in a mouse model of diet-induced obesity (Sylow et al. 2016, Yu et al. 2019). In contrast, hyperglycemia is one of the main dose-limiting side-effects for some SMKIs, including nilotinib, rociletinib, and ceritinib, which has been attributed to impaired pancreatic β-cell insulin secretion, or inhibition of the insulin receptor (IR) and development of insulin resistance in human patients (Novartis Pharmaceuticals Corporation 2014, Zdenek et al. 2014, Sequist et al. 2015, Sakuma et al. 2019). SMKIs have also been reported to have opposing effects on blood insulin levels. Some clinical reports demonstrate increased insulin or c-peptide levels during dasatinib, imatinib, sunitinib, and nilotinib therapy and increased β-cell insulin secretion in vitro, while conflicting reports in the literature suggest nilotinib can also impair insulin secretion in humans (Haap et al. 2007, Ono et al. 2012, Zdenek et al. 2014, Xia et al. 2014, Lutz et al. 2017, Li et al. 2018, Samaha et al. 2019). A schematic summary of the main glycemic and insulinemic effects of some of the most widely used, clinically approved SMKIs is presented in Fig. 1.
Summary of clinically approved SMKIs and the effects on blood glucose and insulin. *Denotes conflicting clinical and experimental effects.
Citation: Journal of Endocrinology 256, 2; 10.1530/JOE-22-0212
Targets regulating glycemic effects of SMKIs
It is difficult to ascertain the mechanism of action underlying the ability of a specific SMKIs to regulate blood glucose due to numerous off-target actions on multiple protein kinases. For example, imatinib is designed against, and is considered highly specific for, non-receptor tyrosine kinase abelson (c-Abl) kinase, but imatinib also inhibits numerous other kinases to a comparable degree, including c-Kit and platelet-derived growth factor receptor β (PDGFRβ). Common overlapping targets for many of the SMKIs that have reported effects on blood glucose and insulin dynamics include cAbl, c-Kit, receptor-interacting serine/threonine protein kinase 2 (RIPK2), EGF receptor, ribosomal protein S6 kinase beta-1 (S6K1), PDGFRβ, c-June N-terminal kinase 1 (JNK1), and the insulin receptor (InsR) (Fig. 2). A number of these SMKI targets has been implicated in metabolic homeostasis, but the key target kinases and molecular signals responsible for each specific SMKI’s ability to cause changes in blood glucose or insulin are unknown and dependent on the effective dose. Using publicly available kinase inhibition assay data from the Harvard Medical School LINCS Center database, we have compiled a table describing many clinically approved SMKIs and their targets relevant to metabolism and the relative degree of inhibition (Table 2). The potential metabolic role of each of these target kinases is discussed in detail below.
Summary of clinically approved SMKIs that alter blood glucose control and their numerous kinase targets.
Citation: Journal of Endocrinology 256, 2; 10.1530/JOE-22-0212
Clinically approved SMKIs inhibit multiple kinase targets. % values correspond to % inhibition at specified SMKI concentration. For SMKIs where no concentration is listed, nanomolar (nM) values given represent Kd50 values.
