Cathegories of AML mutations and their role in leukemogenesis.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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The better knowledge of leukemogenesis has led in the last few years to approval of new target drugs for AML treatment. The availability of these drugs has dramatically changed the AML treatment guidelines, supported by the evidence of their efficacy on a molecular driven basis approach. Neverthless primary resistance and clonal evolution leading to adaptive resistance is a recurring theme even in this setting.
Actually acute myeloid leukemia (AML) is the result of a multi-step sequence of events resulting in impairment of lineage differentiation, hematopoiesis and enhanced self-renewal. Somatic mutations contribute to AML pathogenesis in different manner. Analysis of healthy population exomic and genomic sequencing [1] showed a correlation between pre-leukemic somatic mutations (IDH1/2, SRSF2, U2AF1, TP53, RUNX1, PPM1D) and subsequent development of AML, as first step process towards leukemogenesis. The subsequent acquisition of mutations appeared to be related with different AML phenotypes. The Cancer genome atlas research network [2] identified eight different genetic pathways responsable of leukemogenesis in 200 adult patients, shown in Table 1 (trancriptor factor genes fusion and hyperexpression; nucleophosmin 1 delocalization; tumor suppressor genes inhibition; mutations of: DNA-methylation related genes, activated signaling genes, chromatin-modifying genes, cohesin-complex genes, spliceosoma-complex genes). Afterwards Papaemmanuil et al. [3] identified three other molecular subgroups including: IDH2R172 mutation in 1% of AML, mutually exclusive with NPM1, associated with more severe alterations of metabolic activity in comparison to other IDH2 mutations; CCAAT/enhancer binding protein alpha (CEBPA) biallelic mutated AML and inv3 or t(3;3) AML with MECOM (EVI1) and GATA2 mutations. Furthermore Ibanez et al. [4] analyzed 100 patients with normal karyotype AML, lacking NPM1, FLT3, and CEBPA mutations, identifying thirteen seed-genes involved in leukemogenesis with a mean of 4.89 mutations per sample. The network analysis showed a high heterogeneity of gene mutations in this setting and suggested that a specific alteration could not be essential for leukemogenesis, as the interaction between several deregulated pathways.
Mechanisms of action | Class of mutations | Mutations/translocations (prevalence) |
---|---|---|
Transcription deregulation and impaired hematopoietic differentation. | Class 1 Transcription factor fusions* | t(8;21), t(16;16), t(15;17), MLL fusions (18%) |
Aberrant localizationn of NPM1 and MPM1-interacting protein. | Class 2 NUCLEOPHOSMIN 1 | NPM1 mutations (27%) |
transcriptional deregulation and impaired degradation through the mouse double minute 2 homolog (MDM2) and the phosphatase and tensin homolog (PTEM). | Class 3 Tumor suppressor genes | TP53, WT1, PHF6 (16%) |
Epigenetic modification and accumulation of 2-hydroxyglutarate (2HG) which deregulates DNA methylation | Class 4 DNA-methylation-related genes: DNA hydroxymethylation | TET2, IDH1, IDH2, DNA methyltransferases DNMT3A (44%) |
Proliferative advantage through the RAS– RAF, JAK–STAT, and PI3K–AKT signaling pathways. | Class 5 Activated signaling genes | FLT3, KIT, RAS mutations (59%) |
Deregulation of chromatin modification through methylation of histone or impairment of DOT1L (DOT1-like histone H3K79 methyltransferase). | Class 6 Chromatin-modifying genes | ASXL1, EZH2 mutations, MLL fusions, MLL partial tandem duplications (30%) |
Transcription deregulation | Class 7 Myeloid transcription factor genes* | CEBPA, RUNX1 mutations (22%) |
Chromosome segregation and transcriptional regulation. | Class 8 Cohesin-complex genes | STAG2, RAD21, SMC1, SMC2 (13%) |
Deregulation of RNA processing. | Class 9 Spliceosoma-complex genes | SRSF2, U2AF3S, ZRSR2 (14%) |
Cathegories of AML mutations and their role in leukemogenesis.
Class 1 and 7 mutations are both included in the category of mutations of transcription factors genes.
The perspective of the comprehension of the eterogeneity of the disese inspired recent studies exploring genetic and transcriptomic single leukemic cell analysis with the following aims:
find the correlation with mutations and subclonal architecture;
define a hierarchies of leukemic clones, compared to normal hemapotoiesis;
identify new markers and leukemic stem cell (LSC) specific gene repertoire.
The aknowledgement of these data will promote the finding of future targets for the eradication of the disease even in the biologically chemoresistant setting of LSC.
Uptoday the understanding of leukemogenesis mechanisms have led to the recent approval of FLT3, BCL2 and IDH inhibitors (FLT3i, BCL2i, IDHi).
We briefly resume the mechanisms of leukemogenesis addressed by these drugs. FLT3 tirosin kinase receptor mutations determines the constitutive activation and dimerization status of the receptor itself, indipendently from FLT3 ligand binding, and the downstream activation of leukemic cells prolipheration and pro-survival pathways (RAS-NFKB, JAK–STAT, PI3K, BCL2) as showed in Figure 1 [5]. BCL2 is an antiapoptotic protein of BCL2 family which compete with BH3 for the binding with the pro-apoptotic proteins BAK/BAX [6]. It inhibits the BH3-BAK/BAX domain and its interaction with the mitochondrial membrane, blocking the p53 dependent mitochondrial apoptosis pathway of the leukemic cell (Figure 2). Isocitrate dehydrogenases are cytoplasmic (IDH1) and mitochondrial (IDH2) enzymes cathalyse the reduction of a-ketoglutarate (a-KG) to citrate in krebs cycle in a NADPH-dependent way. NADPH is important for the reduction of glutathione, which in the reduced state is a major antioxidant and protects the cell against reactive-oxygen species (ROS) and other free radicals. IDH mutations have a loss of function effect, producing the accumulation of the oncometabolite R2-hydroxyglutarate (2-HG) which competitively inhibits multiple α-ketoglutarate dependent dioxygenases such as lysine (K)-specific demethylase (KDM) and ten eleven translocation methylcytosine dioxygenase 2 (TET2), causing widespread epigenetic changes with global dysregulation of gene expression and abnormal differentiation and proliferation of leukemic cells (Figure 3) [7]. Furthermore 2-HG activates the EglN family of prolyl 4-hydroxylases (EglN), with consequent ubiquitination and degradation of HIF1a, impairing p53 dependent apoptosis. IDH1 mutations also result in a lack of crucial metabolites including a decrease in the NADPH pool and inhibition of krebs cycle with metabolic changes conferring chemotherapy resistance of leukemic cell. At last 2-HG determines a leukemic status highly BCL-2 dependent, preventing the hypoxia mediated apoptosis, determined by cytochrome c oxidase inhibiton.
FLT3 pathway (green label) and mechanisms of resistance to FLT3i (light yellow labels).
p53 mitochondrial pathway and mechanisms of resistance to Venetoclax (light yellow labels).
Mechanism of leukemogenesis of IDH mutations (green label) and mechanisms of resistance to IDHi (light yellow label).
Recent studies utilizing NGS and single-cell technologies have also illustrated the complex and polyclonal nature of resistance to targeted therapeutics including FLT3, BCL2 and IDH inhibitors (FLT3i, BCL2i, IDHi) [8, 9]. Here we report the results of the principle studies aiming to analyze mechanism of primary and secondary leukemic resistance to new approved target therapies.
FLT3 is a Tirosin Kinase receptor expressed by hematopoietic progenitors and mutated in 25-30% AML. The mutations involve two different domains: the iuxtamembrane domain (FLT3 ITD) in 20-25% AML and the tirosin kinase domain (TKD) in 5-10% AML, expecially at codon D835. They both determine the constitutive activation of the FLT3 receptor tyrosine kinase, inducing cellular proliferation and survival and inhibiting differentiation, through the activation of PI3K/AKT/mTOR pathway, with a critical role in leukemogenesis [10] (Figure 1).
Target drugs inhibiting FLT3 receptor showed different potency of inhibition, activity on FLT3-ITD versus TKD mutations, and on non-FLT3 targets (i.e., kinome specificity), with variable off-target toxicities [11].
