Nominal parameters of two-mass experimental model.
\r\n\t
",isbn:"978-1-83969-642-8",printIsbn:"978-1-83969-641-1",pdfIsbn:"978-1-83969-643-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"5d7f2aa74874444bc6986e613ccebd7c",bookSignature:"Prof. Antonio Morata, Dr. Iris Loira and Prof. Carmen González",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10901.jpg",keywords:"Grape, Wine, Vine Biotechnology, Plant Disease, Vine Physiology, Wine Technology, Winemaking, Fungal Disease, Biological Control, Vigor Management, Aroma Compound, Polysaccharide",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 4th 2021",dateEndSecondStepPublish:"April 1st 2021",dateEndThirdStepPublish:"May 31st 2021",dateEndFourthStepPublish:"August 19th 2021",dateEndFifthStepPublish:"October 18th 2021",remainingDaysToSecondStep:"23 days",secondStepPassed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Prof. Morata is the Spanish delegate at the group of experts in wine microbiology and wine technology of the International Organisation of Vine and Wine (OIV). His team won the international Enoforum award 2019 by the application of UHPH in wines and was among the 5 finalists in 2020 by using PL.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"180952",title:"Prof.",name:"Antonio",middleName:null,surname:"Morata",slug:"antonio-morata",fullName:"Antonio Morata",profilePictureURL:"https://mts.intechopen.com/storage/users/180952/images/system/180952.jpg",biography:"Antonio Morata is a professor of Food Science and Technology at the Universidad Politécnica de Madrid (UPM), Spain, specializing in wine technology. He is the coordinator of the Master in Food Engineering Program at UPM, and a professor of enology and wine technology in the European Master of Viticulture and Enology, Euromaster Vinifera-Erasmus+. He is the Spanish delegate at the group of experts in wine microbiology and wine technology of the International Organisation of Vine and Wine (OIV). He is the author of more than 70 research articles, 3 books, 4 edited books, 6 special issues and 16 book chapters.",institutionString:"Technical University of Madrid",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Technical University of Madrid",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"186423",title:"Dr.",name:"Iris",middleName:null,surname:"Loira",slug:"iris-loira",fullName:"Iris Loira",profilePictureURL:"https://mts.intechopen.com/storage/users/186423/images/system/186423.jpg",biography:"Iris Loira is an assistant professor of Food Science and Technology at the Universidad Politécnica de Madrid (UPM), Spain. 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Her research activity has focused on the field of oenological biotechnology and on the selection of microorganisms (yeasts and BAL) that are of special interest in wine making processes. She has extensive experience in the use of instrumental and sensory tests to assess the quality of alcoholic beverages (wine and beer) and meat products. She has participated in different educational innovation projects and coordinated three of them. These projects have made it possible to coordinate working groups for the implementation of degrees in the EEES, and apply new teaching methodologies that allow the acquisition of horizontal competences by students. 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These mechanical systems are made a request the high-speed response, weight reduction, miniaturization, and high precision requirements for various industrial applications.
\nHence, in industrial field, the system is treated as a multi-mass resonance system, which consists of several inertial moments, torsional shafts, and gear coupling. The first-order approximation model of multi-mass resonances model is two-mass resonance model. For instance, several control methods, which are PID control (Proportional plus Integral plus Derivative Control) with a resonance ratio control using the disturbance observer, coefficient diagram method (CDM), full state feedback control with the state observer, the pole placement method, fractional order PIDk control, and H∞ control method, are effective to control for two-mass resonance system [1–3]. Ikeda et al. [4] have explained the effectiveness of the controller design technique using the pole placement method for the two-mass position control system.
\nHowever, the resonance system is required more high precision and high response speed control in recent years. Therefore, it is necessary to deal with a higher order model of the resonance system. For instance, the drive train of the electric vehicle is constructed the four-mass system. Likewise, the ball screw drive stage is typically four-mass system. The thermal power generation system composed of multiple turbines and generators is modeled as twelve-mass resonance system. Thus, several vibration suppression control methods on three-mass resonance system or more have been proposed [5, 6]. Here, modified-IPD speed controller using Taguchi Method has been proposed in Refs. [7, 8].
\nMeanwhile, the state equations of the controlled object and its parameters are required to design the control systems. Refs. [9, 10] previously proposed a controller gain tuning method for a vibration suppression-type speed controller using fictitious reference iterative tuning (FRIT) for single-input multi-variable control objects without knowledge of the system state equations and the parameters.
\nIn contrast, a fuzzy control system can be assumed as one method for solving these problems. A fuzzy control system using a fuzzy inference is the embodiment of non-mathematical control algorithm, which is constructed by experience and intuition. Several applications brought in the fuzzy control system to motor drive system [11–14].
\nThis chapter proposes a vibration suppression controller by using a fuzzy inference. The control system consists of a speed fuzzy controller and a proportional-integral (PI) current controller to realize precise speed and torque response on two or three inertial resonance system. In the control system, only motor side state variables are utilized for controlling the resonance system. Additionally, this chapter treats with the proposed control system as the digital control system. Here, the proposed control system is new system that I improved to apply the control system which I already proposed for simulation model in Refs. [13, 14] to experimental actual equipment.