| SMKI | 1˚ target | RIPK2 | EGFR | c-Kit | cABL | p-cABL | PDGFRβ | S6K1 | JNK1 | InsR |
|---|---|---|---|---|---|---|---|---|---|---|
| Abemaciclib (1 μM) | CDK4/6 100% | 3% | 0% | 0% | 8% | 2% | 4% | 44% | 96% | 0% |
| Afatinib | ErbB1/2/4 0.3 nM | 2700 nM | 0.3 nM | – | 1300 nM | 570 nM | – | – | – | – |
| Baricitinib (10 μM) | JAK1/2 100% | 1% | 13% | 44% | 81% | 81% | 83% | 56% | 64% | 33% |
| Bosutinib | BCR-Abl 0.12 nM | 3700 nM | 35 nM | 420 nM | 0.12 nM | 0.06 nM | 200 nM | 660 nM | – | – |
| Ceritinib (1 μM) | ALK 100% | 7% | 28% | 13% | 10% | 14% | 11% | 0% | 41% | 98% |
| Crizotinib (10 μM) | ALK, ROS1 91%, 99% | 89% | 18% | 43% | 100% | 100% | 56% | 98% | 0% | 99% |
| Dabrafenib (10 μM) | B-raf 100% | 97% | 24% | 96% n | 99% | 79% | 86% | 91% | 94% | 22% |
| Dasatinib | BCR-Abl 0.03 nM | 31 nM | 120 nM | 0.81 nM | 0.03 nM | 0.05 nM | 0.63 nM | – | – | – |
| Erlotinib | EGFR 0.7 nM | 680 nM | 0.7 nM | 1700 nM | 330 nM | 76 nM | 1400 nM | – | – | – |
| Fedratinib | JAK2 1.1 nM | 6100 nM | 3400 nM | 230 nM | 180 nM | 44 nM | 45 nM | 2000 nM | 260 nM | 850 nM |
| Gefitinib | EGFR 1.0 nM | 530 nM | 1.0 nM | – | 480 nM | 2200 nM | – | – | – | – |
| Ibrutinib (1 μM) | BTK 100% | 95% | 98% | 56% | 93% | 46% | 67% | 37% | 28% | 63% |
| Imatinib | BCR-Abl 1.1 nM | – | – | 13 nM | 1.1 nM | 21 nM | 14 nM | – | 5000 nM | – |
| Lapatinib (10 μM) | ErbB1/2 100% | 94% | 100% | 12% | 0% | 0% | 0% | 20% | 0% | 0% |
| Neratinib | ErbB2 6 nM | – | 1.1 nM | – | – | – | – | – | 3300 nM | 3000 nM |
| Nilotinib | BCR-Abl 10 nM | – | – | 29 nM | 10 nM | 13 nM | 73 nM | – | 450 nM | – |
| Nintedanib | FGFR1/2/3, 92–350 nM | – | – | 5.7 nM | 230 nM | 64 nM | 15 nM | 190 nM | 630 nM | 24 nM |
| Palbociclib (10 μM) | CDK4/6 98–100% | 0% | 10% | 40% | 47% | 51% | 71% | 44% | 98% | 33% |
| Pazopanib | VEGFR1/2/3 14 nM | 580 nM | – | 2.8 nM | 620 nM | 650 nM | 2 nM | – | 2000 nM | – |
| Ribociclib (1 μM) | CDK4/6 99–100% | 0% | 0% | 0% | 17% | 15% | 6% | 22% | 0% | 0% |
| Ruxolitinib | JAK1/2/3 0.1–3 nM | – | – | – | 3700 nM | – | – | – | 4800 nM | – |
| Sorafenib (10 μM) | VEGFR1/2/3 99% | 82% | 15% | 100% | 99% | 75% | 100% | 37% | 0% | 0% |
| Sunitinib | VEGFR2 1.5 nM | – | – | 0.4 nM | 270 nM | 150 nM | 0.1 nM | 48 nM | – | 500 nM |
| Tofacitinib | JAK3 0.2 nM | – | – | – | – | – | – | – | – | – |
| Vandetanib | VEGFR2 820 nM | 4.6 nM | 9.5 nM | 260 nM | 48 nM | 16 nM | 88 nM | 1600 nM | – | – |
| Vemurafenib (10 μM) | B-Raf 100% | 96% | 30% | 99% | 97% | 43% | 99% | 14% | 82% | 22% |
c-Abl
Expressed in the majority of cells, c-Abl is a critical regulator of cell growth, motility, cytoskeleton dynamics, receptor endocytosis, DNA repair, cell survival, and autophagy (Hantschel & Superti-Furga 2004). The focus of c-Abl research has largely been in human cancers, including the oncogenic BCR-Abl fusion, which encodes a highly active kinase and contributes to chronic myeloid leukemia (CML) and acute lymphoblastic leukemia (ALL) (Hantschel & Superti-Furga 2004). Imatinib, nilotinib, dasatinib, bosubtinib, and ponatinib represent the main SMKIs developed against c-Abl/BCR-Abl for use in CML and ALL (Lindholm et al. 2016). Interestingly, c-Abl is highly expressed in the s.c. adipose tissue of obese humans and mice fed a high fat diet (HFD), and in vivo data suggest adipose tissue-specific