zMAO inhibitory properties of known selective mammalian MAO inhibitors. Comparative data for human and rat MAO inhibition are from: aHurtado-Guzmán et al., 2003; bVilches-Herrera et al 2009; cLühr et al, 2010. NE: No effect
\r\n\t
",isbn:"978-1-83881-922-4",printIsbn:"978-1-83881-921-7",pdfIsbn:"978-1-83881-923-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"dcfc52d92f694b0848977a3c11c13d00",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",keywords:"Agricultural Engineering, Technologies, Application, Sustainable Agriculture, Information Technology in Agriculture, Food Security, Renewable Energies, Precision Farming, Smart Agriculture, Farm Mechanization, Robotics, Post Harvest Technologies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 25th 2020",dateEndSecondStepPublish:"December 23rd 2020",dateEndThirdStepPublish:"February 21st 2021",dateEndFourthStepPublish:"May 12th 2021",dateEndFifthStepPublish:"July 11th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Ahmad is a researcher in the field of agricultural mechanization and agricultural equipment engineering, in-charge of Farm Machinery Design Laboratory at Bahauddin Zakariya University, with expertise in modeling and simulation. He applied for two patents at the national level.",coeditorOneBiosketch:"Renowned researcher with a focus on developing energy-efficient heat- and/or water-driven temperature and humidity control systems for agricultural storage, greenhouse, agricultural livestock and poultry applications including HVAC, desiccant air-conditioning, adsorption, Maisotsenko cycle (M-cycle), and adsorption desalination.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.jpg",biography:"Fiaz Ahmad obtained his Ph.D. (2015) from Nanjing Agriculture University China in the field of Agricultural Bioenvironmental and Energy Engineering and Postdoc (2020) from Jiangsu University China in the field of Plant protection Engineering. He got the Higher Education Commission, Pakistan Scholarship for Ph.D. studies, and Post-Doctoral Fellowship from Jiangsu Government, China. During postdoctoral studies, he worked on the application of unmanned aerial vehicle sprayers for agrochemical applications to control pests and weeds. He passed the B.S. and M.S. degrees in agricultural engineering from the University of Agriculture Faisalabad, Pakistan in 2007. From 2007 to 2008, he was a Lecturer in the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan-Pakistan. Since 2009, he has been an Assistant Professor in the Department of Agricultural Engineering, BZ University Multan, Pakistan. He is the author of 33 journal articles. He also supervised 6 master students and is currently supervising 5 master and 2 Ph.D. students. In addition, Dr. Ahmad completed three university-funded projects. His research interests include the design of agricultural machinery, artificial intelligence, and plant protection environment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.jpeg",biography:"Muhammad Sultan completed his Ph.D. (2015) and Postdoc (2017) from Kyushu University (Japan) in the field of Energy and Environmental Engineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese Government) during Ph.D. and Postdoc studies, respectively. In 2019, he did Postdoc as a Canadian Queen Elizabeth Advanced Scholar at Simon Fraser University (Canada) in the field of Mechatronic Systems Engineering. He received his Master\\'s in Environmental Engineering (2010) and Bachelor in Agricultural Engineering (2008) with distinctions, from the University of Agriculture, Faisalabad. He worked for Kyushu University International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years. Currently, he is working as an Assistant Professor at the Department of Agricultural Engineering, Bahauddin Zakariya University (Pakistan). He has supervised 10+ M.Eng./Ph.D. students so far and 10+ M.Eng./Ph.D. students are currently working under his supervision. He has published more than 70+ journal articles, 70+ conference articles, and a few magazine articles, with the addition of 2 book chapters and 2 edited/co-edited books. Dr. Sultan is serving as a Leading Guest Editor of a special issue in the Sustainability (MDPI) journal (IF 2.58). In addition, he is appointed as a Regional Editor for the Evergreen Journal of Kyushu University. His research is focused on developing energy-efficient heat- and/or water-driven temperature and humidity control systems for agricultural storage, greenhouse, livestock, and poultry applications. His research keywords include HVAC, desiccant air-conditioning, evaporative cooling, adsorption cooling, energy recovery ventilator, adsorption heat pump, Maisotsenko cycle (M-cycle), wastewater, energy recovery ventilators; adsorption desalination; and agricultural, poultry and livestock applications.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"8",title:"Chemistry",slug:"chemistry"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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The relatively ease with which large numbers of individuals can be obtained and their inexpensive maintenance makes zebrafish a particularly suitable tool for drug discovery. Thus, in recent years diverse compounds have been assayed both in larval and adult specimens and changes of behavioral patterns, for instance, have been related to anxiolytic, addictive or cognitive effects. In this context, the molecular characterization of drug targets in zebrafish, comparing them to their mammalian counterparts, arises as a subject of paramount importance.
