C and C indicators of {A1}, {A2}, and {A3}
\r\n\t
",isbn:"978-1-83968-388-6",printIsbn:"978-1-83968-387-9",pdfIsbn:"978-1-83968-389-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"61ec2bad4fc3f7060fd64b91fa12e82c",bookSignature:"Ph.D. Vicente Vanaclocha",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10574.jpg",keywords:"Prevalence, Incidence, Worldwide Differences, Red Flags, Moyamoya and School Performance, Medical Treatment, Surgical Treatment, Genetic Markers, Immunologic Factors, Recommended Anesthetic Agents, Source of Intraoperative Complications, Post-Operative ICU Management",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 23rd 2020",dateEndSecondStepPublish:"October 21st 2020",dateEndThirdStepPublish:"December 20th 2020",dateEndFourthStepPublish:"March 10th 2021",dateEndFifthStepPublish:"May 9th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Vicente Vanaclocha is a Chief of Neurosurgery at the University Hospital of Navarra and head of Neurosurgery Service of San Jaime Hospital in Torrevieja. He has over 25 years of experience in neuro-oncology and minimally invasive surgery techniques. He is a pioneer in many areas in neurosurgery (treatment of brain tumors, Chiari Malformation, and sacroiliac joint disorders).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"199099",title:"Ph.D.",name:"Vicente",middleName:null,surname:"Vanaclocha",slug:"vicente-vanaclocha",fullName:"Vicente Vanaclocha",profilePictureURL:"https://mts.intechopen.com/storage/users/199099/images/system/199099.jpeg",biography:"Vicente Vanaclocha is Chief of Neurosurgery. Doctor of Medicine from the University of Valencia, he has over 25 years experience in neuro-oncology, minimally invasive and minimally invasive surgery techniques. Specialist in neurosurgery both nationally and internationally (including the General Medical Register of England and stay at the Groote Schuur Hospital in Cape Town, South Africa) has been Chief of Neurosurgery at the University Hospital of Navarra and head of Neurosurgery Service of San Jaime Hospital in Torrevieja. He was also associate professor of neurosurgery at the Faculty of Medicine of the University of Navarra and is a professor of neuroanatomy at the Catholic University of Valencia also serving as an editorial board member of repute.\nCurrently he is Associate Professor at the University of Valencia.",institutionString:"University of Valencia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"7",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Valencia",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"10928",title:"Simultaneous Topology, Shape and Sizing Optimisation of Skeletal Structures Using Multiobjective Evolutionary Algorithms",doi:"10.5772/9613",slug:"simultaneous-topology-shape-and-sizing-optimisation-of-skeletal-structures-using-multiobjective-evol",body:'\n\t\tA framework or skeletal structure is one of the most used structures in engineering applications. Using such a structure is said to be advantageous since they are simple and inexpensive to construct. It can be employed in many engineering purposes e.g. transmission towers, wind turbine towers, communication towers, civil engineering structures, and mechanical parts. In the past, the design of such a structure was usually carried out in such a way that the conceptual design stage was somewhat ignored and the initial shape of a structure had been formed by an experienced engineering designer. The classical civil engineering design approach is then applied in the preliminary and detailed design stages.
\n\t\t\tRecently topology optimisation, an efficient tool for structural conceptual design, has been studied and used in a wide range of engineering applications. Considerable research work on the conceptual design of truss and frame structures by means of topology optimisation has been conducted in the last two decades (e.g. Hajela & Lee,1995; Ohsaki, 1995; Zhou, 1996; Yunkang & Xin, 1998; Hasancebi & Erbatur, 2002; Kawamura et al., 2002; Stolpe & Svanberg, 2003; Ohsaki & Katoh, 2005; Achtziger & Stolpe, 2007; Svanberg & Werme, 2007; Hagishita & Ohsaki, 2009). By the use of such design technology, the optimised layout of a skeletal structure can be accomplished. Having a structural layout from this design process, the shape and sizing design processes are then implemented. Nevertheless, a more efficient design approach can be achieved if a design problem is posed to have topological, shape, and sizing design variables at the same time. Much research work has been conducted towards combining and performing topology, shape, and sizing optimisation at the same optimisation run (e.g. Wang et al., 2004; Zhou et al., 2004; Tang et al., 2005; Shea & Smith, 2006; Achtziger, 2007; Martínez et al., 2007; Chan & Wong, 2008; Noiluplao & Bureerat, 2008; Zhu et al., 2008). The optimum design problem of the simultaneous topology/shape/sizing optimisation is usually structural weight minimisation subject to stress and other safety constraints. The optimisers employed can be a derivative-based method (Martínez et al., 2007), an evolutionary algorithm (Tang et al., 2005; Shea & Smith, 2006; Noiluplao & Bureerat, 2008), and a hybrid method (Chan & Wong, 2008). From the literature, most if not all of the optimisation problems are formulated to have one objective function, whereas in a practical design of an engineering system, there usually are several design objectives for decision making. If we have an optimiser that can deal with the design problem with such multiple design objectives, it could be useful and beneficial for designers.
\n\t\t\tEvolutionary algorithms (EAs) have been developed and used for several decades. The methods can be viewed as optimisation methods that search for optimum solutions by imitating some kinds of natural behaviours, and relying on a random process. Most EA searches are based upon using a population or a group of design solutions; consequently, they are often called population-based optimisers. Compared to their gradient-based counterparts, the EAs have advantages in that their searching process can be performed without using function derivatives. The methods are robust and simple to use, and can deal with almost all kinds of optimum design problems. As they are dependent on randomisation, EAs however have some drawbacks since they have low convergence rates and lack search consistency.
\n\t\t\tEvolutionary algorithms can deal with both single- and multiple-objective optimisation problems. The best known single-objective evolutionary optimiser is the genetic algorithm. Some other established single-objective EAs are: evolution strategies, particle swarm optimisation, and population-based incremental learning. EAs that can deal with multiple objective functions are called multiobjective evolutionary algorithms (MOEAs). As they use a set of design solutions for searching, on each generation those solutions can be sorted to find the so-called non-dominated design solutions. The set of non-dominated design solutions are improved iteratively, and the final set is taken as an approximate Pareto optimal set. This is the most outstanding and attractive ability of MOEAs as they can search for a group of non-dominated design solutions within one optimisation run. While the other conventional gradient-based optimisers need to be run as many times as the number of Pareto optimal solutions required.