Type I FLT3i (Lestaurtinib, Midostaurin, Gilteritinib, Crenolanib) are active against both FLT3-ITD and TKD mutations because they interact with the gate-keeper domain near to the activation loop or with the ATP binding site, expleting their activity on both active dimeric and inactive monomeric tirosin kinase receptor. Type II FLT3i (Quizartinib and Sorafenib) bind to the hydrophobic region adjacent to the ATP binding site only when the receptor is in an inactive form and are therefore ineffective in the forms with the FLT3TKD mutations where the receptor is always in the dimeric active form.
Midostaurin, a type I FLT3i, also targets c-KIT, PKC, PDGFR, and VEGFR [12] and is FDA, EMA and AIFA approved for the first line treatment of FLT3 mutated (FLT3-mut) AML in association with 7 + 3 in induction and high dose Cytarabine in consolidation, on the basis of the results of the multinational, randomized phase III trial RATIFY (CALBG 10603) [13]. Midostaurin or placebo were given during induction and consolidation, and could be given for up to one year as post-consolidation maintenance, allogeneic transplant was admitted after the stop of the experimental treatment. Midostaurin was associated with a significant improvement in OS (4-year OS rate: 51.4% versus 44.3%; median OS: 74.7 months versus 25.6 months, HR = 0.78; P = 0.009) regardless of the type of
Quizartinib is a type II FLT3i, but also a potent inhibitor of c-KIT, PDGFR, and RET achieving 45-50% marrow remission rates as single-agent in relapsed/refractory (R/R)
Gilteritinib is another potent second-generation type I inhibitor with activity against AXL, a receptor tyrosine kinase that may play a role in mediating resistance to earlier generation FLT3 inhibitors [15]. Gilteritinib was found to be well tolerated as single-agent in a randomized phase III study enrolling R/R FLT3-mut AML, with marrow remission rates of 54% superior to the 22% CRc rate observed after investigator choice salvage chemotherapy (both high- and low-dose chemotherapy), with also a longer median OS (9.3 months vs. 5.6 months, HR = 0.79; P = 0.007) [16]. More patients (26% vs. 15%) were able to proceed to HSCT with gilteritinib compared with salvage chemotherapy. These results led to Gilteritinib FDA approval for the treatment of R/R FLT3-mut AML (both ITD and TKD) in November 2018.
Some concerns on these last two trials have been recently raised by a french retrospective analysis of 160 patients with R/R (114 relapsed and 46 refractory) FLT3-mut AML after a first-line TKI-free treatment, 92 of whom fulfilling the main criteria of the QUANTUM-R study, with CR1 durations <6 months, who received an intensive salvage regimen in 48.9% of cases achieving a 52.8% CRc rate and a bridge to transplant rate of 39.6%, superior to 27% of CR and 11% of bridge to transplant rates observed in the same setting in QUANTUM-R. The Median OS of 7 months observed in the French study was also superior to the Quantum-R OS of 4.7 months. The authors argue that the possible bias, caused by the inclusion in the control arm of patients receiving low-intensity regimens, such as low-dose cytarabine or hypomethylating agents, might compromise the results of similar phase 3 trials [17].
Neverthless hematopoietic stem cell transplant (HSCT) is still necessary and recommended for the cure of the disease, since retrospective studies [18, 19] showed that HSCT improves RFS and OS and reduces incidence of relapse. The favorable predictive role of FLT3 allelic ratio in NPM1 mutated AML is still controversial due to lack of standardization of techniques and thresholds of this factor [20, 21]. Novel FLT3i might increase outcomes in this setting, but researchers have already identified multiple mechanisms of resistance as hereby reported [11].
The acquisition of secondary mutations of single amino acids of the activation loop of the FLT3 receptor (D835, I836, D839, Y842) or of the gate-keeper residue (F691) called ‘TKD’ mutations are reported in 22% of FLT3 AML [22] and are responsible for the resistance specially to type II FLT3i (Quizartinib and Sorafenib) ineffective in targeting TKD mutations [23, 24].
The activity of fibroblast growth factor 2 (FGF2) and CXCL12/CXCR4 pathways in FLT3 mutated leukemic cells can induce their chemoresistance. The increase in FGF2 is an autocrine response mechanism of stromal cells to all phenomena of hematopoietic stress, including that induced by Quizartinib. Paracrine production of FL (FLT3 ligand) by stromal cells also inhibits the action of FLT3 inhibitors with competitive mechanism, but removal of FL from stromal and leukemic cell cultures does not stop the chemoresistance process due to activation of RAS-MAPK mediated by the FGF2-FGFR1 interaction. The increase in FGF2 secreted by stromal cells has been reported to precede the relapse of mutated FLT3ITD mut AML treated with Quizartinib, through activation of the RAS–MEK/MAPK signal [25]. The combination of FGFR and FLT3 inhibitors is being studied, the rationale is represented by the inhibition of the autocrine and paracrine stimulus favoring the survival of the stromal and leukemic cells, respectively.
Furthermore FLT3ITD mutated leukemic cells express CXCR4 and are CXCL12 dependent for growth and survival, which makes them resistant to the action of chemotherapy [26]. Activation of Nutlin-3a reduces mRNA levels and CXCL12 secretion through activation of p53 and consequent down-regulation of HIF-1 alpha. Nutlin-3a also binds MDM2 in the p53 binding domain, inhibiting its interaction with p53 which, remaining free, recovers its function. MDM2 inhibitors such as Nutlin-3a are under study (NCT00623870) and there is a rationale for their association with FLT3 inhibitors [27].
The activation of RAS/RAF/MEK/ERK is independent of the activation of FLT3 as it is constantly present during therapy with FLT3 inhibitors and can represent a mechanism of resistance to inhibitors in some subclones. Inhibitors of these signals could play a role in counteracting resistance to FLT3 inhibitors [28].
The PI3K/AKT/mTOR pathway is activated and upregulated in FLT3 mut AML resistant to FLT3i, suggesting the efficacy of AKT and mTOR inhibitors in this setting. In vitro studies have shown that Sorafenib is able to inhibit FLT3 in leukemia cells of resistant patients without blocking colony formation and survival, due to a replication mechanism independent of FLT3. The GEP analysis of these cells and cell lines resistant to FLT3 inhibitors has shown downstream activation of PI3K/mTOR and in vitro and in vivo studies have shown that Gedatolisib is able, by inhibiting this pathway, to block the formation of colonies and to improve the survival of mice transplanted with Sorafenib-resistant cells [29].
The activation of the FLT3 receptor also promotes leukemogenesis through the trigger of STAT5 and Pim-1 (serine–threonine oncogenic kinase). The FLT3ITD receptor is partially reteined in the endoplasmic reticulum as a 130 kDa protein that interacts with calnexin and heat shock protein 90 (HSP90), resulting in the upregulation of STAT5 and the consequent Pim-1 increase. Pim-1 promotes the phosphorylation at the level of serine and tyrosine 591 of the 130 kDa isoform of FLT3ITD, blocking its glycosylation and degradation, with consequent hyperactivation of calnexin, HSP90 and STAT5 by establishing a FLT3-STAT5-Pim-1 hyperactivation loop that determines the proliferation of mutated FLT3ITD cells and their resistance to FLT3i. STAT5 and Pim-1 inhibitors might therefore have a rationale in resistant FLT3 mut AML [30].
CDK4 and CDK6 regulate the transcription of FLT3 and Pim-1 therefore CDK4 and CDK6 inhibitors, such as Palbociclib, are also useful in this setting [31].
The upregulation of anti-apoptotic proteins Bcl-2, Bcl-xl, Mcl-1 proteins has been described in AML resistant to second generation FLT3 inhibitors. In particular, the FLT3-ITD627E mutation, located in the beta-2 sheet of the first kinase domain, induces a dramatic increase in anti-apoptotic protein MCL-1 suggesting a possible role of MCL-1 inhibitor drugs in blocking resistance to FLT3 inhibitors [32].
The concentration in the marrow at suboptimal doses represents an additional mechanism of resistance, specially of inhibitors that have interactions with drugs metabolized by cytochrome P450 [33].
Recently NGS and single cell analysis were performed in 41 patients with FLT3mut AML relapsing after Gilteritinib monotherapy, permitting the identification of mechanisms of resistance in 22 cases. RAS and FLT3 F691L mutations were found in 15 and 5 patients respectively and acquisition of Bcr Abl fusion protein was found in 2 samples. The FLT3 F691L mutation was more frequent in patients receiving Gilteritinib at dose lower than 200 mg, suggesting a dose dependent resistance and the importance of using higher doses to prevent this mechanism of failure [8]. FLT3 pathway and mechanisms of resistance to FLT3i are illustrated in Figure 1.