\nThe fuzzy controller has three scaling factors, and the PI current controller has two controller gains. In this chapter, a differential evolution algorithm (DE) is utilized the determination of these five controller parameters [13–18]. DE, which was proposed by Price and Storn, is one of the evolutionary optimization strategies. By using DE, it is easy and fast to determine the proper controller parameters.
\nLastly, the validity of the controller design, the robustness, and the control effectiveness of the proposed method was verified using the simulations and the experiments by using the test experimental set up.
\nFigure 1 shows the two-mass resonance model. The model is configured of two rigid inertial masses with a torsional shaft, where ωM, Tdis, ωL, Tin, JM, JL, Ks, and TL denote the motor angular speed, the torsional torque, the load angular speed, the input torque, the inertia of motor, the inertia of load, the shaft torsional stiffness, and the load torque, respectively.
\n2-mass model.
If all the state variables can be observed by several sensors and all the system parameters are known or identified, it is easy to construct the optimal control system. However, in general, it is difficult to measure the state variables of the load side due to constraints on scarce measurement environment and sensor installation location. Therefore, in this chapter, we use only the motor side variables. Furthermore, we contemplate for the current minor control in order to compensate torque response. Eq. (1) shows the continuous state equation of two-mass resonance model, where the viscous friction is not considered.
\nEq. (2) shows the transfer function of two-mass model, which input signal is Tin and output signal is ωM.
\nwhere ωr is a resonance frequency and ωa is an anti-resonance frequency. Here, we use the DC servo motor as the driving motor. Eq. (3) is the voltage equation of dc servo motor, where Ra is the armature resistance, La is the armature inductance, Ke is the back-emf constant, and K0 is the converter gains of the DC power supply. Input torque is calculated by Tin = Ktia, where Kt is the torque constant.
\nFigure 2 is indicative of the block diagram of the two-mass resonance system.
\nBlock diagram of two-mass resonance model.
The inertia ratio R of two-mass model is given by Eq. (4), where JMn and JLn represent the nominal values of the motor and load inertias, respectively.
\nSimilarly to two-mass resonance model, Figure 3 reveals the three-mass model. The model consists of three rigid inertias and two shafts. Here, Jc and JL are the load 1 inertia moment and the load 2 inertia moment, respectively. Furthermore, ωc, ωL, Tdis1, Tdis2, Ks1, and Ks2 denote load 1 angular speed, load 2 angular speed, shaft 1 torsional torque, shaft 2 torsional torque, the shaft 1 stiffness, and the shaft 2 stiffness, respectively.
\nThree-mass model.
The state equation of three-mass resonance model is shown in Eq. (5). Then, Eq. (6) shows the continuous transfer function of three-mass resonance model, which input signal is Tin and output signal is ωM.
\nIn this equation, ω indicates the angular frequency, where ωr1, ωr2, ωa1, and ωa2 are the resonance frequencies, and anti-resonance frequency, respectively. Then, the block diagram realized by using above equations is shown in Figure 4.
\nBlock diagram of three-mass model.
This chapter confirms the effectiveness and performance of the proposed method by experiments using the experimental equipment.
\nFigure 5 is the appearance of the experimental system constructed in this research. The two-mass resonance system is simulated by utilizing the dc servo motor and the dc generator with a finite rigid coupling. The controller is realized on a digital signal processor, which calculates the PWM signal to a four-quadrant dc chopper.
\nExperimental apparatus.
The DSP board (PE-PRO/F28335 Starter Kit, Myway Plus Corp.) consists of the DSP (TMS320F28335PGFA), a digital input/output (I/O), ABZ counters for encoder signals, analog-to-digital (AD) converters and digital-to-analog (DA) converters [19]. The motor and load angles and angular speeds are detected using 5000 pulses-per-revolution encoders. The current of dc servo motor is measured by the current sensor and AD converter.
\nThe control frequency and the detection frequency of the encoder are both 1 ms, and the detection period for the current is 10 μsec. The design language used was C. Then, while considering the application of the system to specific apparatus, we constructed a digital control system that contains a discrete controller. In addition, we used MATLAB/Simulink software for the proposed off-line tuning process based on simulation and constructed the fuzzy control system as a continuous system [20]. The disturbance is added to the dc generator as the torque by using the electric load device on constant current mode. Figures 6 and 7 show the apparatus of the two-mass model and three-mass model used in the experimental set up, respectively. Figure 8 shows the experimental system configuration. For reference, the nominal parameters of the experimental two-mass model and three-mass model are given in Tables 1 and 2, respectively.
\nPhotograph of two-mass resonance model.
Photograph of three-mass resonance model.
Configuration of experimental system (two-mass resonance model).
Symbol | \nParameter | \nValue | \n
---|---|---|
JMn | \nMotor inertia | \n2.744 × 10−4 (kgm2) | \n
JLn | \nLoad inertia | \n2.940 × 10−4 (kgm2) | \n
Ksn | \nShaft stiffness | \n18.5 (Nm/rad) | \n
Nominal parameters of two-mass experimental model.
Symbol | \nParameter | \nValue | \n
---|---|---|
JMn | \nMotor inertia | \n2.744 × 10−4 (kgm2) | \n
Jcn | \nLoad 1 inertia | \n1.112 × 10−4 (kgm2) | \n
JLn | \nLoad 2 inertia | \n2.940 × 10−4 (kgm2) | \n
Ks1n | \nShaft stiffness 1 | \n18.5 (Nm/rad) | \n
Ks2n | \nShaft stiffness 2 | \n18.5 (Nm/rad) | \n
Nominal parameters of three-mass experimental model.