\n\t\t\tThe use of EAs for structural optimisation has been investigated and studied by many researchers around the world. By using EAs, any kind of design variables, constraints, as well as objective function can be defined. With additional strong points i.e. the capability of reaching near global optima of EAs and their robustness, they are even more popular than their gradient-based counterparts. The methods can be used for structural weight or cost minimisation (Benage & Dhingra, 1994, Bureerat & Cooper, 1988), passive vibration alleviation (Keane, 1995; Ton et al., 1998; Moshrefi-Torbati et al., 2003; Alkhatib et al., 2004; Kanyakam & Bureerat, 2007), and other performance maximisation (Xu et al., 2003; Achtziger & Kocvara, 2007).
\n\t\t\tThe work in this chapter, consisting of two parts, is concerned with the demonstration of implementing MOEAs on the optimisation of skeletal structures. In the first part, the performance comparison of a number of MOEAs including non-dominated sorting genetic algorithm (NSGAII), strength Pareto evolutionary algorithm (SPEA2), population-based incremental learning (PBIL), Pareto archive evolution strategy (PAES), and multiobjective particle swarm optimisation (MPSO) are employed to solve seven simultaneous shape and sizing design problems of two structures. The comparative studies are carried out by using the so-called C-indicator. Also, a new indicator C is proposed to be used along with the C parameter. The Pareto optimum results obtained from using the various MOEAs are compared and discussed while some of the best performers are obtained. The second part involves the application of MOEAs to a simultaneous topology/shape/sizing optimisation of a skeletal structure. The design demonstrations are performed and illustrated.
\n\t\tA typical multiobjecitve optimisation problem can be defined as:
\n\t\t\tfind x such that
\n\t\t\tSubject to
\n\t\t\tg\n\t\t\t\n\t\t\t\ti\n\t\t\t(x) 0h\n\t\t\t\n\t\t\t\ti\n\t\t\t(x) = 0where x is a vector of n design variables, f\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t are the m objective functions, g\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t are inequality constraints, and h\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t are equality constraints.
\n\t\t\tIf the problem has one objective, there will be one (global) optimum solution. In cases that the problem has more than one objective function, there will be a set of optimal solutions, which is called a Pareto optimum set. Figure 1 illustrates a particular 2-objective design problem where all the feasible solutions are plotted in the objective domain. The Pareto optimal solutions are the points on the bold frontier, which is called a Pareto frontier or Pareto front. Similarly to the 2-objective case, the Pareto front of a 3-objective design problem is a 3-dimensional surface and so on.
\n\t\t\tPareto front
The basic concept of exploring Pareto optimum points via MOEA search is that, in each generation while a new population is created, non-dominated solutions are classified and carried on to the next generation. The term non-dominated solutions define the local Pareto solutions among the members of the current population during evolutionary search. The definitions associated with non-domination (for minimisation) are given as:
\n\t\t\t\n\t\t\t\tDefinition 1 Dominance Given f\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t(x) for i = 1,…m are objective functions, if f\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t(x1) f\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t(x2) for every i {1,…,m} and there is j such that f\n\t\t\t\t\n\t\t\t\t\tj\n\t\t\t\t(x1) < f\n\t\t\t\t\n\t\t\t\t\tj\n\t\t\t\t(x2), then x1 dominates x2.
\n\t\t\t\n\t\t\t\tDefinition 2 Non-Dominated Solutions (Approximate Pareto Set) Given a set of solutions or population G size N, a solution x\n\t\t\t\t\te\n\t\t\t\t G is a non-dominated solution in G if there does not exist x G such that x dominate x\n\t\t\t\t\te\n\t\t\t\t.
\n\t\t\t\n\t\t\t\tFigure 2 displays the plot of 7 design solutions on a 2-objective domain. The non-dominated solutions are x1, x3, and x7.
\n\t\t\tnon-dominated solutions
In this work, five MOEAs are used including PAES, NSGAII, SPEA2, PBIL and MPSO. The methods are briefly reviewed as follows:
\n\t\t\tPAES was proposed by Knowles & Corne, 2000. The simplest version of PAES is the combination of (1+1)-ES with a Pareto archiving strategy. The algorithm starts with an initial solution called a parent and an external Pareto archive. A candidate solution is then created by mutating the parent. The parent will be replaced by the candidate based on a selection strategy. The decision to discard or add the candidate to the Pareto archive is made. In cases that the archive is full, the adaptive grid algorithm is activated to remove one of the non-dominated solutions from the archive. As the procedure continues, the Pareto archive is iteratively updated until reaching the termination criterion.
\n\t\t\tNSGA was proposed by Srinivas & Deb, 1994, and later the upgraded version NSGAII was presented by Deb et al., 2002. Starting with an initial population, selection, crossover and mutation operators are then used for exploring Pareto solutions. After crossover and mutation are operated with the given probabilities, the children are obtained and a new population is selected by performing non-dominated and crowding distance sorting algorithms on a union set of the parents and their children. The population is updated iteratively until the termination criterion is met.
\n\t\t\tSPEA was proposed by Zitzler & Thiele, 1999, whereas its second version SPEA2 was presented in Zitzler et al., 2002. The search procedure starts with an initial population and an external Pareto set. Fitness values are assigned to the population based upon the level of domination. A set of solutions are then selected to a mating pool by performing the binary tournament selection operator. The new population are created by means of crossover and mutation on those selected solutions. The new external Pareto solutions are the non-dominated solutions of the combination of the old external Pareto set and the new population. In cases that the number of non-dominated solutions in the Pareto archive exceeds its predefined size, the truncation operator also known as the nearest neighbourhood technique is executed to delete some design solutions from the archive. The Pareto archive is updated repeatedly until the termination criterion is met.
\n\t\t\tThe particle swarm optimisation method uses real codes and searches for an optimum by mimicking the movement of a flock of birds, which aim to find food (Reyes-Sierra & Coello Coello, 2006). The search procedure herein is based on the particle swarm concepts combining with the use of an external Pareto archiving scheme. Starting with an initial set of design solutions as well as their corresponding particle velocities and objective function values, an initial Pareto archive is filled with the non-dominated solutions obtained from sorting the initial population. A new population is then created by using the particle swarm updating strategy where the global best solution is randomly selected from the external Pareto archive. Afterwards, the external archive is updated by the non-dominated solutions of the union set of the new population and the previous non-dominated solutions. In cases that the number of non-dominated solutions is too large, the adaptive grid algorithm (Knowles & Corne, 2000) is employed to properly remove some solutions from the archive. The Pareto archive is repeatedly improved until fulfilling the termination criteria.