Crenolanib is a potent novel type I pan-FLT3 inhibitor, effective against both ITD and TKD, but the response in monotherapy is unfortunately transient. Zhang et al. performed WES of samples from R/R FLT3 pos AML patients before and after Crenolanib, administered in a phase II study (NCT 01522469, NCT 01657682). They interestingly observed that patients previously treated with FLT3i expressed RAS mutations at baseline more frequently than naive patients and were less likely to respond to Crenolanib. They identified mutations of NRAS and IDH2 arising in FLT3-independent subclones and of TET2 and IDH1 in FLT3-subclones as possible mechanisms of resistance. Post-crenolanib expansion of mutations associated with epigenetic regulators, transcription factors, and cohesion factors was also detected suggesting diverse genetic/epigenetic mechanisms of crenolanib resistance. Drug combinations in experimental models restore crenolanib sensitivity [34].
FLT3 F691L mutation was shown to be resistant to the majority of FLT3 TKIs including crenolanib, but not ponatinib and pexidartinib (PLX3397) [35]. In addition, a novel FLT3 extracellular mutation at K429E was detected in one patient with high VAF, which showed increased crenolanib IC50. The structural basis for the drug resistance of FLT3 K429E requires further investigations.
Given the expanding spectrum of FLT3 inhibitors FDA-approved, randomized phase III studies of conventional chemotherapy in combination with midostaurin versus gilteritinib (NCT03836209) and with midostaurin versus crenolanib (NCT03258931) are ongoing to establish wich FLT3i should be used in first line. Phase III study of gilteritinib versus placebo and phase II Crenolanib trial as maintenance after HSCT in FLT3-mutated AML are ongoing and may help to more definitively address the benefit of FLT3 inhibition in this setting (BMT CTN 1506; ClinicalTrials.gov identifier: NCT02997202, NCT02400255). Last but not least, the recent findings of intra- and extracellular mechanisms of FLT3i resistance, provided the background of ongoing trials, rationally including combinations with agents targeting specific resistance pathways. Current approaches include adding FLT3i to antiapoptotic drugs such as Venetoclax or milademetan or with drugs inhibiting other target such as PIM kinasi or CDK4/6. The addition of chemotherapy or hypomethylating agents (HMA) to this backbone could be the following step in patients eligible and not to intensive chemotherapy. Table 2 reports the ongoing studies exploring association of FLT3i with other drugs.
Combination regimen | Mechanism of action of combination agent | Mechanism rationale for combination | Clinicaltrials.gov identifier |
---|---|---|---|
LGH447 + midostaurin | Pim kinasi inhibitor | Pim kinasi activity mediates FLT3 inhibitor resistance; combination increases apoptosis | NCT02078609 |
Milademetan (DS-3032b) + quizartinib | MDM2 inhibitor | MDM2 inhibitor restore p53 tumor suppression function | NCT03552029 |
Omacetaxine mepesuccinate + sorafenib Omacetaxine mepesuccinate + quizartinib | Protein synthesis inhibitor | Synergistic with FLT3 inhibitors to suppress leukemic proliferation | NCT03170895 NCT03135054 |
Palbociclib + sorafenib | CDK4/6 inhibitor | CDK4/6 regulate transcription of FLT3 and Pim kinases (mechanism of FLT3 inhibitor resistance) | NCT03132454 |
SEL24 (dual pan Pim/FLT3 inhibitor) | Pim kinase inhibitor | Pim kinase activity mediates FLT3 inhibitor resistance; combination increases apoptosis | NCT03008187 |
Venetoclax + gilteritinb | Bcl-2 inhibitor | Upregolation of anti-apoptotic proteins (e.g. Bcl-2, BCL-xL and Mcl-1) mediates FLT3 inhibitor resistance | NCT03625505 |
Azacitidine + venetoclax + gilteritinib | Hypomethylating agent Bcl-2 inhibitor | Hypomethylation of target genes | NCT04140487 |
Vorinostat + bortezomib + sorafenib | Hystone deacetylase inhibitor (vorinostat) Proteasome inhibitor (bortezomib) | Histone deacetylase inhibitors synergistically induce apoptosis with FLT3 inhibitors; Proteasome inhibitors induce FLT3 ITD degradation through autophagy | NCT101534260 |
Trials with combinations of FLT3i and target drugs.
One of the possible mechanisms of leukemogenesis is represented by the functional loss of p53 or by an altered balance of antiapoptotic and proapoptotic protein expression [36]. Apoptosis is controlled by two parallel pathways, intrinsic and extrinsic, leading to activation of intracellular caspases, ending with cell death. The intrinsic pathway is under the control of the BCL2 family proteins, including antiapoptotic proteins (e.g., BCL2, BCL-XL, and MCL1), proapoptotic BH3-only proteins (e.g., BIM, BAD, PUMA, and NOXA), and proapoptotic effector proteins (e.g., BAK and BAX) (Figure 2). Leukemic cells showed to overexpress BCL2 making of Venetoclax, an oral inhibitor of BCL2, an ideal target therapy. Venetoclax acts as BH3 mimetic protein and restors apoptosis without interacting with other antiapoptotic proteins such as BCL-XL or MCL-1. Based on phase II study outstanding results [37] venetoclax has been approved by FDA, EMA and AIFA for first line treatment of elderly AML over 75 years or unfit for intensive chemotherapy. The trial associated venetoclax at the doses of 400 or 800 mg daily in combination with either decitabine (20 mg/m2, days 1-5, intravenously [IV]) or azacitidine (75 mg/m2, days 1-7, IV or subcutaneously) in 145 AML patients, not eligible to intensive chemotherapy, in first line. CRc (CR and CRi) rate was 73% with a median duration of 11.3 months and a median overall survival of 17.5 months.
Avoidance of apoptosis and the acquisition of BCL-2 mutations such as BCL2 Gly101Val are among the mechanisms currently identified for resistance in chronic lymphocitic leukemia. MCL-1 inhibitors appear to bypass this mutation in preclinical studies and preliminary clinical studies with these agents are ongoing [38].
Mechanisms of resistance to Venetoclax, have been further investigated in AML. A recent study of DiNardo [39] performed NGS at baseline and relapse and follow-up and single cell analysis at baseline and relapse in 81 AML patients receiving HMA (N: 58) or low dose cytarabine (LDAC) (N: 23) with Venetoclax in frontline (NCT02287233 and NCT02203773) at the MD Anderson Cancer Center (Houston) or at the Alfred Hospital (Melbourne). The median age of this elderly cohort was 74 years (range, 62-87 years). The HMA group excluded prior HMA therapy. In contrast, the LDAC group included patients with prior HMA exposure. The target dose of venetoclax was also different in the 2 studies (HMA study: 400 mg/day; LDAC study: 600 mg/day).
Twenty-five cases had adaptive resistance, representing 31% of the total cohort of 81 patients. The median time to relapse was 6.4 months (95% confidence interval, 4.5-10.6 months); 5 patients relapsed after 12 months. To identify dynamic molecular changes indicative of adaptive resistance, the VAFs of individual mutations were compared at diagnosis, in remission, and at relapse to identify clones expanded at relapse. Two important findings emerged: progressive expansion of clones with activated kinases, particularly FLT3-ITD, and in other cases, selection of clones with likely biallelic perturbation of TP53. The single cell analysis of the relapsed clones also showed the selective impact of the expansion of FLT3-ITD or other kinase (CBL, NRAS) in mediating resistance with FLT3-ITD loss of heterozygosity (LOH) at relapse.
In contrast, NPM1mut and IDH2mut were associated with high rates of response and durable remissions. In NPM1mut AML, measurable residual disease (MRD) was eliminated in most cases. Median OS for patients with either NPM1mut or IDH2mut was not reached, with 2-year OS of 71.8% and 79.5%, respectively. In the durable remission group, DNMT3A mut was present in 44% of cases (8/18), and 6 out of 8 of these cases were among patients with concurrent NPM1 or IDH2mut. The association between IDH1mut and prognosis was less clear. There were 2 IDH1mut cases in the durable remission subgroup, and both had a co-occurring NPM1mut. Among the 7 IDH1mut cases occurring in patients with relapsing or primary refractory disease, 5 cases had a concurrent TP53, FLT3-ITD, or RAS mutation. The median OS for patients with IDH1mut was not significantly different from patients with IDH1 wild-type (WT) AML (18.3 vs. 12.7 months; P = 0.79).