deletion of c-Abl in HFD-fed mice mitigated weight gain, hyperglycemia, and hyperinsulinemia (Wu et al. 2017). Consistent with these reports of reduced HFD-induced weight gain, c-Abl has been implicated as a regulator of adipocyte differentiation via PPARβ2 in in vitro studies (Keshet et al. 2014). In contrast, in vitro work has shown that c-Abl is induced by high glucose levels; however, this can be reversed by c-Abl inhibition via administration of imatinib (Jia et al. 2008, Xia et al. 2014). The results are congruent with in vivo findings from our lab demonstrating that imatinib treatment in obese mice lowers hyperglycemia but induces hyperinsulinemia (Duggan et al. 2020).
Imatinib is a potent inhibitor of c-Abl, and although other kinase targets of imatinib may mediate some of the effects on blood glucose and insulin, clinical reports of T1D and T2D and experimental models consistently show that imatinib lowers blood glucose but restores or enhances insulin production in both T1D and T2D (Breccia et al. 2004, Veneri et al. 2005, Haap et al. 2007, Hägerkvist et al. 2007, Louvet et al. 2008, Han et al. 2009, Agostino et al. 2011, Salaroli et al. 2012, Lau et al. 2014, Samaha et al. 2020, Pichavaram et al. 2021). Additionally, nilotinib is a similarly potent inhibitor of c-Abl, but there are contrasting reports on nilotinib’s effects on blood glucose and insulin. Nilotinib has been reported to increase blood glucose in non-diabetic and T2D patients while increasing insulin levels in non-diabetic patients but decreasing insulin levels in T2D. Furthermore, in a rodent model of T1D, nilotinib preserved serum insulin levels while decreasing blood glucose (Saglio et al. 2010, Ito et al. 2013, Zdenek et al. 2014, Samaha et al. 2019). This is further complicated by the fact both imatinib and nilotinib have a similar inhibition profile regarding kinases implicated in metabolic homeostasis, including c-Kit, PDGFRβ, and JNK to various degrees (Fig. 2 or Table 2). An important study by Louvet et al. attempted to address the difficulty in interpreting SMKI’s effects due to overlapping targets by using a systematic combination of SMKIs and neutralizing antibodies in a genetic rodent model of T1D. Imatinib prevented onset of and led to remission of T1D and modestly decreased leukocyte infiltration in pancreatic islets. Similar results were observed with sunitinb, which is primarily targeted against VEGFR, in the same model. The authors suggest that the efficacy of sunitinib eliminated a role for c-Abl in improving T1D; however, kinase assays demonstrate that sunitinib inhibits c-Abl to a significant degree, as well as c-Kit and PDGFRβ (Table 2). More convincingly, experiments using c-Kit monoclonal antibodies only resulted in a marginal effect on blood glucose, while delivery of a soluble form of PDGFR rapidly reversed T1D in these mice (Louvet et al. 2008). Due to the capability of both imatinib and sunitinib to inhibit PDGFRβ, this strongly supports the concept that inhibiting the kinase activity of PDGFRβ rather than c-Kit promotes lowering of blood glucose and possibly protection from T1D in this model. However, this result does not rule out a role for c-Abl in the regulation of blood glucose and insulin secretion, which should be considered a next step in this line of research.