\n\t\t\tPopulation-based incremental learning, probably the simplest form of an estimation of distribution algorithm (EDA), was first proposed by Beluja, 1994, for single-objective optimisation. The method was then extended for multiobjective optimisation (Bureerat & Sriworamas, 2007; Kanyakam & Bureerat, 2007). The algorithm of multiobjective PBIL starts with an external Pareto archive and initial probability matrix having all elements set to be 0.5. A set of binary design solutions are then created corresponding to the probability matrix while their function values are evaluated. The Pareto archive is updated by replacing its members with the non-dominated solutions of the new population and the previous Pareto archive. In cases that the number of non-dominated solutions exceeds the predefined archive size, the normal line method is activated to remove some members from the archive. The probability matrix and the Pareto archive are iteratively updated until the termination criteria are fulfilled.
\n\t\t\tA structural dynamic model can be described as a structure being in a dynamic equilibrium state. It is the state at which the system has minimum potential energy (potential energy herein includes structural elastic potential energy, the work done by external forces, and the work due to inertial forces). The equations of motion basically comprise of kinetic energy, structural restoration (spring and damping) and external dynamic forces. By using the finite element approach, the structural dynamic model is represented by
\n\t\t\twhere is the vector of structural displacements, M is a structural mass matrix, K is a structural stiffness matrix, and F is the vector of dynamic forces acting on the structure.
\n\t\t\tThe computation is traditionally achieved by means of finite element analysis. With the given boundary conditions (say \n\t\t\t\t\tb\n\t\t\t\t = 0) being given, Equation (2) can be partitioned as
\n\t\t\twhere the subscript b indicates the known displacements and unknown reactions at the boundary conditions, and the subscript a denotes unknown displacements and predefined external forces.
\n\t\t\tEquation (3) can be rearranged leading to 2 systems of differential equations as:
\n\t\t\tIn the cases of free vibration analysis, Equation (4) can be written as
\n\t\t\tBy substituting \n\t\t\t\t
Solving such a system of equations leads to N natural frequencies \n\t\t\t\t
\n\t\t\t\t
By using the proportional (Rayleigh) damping concept, a damping matrix can be introduced to the model yielding
\n\t\t\twhere C\n\t\t\t\t\taa\n\t\t\t\t =M\n\t\t\t\t\taa\n\t\t\t\t + K\n\t\t\t\t\taa\n\t\t\t\t, and and are damping coefficients to be defined.
\n\t\t\tFrom equation (9), by substituting \n\t\t\t\t
\n\t\t\t\t
where is the frequency of the input force and its output displacement, and \n\t\t\t\t
The relation between displacement response and input force can be expressed as
\n\t\t\t\n\t\t\t\tFigure 3 illustrates how to measure H(r,s) which represents the ratio of a displacement response at the rth degree of freedom to a harmonic excitation at the sth degree of freedom.
\n\t\t\tFurthermore, by defining force transmissibility (FT), denoted by T(), as the ratio of output harmonic reaction forces to the input external harmonic forces, it can be written as
\n\t\t\tMeasurement of FRF
By letting the reaction force be\n\t\t\t\t
In structural vibration design, FRF and FT determine structural merit. The lower FRF or FT at a particular frequency means the better structural vibration suppression design. Therefore, a design objective can be assigned in such a way that frequency responses at a frequency range of interest are minimised. Moreover, maximising structural natural frequency is an alternative criterion for design of structures under dynamic loadings.
\n\t\t\tApart from dynamic analysis, structural static analysis can be carried out by using Equation (3) ignoring the structural kinetic energy or the mass matrix. The system of equations then becomes
\n\t\t\tThe solutions of (15) are
\n\t\t\tThe displacement vector is used for displacement constraints as well as for computing stresses on structural elements. The reaction F\n\t\t\t\t\tb\n\t\t\t\t is also an important factor since the reaction force can affect a structural foundation. In an optimisation process, it is also common to use many load cases since, in real-world problems, there are many aspects of applied loads acting on one structure.
\n\t\tThe optimisation problems assigned in this study are to find a Pareto optimal set that optimises multiple objective functions subject to stress constraints, which can be expressed as
\n\t\t\t\tsubject to
\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\ta\n\t\t\t\t\t, i = 1, …, N\n\t\t\t\t\t\te\n\t\t\t\t\t\n\t\t\t\t
\n\t\t\t\twhere \n\t\t\t\t\t\ti\n\t\t\t\t\t is a stress on the ith element, \n\t\t\t\t\t\ta\n\t\t\t\t\t is an allowable stress, and N\n\t\t\t\t\t\te\n\t\t\t\t\t is the total number of elements.
\n\t\t\t\tThe multiobjective design problems presented here are similar to the work in Kanyakam & Bureerat, 2007, where the objective functions include structural mass, a natural frequency, an FRF crest parameter, and an FT crest parameter. Figure 4 displays the term FRF crest parameter, which determines the shaded area in a frequency range of interest. As we need
\n\t\t\t\tFRF crest parameter
to reduce the magnitude of the FRF for vibration suppression, the frequency range is thus the interval that bounds a particular natural frequency. Seven design criteria are defined as:
\n\t\t\t\tf1: structural mass
f4: FRF crest parameter at 1\n\t\t\t\t\t\t
f5: mean value of FRF crest parameters at 1, 2 and 3\n\t\t\t\t\t\t
f6: FT crest parameter at 1\n\t\t\t\t\t\t
f7: mean value of FT crest parameters at 1, 2 and 3\n\t\t\t\t\t\t
where \n\t\t\t\t\t\ti\n\t\t\t\t\t is the ith natural frequency.
\n\t\t\t\tThe sets of objective functions can be arranged as:
\n\t\t\t\tP1: min f1, min f2\n\t\t\t\t\t\t
P2: min f1, min f3\n\t\t\t\t\t\t
P3: min f1, min f4\n\t\t\t\t\t\t
P4: min f1, min f5\n\t\t\t\t\t\t
P5: min f1, min f6\n\t\t\t\t\t\t
P6: min f1, min f7\n\t\t\t\t\t\t
P7: min f1, min f5, min f7.
As a result, we have seven multiobjective design problems with the above sets of objective functions and stress constraints. It should be noted that some of these objective function sets have been implemented and examined in Kanyakam & Bureerat, 2007, and it is shown that using P2, P4 and P6 leads to a more effective design based on vibration alleviation. All seven design problems are however used to benchmark the performance of the MOEAS.
\n\t\t\t\tWe apply the above-mentioned problems to design three frame structures made of material with E = 209109 N/m2, and = 7000 kg/m3. The structures are named as:
\n\t\t\t\tST1: a 2D bridge (Figure 5). The structure has 12 nodes and 21 elements where nodes 1 and 12 are fixed. The vertical loads are applied to nodes 2, 4, 6, 8, and 10. The design variables consist of element diameters, and the vertical positions of nodes 3, 5, 7, 9, and 11. Both shape and sizing design variables are treated as being symmetric; as a result, there are 14 continuous design variables. The FRF used as design criteria is the point receptance in the vertical direction of node 7. The FT is the vertical direction transmission from node 7 to the fixed support node 1.