Figure 9 shows an example of experimental result using two-mass model. These step waves are the motor and load angular speeds with direct current voltage input. Similarly, Figure 10 shows an example of experimental result using three-mass model, which are the motor and load angular speeds with same above condition. In these figures, the resonance vibrations can be observed. The purpose of this research is to suppress these resonance vibrations.
\nAngular speeds (ωM and ωL) of the step responses to a DC voltage input (two-mass model).
Angular speeds (ωM and ωL) of the step responses to a DC voltage input (three-mass model).
Fuzzy controller, which is executed by the fuzzy set and the fuzzy inference, can control for nonlinear systems or uncertain model. Figure 11 indicates the proposed fuzzy speed controller in this chapter. The speed controller is based on fuzzy control. The current controller is typical PI controller. Furthermore, the load side state variables are not utilized for control, where S1, S2, and S3 are the parameters to determine the scale of the membership function, which are called scaling factors or scaling coefficient. Kpc and Kic are the current PI controller gains. Eq. (7) shows the transfer function of current PI controller. Additionally, this chapter uses the discrete control system.
\nBlock diagram of the proposed control system.
Figure 12 is indicative of the membership function for the premise variables. This membership function is a shape of triangle with a dense center. Figure 13 indicates the membership function, which is formed uniformly triangle for the consequent variable. Here, the s denotes the scaling factor. PB, PM, PS, ZE, NS, NM, and NB are the linguistic variables of the fuzzy control where, PB indicates positive big, PM indicates positive medium, PS indicates positive small, ZE indicates zero, NS indicates negative small, NM indicates negative medium, and NB indicates negative big, respectively. The premise variables are eωM(k) and ΔeωM(k).
\nMembership functions of the antecedence.
Membership functions of the consequence.
Then, the consequence variable is the variation width of the current input Δiref(k). Therefore, the proposed fuzzy controller is nearly same as the proportional-derivative (PD) type controller.
\nFigure 14 is indicative of the fuzzy rule table. The rule is included the rising correction of the angular speed response.
\nControl rule table.
In this chapter, five parameters (S1, S2, S3, Kpc and Kic) of the proposed controller have to be designed. However, it is difficult to determine them by trial and error or some. Therefore, this chapter proposes the differential evolution (DE) to search the optimal controller parameters. Here, DE is one of evolutionary optimized solution search methods. DE is the optimization method-based multi-point search method. In particular, basic GA expresses parameter by binary coding, whereas DE uses the parameters by real variable vector. The DE design is conducted by the initial population, the mutation, the crossover, and the selection. The design flow of DE is shown in Figure 15. In this chapter, DE/rand/1/bin design strategy is used for the determination of five controller parameters.
\nFlow of DE algorithm.
where D is the number of design parameter vectors, NP is the number of members in each population. Each parameter vector is represented by the parameter vector (target vector) xi,G, where G denotes one generation. The mutation vector vi,G is calculated by Eq. (10). From this equation, F indicates the step width (scaling factor) of DE design, and CR indicates of the crossover rate, where r1, r2, and r3 are different values.
\nIn Eq. (11), uj,G+1 is the vector of trial parameter, the rand is random value, and ST indicates the start point. The selection is utilized next algorithm,
\nAs previously described, the proposed method uses five control parameters (S1, S2, S3, Kpc, and Kic). The population size is 2000, the order of each vector is 20, and the coefficient of membership function F is 0.5. Moreover, the rate of crossover CR is 0.9. Then, the performance index function is shown in Eq. (13). Meanwhile, this chapter utilizes the inverse of y as a fitness function.
Next, the simulation results of the proposed method are demonstrated by computer simulation.
\nTable 3 shows the results of design parameter using the proposed method for two-mass model. Figure 16 is indicative of the transition of the maximum fitness function. In this simulation design, the step response and the disturbance response have been evaluated. Furthermore, the inertia ratio R is 1.07, and the stiffness of shaft Ksn has been set to 18.5 Nm/rad in the simulation design.
\nS1 | \nS2 | \nS3 | \nKpc | \nKic | \n
---|---|---|---|---|
8.486 | \n0.4802 | \n0.4001 | \n4.678 | \n1.0 × 10−6 | \n
Results of design parameter calculated by DE.
Convergence of index function y.
Figures 17 and 18 show the step responses that were obtained for the motor and load angular speeds, and armature current when using the proposed method. In this chapter, ωref is 30 rad/s, the DC voltage input is 25 V, and the disturbance input TL is changed from 0 to 20% at t = 0.3 s. As shown by these figures, good waves are observed for the reference-following, vibration suppression, and the disturbance performance. Figure 19 is indicative of the search process of the S1 vector. Similarly, Figures 20–23 show the transition of the S2 vector, S3 vector, Kpc vector and Kic vector, respectively. In particular, from Figure 23 and Table 3, Kic is 1.0 × 10−6 of the design limitation value. Therefore, integral gain of the current PI controller can be omitted for this control object.
\nSimulation results ωM and ωL (two-mass, R = 1.07, Ksn = 18.5 Nm/rad).