Primary refractory AML had 3 patterns of resistance: TP53 abnormality, RUNX1 and activating kinase mutations (FLT3-ITD, N/KRAS, CBL, or KIT). The VAF of TP53 mutation was higher in refractory patients, while RUNX1 mutations were also found in responder patients, in association with IDH1 and SRSF2 mutations, suggesting that larger studies are needed to refine their role in resistance to Venetoclax. Figure 2 shows the mithocondrial pathway of p53 and the mechanisms of resistance to venetoclax.
On the basis of the results of the analysis performed by DiNardo et al. [39], a baseline molecular characterization may allow patients to be risk stratified into a favorable risk NPM1mut subgroup, where molecular MRD monitoring, and even consideration of treatment cessation, could be employed within a future clinical trial. Patients with IDHmut could be considered for postremission IDH inhibitor maintenance-based approaches, aimed at eradicating residual molecular disease, and patients with FLT3-ITD mutations could also benefit from the addition of targeted FLT3 inhibitors to prevent failures. Relapsed and refractory patients with TP53 mutations actually still represent an unmet medical need. Clinical trials incorporating new agents targeting TP53mut/del(17p) drug resistant clones should be pursued. APR 246 showed encouraging results in a phase I/IIa clinical trial of patients with hematological malignancies or prostate cancer [40] due to the reactivation of the disrupted TP53 through the conversion to methylene quinuclidinone (MQ), a Michael acceptor that reacts with cysteines in the p53 core domain restoring its activity [41]. However, the mechanism by which APR-246/MQ reactivates mutant p53 is not fully understood. In early results from an ongoing phase Ib/II study in patients with high-risk
Transcriptomic analysis after run-in of single-agent APR-246 confirmed on-target effects, including transcriptional activation of p53 targets. A phase III randomized study of azacitidine with or without APR-246 in MDS and AML with 20–30% blasts is ongoing (NCT03745716).
Preclinical studies showed sinergy between MCL1 inhibition and venetoclax [43] and therefore MCL1 inhibitors are now being explored in early clinical trials, both as single agents and in combination with venetoclax. Interestingly BH3 profiling might predict sensitivity to specific BH3 agents helping the choice between BCL2 or MCL1 inhibition or a combination of both [44].
In vitro studies also showed a synergistic effect of MDM2 inhibitors in combination with BCL2i due to the downregulation of MCL1 through the inhibition of RAS–RAF–MEK–ERK pathway. In an ongoing international phase Ib study of venetoclax and idasanutlin, in patients aged >60 years with relapsed or refractory AML, the marrow remission rate was 37% (11 of 30 evaluable patients) in the entire population, and 50% (9 of 18 evaluable patients) at the recommended phase II dose of venetoclax. As with other venetoclax-based regimens, higher ratios of BCL2/BCL-XL and BCL2/MCL1 were predictive of response [45].
IDH1 and 2 are targetable mutations occurring in approximately 20% of Acute Myeloid Leukemia (AML) patients [IDH1 (8%) and IDH2 (12%)] and are more common in the elderly (25-28%). They are usually associated with intermediate-risk cytogenetics, FLT3 and NPM1 mutations [46, 47] and mutually exclusive with the TET2 mutation [48].
Hotspot IDH1 mutations, affecting the catalytic domains, commonly involve a cysteine (R132C) or histidine (R132H) substitution for arginine. In IDH2 mutations, arginine is most often replaced by glutamine at residue 140 (R140Q) or by lysine at residue 172 (R172K) [49]. Further, IDH2 R172 and NPM1 mutations were not detected in the same patient samples [50]. The incidences of IDH1 and IDH2 mutations are equivalent and mutually exclusive [51]; however, the incidence rate of IDH2 R140Q was found to be higher than that of IDH2 R172K (9.2% vs. 2.9%) [52]. Somatic mutations in catalytically active arginine residues decrease their enzymatic activity as well as confer a gain of function activity leading to the production of the oncometabolite 2 hydroxyglutate (2-HG) instead of alpha-ketoglutarate (α-KG). 2-HG competitively inhibits the function of αKG-dependent oxygenases involved in DNA or histone demethylation, increases the production of ROS (reactive oxygen species) through the oxydation of Glutathione and determines metabolic changes interfering with NF-Kb and BCL-2 proteins, such as ten-eleven translocation (TET2) DNA methylases, and Jumonji C (JmjC) domain containing histone demethylases, resulting in global DNA hypermethylation of regulatory genes and arrested myeloid differentiation [53] (Figure 3). Mutant IDH has therefore become a viable target in AML treatment.
The prognostic value of the different mutation isoforms remain controversial [54]. Some AML patients with IDH mutation, especially IDH2 R172 mutation, have a poor response to traditional chemotherapy and have a higher relapse rate [55]. Therefore, individualized treatment, specially targeted therapy for IDH mutations, may be an important option for such patients. In recent years, IDH inhibitors have shown good clinical response in AML patients. Based on phase 1/2 clinical trials, enasidenib and ivosidenib have been approved by the FDA on 1 August 2017 and 20 July 2018 for the treatment of adult R/R AML with IDH2 and IDH1 mutations, respectively [56, 57]. Ivosidenib 500 mg/day in combination with subcutaneous azacitidine was associated with an ORR of 78% (18/23) and 30% of CRs with a median OS of 12.6 months, while in patients with IDH2 mutation, enasidenib (100 mg once daily) plus azacitidine was associated with an ORR of 67% with 20.6% of CRs and median OS of 9.3 months. No patient in the ivosidenib group and one patient in the enasidenib group had progressive disease.
To fully characterize the mechanisms of response and relapse to ivosidenib monotherapy, Choe et al. [58] conducted a comprehensive genomic analysis of samples from a cohort of 179 patients with m
Single-cell DNA sequencing analyses also found co-occurring mutations at single-cell resolution, including genes of the RTK pathway (eg,
The analysis of baseline mutational profiles of 101 mIDH1 AML patients [59] showed similar results with the following comutations: DNMT3A (35%), NPM1 (26%), SRSF2 (24%), ASXL1 (18%), RUNX1 (18%), NRAS (14%), and TP53 (13%); FLT3TKD (9%), FLT3ITD (2%), TET2 (14%). The achievement of CR was analogously related to lower genomic complexity with lower number of comutations in responders compared to non responders (2.8 vs. 3.7), with P < .001. RTK pathway mutations, along with an increased number of mutations, are conversely associated with primary treatment resistance.
Actually, although 2-HG–restoring mutations are a major pathway of resistance, other 2-HG-independent pathways, such as RAS and FLT3 mutations, are important and may be dominant over 2-HG restoration.
This finding is consistent with a similar work showing an association between NRAS mutations and a lower likelihood of response to enasidenib in patients with mIDH2 R/R AML [60].
Amatangelo et al. showed emergence of AML-related mutations, such as
Some reports have shown that FLT3 inhibitors induce granulocytic differentiation and differentiation syndrome symptoms in some patients with
These findings highlight the interplay among baseline mutation profiles, response, and clonal evolution during ivosidenib therapy. The complex and polyclonal mechanism of resistance to ivosidenib has implications for mIDH1/2 inhibitor treatment strategies, and supports the use of combination therapies or sequential treatment modifications at early relapse before overt clinical progression, rather than monotherapy with mIDH1/2 inhibitors. It will also be important to understand whether or not these patterns of resistance are replicated with combination therapies. Because individual patients often show multiple resistance mechanisms at relapse, combination of ivosidenib with nontargeted agents, such as intensive chemotherapy/cytotoxic therapies, hypomethylating agents, and venetoclax (BCL-2 inhibitor), may improve responses and decrease the likelihood of relapse.
2-HG accumulation lead to cytochrome c oxidase activity, mimicking an oxygen-deprived state and decreasing the mitochondrial threshold for induction of apoptosis.
The association of IDHi to BCL2i might represents the exciting possibility of a chemotherapy free oral combination for IDH mut AML and a phase Ib/II clinical trial (NCT03471260) confirmed a surprisingly high 75% CRc rate in a cohort of 12 patients [66, 67].