Monoamine oxidase (MAO) is the main catabolic enzyme of monoamine neurotransmitters and the primary target of several clinically relevant antidepressant and antiparkinsonian drugs. In mammals, it exists in two isoforms termed MAO-A and MAO-B, which share a number of structural and mechanistic features, but differ in genetic origin, tissue localization and inhibitor selectivity. High-resolution structures of MAOs from rat and human have been reported during the last decade, allowing detailed comparison of their overall structures and respective active sites. On the other hand, a few studies have shown that zebrafish contains a single MAO gene and that enzyme activity is due to a single form (zMAO) which resembles, but is distinct from, both mammalian MAO-A and MAO-B. No three-dimensional structural data exist thus far for zMAO. Sequence comparison of the putative substrate binding site of zMAO with those of human MAO isoforms suggests that the fish enzyme resembles mammalian MAO-A more than MAO-B. Nevertheless, biochemical studies have shown that zMAO exhibits such unique behavior toward MAO-A and -B substrates and inhibitors, that the results of studies using zebrafish MAO function, either as a disease model or for drug screening, should be considered with caution.
Functional and evolutionary relationships between proteins can be reliably inferred by comparison of their sequences, structures or binding sites. From a drug-discovery perspective, the study of binding site similarities (and differences) can be particularly insightful since it aids the design of selective or non-selective ligands and the detection of off-targets. In addition, knowledge of ligand-binding site similarity could increase our understanding of divergent and convergent evolution and the origin of proteins, even in those cases where no obvious sequence or structural similarity exists. In recent years, a number of algorithms have been developed for the identification and comparison of ligand-binding sites. Even though each method has its own merits and limitations, the performance of these computational tools is continuously improving. Advances in this field, associated with the increasing availability of structural data and reliable homology models of thousands to millions of protein molecules, provide an unprecedented framework to investigate the mechanisms underlying the molecular interactions between these proteins and their ligands, as well as to evaluate the similarities between the binding sites of related and unrelated proteins
On the basis of the foregoing, the first section of this chapter provides an overview on: a) the relevance of zebrafish as an animal model of increasing interest in pharmacology; b) the impact that MAO crystal structures and molecular simulation approaches have had on the development of novel MAO inhibitors, as well as comparative structural and functional information about zMAO and its mammalian counterparts; c) recent developments in computational methods to evaluate similarities between ligand-binding sites, emphasizing their usefulness for the rational design of multitarget (promiscuous) drugs.
The second part of the chapter describes unpublished results regarding a further characterization of zMAO activity and its comparison with MAOs from mammals. Specific topics in this section include: a) the construction of homology models of zMAO, built using human MAO-A and -B crystal structures as templates; b) a three-dimensional analysis of the binding site similarities between MAOs from different species using a statistical algorithm; c) a functional evaluation of zMAO activity in the presence of a small series of reversible and selective MAO-A and -B inhibitors.
In order to understand complex behaviors observed in nature, scientists have always tried to develop models that could be used and tested under controlled conditions in the laboratory. In the last 30 years a new animal model, zebrafish (Danio rerio), has emerged as a powerful tool mostly for studying developmental biology. The scientific potential of zebrafish was originally assessed by George Streisinger (Streisinger et al., 1981). This work was the starting point for rapid progress in molecular and genetic analysis of zebrafish neurodevelopment, which allowed the construction of many genetic mutants and the identification of several genes that affect different brain functions such as learning and memory (Norton & Bally-Cuif, 2010). During the last decade zebrafish has also become an attractive model for behavioral and drug discovery studies, particularly those related to actions in the central nervous systems (Chakraborty & Hsu, 2009; King, 2009; Rubinstein, 2006; Zon & Peterson, 2005).