\n\t\t\t\tbridge ST1
ST2: a walking tractor handlebar (Figure 6). The finite element model of the handlebar structure (Kanyakam & Bureerat, 2007) consists of 16 nodes and 27 elements with four nodes being fixed. The structure is subjected to two static load cases, one is the load for turning the tractor and the other is the load for balancing and controlling the tractor. The total number of continuous shape and sizing design variables is 24. The FRF being used is the point receptance in the vertical direction of node 6. The FT is the vertical direction transmission from node 6 to the fixed support node 1.
\n\t\t\t\tWalking tractor handlebar structure ST2
ST3: a 2D bridge (Figure 7). The ST3 structure is modified from ST1 for the demonstration of simultaneous topology/shape/sizing optimisation. The structure in Figure 7, which is set as a ground structure for topology optimisation, has 12 nodes and 25 elements. The design variables are element diameters, and the vertical and horizontal positions of nodes 3, 5, 7, 9, and 11, which have a total of 18 variables after symmetry treatment. The diameters are discrete and allowed to have nearly zero size so that we can have various structural topologies. The position of FRF and FT measures are the same as that of ST1.
\n\t\t\t\tFinite element analysis is carried out by using the 2-node 3-dimensional 12 d.o.f. beam element. The ST1 and ST2 structures are used for the investigation of a comparative performance of MOEAs, while the ST3 structure is assigned for the demonstration of performing topology/shape/sizing optimisation at the same time.
\n\t\t\tbridge’s ground structure for simultaneous topology/shape/sizing optimisation ST3
Seven multiobjective evolutionary strategies are employed in this study, which are designated as:
\n\t\t\t\tBPBIL: PBIL using binary codes with 0.05 mutation rate, and 0.2 mutation shift
BPAES: PAES using binary codes
BNSGA: NSGAII using binary codes with crossover and mutation rates as 1.0 and 0.5 respectively
BSPEA: SPEA2 using binary codes with crossover and mutation rates as 1.0 and 0.5 respectively
RSPEA: SPEA2 using real codes (Sirsomporn & Bureerat, 2008) with crossover and mutation rates as 1.0 and 0.5 respectively
RNSGA: NSGAII using real codes with crossover and mutation rates as 1.0 and 0.5 respectively
RMPSO: MPSO (Reyes-Sierra & Coello Coello, 2006) using real codes with a starting inertia weight, an ending inertia weight, a cognitive learning factor, and a social learning factor as 0.5, 0.01, 0.5, and 0.5 respectively.
For the comparative performance study of optimising ST1 and ST2, starting with the same initial population, the number of generations of MOEAs is set to be 100 while both the population and archive size are set as 50. Each optimiser is applied to solve each design problem for designing each structure with 10 optimisation runs. The last updated Pareto archive is set to be a Pareto optimal set. For the design of ST3, one of the best MOEAs obtained from the first two design case studies is employed to solve the P2, P4, and P6 design problems with the number of generations being 150 while the population and the external archive are sized 100. The non-dominated sorting scheme proposed in Deb et al. (2001) is used to handle the stress constraints.
\n\t\t\t\tThe performance assessment is accomplished by using the C-parameter (Zitzler et al., 2000). Having two particular non-dominated fronts {A} and {B}, C\n\t\t\t\t\t\tA,B\n\t\t\t\t\t determines the number of solutions in {B} that are dominated or equal to some solution in {A} divided by the total number of solutions in {B}. On the other hand, C\n\t\t\t\t\t\tB,A\n\t\t\t\t\t determines the number of solutions in {A} that are dominated or equal to some solution in {B} divided by the total number of solutions in {A}. From the definition, if C\n\t\t\t\t\t\tA,B\n\t\t\t\t\t> C\n\t\t\t\t\t\tB,A\n\t\t\t\t\t, we can say that front {A} is better than front {B} or vice versa. Figure 8 displays two approximate Pareto fronts {A} and {B} where C\n\t\t\t\t\t\tA,B\n\t\t\t\t\t = 0.5000 and C\n\t\t\t\t\t\tB,A\n\t\t\t\t\t = 0.4000. This implies that {A} is better than {B}. However, we can observe from the shaded areas between the two fronts dominating each other that the dominating area of {B} is larger than {A}. These shaded areas are also meaningful for front comparison. Nevertheless, it is difficult or even impossible to calculate such areas of hundred pairs of Pareto fronts directly. We can laterally calculate them and define a new performance indicator as:
\n\t\t\t\twhere
\n\t\t\t\tIf V\n\t\t\t\t\t\tA\n\t\t\t\t\t = V\n\t\t\t\t\t\tB\n\t\t\t\t\t = 0, C\n\t\t\t\t\t\tA,B\n\t\t\t\t\t = C\n\t\t\t\t\t\tB,A\n\t\t\t\t\t = 0.5.
\n\t\t\t\tFrom Figure 8, we have C\n\t\t\t\t\t\tA,B\n\t\t\t\t\t = 0.4556 and C\n\t\t\t\t\t\tB,A\n\t\t\t\t\t = 0.5444, which means {B} is the better front than {A}. Based on these two indicators, we have two different views of comparing approximate Pareto fronts. The special characteristic of the C parameter is illustrated in Figure 9, which shows three particular non-dominated fronts {A1}, {A2}, and {A3}. The C and C comparisons of the three fronts are given in Table 1. In the Table, the value in the ith row and jth column represents C\n\t\t\t\t\t\tAi,Aj\n\t\t\t\t\t. From the C comparison, it can be concluded that the front {A3} is the best method while the second best is {A1}. However, in cases of C comparison, we can see that {A2} is better than {A1}, {A1} is better than {A3}, and {A3} is better than {A2}. This scenario can be called a scissor-paper-stone situation, which can happen in comparing approximate Pareto fronts obtained from using MOEAs. The two indicators, in fact, are not always conflicting to each other but they should be used together to provide different aspects of front comparing.