PDGFRβ
PDGFRβ belongs to a family of membrane-bound receptors activated by platelet-derived growth factors (PDGFs), which control fundamental aspects of cell function, including growth and proliferation. PDGF-PDFRβ signaling regulates hematopoiesis, blood vessel formation, and wound repair (Chen et al. 2013a ). Overactive PDGFR signaling has been implicated in proliferative diseases, including various cancers and atherosclerosis (Chen et al. 2013a ). PDGFRs have been reviewed elsewhere as cancer drug targets (Pietras et al. 2003). Sunitinib and sorafenib are two of the main SMKIs that have been developed against PDGFRβ, but these SMKIs also inhibit cKIT and fms-like tyrosine kinase 3 (FLT3) with equal or even greater potency, according to available kinase assay data (Levitzki 2004, Davis et al. 2011). Nonetheless, sunitinib and sorafenib consistently yield improvements in blood glucose level, lower blood glucose, and/or reductions in insulin requirements in T1D and T2D patients and rodent models (Billemont et al. 2008, Louvet et al. 2008, Templeton et al. 2008, Agostino et al. 2011, Imarisio et al. 2012, Oh et al. 2012, Holstein et al. 2013, Mukai et al. 2014, Di Costanzo et al. 2017, Peng et al. 2018, Karbownik et al. 2020, Mahdi et al. 2022).
PDGFRβ is increased in the white adipose tissue (WAT) of HFD-fed mice. Additionally, adipose-specific knockdown of Pdgfrb resulted in lower accumulation of visceral and s.c. fat and improved blood glucose control and lower blood insulin in HFD-fed mice, coincident with a reduction in new blood vessel formation in adipose tissue (Onogi et al. 2017, Shao et al. 2018). The effects of inhibiting PDGFRβ are not restricted to obesity and T2D, since Louvet et al. showed that targeting PDGFRβ can mitigate aspects of T1D in lean mice (Louvet et al. 2008). There is not much evidence that contradicts the concept of PDGFβ inhibition and lower blood glucose, but some work has implicated PDGFβ as a growth factor that stimulates insulin-independent glucose transport and glucose uptake in adipocytes (Whiteman et al. 2003). In theory, this suggests that inhibition of PDGFRβ could limit blood glucose disposal and increase blood glucose in models of insulin resistance or insufficiency. However, the impact of adipocyte glucose uptake on systemic blood glucose control is limited and any potential effect on adipocyte glucose uptake on blood glucose appears outweighed by the totality of SMKI evidence showing that inhibiting PDGFβ lowers blood glucose. It is not yet clear how targeting PDGFRβ participates in development of, or protection against, T1D and T2D despite the potential for reduced insulin-independent adipocyte glucose uptake.
EGFR and VEGFR
EGF and related vascular endothelial growth factor (VEGF) signaling pathways regulate growth, survival, proliferation, and differentiation in mammalian cells (Press & Lenz 2007). Mutations leading to hyperactive EGFR or VEGFR have been associated with a number of cancers including types of lung and epithelial cancers, renal and thyroid cancers, and glioblastomas (Sullivan & Planchard 2016). Some of the first SMKIs approved for clinical use included the EGFR inhibitors, gefitinib and erlotinib, in 2012 and 2013, respectively, and numerous EGFR and VEGFR inhibitors have been approved in years since (Wu et al. 2015). EGFR inhibitors gefitinib and erlotinib have been demonstrated to reverse hyperglycemia in clinical reports of T2D and improve glucose tolerance and insulin sensitivity in rodent models of T2D, while other EGFR-targeted inhibitors including panitumumab and rociletinib are associated with clinical hyperglycemia in human non-diabetic and T2D cancer patients (Costa & Huberman 2006, Brooks 2012, Zhang et al. 2014, Goldman et al. 2016, Li et al. 2018, Duggan et al. 2020). Although not approved for clinical use, another EGFR SMKI, dubbed PD153035, has been shown to reduce inflammation and improve glucose tolerance and insulin sensitivity in HFD-fed mice (Prada et al. 2009). In this study and others, authors conclude that inhibition of EGFR contributes to the observed therapeutic effects; however, gefitinib, erlotinib, and PD153035 are not specific for EGFR. These SMKIs have been shown to inhibit many other kinases involved in glycemic control, including RIPK2, both in vitro and in vivo (Duggan et al. 2017). Increased expression and activity of EGF have been implicated in diabetes-induced vascular hypertrophy, nephropathy, and vasoconstriction relevant to cardiovascular disease outcomes, but it is unclear if and how EGF signaling may mediate blood glucose control (Gilbert et al. 2000, Chen et al. 2015, Prado et al. 2018). Current evidence is insufficient to determine specific kinase target(s) of EGFR SMKIs that are responsible for these contrasting effects on glycemic control.