Zebrafish develop rapidly and almost all organs are developed at 7 days post-fertilization. Their fecundity makes it easy to obtain large numbers of individuals for experimentation, which are relatively inexpensive to maintain. In addition, they can absorb chemical substances from their tank water, and their genome has been almost fully sequenced, which makes genetic manipulation more accessible. These characteristics have stimulated the use of zebrafish in medicinal chemistry to assay the effects of different compounds in whole animals (Goldsmith, 2004; Kaufman & White, 2009). Another attractive characteristic of zebrafish is its potential for use in in vivo high-throughput screening assays. Consequently, a number of studies which take advantage of this possibility have been reported recently (Kokel et al., 2010; Kokel & Peterson, 2011; Rihel et al., 2010; Zon & Peterson, 2005).
Zebrafish exhibit many social characteristics that can be assimilated to those observed in mammals. They recognize each other by sight and odor (Tebbich et al., 2002) and display an interesting social learning (Reader et al., 2003). This teleost also shows a characteristic aggressive behavior (Payne, 1998), a pheromone-mediated danger alarm (Suboski, 1988; Suboski et al., 1990), cognitive and adaptive behaviors such as habituation (Miklosi et al., 1997; Miklosi & Andrew 1999), spatial navigation abilities and Pavlovian conditioning (Hollis, 1999). These features make this species a valuable tool for either the development or the adaptation of behavioral paradigms. Thus, behavioral protocols such as an aquatic version of the T-maze, which is used for studies of discrimination, reinforcement and memory in rodents, had been used to assess color discrimination in zebrafish (Colwill et al., 2005). Another interesting model is the aquatic version of conditioned place preference (CPP), where the fish can be exposed to different stimuli in two separate compartments and is then allowed to freely explore the apparatus without partition (Darland & Dowling, 2001). A further paradigm, the novel tank diving test, has been used by different research groups (Bencan & Levin 2008; Bencan et al., 2009; Egan et al., 2009; Levin et al., 2007) as a model for anxiety. It is conceptually similar to the rodent open field test, because it takes advantage of the instinctive behavior of both zebrafish and rats to seek refuge when exposed to an unfamiliar environment (Levin et al., 2007). In the case of the novel tank diving test, the fish dives to the bottom of the tank and remains there until it presumably feels safe enough to explore the rest of the tank and gradually starts to explore the upper zone (Egan et al., 2009). Similar observations can be made in an open field test for rodents, where initially they spend a lot of time near the walls, which is considered as an indication of an anxious state. The time spent by the zebrafish in the lower or upper part of the tank, as well as erratic movements, have been established as anxiety indices (Egan et al., 2009). It is considered that the zebrafish is anxious when it shows a longer latency to enter the upper part of the tank, or when the time spent at the top is reduced. Conversely, when an anxiolytic drug is administered, animals spend much more time in the upper portion of the tank. Figure 1 illustrates this response by showing the typical traces of motor activity observed for control animals (left) and for animals exposed to nicotine (right), which has been reported to have anxiolytic properties in this paradigm (Levin et al., 2007).
Based on these findings, the potential of zebrafish for neurobehavioral studies is increasingly recognized (Bencan & Levin, 2008; Eddins et al., 2010). Thus, this animal has been used as a model in studies of memory (Levin & Chen, 2006), anxiety (Bencan et al., 2009; Levin et al., 2007), reinforcement properties of drugs of abuse (Ninkovic & Bally-Cuif, 2006), neuroprotection of dopaminergic neurons (McKinley et al., 2005), and movement disorders (Flinn et al., 2008).
Representative traces of characteristic behavior of control-saline- (left) and nicotine- (right) treated zebra fish. Traces were recorded during 5 min in a glass trapezoidal test tank (22.9 cm long at the bottom, 27.9 cm long at the top, 15.2 cm high, 6.4 cm wide), filled with 1.5 L of artificial sea water. Nicotine was administered 5 min before the test. All other experimental conditions were as previously published (Levin et al., 2007).