\n\t\t\t\tComparing two non-dominated fronts
Scissors-paper-stone situation
C -indicator | \n\t\t\t\t\t\t\tC -indicator | \n\t\t\t\t\t\t||||
{ A 1 } | \n\t\t\t\t\t\t\t0.5714 | \n\t\t\t\t\t\t\t0.1667 | \n\t\t\t\t\t\t\t{ A 1 } | \n\t\t\t\t\t\t\t0.4929 | \n\t\t\t\t\t\t\t0.5029 | \n\t\t\t\t\t\t
0.4286 | \n\t\t\t\t\t\t\t{ A 2 } | \n\t\t\t\t\t\t\t0.5000 | \n\t\t\t\t\t\t\t0.5076 | \n\t\t\t\t\t\t\t{ A 2 } | \n\t\t\t\t\t\t\t0.4908 | \n\t\t\t\t\t\t
0.4286 | \n\t\t\t\t\t\t\t0.5714 | \n\t\t\t\t\t\t\t{ A 3 } | \n\t\t\t\t\t\t\t0.4971 | \n\t\t\t\t\t\t\t0.5092 | \n\t\t\t\t\t\t\t{ A 3 } | \n\t\t\t\t\t\t
C and C indicators of {A1}, {A2}, and {A3}
The performance comparison of 7 MOEAs is displayed in Figures 10 – 17. In Figure 10, the box-plots of C parameters comparing the various MOEAs when solving the P1-P7 design problems for the case of ST1 are illustrated. Each box-plot represents 1010 C values of comparing 10 fronts obtained from using a method X to 10 fronts obtained from using a method Y when solving a particular design problem. The box-plots at the ith row and jth column display the C values comparing non-dominated fronts of the seven design problems obtained from the ith optimiser with respect to those obtained from using the jth optimiser. For example the box-plots at the first row and second column in the figure present the C values of the fronts obtained from using BNSGA to those obtained from using BSPEA. We also need the box-plots in the second row and first column to compare these two optimisers. From the Figure, it can be concluded that BPBIL is the best for all the design problems. The C comparison of these four methods is given in Figure 11. It can be shown that BPBIL is the best method except for the P7 design problem, which BNSGA gives the best results.
\n\t\t\tThe best multiobjective evolutionary algorithm using binary codes BPBIL is taken to be compared with the methods using real codes as shown in Figures 12-13. From the C comparison in Figure 12, the overall best method is BPBIL with RSPEA being the second best. In Figure 13, it can be seen that RSPEA is as good as BPBIL based on C comparison. This occurrence is probably similar to that illustrated in Figure 8. However, when taking an account of both indicators, the best method for designing ST1 is BPBIL.
\n\t\t\tBox-plot of C indicator of ST1 – I
Box-plot of C indicator of ST1 – I
Box-plot of C indicator of ST1 – II
Box-plot of C indicator of ST1 - II
The comparative performance of MOEAs for solving the P1-P7 optimisation problems for the case of ST2 is illustrated in Figures 14-17. The best method that use binary codes based on the C indicator is BPBIL whereas the second best method is BNSGA as shown in Figure 14. Based on the C indicator, the best method is BPBIL while the close second best is BNSGA. BNSGA even outperforms BPBIL in cases of the P3 and P4 design problems. This situation is similar to that displayed in Figure 8.
\n\t\t\tThe best evolutionary optimiser among the methods using binary codes (BPBIL) is taken to be compared with the optimisers using real codes as shown in Figures 15 and 16. Based on the C indicator, the best optimiser is RSPEA whereas the second best is BPBIL. For the C comparison, the best optimiser is still RSPEA with BPBIL being the second best. From both ST1 and ST2 case studies, it can be concluded that BPBIL tends to be more efficient when dealing with design problems with a smaller number of design variables while RSPEA is the better optimiser for the design problems with a greater number of design variables.
\n\t\t\tBox-plot of C indicator of ST2 – I
Box-plot of C indicator of ST2 - I
Box-plot of C indicator of ST2 – II
Box-plot of C indicator of ST2 - II
The BPBIL algorithm is chosen to solve the P2, P4 and P6 design problems where the structure to be optimised is ST3. In this design case, some structural elements are allowed to be removed if their diameters are too small. The Pareto optimum results of P2 for optimising ST3 obtained from using BPBIL are plotted in Figure 18. Some selected non-dominated solutions are labelled whereas their corresponding structures are given in Figure 19. It can be seen that the structures have various layouts and shapes as well as various element sizes. The approximate Pareto front of P4 is plotted in Figure 20 whereas some selected structures from the front are illustrated in Figure 21. Similarly to the P2 design case, there consist of various structural topologies, shape and element sizes. The non-dominated front of P6 obtained from using BPBIL is given in Figure 22 whilst the selected solutions are shown in Figure 23. Similarly to the first two cases, the structures have various topologies, shapes, and element sizes. The obvious advantage of using MOEA for solving the simultaneous topology/shape/sizing optimisation is that they are robust and simple to use, and we can have a set of Pareto optimal solutions for decision making within one simulation run.
\n\t\t\tPareto front of P2 problem and ST3 structures
Some selected structures of P2
Pareto front of P4 problem and ST3 structures
Some selected structures of P4
Pareto front of P6 problem and ST3 structures
Some selected structures of P6
The search procedures of the various MOEAs as well as the static and dynamic analysis of skeletal structures are briefly detailed. Seven multiobjective optimum design problems are posed for benchmarking the performance of the MOEAs. The design problems are applied to three skeletal structures. The first two structures have simultaneous shape/sizing design variables while the third structure is used for design demonstration of simultaneous topology/shape/sizing optimisation. From the optimum results, it can be concluded that SPEA2 using real codes is the best performer for the ST2 structure which has greater number of design variables. The PBIL using binary string is said to be the best method for ST1, which has a smaller number of design variables. From the design demonstration of using topological, shape, and sizing design variables at the same time, it is shown that BPBIL is an efficient and effective optimisation tool for such a type of structural optimum design. The C and C indicators should be used together to provide some insight that is missing from using either of them solely. The application of MOEAs for design optimisation of skeletal structures is said to be advantageous since they are robust and simple to use. The method can cope with all kind of design criteria as demonstrated in this work. Most importantly, we can have a set of non-dominated solutions for decision making within one optimisation.
\n\t\tChronic Obstructive Pulmonary Disease (COPD) is a common condition, usually affecting people of >40 years of age significantly exposed to noxious particles or gases [1]. Although considered both preventable and treatable [1], COPD remains a leading cause of morbidity and mortality [2, 3], affecting an estimated 384 million people worldwide [4]. The COPD prevalence is projected to increase in the coming decades [5], as well as its position among the leading causes of mortality [4].
\nActive or passive cigarette smoking is the most commonly encountered risk factor for COPD across the world [1]; however other factors may play a role in the disease pathogenesis, such as genetic factors [6, 7], exposure to indoor and outdoor air pollutants [8, 9, 10, 11], exposure to occupational dusts, chemical agents or fumes [12], infections (HIV, tuberculosis) [13, 14], and socioeconomic status [15].
\nThe normal lung response to the inhalation of noxious factors is an inflammatory reaction of the airways. In patients who develop COPD, the excessive inflammatory response is further enhanced by the oxidative stress and an imbalance of the protease-antiprotease system, leading to the destruction of the lung parenchyma and disruption of normal repair and defense mechanisms. Emphysema and small airway fibrosis are the consequences of these processes, which translate into gas trapping and chronic airflow limitation [1].