Simulation results ia (two-mass, R = 1.07, Ksn = 18.5 Nm/rad).
Transition of scaling factor S1.
Transition of scaling factor S2.
Transition of scaling factor S3.
Transition of current proportional gain Kpc.
Transition of current integral gain Kic.
Next, the experimental results by using the proposed method are illustrated in this section. Figures 24 and 25 show the experimental results of two-mass model using the proposed method, where the condition (R = 1.07, Ksn = 18.5 Nm/rad) is same as the above simulation results shown in Figures 17 and 18. From these figures, it is observed that the resonance vibrations between the motor and the load angular speed (ωM and ωL) have been suppressed very well. Furthermore, after inputting disturbance, it can be seen that the angular speeds immediately have followed the reference speed ωref without resonance vibrations. Hence, the validity of the control system, which consists of the proposed method, can be confirmed.
\nExperimental results for ωM and ωL obtained using the proposed method (two-mass, R = 1.07, Ksn = 18.5 Nm/rad).
Experimental results for ia obtained using the proposed method (two-mass, R = 1.07, Ks = 18.5 Nm/rad).
Robustness verification results (two-mass, R = 0.42, Ksn = 18.5 Nm/rad).
Robustness verification results (two-mass, R = 2.67, Ksn = 18.5 Nm/rad).
Next, it is described the effectiveness of robustness by the proposed design method. This section evaluates the robustness to variations in the ratio of inertia and the stiffness of the rigid shaft based on a nominal value.
\nFigures 26 and 27 show the experimental results of the motor and load angular speeds obtained for the inertia ratio variation when using the same controller gains that were designed using the proposed method, when R = [0.42, 2.65], where the disturbance torque input was skipped. From these figure, although it can be observed some overshoot and resonance vibration, the good results can be confirmed that were obtained for the design condition.
\nFigure 28 shows the experimental results of the motor and load angular speeds obtained for the stiffness of shaft variation using the same controller gains, when Ksn = 70.7. From this figure, it can be seen some resonance vibrations. However, the vibrations rapidly have been suppressed well.
\nRobustness verification results (two-mass, R = 1.07, Ksn = 70.7 Nm/rad).
Similarly, Figure 29 shows the experimental results when Ksn = 3.1. As can be seen, the motor and load angular speeds oscillated and overshot. Therefore, if the stiffness of shaft of the experimental model is less than the design value, the settling time to suppress the resonance vibration becomes longer, although the proposed control system is not unstable. In addition, Figure 30 shows the experimental result when the control parameter redesigned with the stiffness of shaft Ksn as the nominal value of experimental model. Good responses can be observed in this figure.
\nRobustness verification results (two-mass, R = 1.07, Ksn = 3.1 Nm/rad).
Experimental results for ωM and ωL redesigned using the proposed method (two-mass, R = 1.07, Ksn = 3.1 Nm/rad).
Furthermore, the proposed fuzzy control system is applied to a three-mass resonance model. Figure 31 shows the experimental results of the motor and load angular speeds when using the same controller gains designed for two-mass model (R = 1.07, Ksn = 18.5 Nm/rad, where the nominal parameters of the three-mass experimental setup are JMn = 2.774 × 10−4 kgm2, JLn = 2.940 × 10−4 kgm2, Ks1n = 18.5 Nm/rad, Ks2n = 18.5 Nm/rad. From this figure, the effectiveness of the proposed method can be confirmed in a similar manner to the two-mass model case.
\nRobustness verification results (three-mass, JMn = 2.774 × 10−4 kgm2, Jcn = 1.112 × 10−4 kgm2, JLn = 2.940 × 10−4 kgm2, Ks1n = 18.5 Nm/rad, Ks2n = 18.5 Nm/rad).
This chapter proposed the speed control system to suppress the resonance vibration of multi-inertial model, especially two-mass system and three-mass system. The controller has been constructed with the digital fuzzy controller for speed control and the digital PI controller for current control. In the control system, only motor side state variables have been used for controlling the resonance system. Additionally, this chapter utilized the DE to determine these five controller parameters. Finally, the validity of the controller design, the robustness, and the control effectiveness of the proposed method has been verified using the simulations and the experiments by using the test experimental set up.
\nMetformin or 1,1-dimethylbiguanide is a derivate of isoamylene guanidine, a substance found in the plant Galega officinalis [1]. This drug is widely used in metabolic disorders as type 2 diabetes mellitus, metabolic syndrome, and gestational diabetes [2, 3]. Besides, metformin is used as a treatment for polycystic ovarian syndrome [4], which is characterized by the dysfunction of reproductive tissues such as the ovary and endometrium. In this context, metformin improves ovarian follicle dynamics and frequency of ovulation [5, 6], and it increases the expression of endometrial GLUT4 (insulin-regulated glucose transporter), which may improve endometrial physiology in these patients [7].
In the last decades, metformin has been studied in the context of cancer, especially after an initial report by Evans et al., performed with a Scottish database, who found that metformin intake reduces the risk of cancer in type 2 diabetic patients [8].