Sorafenib, sunitinib, and panzopanib are some of the most widely used SMKIs of VEGFR, and reports in the literature consistently show decreases in blood glucose or hypoglycemia in T1D, T2D, and non-diabetic cancer patients and in rodent models following treatment with these inhibitors (Billemont et al. 2008, Louvet et al. 2008, Templeton et al. 2008, Böhm et al. 2010, Agostino et al. 2011, Oh et al. 2012, Imarisio et al. 2012, Chen et al. 2013b , Holstein et al. 2013, Demirci et al. 2014, Huda et al. 2014, Mukai et al. 2014, Tyrrell & Pwint 2014, Di Costanzo et al. 2017, Lutz et al. 2017, Peng et al. 2018, Karbownik et al. 2020, Mahdi et al. 2022). However, these SMKIs also target c-Kit, c-Abl, and PDGFRβ and there is no clearly established role for VEGFR in blood glucose control. Furthermore, there are conflicting reports on the therapeutic benefits of altering VEGF signaling, and its contribution to angiogenesis during adipose tissue expansion (Tam et al. 2009, Robciuc et al. 2016). It remains unknown if off-target kinases are involved in the VEGF SMKI-mediated changes in blood glucose.
c-KIT
CD117, also known as c-Kit, is a transmembrane stem cell factor receptor expressed predominantly in bone marrow and progenitor cell populations. Gain-of-function mutations in c-Kit have been implicated in human hematopoietic and gastrointestinal cancers (Babaei et al. 2016). Imatinib, sorafenib, and sunitinib are some of the most potent inhibitors of c-Kit with blood glucose-lowering effects (Babaei et al. 2016). c-Kit expression has been found to be increased in the synovium of patients with obesity-associated osteoarthritis (Uchida et al. 2019). Streptozotocin (STZ)-induced hyperglycemia or HFD-induced obesity in rodents is associated with a decrease in the number of c-Kit+ bone marrow progenitor cells (Pierpaoli et al. 2016, Van Den Berg et al. 2016). However, specific inhibition of c-Kit using a monoclonal antibody approach did not affect the development or disease progression in a STZ-induced T1D rodent model, whereas the SMKIs imatinib and sunitinib protected against T1D development (Louvet et al. 2008). This suggests that although imatinib and sunitinib are designed to inhibit c-Kit, the actions of these SMKIs are propagated through other kinases that alter glycemic outcomes, although there is a current lack of convincing evidence identifying the specific kinase(s) and specific mechanism(s) underlying the distinct effects on blood glucose, insulin sensitivity, and β-cell function (Louvet et al. 2008). Multiple studies have identified that c-Kit is required for pancreatic β-cell growth and function, where decreased expression of c-Kit in mutant mouse models results in hyperglycemia and impaired glucose control. These manifestations have been observed as decreased insulin gene expression, β-cell mass, and impaired glucose-stimulated insulin secretion in vivo and in isolated islets. (Krishnamurthy et al. 2007, Feng et al. 2015). Thus, inhibition of c-Kit by SMKIs may be deleterious to glycemic control and limit their therapeutic benefits in T1D and T2D.