\nBy definition, COPD is a chronic condition, and the major symptoms exhibited by the patients suffering from this disease, dyspnea, cough, and sputum production, are usually persistent and/or progressive and have a considerable negative effect on the patient’s quality of life. The Global Burden of Disease Study highlighted that COPD is a major contributor to disability and mortality around the world, by ranking COPD as the fifth leading cause of disability-adjusted life years (DALYs) lost in 2013 [16].
\nThe natural course of the disease is grafted by acute episodes of worsening of symptoms triggered by infectious agents, air pollution, and other factors. These events are referred to as “exacerbations” and usually require a change in medication and/or hospitalization. Exacerbations are associated with accelerated lung function decline, reduced quality of life, and increased mortality [17] and, not surprisingly, have been surnamed as “chest attacks” or “strokes of the lung” [18, 19].
\nThe main goals for the management of stable COPD are improvement in quality of life by relieving symptoms and increasing exercise tolerance and reduction of mortality risk by preventing exacerbations and disease progression [1].
\nSeveral inhaled, oral, and systemically administered drugs improve lung function, decrease the frequency and severity of COPD exacerbations, and improve patients’ quality of life [20].
\nNon-pharmacological therapies in COPD, including smoking cessation strategies, pulmonary rehabilitation, vaccinations, surgical or bronchoscopic interventions, and noninvasive ventilation have their established role in the management of the disease; however they are not discussed here, as the focus of this chapter is on the pharmacological treatment.
\nBack in 2001 when the first edition of the GOLD document was released [21], the pharmacological arsenal for the treatment of COPD was rather limited, comprising of short-acting beta2-agonists and anticholinergics, long-acting beta2-agonists, theophyllines, and mucoactive agents. Inhaled corticosteroids, although available as single medication, were never widely recommended for the treatment of COPD in monotherapy and have no current authorization for use outside fixed-dose combinations.
\nNowadays, there is a broader range of molecules recommended for the treatment of stable COPD that can be classified in the following classes of pharmacological agents:
Beta2-agonists: short-acting (SABA) and long-acting (LABA)
Anticholinergics: short-acting (SAMA) and long-acting (LAMA)
Fixed-dose combinations: SABA/SAMA, LABA/ICS, LABA/LAMA, LABA/LAMA/ICS
Methylxanthines
Phosphodiesterase-4 (PDE4) inhibitors
Mucolytics
Antibiotics
Additionally, a new acquisition in the bronchodilator portfolio could be the potential use of dual agents or bifunctional muscarinic antagonists and beta2-agonists (MABAs), which combine both antimuscarinic and adrenergic properties in a single molecule [22]. Some of these molecules are already in clinical trials, but a major caveat is the difficulty to balance the antimuscarinic and adrenergic activities, without expressing a tendency toward one of them [23].
\nEfforts have been made for the discovery of new pharmacological agents, either belonging to the mentioned classes or addressing new therapeutic targets: new corticosteroids, novel classes of bronchodilators, kinase inhibitors, mediator antagonists (including biological therapies, such as cytokine inhibitors), antioxidants, etc. Unfortunately, many of these molecules never made it to the market or were not granted approval for COPD due to safety, efficacy, or delivery issues; several others are still in the development process [23].
\nCurrently available pharmacological agents and other therapies are mainly used as pathogenic or symptomatic treatment.
\nChronic airflow limitation is a central characteristic of COPD and is the result of the combination in varying degrees of several pathological processes such as narrowing of the airways, mucus hypersecretion, and loss of small conducting airways [24]. The consequences of these anatomic changes are expiratory airflow limitation, air trapping, and ventilation-perfusion mismatch [22, 25]. Additionally, the loss of elastic recoil and hyperinflation adversely affect thoracic and diaphragmatic mechanics, increasing the work of breathing and ultimately leading to dynamic hyperinflation [26]. Hyperinflation is an independent predictor of mortality in COPD [27].
\nThe clinical expression of airflow limitation is chronic, progressive dyspnea, which typically worsens with physical exercise. Chronic cough with or without sputum production is usually a reflection of the ongoing inflammatory process in the airways of COPD patients. However, there is no linear correlation between the severity of the airflow limitation and the level of symptoms. Some patients may have little subjective complaints, although the lung function testing reveals various degrees of airflow limitation, while other patients may have significant complaints, with little or no evidence of airflow obstruction [28]. In some cases, the symptoms may precede the development of airflow limitation by many years [1].
\nTreatment with inhaled bronchodilators can reduce hyperinflation, improve dyspnea, and increase exercise tolerance [29], and therefore, bronchodilators are considered as a cornerstone in the management of stable COPD [30].
\nWhile short-acting bronchodilators are an option for patients with occasional dyspnea at low risk of exacerbations, the majority of patients have breathlessness leading to exercise limitation at the time of diagnosis and may require more intensive treatment than short-acting bronchodilators alone [30]. For these patients, whether or not they are also at higher risk of exacerbations, long-acting bronchodilators (as monotherapy or in combination) are recommended as a preferred treatment choice in the GOLD strategy report [1].
\nAirway tone is controlled by both the sympathetic and parasympathetic nervous systems. These mechanisms interact and may potentiate each other and are employed alone or in combination therapeutically. Relaxation of airway smooth muscle is caused by blockade of acetylcholine activity at the receptor (muscarinic antagonist) or stimulation of the G protein-coupled receptor (beta-agonist) [31].
\nAnticholinergic drugs in the form of smoked alkaloids were among the first effective treatments for asthma [32]. In the mid-twentieth century, parenteral muscarinic antagonists and beta-agonists were used for acute attacks of asthma [33]. The major disadvantages of the systemic delivery were the side effects and a short duration of benefit. As such, subsequent work has both optimized the receptor specificity and the duration of action [22].
\nBeta-agonists were in use in Chinese medicine for millennia in the form of ephedra. Developments in the mid-twentieth century yielded compounds that specifically target the beta2-adrenergic receptor, reducing the side effects from beta1-agonists [31].
\nSince the approval by the US Food and Drug Administration (FDA) in 2004 of the first LAMA, tiotropium, long-acting bronchodilators have begun to play a central role in the management of stable COPD. Currently available molecules for inhalation delivery are summarized in Table 1.