As we have seen that RAS mutation are often involved in resistance to all new approved target therapies, RAS pathway–targeting agents may be useful, either concomitantly with FLT3i or IDHi to avoid primary resistance in patients with RAS mutations at baseline, or sequentially in patients who have a newly detectable RAS mutation while on therapy with a FLT3, IDH, or BCL2 inhibitor. MEK1/MEK2 inhibitors selumetinib and trametinib unfortunately did not confirm this potential efficacy since modest response rates of 17–20% were seen in relapsed or refractory RAS-mutated AML [68]. An established mechanism of resistance to MEK inhibition is the compensatory activation of PI3K–AKT–mTOR pathway.
The targeted downmodulation of pERK and pS6 was shown in a study of 23 RAS-mutated AML patients treated with trametinib plus an AKT inhibitor (GSK2141795), without producing any clinical response [69].
At last but not least immune evasion represents one of the main mechanism of resistance common to all target drugs. Actually the exhaustion of the host’s own immune system contribute to cancer growth. Murine and human studies have shown association between AML and increased infiltration of T-regulatory cell and expression of immune checkpoint proteins on CD8 positive T cells, including PD-1, TIM3, and LAG3 [70] which might induce immune exhaustion and early relapse [71]. The immunotherapies with antibody targeting leukemic antigens, such as CD123 might be an effective strategy to target measurable residual disease (MRD) in maintenance therapy in high-risk AML. The IL3 receptor alpha chain, CD123, is notably expressed on leukemic stem cells (LSC) and is expressed at lower levels on normal hematopoietic stem cells (HSC) than CD33 [72].
Antibody-drug-conjugate (ADCs) and bispecific antibodies targeting CD123 have shown promising clinical activity in phase I studies and are rapidly moving to multicenter studies as single-agent expansions and in combination approaches [73, 74]. Pre therapy low levels of bone marrow CD3+ or CD8+ and overexpression of CTLA4 predict resistance to check point inhibitors [75]. These may be potential biomarkers to prospectively select patients most likely to respond. Inhibitors of “macrophage checkpoints” could be another interesting chance for immunomodulation. In particular CD47, highly expressed on LSCs, is associated with unfavorable outcomes [76]. Upregulation of CD47 on AML cells allows the binding to the signal-regulatory protein-α (SIRPα) receptor on macrophages, providing a “don’t eat me” signal [77].
Hu5F9-G4 is an anti-CD47 antibody that inhibits the binding of LSC with SIRPα, promoting macrophage-mediated phagocytosis of leukemic cells [78].
The combination of Hu5F9-G4 with azacitidine in unfit newly diagnosed AML produced 64% of CR/CRi/MLFS (9/14) [79]. The study is ongoing at multiple centers (NCT03248479). Unfortunately the lack of an AML specific antigen restricted to the leukemic cell is the main reason of unsatisfactory results of vaccines and chimeric antigen receptor (CAR) T-cell therapies in this setting [80]. CD33, CD123, CLEC12A are expressed on normal cells leading to potential “on-target, off-tumor” toxicity.
In conclusion the knowledge of the mechanism of resistance might help the design of future studies with sequences (Figure 4) or combinations (Figure 5) of new target drugs. Furthermore the policlonal nature of leukemia resistance might reduce the efficacy of target therapies leaving a role to immune therapies such as checkpoint inhibitors, vaccines, and adoptive T-cell therapies, in decreasing the burden of residual disease. Several studies of consolidative or maintenance immune modulation in this context are ongoing [81].
Model of kinetics of chemoresistant leukemic clones after treatment with hypomethylating agents plus Venetoclax when target drugs are administered sequentially at the onset of chemoresistant clones.
Model of kinetics of chemoresistant leukemic clones after a total therapy approach including treatment with hypomethylating agents plus Venetoclax, plus combination of target drugs and immune therapies, preventively administerd to avoid emergence of chemoresistant leukemic clones. Consolidation with immune based therapies might be administered in order to reduce MRD and possibly cure AML.
These approaches may be particularly appealing in patients not eligible to allogeneic transplant. The polyclonal mechanisms of resistence to new drugs, hereby illustrated, underline an urgent need for future trials in this setting, based on total therapy approach, including initial chemotherapy or HMA with targeted or apoptosis-inducing drugs, sequentially adjusted, on the basis of emerging early clones, with immune or target-based therapies, to eradicate reservoirs of residual disease (Figure 5).
The global burden of chronic kidney disease (CKD) was estimated to be 10–15% in 2017, which accounts for 850 million people living with this condition worldwide [1, 2]. In 2021, the Centers for Disease Control and Prevention (CDC) established that approximately one in seven adults have CKD in the United States [3]. The most common cause of CKD is in fact diabetes.
Another pressing statistic is the mortality attributed to kidney disease. In 2017, CKD ranked as the 12th leading cause of death [1]. However, besides its direct impact in morbidity and mortality, kidney disease is also an independent risk factor for cardiovascular events [4]. Globally, CKD accounted for 61.3 million disability-adjusted life-years (DALYs) with 41.6% of those being directly attributed to cardiovascular disease. DALYs mainly resulted from ischemic heart disease, stroke, and peripheral artery disease [1]. Further, a rising CKD population leads to a higher number of patients with end-stage kidney disease (ESKD). It has been predicted that the incidence of ESKD will increase from 11 to 18% between 2015 and 2030 [5]. These trends, together with higher cardiovascular disease, will intensify the burden on healthcare systems.
Diabetes stands as the primary cause of CKD contributing to approximately half of the cases [6]. Currently, diabetes affects roughly 460 million persons worldwide with a projected increase to around 580 and 700 million cases by 2030 and 2045 respectively [7, 8]. It was estimated that 10.5% of the US population had diabetes in 2018, with a projected increase to 14% by 2030 [9, 10].
Clustering of diabetes and diabetic kidney disease (DKD) has been observed among specific racial and ethnic groups. As such, African Americans, Native Americans, and Latinx have higher prevalence of diabetic nephropathy compared with Caucasians. This is explained not only by epigenetic factors associated with the environment and access to care but also by a genetic component [11]. Although, DKD does not represent a simple Mendelian inheritance, several genes have been identified as potential candidates that may differ between populations. This includes the glucose transporter 2, transforming growth factor β, APOL1, and endothelial nitric oxide synthase genes. Further, studies suggest a relationship between polymorphisms of the angiotensin converting enzyme (ACE) gene and DKD. Specifically, there is evidence that ACE gene may play a role in determining which patients may respond to renin angiotensin aldosterone system (RAAS) blockade therapy [12].
Even though the pathogenesis of DKD involves several hemodynamic and metabolic disorders, hyperglycemia is the cardinal feature of diabetic nephropathy. Elevated blood glucose leads to activation of many biochemical pathways resulting in increased oxidative stress, advanced glycation end-products (AGE), cytokine and growth factor release, and hormonal imbalance [12]. Collectively, these factors affect glomerular endothelial, mesangial, tubular and epithelial cells, or podocytes [11].
Renal hemodynamic changes happen with diabetes onset. Even in the absence of hypertension, animal models have shown an elevated intraglomerular pressure that is mediated by angiotensin II and endothelin [13]. The main mediators between the metabolic and hemodynamic pathways are the transforming growth factor beta (TGF-β) and vascular endothelial growth factor (VEGF) [11].
Angiotensin II is a potent hemodynamic and growth factor mediator. It increases sodium reabsorption and volume expansion in the proximal and distal nephron. In the proximal tubule, angiotensin II directly activates the sodium-hydrogen transporter, thereby increasing proximal sodium reabsorption. In addition, angiotensin II acts on the zona glomerulosa in the adrenal cortex and stimulates the release of aldosterone, which in turn increases distal sodium reabsorption and potassium excretion. Further, this vasoactive hormone causes systemic vasoconstriction through G-protein-coupled receptors. In the glomeruli, the afferent and efferent arteriole resistance increases in response to angiotensin II, although there is a greater effect in the efferent one [14]. Lastly, as a growth factor, this hormone affects the proximal tubular and the mesangial cells through direct and indirect pathways [15].