SMKIs alter metabolism
SMKIs may alter the connection between the immune system and metabolism. Changes in immune cell populations and immunological messengers (i.e. cytokines) have been implicated in the pathogenesis of metabolic disease and can alter blood glucose, insulin, and insulin resistance during obesity (Hotamisligil 2006, Osborn & Olefsky 2012). SMKIs can alter kinases, such as RIPK2 and Bruton’s tyrosine kinase (BTK) involved in innate immune responses propagating inflammation from pattern recognition receptors (PRRs) and the NLR family pyrin domain containing 3 (NLRP3) inflammasome. Inhibition of specific host kinases by SMKIs can also alter the connection between immunity and metabolism, which has implications for blood glucose and insulin.
RIPK2
RIPK2 is the common adapter for the pattern recognition receptors nucleotide-binding oligomerization domain-containing protein (NOD)1 and NOD2. Our lab and others have defined the innate immune sensors NOD1 and NOD2 as points of convergence between immunity and blood glucose control in mice models. Specifically, activation of NOD1 triggers key features of metabolic syndrome, including lipolysis, inflammation, and dysglycemia. In contrast, activation of NOD2 alleviates metabolic dysfunction and improves blood glucose control during low-dose endotoxin or diet-induced obesity (Schertzer et al. 2011, Chi et al. 2014, Cavallari et al. 2017, 2020, Duggan et al. 2017). Consistent with this, deletion of hematopoietic NOD1 during obesity improves blood glucose control, whereas deletion of NOD2 during obesity worsens blood glucose control (Schertzer et al. 2011, Denou et al. 2015, Chan et al. 2017).
Propagation of NOD1 and NOD2 signaling requires the kinase activity of the downstream adaptor protein, RIPK2. We have shown that RIPK2 is an obligate component of the contrasting NOD1-driven and NOD2-driven glycemic responses (Duggan et al. 2017, 2020). RIPK2 deletion results in exacerbated obesity and worsened insulin resistance during HFD-feeding in mice (Wang et al. 2013a ), although the relative importance of NOD1-RIPK2 vs NOD2-RIPK2 metabolism during obesity and their individual contributions to glycemic control in metabolic disease states are not well-understood. Since many SMKIs are potent inhibitors of RIPK2, including gefitinib and erlotinib, RIPK2 was positioned as a potential mediator of SMKI effects on blood glucose. Although RIPK2 is not the intended target, many SMKIs with reported inhibitory activity against RIPK2 (Table 2) can attenuate the glycemic consequences of acute NOD1 and NOD2 signaling (Duggan et al. 2017, 2020). Thus, in the context of obesity, inhibition of the NOD1–RIPK2 axis represented a potential mechanism by which SMKIs lower blood glucose. In contrast, inhibition of the NOD2-RIPK2 axis by SMKIs could limit the therapeutic benefits of these drugs on blood glucose. Using a series of genetic knockout mice models, we showed that gefitinib-mediated blood glucose lowering during obesity was entirely independent of NOD1, NOD2, or RIPK2 signaling. Importantly, we found that gefitinib treatment also lowered plasma insulin during a glucose challenge and improved insulin resistance only in Ripk2−/− mice, suggesting that deletion of Ripk2 improves the insulin sensitizing effect of gefitinib during obesity. These findings highlight that inhibition of RIPK2 by SMKIs may be limiting their therapeutic potential to not only lower blood glucose but improve insulin sensitivity in obesity and T2D (Duggan et al. 2020) (Fig. 3).
SMKIs for RIPK2 attenuate acute NOD1 and NOD2 glycemic consequences, but inhibition of RIPK2 limits the insulin sensitizing potential of a SMKI during obesity.