A final word of caution should be said regarding the apparent usefulness of zebrafish as a research tool. One critical aspect to be considered when using animal models to understand a specific behavior is its validity. Mammals such as rats and mice have been widely used as models to study several functions since, among other characteristics, many brain regions and their neurotransmitter systems are well characterized. Thus, even though genome and the genetic pathways controlling signal transduction and development appear to be highly conserved between zebrafish and humans (Postlethwait et al., 2000), further validation of this model is needed, particularly if human systems or conditions are the final aims to be addressed.
Monoamine oxidase (monoamine oxygen oxidoreductase (deaminating) (flavin-containing); EC 1.4.3.4; MAO) is a key enzyme in the inactivation of neurotransmitters such as serotonin, dopamine and noradrenaline. In mammals it exists in two isoforms termed MAO-A and MAO-B which have molecular weights of ~60 kDa. Both proteins are outer mitochondrial membrane-bound flavoproteins, with the FAD cofactor covalently bound to the enzyme. MAO-A and MAO-B are encoded by separate genes (Kochersperger et al., 1986; Lan et al., 1989) and the isoforms from the same species show about 70% sequence identity, whereas 85-88% identity is observed between the same isoforms from human and rat (Nagatsu, 2004). Both neurological and psychiatric diseases have been related to MAO dysfunction. Consequently, the search for inhibitors of each isoform has lasted decades. Currently, selective inhibitors of MAO-A are used clinically as antidepressants and anxiolytics, while MAO-B inhibitors are used to reduce the progression of Parkinson’s disease and of symptoms associated with Alzheimer’s disease (Youdim et al., 2006).
In 2002, Binda and colleagues (Binda et al., 2002) published a groundbreaking article showing the high-resolution structure of human MAO-B in complex with the irreversible inhibitor pargyline. Subsequent structures of this enzyme (Binda et al., 2003, 2004), as well as that of rat MAO-A (Ma et al., 2004), and more recently human MAO-A (De Colibus et al., 2005; Son et al., 2008), have allowed a detailed comparison of the overall structures of both isoforms, and new insights regarding their active sites (Edmondson et al., 2007,\n\t\t\t\t\t\t2009; Reyes-Parada et al., 2005). Based on these findings, the substrate/inhibitor binding site of both isozymes can be described as a pocket lined by the isoalloxazine ring of the flavin cofactor and several aliphatic and aromatic residues (in the second part, close ups of this binding site are depicted in Figures 5 and 8). In particular, two conserved tyrosine residues (Y407, Y444 and Y398, Y435 in MAO-A and -B, respectively), whose aromatic rings face each other, are located almost perpendicularly to the isoalloxazine ring defining an “aromatic cage”. This conformational arrangement provides a path to guide the substrate amine towards the reactive positions on the flavin ring and therefore seems to be essential for catalytic activity. In addition, a critical role of residues G215 and I180 of MAO-A (G206 and L171 being the corresponding residues in MAO-B) in the orientation and stabilization of the substrate/inhibitor binding can be inferred from the X-ray diffraction data. In MAO-B, the substrate/inhibitor binding site is a cavity (~400 Å3, termed the “substrate cavity”) which can be distinguished, in some cases, from another hydrophobic pocket (~300 Å3, termed the “entrance cavity”) located closer to the protein surface. It has been demonstrated that the I199 side-chain can act as a “gate” opening or closing the connection between the two cavities by modifying its conformation (Binda et al., 2003). In contrast, the MAO-A binding site consists of a single cavity (De Colibus et al., 2005; Ma et al., 2004). It should be noted that, although residues lining the binding site of human and rat MAO-A are identical, the human MAO-A cavity is larger (~550 Å3) than that in rat MAO-A (~450 Å3). Remarkably, an exchanged location of aromatic and aliphatic nonconserved residues in the active sites of MAO-A and MAO-B (F208/I199 and I335/Y326, respectively) has been implicated in the affinity and selective recognition of substrates and inhibitors, and provides a molecular basis for the development of specific reversible inhibitors of each isoform (Edmondson et al., 2009).