The role of angiotensin II in the tubuloglomerular feedback (TGF) is worth discussing separately. As shown in Figure 1, TGF is an autoregulatory mechanism within each single nephron, where specialized cells that belong to the juxtaglomerular apparatus (JGA) interact to maintain glomerular filtration rate (GFR). The JGA is formed by the macula densa, which is located between the ascending limb of the loop of Henle and the distal tubule, the juxtaglomerular cells of the afferent and efferent arterioles, and the mesangial cells. The macula densa senses distal tubular NaCl concentration. In states of reduced NaCl delivery, the macula densa releases renin resulting in efferent arteriole vasoconstriction [mediated by angiotensin II] and afferent arteriole vasodilation [mediated by a decrease in adenosine, which is a renal vasoconstrictor]. Therefore, in states of volume depletion with resulting decreased distal NaCl delivery, the net effect is an increase in single nephron GFR. In contrast, increased distal NaCl delivery decreases renin levels and stimulates the release of adenosine, which reduces single nephron GFR. Figure 1 shows the normal physiology of the tubuloglomerular feedback [12].
Tubuloglomerular feedback. This autoregulatory mechanism is present within each single nephron. The macula densa, located in the distal nephron, senses tubular NaCl concentration and regulates the diameter of the renal arterioles to maintain GFR.
The diabetic milieu alters the tubuloglomerular feedback due to an increased activity of the sodium–glucose cotransporter 2 (SGLT2) in the proximal tubule. Proximal reabsorption of Na+ and glucose via sodium glucose cotransporter 2 leads to a decreased distal delivery of NaCl, which in turn increases single nephron GFR (SNGFR) and leads to hyperfiltration as shown in Figure 2 [12].
Pathogenesis in diabetic nephropathy. In diabetes, there is an increased activity of the sodium glucose cotransporter 2 (SGLT2). This results in higher proximal NaCl reabsorption leading to a decreased distal delivery of NaCl. This is followed by a release of renin from the juxtaglomerular apparatus, RAAS activation with vasoconstriction of the efferent arteriole, and a decrease in adenosine that causes afferent arteriole vasodilation. The net effect is an increase in single nephron GFR (SNGFR).
DKD is characterized by different phases. Hyperfiltration is the initial stage and is followed by proteinuria in most cases and a subsequent decline in GFR secondary to a loss of nephron mass [16].
Hyperfiltration is defined as a GFR of at least two standard deviations above the mean GFR from its nondiabetic counterparts. It can be present in 10–67% of patients with type 1 and 6–73% of those with type 2 diabetes [11]. Hypertension and obesity frequently coexist with diabetes and contribute to glomerular hyperfiltration by causing glomerular enlargement [17, 18]. This results in increased renal plasma flow, glomerular capillary hyperperfusion, and higher glomerular transcapillary hydrostatic pressure gradient, which collectively lead to an increase in GFR.
The structural lesions of DKD start with thickening of the glomerular basement membrane followed by mesangial expansion and podocyte damage [19–21]. These changes lead to defects in selective glomerular capillary permeability, albuminuria, and protein extravasation into the mesangium. In fact, segmental mesangiolysis and Kimmelstiel-Wilson nodules are signs of progression of DKD [22, 23]. Interstitial fibrosis and glomerulosclerosis eventually take place manifesting as a decline in GFR [24, 25].
The presence of proteinuria in DKD is the single biggest predictor of CKD progression. Approximately 25% of patients with type 2 diabetes develop moderate albuminuria by 10 years [26]. Further, any degree of albuminuria also increases cardiovascular risk in this population [27–29]. It is important to note that roughly 20% of patients with diabetes develop DKD in the absence of albuminuria, and they have shown slower rates of CKD progression [30–32].
Diabetic nephropathy is usually identified after 5 years of diagnosis of type 1 diabetes. Because type 2 diabetes is recognized later in life and tends to be accompanied by elevated blood pressure and poor glycemic control, DKD may be evident at the time of diabetes diagnosis. Despite the differences in timing of recognition of kidney impairment, the clinical manifestations are similar in both types of diabetes [11]. Importantly, only 30–40% of patients with diabetes develop DKD, so nondiabetic glomerulopathies should also be considered in this population [33].
The natural history of DKD and the presence of other microvascular complications, such as retinopathy and/or neuropathy, can help determine the likelihood of kidney involvement. Among those, diabetic retinopathy has perhaps been the most widely used clinical feature. In fact, a population-based study in Wisconsin reported retinopathy in 98% of patients with type 1 diabetes mellitus (DM) compared with 78% of those with type 2 DM by 15 years [34, 35]. Further, in a meta-analysis of 2012 patients, the pooled positive predictive value of diabetic retinopathy for DKD was 0.72 (95% CI 0.68–0.75), while the negative predictive value was 0.69 (95% CI 0.67–0.72) [36]. In other words, diabetic retinopathy is concordant with diabetic nephropathy in almost all patients with type 1 diabetes, but it is less frequent in those with type 2 diabetes.
In regard to diabetic neuropathy, the correlation is less clear. The European Diabetes (EURODIAB) Prospective Complications Study showed that 23.5% of persons with type 1 DM had developed neuropathy by 7 years [37]. Other studies have reported that up to 50% of patients with types 1 and 2 diabetes have diabetic peripheral neuropathy [38].
However, it is important to highlight that the prevalence of nondiabetic kidney disease (NDKD) may vary between 10 and 85% depending on the study [39–41]. For example, a review of 233 kidney biopsies performed in persons with diabetes identified NDKD in 53% of the cases. The most common conditions observed included focal segmental glomerulosclerosis, minimal change disease, IgA nephropathy, and membranous glomerulonephritis [40].
Therefore, it is of utmost importance to identify when to pursue a kidney biopsy, which remains the gold standard for diagnosis. In general, a biopsy is indicated in the following clinical settings: nephrotic range proteinuria or GFR decline in the absence of diabetic retinopathy, onset of proteinuria less than 5 years from the diagnosis of type 1 diabetes, nephrotic range proteinuria with normal kidney function, active urinary sediment, such as microscopic hematuria, rapid decline in kidney function in patients with previously stable CKD [11].
The treatment of DKD is focused on four major pillars: cardiovascular risk reduction, blood pressure and glycemic control, and inhibition of the renin angiotensin aldosterone system (RAAS). Cardiovascular risk reduction is achieved by proper blood pressure and glycemic control, management of dyslipidemia, and smoking cessation [11].
Blood pressure goals in patients with diabetes have been highly debated. The Action in Diabetes and Vascular Disease: PreterAx and Diamicron-MR Controlled Evaluation (ADVANCE) trial showed a 21% risk reduction of kidney endpoints with systolic blood pressure levels below 135 mmHg compared with those values ~140 mm Hg [42]. Further, a review of studies showed that less GFR decline is achieved at a mean arterial pressure of 89 mm Hg (equivalent to a systolic blood pressure of 120 mm Hg and a diastolic of 75 mm Hg) [43].
The KDIGO guidelines recommend a blood pressure goal below 140/90 mmHg for DKD and a lower goal below 130/80 mmHg in the presence of albuminuria [44].
Optimal glycemic control reduces CKD progression, cardiovascular events, and death. Most studies focus on targeting the hemoglobin A1c (HbA1c) since it is a surrogate of the 3-month average blood glucose. However, data have also shown that glucose variability predicts worse kidney and cardiovascular outcomes [45].
In patients with type 1 diabetes, the Diabetes Control and Complications Trial (DCCT) showed that intensive glycemic control (goal HbA1c < 6.05%) conferred a lower risk for the development of microalbuminuria by 39% and by 56% for overt proteinuria. Importantly, the median HbA1c concentration was 9.1% in the conventional group compared with 7.3% in the intensive group. Other microvascular complications were also lower in the intensive control arm [46].
In regard to patients with type 2 diabetes, the United Kingdom Prospective Diabetes Study (UKPDS) showed a 33% reduction in albuminuria in the group that achieved a median HbA1c of 7% compared with 7.9% in the conventional arm [47].
Further studies addressing whether lower A1c goals would have an additional benefit have shown mixed results. The ADVANCE, Action to Control Cardiovascular Risk in Diabetes (ACCORD), and VA Diabetes Trial (VADT) targeted and achieved HbA1c levels of ∼6.0% compared with a control arm of ∼7.0% [48–50]. ADVANCE trial showed positive kidney outcomes but no difference in cardiovascular events [48]. Contrarily, ACCORD and VADT established no cardiovascular or kidney benefit [49, 50].
Taking all this information together, the KDIGO guidelines recommend an individualized HbA1c target ranging from < 6.5 to < 8% [44].