Citation: Journal of Endocrinology 256, 2; 10.1530/JOE-22-0212
RIPK1 and RIPK3
RIPK1 and RIPK3 regulate immune, cellular stress, and cell survival responses. These kinases also work to mediate cell apoptosis and necroptosis, which primarily engage RIPK3 (Wegner et al. 2017). Sorafenib, sunitinib, pazopanib, ponatinib, and dabrafenib are all potent inhibitors of RIPK1 and/or RIPK3 and are being investigated for therapeutic repurposing in various inflammatory conditions. Interestingly, emerging evidence connects RIPK1 and RIPK3 to glucose homeostasis and metabolic disease. Overexpressing variants of the Ripk1 gene are associated with obesity in humans, and Ripk1 silencing in mice fed a HFD results in decreased body weight, adiposity, improved insulin sensitivity, and reduced markers of non-alcoholic steatohepatitis (NASH) (Karunakaran et al. 2020, Tao et al. 2021). This evidence positions RIPK1 as a potential therapeutic target for obesity-associated metabolic disease and as a potential mechanism underlying the blood glucose-altering effects of some SMKIs. Similarly, Ripk3 is overexpressed in the adipose tissue of obese mice and humans, and hepatic Ripk3 expression is correlated with the severity of fatty liver disease. In contrast to RIPK1, genetic knockdown of Ripk3 exacerbates inflammation and glucose intolerance during HFD-feeding (Gautheron et al. 2016, Afonso et al. 2021). A recent study established a role for RIPK3 in mediating IL-1β-dependent macrophage infiltration and β-cell dysfunction in response to ER stress or overnutrition, and inhibition of RIPK3 protected against islet inflammation and β-cell dysfunction in mouse pancreatic β-cell line (Yang et al. 2020). While current available evidence suggests that RIPK1 may be a therapeutic target of SMKIs that can contribute to improvements in glycemic control, there is conflicting evidence for the role of RIPK3 in protecting against inflammation and glucose tolerance during obesity versus precipitating β-cell inflammation and potential insulin secretion defects. Therefore, the role of RIPK3 in mediating SMKI’s effects on blood glucose and insulin remains unclear. Given that RIPK1 and RIPK3 have emerged as regulators of metabolism, further studies involving these kinases as potential targets of SMKIs are needed.
Bruton's tyrosine kinase
BTK was initially discovered as the genetic cause of X-linked agammaglobulinemia (XLA), a condition characterized by defective development of B cells resulting in impaired immunoglobulin production (Vetrie et al. 1993). This non-receptor tyrosine kinase is crucial for the activation of B cells and other cells of the hematopoietic system (Liu et al. 2017). BTK influences proliferation, differentiation, and survival of B-cells, which makes this kinase a prime treatment target in many B-cell malignancies (Weber et al. 2017, Pal Singh et al. 2018). BTK has also been implicated in the activation of toll-like receptor (TLR) signaling pathways, demonstrating a role for BTK in mediating both innate and adaptive immunity (Weber et al. 2017). Ibrutinib and acalabrutinib represent the two main FDA-approved SMKIs for BTK, used in the treatment of relapsed/refractory chronic lymphocytic leukemia (CLL), mantle-cell lymphoma (MCL), among several other malignancies (Byrd et al. 2013, Wang et al. 2013b ). Ibrutinib and acalabrutinib are designed to bind Cysteine-481 on BTK; however, off-target binding of several other homologous cysteine-containing kinases, such as the TEC family kinases (ITK, BMX, TEC), EGFR, T-cell X chromosome kinase (TXK), and Janus kinase 3 (JAK3), has been reported, which can lead to adverse side-effects (Honigberg et al. 2010, Byrd et al. 2016, Herman et al. 2017). Although data are not available for acalabrutinib, kinase assay data for ibrutinib indicate that it inhibits RIPK2, EGFR, and c-ABL to a similar degree as BTK and ibrutinib is approximately half as potent for inhibition of c-Kit and PDGFRβ targets (Table 2).