The availability of the aforementioned crystal structures has made an enormous impact on our knowledge about the function and regulation of the enzyme and has also allowed a quicker pace in the rational design of novel MAO inhibitors. Different theoretical approaches and computational methods have been used since, to explore how, where and why some interactions are central in MAO-ligand complexes. For instance, quantum mechanics calculations have been used to obtain insights about the mechanism by which amines are oxidized by MAO (Erdem & Büyükmenekşe, 2011), whereas molecular dynamics simulations have been recently employed to study specific interactions involved in the access of reversible MAO inhibitors to their binding site (Allen & Bevan 2011). In addition, a number of studies describing potent and selective inhibitors have been reported during the last decade and in most of them molecular simulation approaches have been used to rationalize and/or to predict the functional interactions between the proteins and their inhibitors. Figure 2 illustrates this situation by showing the progression of published articles about MAO in which computational methodologies were used.
It should be pointed out however, that crystal structures only provide a snapshot of one of the many conformations available to proteins. Therefore theoretical (and experimental) approaches, adequately considering dynamic aspects, will grow in importance in order to better understand the physiological functioning of these enzymes.
Progression of research articles involving docking studies on MAO before and after (2002) the first three-dimensional structure of MAO was deposited in the Protein Data Bank. Data from PubMed. “MAO” and “docking” were used as keywords.
Unlike mammals, zebrafish have only one MAO gene (Anichtchik et al., 2006; Setini et al., 2005). This gene is located in chromosome 9 and exhibits an identical intron-exon organization as compared to mammals, which suggests a common ancestral gene (Anichtchik et al., 2006; Panula et al., 2010). Sequencing studies have shown that zebrafish MAO (zMAO) contains 522 amino acids and has a molecular weight of about 59 kDa (Setini et al., 2005), which is very similar to that found in mammalian MAO-A and MAO-B. zMAO displays about 70% identity with human MAO-A or -B, and its predicted secondary structure indicates that the flavin-binding-, the substrate- and the membrane-binding- domains, which are typical in other MAOs, should also be present in the fish enzyme. Indeed, a recent study (Arslan & Edmondson, 2010) has demonstrated that (like the mammalian isoforms), zMAO is also a mitochondrial enzyme, presumably bound to the outer membrane, and that the flavin cofactor is covalently bound to the protein via an 8α-thioether linkage likely established with C406. Beyond its overall identity, the amino acid sequence of the presumed zMAO binding domain shows ~67% and ~83% identity with the corresponding binding sites of human MAO-B and MAO-A respectively (Panula et al., 2010). Interestingly, some residues that have been shown to be critical for inhibitor and substrate selectivity in human MAOs such as the pairs F208/I335 (in MAO-A) and I199/Y326 (in MAO-B), are identical or conservatively replaced in zMAO (F200/L327) as compared with MAO-A.
Regarding functional studies, recent data obtained using para-substituted benzylamine analogs as substrates suggest that, as in mammalian MAOs, α-C-H bond cleavage is the rate-limiting step in zMAO catalysis (Aldeco et al., 2011). Furthermore, a variety of substrates and inhibitors have been tested against zMAO. Preferential substrates of both MAO-A (e.g. serotonin) and MAO-B (e.g. phenethylamine, benzylamine, MPTP) as well as non-selective substrates such as tyramine, dopamine or kynuramine, have been shown to be deaminated, although with different catalytic efficiency, by zMAO (Aldeco et al., 2011; Anichtchik et al., 2006; Arslan & Edmondson, 2010; Sallinen et al., 2009; Setini et al., 2005). In addition, irreversible selective inhibitors such as clorgyline (MAO-A) or deprenyl (MAO-B) exhibit similar inhibitory profiles toward zMAO (Anichtchik et al., 2006; Arslan & Edmondson, 2010; Setini et al., 2005). Interestingly, the in vivo administration of deprenyl to zebrafish increases serotonin levels about 10-fold while levels of dopamine remain unchanged (Sallinen et al., 2009). These data indicate that zMAO is essential for serotonin metabolism in zebrafish, but also underline the distinctive character of this enzyme since in rodents dopamine concentrations are increased after deprenyl treatment, whereas serotonin levels remain unchanged. Structurally diverse reversible MAO inhibitors such as harmane, tetrindole, methylene blue, amphetamine, 8-(3-chlorostyryl)-caffeine, 1,4-diphenyl-1,3-butadiene, farnesol, safinamide or zonisamide display a wide range of inhibitory potencies, from nM to µM to no effect, against zMAO (Aldeco et al., 2011; Binda et al., 2011). Remarkably, methylene blue is the most potent zMAO inhibitor tested thus far, exhibiting a Ki value of 4 nM.