\n\n | Delivery type | \nDuration of action (h) | \n
---|---|---|
Long-acting beta2-agonists (LABA) | \n||
Arformoterol | \nNebulized | \n12 | \n
Formoterol | \nDPI | \n12 | \n
Indacaterol | \nDPI | \n24 | \n
Olodaterol | \nSMI | \n24 | \n
Salmeterol | \nMDI, DPI | \n12 | \n
Long-acting anticholinergics (LAMA) | \n||
Aclidinium bromide | \nDPI, MDI | \n12 | \n
Glycopyrronium bromide | \nDPI | \n12–24 | \n
Tiotropium | \nDPI, SMI | \n24 | \n
Umeclidinium | \nDPI | \n24 | \n
Currently available LABAs and LAMAs as monotherapy.
DPI = dry powder inhaler; MDI = metered dose inhaler; SMI = soft mist inhaler.
The benefits of long-acting bronchodilator monotherapy have been well proven across a range of clinical studies [30] and include improvement of the airflow limitation [34, 35, 36, 37, 38, 39], dyspnea [34, 35, 39], physical activity/exercise capacity [29, 40, 41, 42], health status [34, 35, 37, 38, 39], and prevention of exacerbations [35, 39, 43, 44]; however, many patients remain symptomatic despite treatment [45].
\nDual bronchodilation improves lung function compared with a single bronchodilator [30]. Long-acting beta2-agonists and long-acting muscarinic antagonists act via different mechanisms; when used together in patients with COPD, they exert additional bronchodilating effects [46]. Multiple studies have assessed [30] and demonstrated that the use of LABA/LAMA dual bronchodilation results in additional improvements in lung function, exacerbation rates, health status, and other outcome measures when compared with monobronchodilation, while the safety profile of the dual bronchodilators was similar to that observed with placebo and individual monocomponents. Currently available LABA/LAMA combinations are listed in Table 2.
\n\n | Delivery type | \nDuration of action (h) | \n
---|---|---|
Fixed-dose combinations of LABA and LAMA (LABA/LAMA) | \n||
Formoterol/Aclidinium | \nDPI | \n12 | \n
Formoterol/Glycopyrronium | \nMDI | \n12 | \n
Indacaterol/Glycopyrronium | \nDPI | \n12–24 | \n
Olodaterol/Tiotropium | \nSMI | \n24 | \n
Vilanterol/Umeclidinium | \nDPI | \n24 | \n
Currently available fixed-dose combinations of LABA/LAMA.
DPI = dry powder inhaler; MDI = metered dose inhaler; SMI = soft mist inhaler.
According to current guidelines and strategy reports, long-acting bronchodilators in monotherapy are adequate options for the majority of COPD patients, regardless of the disease severity. However, in the GOLD report 2019 [1], the authors provide a clarification of the concept of “escalation” and “de-escalation” of the COPD therapy, which was introduced in a previous version. While “de-escalation” is mainly employed for the withdrawal of ICS due to lack of response or side effects, such as pneumonia, the “escalation” of treatment should be prompted by either inappropriate symptomatic response to the initial therapy or by the presence of exacerbations despite regular treatment and consists of adding a second class of bronchodilator and/or an ICS and/or other pharmacological agents (azithromycin, roflumilast) in order to ensure maximal symptom relief and to curb the risk of exacerbations.
\nThe choice of the bronchodilator treatment should take into account several factors, such as physiological impairment, symptom burden, and exacerbation risk, and should be individualized according to the drug safety profile, cost, and patients’ preference for device and medication [1, 20].
\nOne of the current controversies in COPD [20] is the following: what is best, a progressive escalation of bronchodilator therapy or “maximizing” bronchodilator therapy with dual bronchodilator therapy ab initio? The members of the GOLD Scientific Committee suggest that ensuring a maximal bronchodilation from the beginning could be a reasonable approach for both patients with high symptom burden and patients less severely affected. The latter may underreport their symptoms, masking an underlying resting and exercise lung hyperinflation, which is further linked to increased mortality and risk of severe exacerbations [20]. However, if a single agent is preferred, currently available evidence supports the use of a LAMA (tiotropium) since it improves lung function and health status even in patients with milder disease [47].
\nCOPD exacerbations represent acute worsening of symptoms requiring changes in medication and/or hospitalization [1]. Anthonisen and colleagues’ criteria [48] have been used for decades now in an attempt to standardize the evaluation of these events; however COPD exacerbations still have no universally established definition [49] and are subject to diagnostic uncertainty [50].
\nHistorically, the level of healthcare resource use (HCRU) required for the management of COPD exacerbations was used both to define and quantify the severity of the exacerbations, with moderate exacerbations requiring administration of oral steroids and/or antibiotics and severe exacerbations requiring hospitalization [49, 51, 52, 53]. However, healthcare use in COPD varies widely depending on access, leading to disparities across different healthcare systems [54]. Furthermore, in order to be treated, an acute event should be reported to healthcare professionals; hence unreported events may not be captured by HCRU definitions. In some reports, such events comprise up to two-thirds of exacerbations and can impair health-related quality of life [55, 56] and increase the risk of hospitalization [57].
\nAnother approach to define exacerbations is based on the systematic and standardized assessment of daily symptoms recorded using specific questionnaires (diaries) administered to the patients on paper or electronically. These questionnaires were developed with the ability to detect worsening of symptoms beyond a pre-specified threshold, based on patients’ reporting of their daily symptoms [58, 59]. Advantages of a standardized, validated assessment of COPD symptoms in exacerbation studies include uniform metrics, reduced recall bias, and the ability to fully characterize exacerbations of COPD, including the estimated 50–70% of events that are unreported [55, 56, 59]. Although attractive, this kind of approach is more difficult to implement outside the clinical trial setting, and the concordance with the HCRU-defined events is modest [54, 60].
\nCOPD exacerbations have a marked negative effect on both the patient and underlying disease processes [61] and can result in hospitalization and readmission, an increased risk of death [62], and a significant reduction in health status [55]. Exacerbations are also associated with long-term decline in lung function and a high socioeconomic cost [63, 64]. A history of frequent exacerbations is a good predictor for future exacerbation risk and defines the “frequent exacerbator” phenotype [65]. Thus, optimizing the prevention and management of COPD exacerbations are important clinical issues [61].
\nThe GOLD strategy report stratifies COPD patients based on the severity of their airflow limitation, symptom burden, and the risk of exacerbations; however the recommendations for the pharmacological treatment rely exclusively on the level of symptoms and exacerbation risk [1].
\nWhile the initial assessment of exacerbation risk may be biased by the patients’ ability to recall historical episodes of symptom worsening prior to being diagnosed with COPD, the reassessment of risk after initial pharmacological treatment should be able to identify patients requiring an escalation of treatment for a better prevention of future exacerbation episodes.
\nThe preferred treatment options for patients at high risk of exacerbation are a LAMA in monotherapy, a LABA/LAMA, or a LABA/ICS combination [1].