RAAS blockade has been the single most effective therapy for slowing the progression of DKD for decades, and it has been studied across all stages of diabetic nephropathy. As explained in the pathogenesis, RAAS is activated in diabetic nephropathy resulting in vasoconstriction of the efferent arteriole, which plays a role in increasing SNGFR. Figure 3 shows the nephroprotective effect of RAAS [12].
Hyperfiltration in early diabetic nephropathy with concomitant RAAS blockade. Angiotensin II is a potent vasoconstrictor of both renal arterioles but predominantly of the efferent one. Therefore, Renin Angiotensin Aldosterone System (RAAS) blockade results in vasodilation of the efferent arteriole and decreases single nephron GFR (SNGFR).
In initial stages when albuminuria has not occurred, the use of RAAS inhibitors is controversial. In fact, studies conducted in patients with Type 1 diabetes have failed to show that RAAS blockade prevents the development of microalbuminuria, although this was a secondary endpoint [51, 52]. On the other hand, the Bergamo Nephrologic Diabetes Complications (BENEDICT) and the Randomized Olmesartan and Diabetes Microalbuminuria Prevention (ROADMAP) Trials showed that therapy prevented or delayed the development of microalbuminuria independent of blood pressure reduction [53, 54]. In 2016, a meta-analysis of 16921 patients further confirmed a potential role for RAAS blockade in the prevention of DKD, establishing a 16% risk reduction of development of microalbuminuria in patients with type 2 diabetes [55]. In regard to the cardiovascular risk associated to diabetes, the Health Outcomes and Prevention Evaluation (HOPE) study established that ACE inhibition decreased the risk of cardiovascular death, stroke, and myocardial infarction in patients with and without kidney disease [56]. Overall, RAAS blockade appears to play a role in prevention of DKD and cardiovascular protection irrespective of nephropathy. It is worth mentioning that current guidelines do not yet recommend it for the prevention of DKD [44].
Once hyperfiltration ensues and microalbuminuria develops, patients are at risk for transitioning from microalbuminuria to overt proteinuria. The Irbesartan in Patients with Type 2 Diabetes and Microalbuminuria (IRMA-2) trial showed a dose-dependent effect favoring the use of irbesartan over placebo for the development of overt proteinuria, which was defined as ACR > 200 mg/day [57]. Similarly, in the Irbesartan Diabetic Nephropathy Trial (IDNT), RAAS blockade had superior kidney outcomes, independent of the effect on blood pressure, when compared with both the amlodipine and the placebo groups [58].
Maintaining RAAS blockade during the last stage of DKD where there is a progressive decline in GFR is still recommended. The Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan (RENAAL) study showed a 25% risk reduction of doubling of serum creatinine, 28% reduction of risk of ESKD, and 16% reduction in the composite endpoint, which was defined as doubling of serum creatinine or ESKD or death after 3.4 years of follow-up. Moreover, the therapy group showed 35% reduction of proteinuria assessed as secondary endpoint [59].
Another important clinical question is whether dual blockade of the RAAS provides additional benefit. Landmark trials including the ONTARGET, VA NEPHRON-D and the ALTITUDE presented safety concerns due to significant rates of hyperkalemia, kidney injury, or hypotension [60–62]. Collectively, these studies suggest avoiding dual blockade of the RAAS system in type 2 diabetes.
The twenty-first century began with this overwhelming evidence favoring the use of RAAS inhibitors across all clinical phenotypes of DKD. Unfortunately, the prevalence of RAAS blockade agents use decreased from 45% in 2006–2008 to 36% in 2012–2014 [63].
GLP-1 receptor agonists, also called incretins, are part of the arsenal of medications for the treatment of diabetes since 2005. However, only since 2019 an oral formulation has been available and approved by the FDA. GLP-1 receptor agonists initially gained popularity because of their ability to induce weight loss. They act by stimulating glucose-dependent insulin secretion and decrease glucagon secretion, gastric emptying, and appetite [64].
Another positive effect of GLP-1 receptor agonists is their cardiovascular protection. A meta-analysis of 56,004 patients with diabetes showed a significant 12% relative risk reduction in major adverse cardiovascular events [65]. In 2021, the Cardiovascular and Renal Outcomes with Efpeglenatide in Type 2 Diabetes (AMPLITUDE-O) trial showed a 27% risk reduction of incident major adverse cardiovascular events in the efpeglenatide group. Even though the kidney outcomes were a secondary endpoint, a composite kidney outcome of decrease in kidney function or macroalbuminuria was 32% lower in the medication arm [66].
The sodium glucose cotransporter 2 is responsible for the uptake of roughly 90% of glucose and majority of the sodium that is reabsorbed in the proximal tubule [12].
It has been shown that SGLT2is significantly reduce the renal threshold for glucose excretion from ~10 mmol/l (180 mg/dl) to ~ 1.2 mmol/L (21 mg/dL) in diabetic and to ~2 mmol/L (37 mg/dL) in nondiabetic subjects [67]. As a result, a better glycemic control addresses hyperglycemia, which is the main driving factor of the diabetic milieu. It is worth mentioning that glucose uptake by kidney cells is not insulin-dependent, so the effect of SGLT2is is present even in states of insulin deficiency [12].
In addition to their effect on glucosuria, the benefits of the SGLT2is result from their natriuretic action and the restoration of the tubuloglomerular feedback. The increased distal delivery of sodium ameliorates the altered TGF characteristic of diabetic nephropathy by decreasing SNGFR, hyperfiltration, and intraglomerular pressure as shown in Figure 4 [68].
Effect of RAAS blockade and SGLT2is in diabetic nephropathy. Sodium glucose cotransporter 2 inhibitors (SGLT2is) increase the distal delivery of NaCl to the macula densa, thereby restoring the tubuloglomerular feedback. Renin angiotensin aldosterone system (RAAS) blockade decreases single nephron GFR (SNGFR) through vasodilation of the efferent arteriole; however, the effect of SGLT2is lowers SNGFR further through vasoconstriction of the afferent arteriole.
Cardiovascular outcome trials with SGLT2is inhibitors, although not powered for secondary (renal) endpoints, showed beneficial renal effects by 40–70% [69–72]. Notably, all these studies included patients with CKD, but most of them had early, if any, kidney disease. The Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes (EMPA-REG OUTCOME) trial included subjects with a mean eGFR of 74 mL/min/1.73 m2, and 60% of the patients enrolled did not have albuminuria [69, 70]. Further, the Canagliflozin Cardiovascular Assessment Study (CANVAS) observed patients with a mean eGFR of 76.5 mL/min/1.73 m2 and a median UACR of 12 mg/g [71]. Similarly, the Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes (DECLARE-TIMI 58) included patients with a mean eGFR of 85 mL/min/1.73 m2 while UACR data was not provided [72].
The Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy (CREDENCE) was the first study to establish primary renal endpoints. It included patients with stage 3 CKD with a mean eGFR of 56 mL/min/1.73 m2 and a median UACR of 927 mg/g. The CREDENCE trial showed a significant 30% risk reduction in the composite renal outcome without significant adverse events. This trial led to the FDA approval of canagliflozin for DKD [73].
In 2020, the Dapagliflozin in Patients with Chronic Kidney Disease (DAPA-CKD) trial showed a 39% risk reduction of the primary composite renal outcome in the dapagliflozin arm. Of note, the positive effect was observed across all subgroups including those with and without diabetes. The mean eGFR in this study was 43 mL/min/1.73 m2 with a median UACR of 965 mg/g [73].
Neither CREDENCE nor DAPA-CKD showed an increased risk of amputations, hyperkalemia, or hypoglycemia. However, CREDENCE showed a significantly higher risk of diabetic ketoacidosis in the canagliflozin group. This was not observed in the DAPA-CKD [73, 74]. In addition, patients treated with dapagliflozin developed more urinary tract infections and genital infections compared with placebo.
Randomized controlled trials with dedicated kidney outcomes have shown that SGLT2is are beneficial and safe in patients with CKD and proteinuria, regardless of diabetes status [73, 74]. Even though the majority of patients from the cardiovascular outcome trials included patients without proteinuria, subgroup analysis was not provided [69–72]. Notably, the lowest GFR studied thus far is 20 mL/min/1.73 m2 in the EMPEROR- reduced trial, which also showed positive secondary kidney endpoints [75]. Finally, an ongoing clinical trial, The Study of Heart and Kidney Protection with Empagliflozin (EMPA-KIDNEY) with primary renal and cardiovascular outcomes also enrolled patients with an eGFR ≥ 20 mL/min/1.73 m2. [NCT03594110]
Table 1 summarizes trials of SGLT2is and selective mineralocorticoid receptor antagonist in DKD patients.