Based on sequence similarity, substrate preference and inhibitor sensitivity, it has been consistently suggested that the functional properties of zMAO resemble more strongly those of MAO-A than those of MAO-B. Nevertheless, virtually all articles published so far recognize that, although some overlapping properties can be detected, zMAO also shows characteristics of its own that distinguish it from its mammalian counterparts.
The concept of protein binding-site similarity and the development of methods to evaluate it are receiving much attention. This is viewed as a step forward in protein classification, as compared with classical sequence-based approaches, since it should allow proteins with low sequence similarity but high similarity at their binding sites to be related (Milletti & Vulpetti, 2010). On the contrary, as will be analyzed below, this approach can also detect subtle differences between highly homologous proteins, and therefore be useful to determine the suitability of non-human proteins as models for drug design aimed to the treatment of human conditions.
One of the newest applications of the study of binding site similarities is polypharmacology. Thus, the classical idea that selective drugs acting on a single target related to one disease will have maximal efficacy has been challenged by increasing evidence showing that most clinically effective drugs bind to several targets, even if these targets are not originally related to the disease (Keiser et al., 2009; Schrattenholz & Soskić 2008). Even though this pharmacological promiscuity may be seen as a negative property, primarily related with the incidence of side effects, recent observations increasingly indicate that multitarget compounds might have better profiles regarding both efficacy and side effects, since they would be acting on a pharmacological network, where several nodes underlie the physiopathology of the disease (Apsel et al., 2008; Hopkins 2008). Thus, the concept of polypharmacology has motivated several groups to find new drug-target associations, based on the idea that a given compound can interact simultaneously with two or more relevant targets if they have similar binding sites. It should be stressed that these associations are pursued considering that two proteins could share a ligand even if they are structurally or functionally very different (Kahraman et al., 2007).
One aspect that has critically fueled this field is the increasing availability of 3D protein structures in public databases (almost 75.000), which allows us to explore the complexity of protein-ligand interactions. This exploration has yielded important insights in order to obtain a good characterization of the binding sites and has confirmed the notion that protein-ligand binding depends not only on shape complementarity but also on complementary physicochemical features (Henrich et al., 2010).
Several algorithms have been developed to compare binding sites of different proteins. In most of them, two main steps are present: the creation of a database that requires the calculation of fingerprints describing each binding site and a pocket screening that requires multiple similarity alignments between the query pocket and the database. These applications are used as a strategy to assess specific issues, such as off-target identification for drug re-purposing (Cleves & Jain, 2006; Keiser et al., 2009; Moriaud et al., 2011), functional classification of unknown proteins (Kinnings & Jackson, 2009; Russell et al., 1998), drug discovery by sequence analysis (Xie et al., 2009), detection of evolutionary relationships (Xie & Bourne, 2008) and polypharmacology predictions (Milleti & Vulpetti, 2010; Pérez-Nueno & Ritchie, 2011). The main step before finding similarity between two or more binding sites is their characterization. Several methodologies have been proposed with this purpose: geometrics approaches, which mainly analyze cavities through the exploration of the solvent-accessible protein surface (Weisel et al., 2007); energetics approaches, which use van der Waals and electrostatic energies to define cavities (Laurie & Jackson, 2005); structure and sequence comparison approaches, which use the information of known binding sites to compare and define unknown cavities through the analysis of sequence and structural similarity (Brylinski & Skolnick, 2009); and approaches involving the dynamics of protein structures, which use dynamics simulations to include the natural flexibility of proteins and possible allosteric modifications of binding sites (Landon et al., 2008). Although the determination of similarities between binding sites could seem a simple mathematical method, several approaches have been developed using different characteristics. For example, the Isocleft algorithm measures the similarity by initially defining a cleft in any protein to be compared. These clefts are determined by a set of overlapping