Type 2 diabetes and obesity affect a significant percentage of the world population [9, 10] whose food habits and lifestyle have been changing in the last decades. Both obesity and type 2 diabetes are pathologies associated with increased incidence and poor prognosis of ovarian cancer by several authors [11, 12, 13]. These observations could be explained because obesity and type 2 diabetes are characterized by molecular changes that could encourage tumoral transformation and progression, such as hyperinsulinemia, hyperglycemia, dyslipidemia, increased insulin-like growth factors (IGF), adipose tissue factors, and inflammatory components [14, 15, 16, 17, 18, 19].
By its chemical nature, metformin gets into the cell through organic cation transporters (OCTs) and multidrug and toxin extrusion transporters [20]. Because metformin cannot be metabolized, almost its entirety is excreted by the kidneys; the plasmatic levels of this drug do not reflect its intracellular concentration, mainly by its high apparent volume of distribution and prolonged half-life [21, 22]. Therefore, metformin is accumulated in tissues, and its plasmatic concentration is probably lower than of organs that express OCT transporters. This observation supports most in vitro studies that use high concentrations of metformin to study its antitumoral properties. Importantly, these transporters are present in the ovary [23, 24], so ovarian cancer cells could be a target for metformin action.
It is discussed that metformin could display direct and indirect antitumoral effects. The systemic effects of this drug include the decrease of blood glucose and insulin levels by action in its classical target organs: liver, muscle, and fat tissues. In humans, metformin decreases the hepatic gluconeogenesis and the release of glucose from hepatic reserves, which produces an increase in the peripheral uptake of glucose and its metabolism, decreasing patients’ hyperglycemia and hyperinsulinemia [1, 2, 25]. These conditions (hyperglycemia and hyperinsulinemia) favor tumoral growth and are associated with cancer incidence, by two possible mechanisms: (1) high availability of glucose for cancer cells and (2) high levels of insulin, which could act in insulin-like growth factor (IGF) receptors [14, 15, 16]. IGF/IGF receptors display an important role in the ovary, because 100% of the ovarian carcinomas express IGF receptors [26].
In fat tissue, metformin decreases the activity of lipogenic enzymes such as HMG-CoA reductase, acetyl-CoA carboxylase (ACC), and fatty acid synthase, decreasing the endogen production of cholesterol and the fatty acid synthesis [1, 27, 28]. This produces a decrease in the plasma levels of lipids in patients using metformin [29, 30, 31, 32], which in addition to metformin-hypoglycemic properties, decreases the readiness of energy substrates of tumoral cells.
All these metformin-mediated changes impair survival and mitogenic signaling and decrease nutrient availability for ovarian cancer cells.
Several studies have shown that metformin displays direct antitumoral effects. Most of these studies have been performed in ovarian cancer cell lines, where metformin impairs cell proliferation, migration, and angiogenesis potential and enhances the chemotherapy sensibility [33, 34, 35, 36].
The direct antitumoral effects of metformin are commanded by metabolic changes in cancer cells. Because metformin is a drug with pleiotropic effects, several molecular targets at different levels of the tumoral cell have been described. One of the most studied targets for metformin is the adenosine monophosphate-activated protein kinase (AMPK), a key sensor of the energetic status of the cell [37], and it was described that metformin treatment can activate AMPK in in vitro and in vivo experiments of ovarian cancer models [33, 38]. The activation of AMPK occurs by increasing the AMP/ATP ratio [39] which exposes the activation loop of AMPK to be phosphorylated in the residue threonine 172 by serine/threonine kinases such as liver kinase B1 (LKB1) [40]. Activated AMPK phosphorylates several proteins; the phosphorylation can either activate or repress protein function at the cellular level [41, 42]. Despite that an important part of the studies indicates that the antitumoral effect of metformin could be AMPK-dependent; in the absence of AMPK, metformin preserves most of its antitumoral effects [43], indicating that the mechanism of this drug is more complex.
One of the characteristic hallmarks of cancer cells is an increased cell proliferation. To do so, ovarian cancer cells overexpress several growth factors and its receptors, which produce an enhanced cell signaling related with survival and proliferation in these cells [44, 45, 46].
In ovarian cancer, growth factors can activate protein kinase B (AKT) and the extracellular signal-regulated kinase (ERK) signaling pathways, among others [47, 48, 49]. These signaling pathways are associated with an increase of cell proliferation in most kinds of cancer cells [50, 51]. Some studies have shown that metformin treatment decreases IGF-1 and insulin levels, in a mice model with ovarian cancer [51], and also metformin treatment blocks the pro-tumoral effects of the nerve growth factor (NGF) in epithelial ovarian cancer cells [35] or the insulin/IGF-I signaling in uterine serous carcinoma [52].
The activation by growth factors of AKT and ERK signaling in ovarian cancer cells induces the activation of mechanistic target of rapamycin complex 1 (mTORC1), which controls protein translation and cell growth [53, 54, 55]. It is described that metformin-activated AMPK inhibits mTORC1 signaling in ovarian cancer cells [56, 57], which could impair its cell potential to proliferate and fend it in unfavorable conditions. Additionally, one key point in the antitumoral effect of metformin is that AMPK decreases the signaling pathways mediated by AKT and ERK in several types of cells, including cancer cells [38, 57, 58]. These signaling pathways are associated with the increase of most oncoproteins, for example, the transcription factor c-MYC and the inhibitory apoptotic protein survivin (BIRC5) [59, 60, 61, 62]. c-MYC is a proto-oncogene that controls several genes related with cell growth and cell proliferation, and some reports show that metformin decreases c-MYC protein levels in ovarian cancer cell lines [63, 64]. In addition, metformin decreases the mRNA levels of survivin in metastatic ovarian cancer cells [65].