\nThere is evidence that both LABAs and LAMAs significantly improve the exacerbation rate versus placebo [66, 67, 68]; however, clinical trials have shown a greater effect on exacerbation rates for LAMA treatment (tiotropium) versus LABA treatment [69, 70].
\nThere is a strong evidence that treatment with fixed-dose combinations of LABA/LAMA improves lung function, symptoms, and health-related quality of life compared to placebo or its individual bronchodilator components [71, 72, 73]. The superiority of dual bronchodilation in the prevention of exacerbations compared to monocomponents was demonstrated for a LABA/LAMA combination [74], while another large study found that combining a LABA with a LAMA did not reduce exacerbation rate as much as expected compared to LAMA alone [75].
\nSimilarly, an ICS combined with a LABA is more effective than the individual components in improving lung function and health status and reducing exacerbations in patients with a history of exacerbations and moderate to very severe COPD [76, 77]. Currently available ICS/LABA combinations are listed in Table 3.
\n\n | Delivery type | \nDuration of action (h) | \n
---|---|---|
Fixed-dose combinations of LABA and ICS (LABA/ICS) | \n||
Formoterol/Beclometasone | \nMDI | \n12 | \n
Formoterol/Budesonide | \nMDI, DPI | \n12 | \n
Formoterol/Mometasone | \nMDI | \n12 | \n
Salmeterol/Fluticasone | \nMDI, DPI | \n12 | \n
Vilanterol/Fluticasone furoate | \nDPI | \n24 | \n
Currently available fixed-dose combinations of LABA/ICS.
DPI = dry powder inhaler; MDI = metered dose inhaler.
Furthermore, another study demonstrated the superiority of a LABA/LAMA combination versus an ICS/LABA combination in the prevention of exacerbations in patients with moderate to very severe COPD and a history of exacerbations, regardless of baseline blood eosinophils [78].
\nIn a recently published review, a group of experts critically evaluated mechanisms potentially responsible for the increased benefit of LABA/LAMA combinations over single long-acting bronchodilators or LABA/inhaled corticosteroids in decreasing exacerbation. These included effects on lung hyperinflation and mechanical stress, inflammation, excessive mucus production with impaired mucociliary clearance, and symptom severity [79].
\nAlthough triple therapy in separate inhalers is already in use for COPD patients for a couple of years now, fixed triple therapy combining an ICS, a LABA, and a LAMA in a single inhaler recently emerged on the market. Currently, there are only two products approved by the European Medicines Agency (EMA) for use in COPD, and a third one was recently approved in Japan (see Table 4) [80].
\n\n | Delivery type | \nDuration of action (h) | \n
---|---|---|
Fixed-dose combinations of LABA, LAMA and ICS (LABA/LAMA/ICS) | \n||
Formoterol/Glycopyrronium/Beclometasone | \nMDI | \n12 | \n
Vilanterol/Umeclidinium/Fluticasone furoate | \nDPI | \n24 | \n
Formoterol/Glycopyrronium/Budesonide | \nMDI | \n12 | \n
Currently available fixed-dose combinations of LABA/LAMA/ICS.
DPI = dry powder inhaler; MDI = metered dose inhaler.
Several recent studies have demonstrated that single-inhaler triple therapy is more effective in reducing the exacerbation than LAMA alone, a LABA/ICS, or a LABA/LAMA combination [81, 82, 83, 84].
\nThe GOLD algorithm for the escalation of treatment in patients with persistent risk of exacerbations despite regular treatment provides that patients taking a single bronchodilator should be switched to dual bronchodilation and then to triple therapy and/or additional therapies. Alternatively, some patients with high blood eosinophils may benefit from a LABA/ICS combination prior to receiving triple therapy [1].
\nThe use of ICS in COPD has become very controversial in the last years, owing on the one hand to the limited effect on lung function and on the other hand to potential side effects associated with long-term use at the higher doses recommended for the treatment of COPD. These include:
Risk of infections such as pneumonia [85], tuberculosis and non-tuberculous mycobacterial disease [86], and oropharyngeal candidiasis [87]
Skin lesions [88]
Diabetes onset and progression [89]
Increased risk of bone fractures [90]
Cataracts [91]
The use of ICS alone is discouraged in COPD [20]; however several studies have demonstrated a consistent effect on exacerbation reduction of LABA/ICS fixed-dose combinations versus individual monocomponents [76, 77, 87, 92, 93].
\nThe need for biomarkers accurately assessing disease activity and response to therapy in order to develop better COPD treatment is well acknowledged [94]. Peripheral blood eosinophil level has emerged in the recent years as a promising biomarker, showing capabilities to predict both the risk of exacerbation and the magnitude of response to ICS therapy [95, 96, 97]. Thus, several post hoc or pre-specified analyses of clinical trials have shown that blood eosinophil levels may indicate which patients can benefit from a reduction of exacerbations by the treatment with ICS-containing regimens [84, 96, 98]. Various cutoff points were proposed for the level of blood eosinophils in order to identify the patients who would benefit most from the ICS therapy. A recent pooled analysis (n = 4528) evidenced that a level of blood eosinophils >300/mmc3 suggests a beneficial role of ICS, while a low level of blood eosinophils (<100/mmc3) may be a negative predictor of the ICS effects. This was previously observed in other two post hoc analyses [99, 100] and was confirmed in a pre-specified analysis of another randomized clinical trial [101].
\nOther classes of pharmacological agents, such as PDE4-inhibitors (roflumilast) or antibiotics (azithromycin) administered orally on top of inhaled therapy, may bring an additional benefit in reducing exacerbations [102, 103]. The side effects, however, limit their use to selected patients only.
\nTwo large clinical trials have failed to demonstrate a positive effect of the active treatments (LABA/ICS, ICS alone, and LABA alone) versus placebo on the mortality risk [36, 104].
\nSmoking cessation, vaccinations, supplemental oxygen for hypoxemic patients, and lung volume reduction surgery in selected patients are the only therapies that have been proven to improve survival; smoking cessation also attenuates disease progression [20].
\nInhaled long-acting bronchodilator treatment plays a central role in the management of stable COPD. Anti-inflammatory treatment with inhaled corticosteroids in combination with a long-acting beta2 agonist or with dual bronchodilation (LABA and LAMA) as part of the triple therapy improves outcomes especially in patients with high blood eosinophil level.
\nDespite all the progress made in the recent years in the field of COPD, we are still lacking drugs that can effectively modify the course of the disease [23].
\nThe unmet needs in COPD warrant further research for the discovery of new biomarkers and effective therapeutic agents able to radically improve short-term and long-term outcomes in patients suffering of this disease.
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