spheres that are represented by the van der Waals radii of atoms in the binding sites. Finally each cleft is viewed like a graph and the similarity is measured by finding the largest common subgraph (Najmanovich et al., 2008). The SitesBase algorithm uses a triangular geometric determination of binding sites establishing the cutoff at 5 Å. Similarity is measured by an atom–atom score which finds the largest possible matching constellation (similar atom types with a similar spatial orientation) (Gold & Jackson, 2006). The ProFunc server uses sequence and structural information to find similarities between binding sites. This process includes a phylogenetic component that is used for the identification of homologous proteins (Laskowski et al., 2005). The Sumo algorithm flags each functional group as a node in a graph. Then the similarity is measured through a strategy that does not necessarily find the maximal common subgraph between a pair of binding sites (Jambon et al., 2003). The FLAP algorithm utilizes GRID methodology to calculate the energy of interaction between a molecular probe and the binding sites. These interactions, which include van der Waals and electrostatic terms, are then compared through a geometric approach (Baroni et al., 2007). In another recently developed algorithm (Hoffmann et al., 2010) the binding sites are represented as a set of atoms in the 3D space described by 3D vectors. Initially the algorithm calculates the similarity between two binding sites comparing vectors that only consider the atom coordinates, although different additional parameters such as atom type and charges could be included in the algorithm. The PocketMatch algorithm involves three basic steps: a) each binding site is represented as a sort list of distances between three selected points in every amino acid present at one specific distance from the ligand, b) the two sets of sorted distances are aligned and c) finally the similarity percentage is calculated (Yeturu & Chandra, 2008).
Although most algorithms used to measure the similarities between binding sites have shown high performance when the comparison involves related proteins, doubtful results are obtained when the proteins are not related. In these cases it is very important to select the best algorithm taking into account some critical issues: a ligand may change its orientation in different binding sites; some protein-ligand conformations may have a favorable binding energy, but natural allosteric regulations (not always considered) might not favor such conformations; protein structures from databases could have been determined in different conformational states (active, inactive, closed, open, etc.); finally, it is also very important to consider the solvent and ion concentrations in every system.
Beyond these considerations, the continuous increase in both the number of protein structures and computational power, augurs the development of ever more accurate similarity searching tools, which likely will allow not only better results in virtual screening programs but also a novel view on the evolution of structure and function of proteins.
As mentioned, even though amino acids lining the zMAO binding site exhibit a high level of identity with those of rat and human MAOs, a few studies have shown that the fish’s enzyme shows unexpected sensitivities for known specific substrates and inhibitors. Since zebrafish has been proposed as a model that could be useful for the identification of novel MAO inhibitors (Kokel et al., 2010), we further characterized zMAO using three different approaches. First, we determined the inhibitory potency of a small series of compounds which have been previously evaluated against rat and human MAOs. Then, we built homology models of zMAO based on the crystal structures of human MAO-A or MAO-B and performed docking experiments with a drug selected from the biochemical evaluations. Finally, we used the recently described algorithm PocketMatch (Yeturu & Chandra, 2008) to explore similarities and differences between MAO isoforms from human, rat and zebrafish.
4-Methylthioamphetamine (MTA), 2-naphthylisopropylamine (NIPA), (6-methoxy-2-naphthy)lisopropylamine (MeONIPA), all as hydrochloride salts, 2-(4’-butoxyphenyl)thiomorpholine (BTI), 2-(4’-benzyloxyphenyl)thiomorpholine (ZTI), both as oxalate salts, as well as 2-(4’-butoxyphenyl)thiomorpholin-5-one (BTO) and 2-(4’-benzyloxyphenyl)thiomorpholin-5-one (ZTO) were synthesised following published methods (Hurtado-Guzmán et al., 2003; Lühr et al., 2010; Vilches-Herrera et al., 2009). The expression and purification of zMAO in Pichia pastoris was performed as previously described (Arslan & Edmondson, 2010). Enzyme kinetic studies were done spectrophotometrically in 50 mM potassium phosphate buffer (pH = 7.4), 0.5% (w/v) reduced Triton X-100 with kynuramine as substrate. The spectrophotometer used was a Perkin-Elmer Lambda-2 UV–Vis at 25 °C.