According to current evidences, c-MYC controls the transcription and cell cycle inhibitors [66]. In agreement with the metformin-depending decrease of c-MYC in ovarian cancer cells, metformin induces the degradation of cyclin D1 [33, 38], a protein required for progression from G1 to S phase of the cell cycle, and increases p21 expression (a negative regulator of cell cycle) [67]. These results are consistent with experiments performed in primary ovarian cancer cell cultures and ovarian cancer cell lines, which show that metformin induces cell cycle arrest in the G0/G1 phase and decreases the percentage of cells in S phase of the cellular cycle [35, 68, 69]. These findings highly suggest that metformin decreases the progression of the cell cycle in ovarian cancer cells.
Even more, several authors have shown that metformin can elicit cytostatic or cytotoxic effects in ovarian cancer cells. A key point for a better understanding of these differences is that metformin inhibits tumor cell proliferation in the presence of glucose (with a cytostatic effect) but induces apoptosis in low-glucose conditions [70]. For example, ovarian cancer cells are more sensitive to metformin at concentrations of 2.5 millimolar than in 25 millimolar of glucose (found in culture conditions). This is a consequence of reactive oxygen species accumulation, which increase cell apoptosis and endoplasmic reticulum stress and decrease of c-MYC protein levels [63, 70].
For cell proliferation, the cancer cell has high requirements of substrates for synthesis of structural components and signaling. One target of AMPK is the sterol regulatory element-binding protein 1 (SREBP1), a lipogenic transcription factor [71], which increases cellular biosynthesis of fatty acids and cholesterol by transcription of the enzymes ACC, HMG-CoA reductase, and fatty acid synthase [72], not only in fat tissue but also in ovarian cancer cells [73]. Because ACC is involved in the taxol-mediated cytotoxic effect of ovarian cancer cells [74], besides the fact that the inhibition of ACC suppresses ovarian cancer cell growth in vivo and in vitro [75], it is possible to conclude that ACC inhibition could contribute to an important part of the antitumoral effects of metformin.
Angiogenesis, defined as the generation of new blood vessels from preexisting ones [76], is an essential process to supply oxygen and nutrients to normal and tumoral ovarian cells. Unfortunately, this process is exacerbated in ovarian cancer cells, which overexpress some growth factors, such as vascular endothelial growth factor (VEGF) or NGF [77, 78] which promotes angiogenesis.
The relevance of metformin in the vascular context is recognized; however, its action depends on the cell type, metabolic status, and nutrient availability. For example, some pro-angiogenic properties have been attributed to metformin under hypoxia and hyperglycemia, similar characteristics to myocardial infarction in diabetic patients. In this context, metformin enhances endothelial cell survival, migration, and apoptosis inhibition [79, 80]; this strongly suggests that the use of metformin could be beneficial in the context of cardiovascular diseases in diabetic patients. On the other hand, metformin could have an opposite effect in endothelial cells under hypoglycemic conditions (as tumor endothelial cells), where metformin produces an inhibition of its cell proliferation and angiogenesis potential, as will be discussed later.
In the ovary, the correct formation and regression of blood vessels during each ovarian cycle is indispensable for proper follicular development, ovulation, and corpus luteum formation, so that angiogenesis displays a key role in ovarian homeostasis and pathogenesis [81]. In patients with polycystic ovary syndrome, an increased expression of VEGF is described, and it is hypothesized that part of the beneficial metformin-associated effects will be mediated by a decrease or normalization of its VEGF levels. For example, it is described that in a rat model with dehydroepiandrosterone-induced polycystic ovaries, metformin administration restores the ovarian-increased levels of VEGF and angiopoietin 1, both angiogenic factors [82]. In addition, women with polycystic ovarian syndrome who take metformin have decreased their levels of plasmatic endothelin 1 and plasminogen activator inhibitor-1 [83, 84], molecules that also promote angiogenesis.
The angioprotection is an antitumoral mechanism that has been explored in ovarian cancer. Considering that the most studied angiogenic factor is VEGF, a monoclonal antibody against VEGF called bevacizumab has been developed and was approved for the use in advanced stages of ovarian cancer [85, 86]. In ovarian cancer models, the main knowledge of anti-angiogenic characteristics of metformin comes from VEGF modulation. Several in vitro models have shown that metformin decreases both VEGF mRNA and protein levels in ovarian cancer cell lines and then, its angiogenic potential [33, 64]. In a mice model with ovarian cancer, metformin decreases VEGF levels in plasma and ascitic fluid, with a consistent decrease of the ovarian tumor growth [51]. Interestingly, metformin reduces the vascular density (showed by CD31 staining) of ovarian cancer xenografts in mice, and metformin-/cisplatin-treated mice have significantly less vascular density than either metformin or cisplatin alone [33]. Because cisplatin/carboplatin and paclitaxel are drugs used in the first-line chemotherapy in ovarian cancer [87, 88], these results suggest that metformin could potentiate the anti-angiogenic effects of chemotherapy during ovarian cancer treatment.