Beyond cancer treatment, studies investigating the deficiency or inhibition of BTK have demonstrated therapeutic potential in numerous inflammatory disease models like autoimmune arthritis, ischemic brain injury, and colitis, among others (Chang et al. 2011, Ito et al. 2015, Mao et al. 2020). The idea of BTK as a point of regulation for NLRP3-associated inflammation has gained traction in recent years (Liu et al. 2017, Purvis et al. 2020, Bittner et al. 2021). The NLRP3 inflammasome mediates the activation and production of several inflammatory cytokines, which can exacerbate insulin resistance, and NLRP3-mediated IL-1β activation contributes significantly to the development of obesity and T2D (Xu et al. 2003, Zhao et al. 2014). Ibrutinib treatment impairs activation of the NLRP3 inflammasome, thereby lowering IL-1β processing and release in human primary macrophages and tumor-associated macrophages (Liu et al. 2017, Benner et al. 2019, Purvis et al. 2020). A recent study demonstrated that BTK is highly expressed in visceral adipose tissue samples collected from obese and diabetic human subjects and a positive correlation has been established between BTK gene expression and pro-inflammatory IL-1 expression in these samples. By targeting BTK via inhibition with ibrutinib, improvement in insulin resistance and increase in glucose uptake were observed in a human liver cancer cell line (Althubiti et al. 2020).
Consistent with this, HFD-feeding in mice has been shown to increase BTK expression and activation in liver, and treatment of HFD-fed mice with ibrutinib led to significantly lower levels of plasma insulin and non-fasting blood glucose and an improvement in insulin signaling in liver (Purvis et al. 2020). As newer generation inhibitors, little to no clinical reports of ibrutinib or acalabrutinib lowering blood glucose in diabetic cancer patients exist, but in vitro and rodent models support a role for targeting BTK to improve glycemic control in the context of metabolic disease, although inhibition of BTK versus other clinically relevant kinase targets (RIPK2, c-ABL, c-Kit, PDGFRβ) must also be considered as an underlying mechanism of action.
Conclusions
Understanding how specific SMKIs can alter blood glucose, insulin, and insulin sensitivity is important for both safe and effective use of SMKIs in their primary therapeutic indications, such as cancer or inflammatory disease. In addition to concerns of SMKI-induced acute hypoglycemia, higher blood glucose can affect the disease course of cancer and inflammatory conditions, and therefore, it is important to understand which SMKIs alter blood glucose and insulin and which kinase targets mediate these effects. The effects of SMKIs on blood glucose and insulin are complex, depend on the effective dose, and are complicated by the multiple kinases inhibited by a given SMKI (Fig. 4). It is important to distinguish between studies that use inhibitors to test the role of a given kinase versus genetic deletion of the target kinase, and ideally, genetic knockout models can be used to validate the proposed target kinase that is mediating the effects of a given SMKI effect. In addition, SMKI drugs in development include inhibitors of phosphoinositide 3-kinases (PI3K) and protein kinase B (PKB)/AKT, which are critical nodes in insulin signaling pathways, where some potential signaling redundancies could be exploited (Liu et al. 2009, Molinaro et al. 2019). How these new SMKIs impact glycemic control and insulin dynamics will need to be carefully considered. An important future goal will be to assess the potential synergy between inhibiting multiple kinases, which occurs with each SMKI, as well as how SMKIs impact the host–microbe relationship, which can have important outcomes in cancer and diabetes progression and therapy.
SMKIs alter insulin production, insulin sensitivity, and blood glucose control through multiple kinases.
Citation: Journal of Endocrinology 256, 2; 10.1530/JOE-22-0212
Declaration of interest
The authors have no conflict of interest related to this research.
Funding
This work was supported by a grant to JDS from the Natural Science and Engineering Research Council (RGPIN-2020-05707). JDS holds a Canada Research Chair in Metabolic Inflammation. DMM was supported by a Canadian Institute for Health Research graduate scholarship. Figures were created with Biorender.com.
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