Trial | Patient population | Intervention | Primary endpoint | Follow-up (median, years) | Results |
---|---|---|---|---|---|
EMPA-REG OUTCOME (2015) | N = 7020 T2DM and CVD GFR> 30 No UACR criteria 60% had a UACR < 30 | Empagliflozin vs Placebo | MACE | 3.1 | Primary outcome: HR 0.86, 95% CI: 0.74–0.99 Secondary (renal) outcome: Incident or worsening nephropathy 39% lower in the empagliflozin group HR 0.61; 95% CI: 0.53–0.70 |
CANVAS (2017) | N = 10,142 T2DM and CVD GFR ≥ 30 No UACR criteria 70% had a UACR < 30 | Canagliflozin vs Placebo | MACE | 2.4 | Primary outcome: MACE: HR 0.86, 95% CI: 0.75–0.97 Secondary (renal) outcomes: Composite 40% reduction in GFR, renal replacement therapy or death from renal causes: HR 0.60; 95% CI, 0.47 to 0.77 Progression of albuminuria HR 0.73, 95% CI: 0.67–0.79 |
CREDENCE (2019) | N = 4401 T2DM GFR 30–90 UACR 300–5000 Maxim tolerated RAS blockade | Canagliflozin vs Placebo | Composite of ESKD, doubling of serum creatinine, or kidney/CV related death | 2.6 | Primary outcome: HR 0.70, 95% CI: 0.59–0.82 |
DECLARE-TIMI 58 (2019) | N = 17,160 T2DM and CVD GFR ≥ 60 No UACR criteria | Dapagliflozin vs Placebo | MACE; Composite CV death or hospitalization for HF | 4.2 | Primary outcome: MACE: HR 0.93, 95% CI: 0.84–1.03 Composite CV death or hospitalization for HF HR 0.83, 95% CI: 0.73 – 0.95 Secondary (renal) outcome: Composite of >40% decrease in GFR to < 60 ml/min, ESKD, or renal death: HR 0.53, 95% CI: 0.43–0.66 |
DAPA-CKD (2020) | N = 4304 T2DM: 67% GFR 25 to 75 UACR 200 to 5000 | Dapagliflozin vs Placebo | Composite of sustained decline in GFR of at least 50%, ESKD, or death from kidney or cardiovascular causes | 2.4 | Primary outcome: HR 0.61, 95% CI: 0.51–0.72 Primary outcome according to subgroups: T2DM: HR 0.64 (95% CI: 0.52–0.79) No T2DM: HR 0.50 (95% CI: 0.35–0.72) |
EMPEROR-REDUCED (2020) | N = 3730 HFrEF GFR ≥ 20 No UACR criteria | Empagliflozin vs Placebo | Composite of CV death or hospitalization for HF | 1.3 | Primary outcome: HR 0.75, 95% CI: 0.65–0.86 Secondary (renal) outcomes: Mean slope of change in GFR (hierarchical testing): HR 1.73 (95% CI: 1.10–2.37) Composite renal outcome: HR 0.50 (95% CI: 0.32–0.77) |
FIDELIO-DKD (2020) | N = 5734 T2DM GFR 25 to 75 UACR 30 to 5000 | Finerenone vs Placebo | Composite of kidney failure, a sustained GFR decrease of at least 40%, or death from renal causes | 2.6 | Primary outcome: HR 0.82; 95% CI: 0.73–0.93 Secondary outcome: Reduction in UACR: HR 0.69; 95% CI: 0.66–0.71 |
Recent chronic kidney disease randomized clinical trials of SGLT2is and selective mineralocorticoid receptor antagonist.
MACE: Major Advere Cardiovascular Events, T2DM: Type 2 Diabetes Mellitus, CVD: Cardio Vascular Disease, and HFrEF: Heart Failure with reduced Ejection Fraction, GFR: Glomerular Filtration Rate (mL/min/1.73 m2), UACR: Urine Albumin to Creatinine Ratio (mg/g).
Nonetheless, it is still recommended to avoid STGL2is in patients with recurrent genito-urinary tract infections, patients with urinary bladder catheterization or suprapubic catheters, or when there is a concern for volume depletion or prolonged fasting.
Aldosterone binds to the mineralocorticoid receptor (MR) of principal cells in the cortical collecting duct of the kidney and activates the epithelial sodium channel (ENaC), which will regulate salt excretion, extracellular volume, and blood pressure. MR is also present in nonepithelial cells such as cardiomyocytes, endothelial cells, vascular smooth muscle cells, mesangial cells, and podocytes. Importantly, cortisol also has high affinity to MR in nonepithelial cells due to the lack of the enzyme 11β-hydroxysteroid dehydrogenase type 2 (11βHSD-2), which converts cortisol to cortisone, the nonactive form. Activation of the mineralocorticoid receptor stimulates the formation of reactive oxygen species, endothelial exocytosis, and adhesion. These sequences stimulate an
As such, a novel selective MR antagonist has also been added to the armamentarium against DKD. Finerenone and eplerenone are selective MR antagonists. However, finerenone is equally saturated in the kidneys and heart tissues, while eplerenone is more saturated in the kidneys. Spironolactone is a nonselective MR antagonist, which is also more saturated in the kidneys and can bind with the estrogen receptors in the mammary glands causing gynecomastia. By the end of 2020, the Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes (FIDELIO-DKD) trial showed promising renal outcome. Finerenone had a significant 18% reduction of a primary composite of kidney failure, sustained decreased of at least 40% in eGFR, and death from renal causes. Further, it showed a 31% greater reduction in albuminuria by month 4 compared with placebo [77].
In FIDELIO-DKD trial, the incidence of all serious adverse events was similar in both groups; however, the incidence of hyperkalemia leading to discontinuation of the trial regimen was higher in the finerenone group compared with placebo (2.3% vs. 0.9%, respectively) [77].
The positive kidney and cardiovascular outcomes seen in the FIDELIO-DKD persisted irrespective of SGLT2 inhibitor status [78].
The major therapeutic breakthroughs for DKD have led to updates in the current guidelines. The KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease recommends the use of SGLT2is in patients with Type 2 diabetes, CKD and GFR > 30 ml/min/1.73 m2. KDIGO also suggests initiating GLP-1 receptor agonists in patients with type 2 diabetes mellitus and CKD if glycemic targets are not met despite using metformin and SGLT2is or if unable to use those medications [44].
Similarly, the American Diabetes Association (ADA) recommends using GLP1-receptor agonists or SGLT2is in persons with type 2 diabetes and cardiovascular disease. However, when CKD or heart failure are predominant, SGLT2is are the preferred option [79].
We expect those guidelines to incorporate finerenone in the management of DKD given its beneficial role in CKD progression and cardiovascular outcomes in patients with diabetes mellitus Type 2.
We want to thank Dr. Bernard Jaar for giving us the opportunity to participate in this book chapter and for his ongoing professional support.
Dr. Hanouneh is a speaker for AstraZeneca and Bayer.
Dr. Cervantes has no conflicts of interest.
Diabetes mellitus is the leading cause of chronic kidney disease (CKD) worldwide, and its rising prevalence explains the significant increase in the number of patients receiving renal replacement therapy. Additionally, CKD represents a major cause of morbidity and mortality in patients with diabetes. Hyperglycemia is the cardinal feature that leads to diabetic nephropathy. In fact, elevated glucose levels initiate several inflammatory and metabolic pathways that result in activation of the renin angiotensin aldosterone system (RAAS) and dysregulation of the tubuloglomerular feedback (TGF). For decades, the standard of care to prevent or delay the progression of diabetic kidney disease has included RAAS inhibitors and optimal blood pressure and glycemic control. Fortunately, newer medications have joined the armamentarium of drugs against diabetic nephropathy. Specifically, we will review sodium glucose cotransporter 2 inhibitors (SGLT2is) and the selective mineralocorticoid receptor antagonist (MRA), finerenone, which have proven to decrease the progression of diabetic kidney disease and cardiovascular risk.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. 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He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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