On the other hand, metformin treatment (in millimolar concentrations) displays direct effects in the endothelial cells, by reducing cell proliferation in human umbilical vein endothelial cells (HUVEC) and endothelial progenitor cells [89, 90]. Similar results were replicated by our group where metformin decreases cell proliferation of the endothelial cell line EA.hy926, in a dose-dependent manner [35], as well as, the endothelial cell differentiation (Figure 1). These results suggest that metformin affects in a direct manner the angiogenesis potential of endothelial cells.
Effect of metformin on the differentiation of endothelial cells. Metformin reduces the multicellular junctions and polygonal structures of endothelial cells EA.hy926 in a matrigel assay (4 h). Upper insert: positive control (NGF 100 ng/ml). Magnification bar: 50 μm.
In the ovarian cell, posttranscriptional regulations control gene expression at RNA level [91]. The micro-RNAs (miRs) are short non-codificant RNAs that regulate the expression of approximately 60% of protein-coding genes of the human genome [92]. miRs bind to a messenger RNA target, producing its degradation or translational repression depending of complementary degree [93]. The machinery for expression, processing, and exportation of miRs depends on several proteins as RNAse III DICER and exportins [93]. It is described that DICER downregulation is an oncogenic event that enhances epithelial-mesenchymal transition (EMT) and metastatic dissemination in cancer cells [94]. An important antecedent is that metformin elicits anticancer effects through the sequential modulation of DICER and c-MYC in breast cancer cells, increasing oncosuppressor miRs [95]. These mechanisms have not been investigated in ovarian cancer cells; nevertheless, preliminary results from our group show that metformin increases the oncosuppressor miRs 23-b and miR-145 in the epithelial ovarian cells [96].
As already mentioned in point 3.3, the activation of AMPK by metformin produces an inhibitory phosphorylation of acetyl-CoA carboxylase, an enzyme that regulates lipid metabolism. Importantly, intermediaries of lipid metabolism participate in cell signaling and chromatin structure, modulating processes as cell histone acetylation that depends on cytosolic acetyl-CoA [97]. The decrease of the conversion of acetyl-CoA to malonyl-CoA leads to an increase in the acetylation of histones in the chromatin and altered gene expression in ovarian cancer cells [67]. Because acetylation of nucleosomal histones is linked to nuclear processes as transcription, replication, and repair among other functions [98], it is possible that several antitumoral effects of metformin could be regulated by protein acetylation and transcriptional regulation of several oncosuppressor proteins.
The summary of the main studied antitumoral effects of metformin is shown in Figure 2.
Main antitumoral mechanism of metformin in ovarian cancer cells. Metformin enters the cell through organic cationic transporters (OCT) and produces the activation of liver kinase B1 (LKB1) and an increase of AMP/ATP ratio, which results in the activation of AMPK. This kinase has several targets as sterol regulatory element-binding protein 1 (SREBP) and acetyl-CoA carboxylase (ACC); the mechanistic target of rapamycin complex 1 (mTORC1) and AKT/ERK signaling; key proteins in the fatty acid synthesis and cell growth, survival, proliferation, and migration; and the processes of epithelial-mesenchymal transition (EMT). On the other hand, metformin can block the growth factor (GF) signaling dependent or independent of AMPK activation. Also metformin decreases the angiogenic potential of ovarian cancer cells, impairs the expression of vascular endothelial growth factor (VEGF), or acts directly on the endothelial cells.
A recent meta-analysis shows that among available studies of relationship between metformin intake with ovarian cancer incidence and prognosis in diabetic patients, the majority of the studies indicate a negative correlation between the use of metformin and the incidence of ovarian cancer, as well as, a positive correlation with better prognosis [99]. The same study shows that metformin treatment in diabetic patients has a reduction of 24% risk of ovarian cancer occurrence and also a 42% of reduction in mortality [99]. The main studies that showed metformin benefits in the context of ovarian cancer diabetic patients are summarized in Table 1.
Research | Study and population | Main finding |
---|---|---|
Wang et al. [12] | Retrospective cohort study N = 568, China | |
Bar et al. [114] | Retrospective cohort study N = 143, Israel |
|
Tseng et al. [115] | Retrospective cohort study N = 479,475, China |
|
Kumar et al. [116] | Case-control study 72 cases (OvCa, metformin users), 142 controls (OvCa, non-metformin) USA |
|
Romero et al. [102] | Retrospective cohort study N = 341, USA |
|
Bodmer et al. [117] | Case-control study 1611 cases (OvCa) and 9170 controls (non-OvCa), UK | Metformin use was associated with a decreased of risk of OvCa |
Summary of studies that evaluated incidence and prognosis of ovarian cancer (OvCa) patients using and not using metformin.
PFS: progression-free survival (length of time during and after the treatment of OvCa that a patient lives with the disease but it does not get worse).
DSS: disease-specific survival (percentage of people in a study or treatment group who have not died from OvCa in a defined period of time).
Although several observational studies show positive effects of metformin in diabetic patients, it has not yet been elucidated if metformin could be beneficial in nondiabetic patients. In addition, ovarian cancer has a low incidence, and the number of participants in some of the available studies is low; therefore, the evidence should be interpreted with caution.
Because of the increased interest in the possible use of metformin in nondiabetic patients, there are currently six clinical trials inscribed in NIH
Besides the abovementioned benefits, metformin treatment has a relevant role in the metastasis and chemoresistance prevention of several ovarian cancer models. For example, in vitro experiments have shown that metformin decreases the adhesion capacity, invasion, and migration of ovarian cancer cell lines [101]. In rodents, metformin treatment inhibits the growth of metastatic nodules in the lung product of ovarian cancer [33], and importantly, the use of metformin in diabetic women decreases the probability of disease recurrence [102].
The cancer stem cells, recently called “tumor-initiating cells,” are a tumoral cell subpopulation with critical role in therapy resistance and metastasis [103, 104, 105]. There are several markers to identify them, as lactate dehydrogenase (LDH), aldehyde dehydrogenase (ALDH), or cell-surface antigens as CD44, CD133, or CD117 [106, 107, 108]. Metformin treatment decreases the abundance of ovarian cancer LDH+ and decreases its ability to form tumor spheres, an attachment-independent growth characteristic of these kinds of cells [109]. At the same time, a low dose of metformin (micromolar concentration) decreases the abundance of CD44+/CD117+ ovarian cancer cells selectively, whereas CD133+ or ALDH+ cell subpopulation were more sensitive to millimolar concentration of this drug [109, 110].
Another key point is that metformin decreases the expression of classical markers related with EMT. This process is necessary to confer an increased migratory capacity to tumor cells, participating in the intra-/extravasation and hence, in the tumor cell dissemination. In CD44+/CD117+ ovarian cancer cells, metformin treatment decreases snail2, twist, and vimentin protein levels (these are mesenchymal markers), increasing E-cadherin protein levels (a known epithelial marker) [110]. These observations are related with a study performed in diabetic patients with endometrial cancer, where in the biopsies of these patients using metformin were found increased levels of E-cadherin [111]. These findings suggest that metformin decreases the process of EMT in ovarian cancer cells, affecting preferentially tumor-initiating cells, which constitutes a relevant advantage, because this type of cells is not affected by traditional chemotherapy.
One important aspect in ovarian cancer treatment is the high percentage of chemoresistance developed by patients. In this context, metformin stands as a promising drug, since several studies showed that it could increase the susceptibility of ovarian cancer cells to chemotherapy and revert its acquired chemoresistance [34, 112, 113]. One recent study performed in ovarian cancer cell lines treated for 6 months with cisplatin and paclitaxel (for the acquirement of chemoresistance phenotype) shows that metformin treatment increases drug sensitivity and reduces migratory abilities of these ovarian cancer cells. In addition, the same study shows that metformin decrease the ovarian cancer stem cell population and the expression of specific biomarkers of pluripotent genes [112].
Metformin is an antidiabetic drug that displays antitumoral effects in several in vivo and in vitro models of cancer, including ovarian cancer. The mechanism of its antitumoral effects could be either dependent or independent of AMPK, a key sensor of the cell energetic status. Metformin has several cell targets which include transcription factors and cell cycle regulators; wherewith it impairs cell proliferation by the arrest of the cell cycle. In addition, metformin modulates enzymes of metabolic pathways and lipid metabolism, as well as epigenetic and posttranscriptional regulation of the ovarian cancer cells, which can explain its pleiotropic actions. Another important point is that metformin regulates angiogenesis in the ovarian cancer cells, mainly decreasing VEGF expression, which impairs the angiogenic potential of these cells. On the other hand, metformin acts directly in endothelial cells, decreasing its proliferation, migration and differentiation, which complement its anti-angiogenic effect.
An important niche for metformin treatment could be its selective effect in ovarian cancer cells with stem cell phenotype, which are responsible for ovarian cancer dissemination and chemotherapy resistance. Several studies show that metformin reduces ovarian cancer stem cells abundance and that it could have a chemosensitivity role when used in combination with first-line chemotherapy agents. This opens the possibility to the potential use of metformin as a coadjuvant agent in ovarian cancer treatment.
Finally, there are several observational studies in diabetic women with ovarian cancer which show that metformin is associated with less ovarian cancer incidence and better prognosis. However, it is important to consider that the number of participants using metformin in some of these studies is low and that several in vitro experiments have shown that metformin action depends on the metabolic context and nutrient and oxygen availability of ovarian cancer cells. For these reasons, the use of metformin in nondiabetic women with ovarian cancer should be considered with caution.
Currently, there are several clinical trials performed in women with ovarian cancer. These trials are studying the effect of metformin treatment together with standard chemotherapy in the ovarian cancer prognosis and clinic-pathological markers, which could be helpful to elucidate whether this drug could be considered as a coadjuvant alternative in the treatment of ovarian cancer.
The authors would like to thank the National Fund for Scientific and Technological Development (FONDECYT) #1160139.
The authors declare no conflict of interest.
acetyl-CoA carboxylase activate protein kinase B adenosine monophosphate-activated protein kinase aldehyde dehydrogenase epithelial-mesenchymal transition extracellular signal-regulated kinase human umbilical vein endothelial cells insulin-like growth factor lactate dehydrogenase liver kinase B1 mechanistic target of rapamycin complex 1 micro-RNAs nerve growth factor organic cationic transporters sterol regulatory element-binding protein 1 vascular endothelial growth factor
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