Figure 3 shows the chemical structures of the inhibitors evaluated.
Chemical structures of the compounds used in the biochemical evaluation
Table 1 summarizes the effects of these compounds upon zMAO and also includes, for comparative purposes, the reported values of their inhibitory activities against MAO-A and -B from human and rat (Fierro et al., 2007; Hurtado-Guzmán et al., 2003; Lühr et al., 2010; Vilches-Herrera et al., 2009).
Compound | \n\t\t\t\n\t\t\t\tK\n\t\t\t\ti (µM) | \n\t\t||||
zMAO | \n\t\t\thMAO-A | \n\t\t\trMAO-A | \n\t\t\thMAO-B | \n\t\t\trMAO-B | \n\t\t|
MTA a\n\t\t\t | \n\t\t\tNE | \n\t\t\t0.13 ± 0.02 | \n\t\t\t0.25 ± 0.02 | \n\t\t\tNE | \n\t\t\tNE | \n\t\t
NIPAb\n\t\t\t | \n\t\t\t17.7 ± 2.6 | \n\t\t\t0.48 ± 0.31 | \n\t\t\t0.42 ± 0.04 | \n\t\t\t>100 | \n\t>100 | \n
MeONIPAb\n\t | \n\t4.8 ± 0.4 | \n\t0.24 ± 0.02 | \n\t0.18 ± 0.05 | \n\t5.1 ± 0.4 | \n\t16.3 ± 7.8 | \n
BTOc\n\t | \n\tNE | \n\t10.0 ± 0.3 | \n\t50.9 ± 6.1 | \n\t0.46 ± 0.18 | \n\t0.16 ± 0.01 | \n
ZTOc\n\t | \n\tNE | \n\t>100 | \n27.5 ± 4.6 | \n0.048 ± 0.03 | \n0.074 ± 0.003 | \n
BTIc\n\t | \n\t30.4 ± 3.8 | \n\t2.5 ± 0.2 | \n\t14.1 ± 1.2 | \n\t0.068 ± 0.05 | \n\t0.27 ± 0.02 | \n
ZTIc\n\t | \n\tNE | \n\t>100 | \n19.0 ± 0.4 | \n0.038 ± 0.003 | \n0.13 ±0.01 | \n
zMAO inhibitory properties of known selective mammalian MAO inhibitors. Comparative data for human and rat MAO inhibition are from: aHurtado-Guzmán et al., 2003; bVilches-Herrera et al 2009; cLühr et al, 2010. NE: No effect
The amphetamine derivative MTA, which is a potent and selective inhibitor of rat and human MAO-A (Fierro et al., 2007; Hurtado-Guzmán et al., 2003), showed no significant effect upon zMAO activity. Similarly, the 2-arylthiomorpholine analogue ZTI, and the 2-arylthiomorpholin-5-one derivatives BTO and ZTO, which are highly selective MAO-B inhibitors (Lühr et al., 2010), did not inhibit the fish’s enzyme. In contrast, naphthylisopropylamine derivatives NIPA and MeONIPA, which are selective inhibitors of MAO-A (Vilches-Herrera et al., 2009), as well as the 2-arylthiomorpholine derivative BTI which selectively inhibits MAO-B (Lühr et al., 2010), exhibited zMAO inhibitory properties with Ki values in the micromolar range. MeONIPA was the most potent compound of the series evaluated, showing a Ki value (4.8 µM) very similar to that found against human MAO-B (5.1 µM). These results agree with a notion that can be inferred from previous data (Aldeco et al., 2011; Anichtchik et al., 2006), indicating that effects on zMAO cannot be straightforwardly used to predict an effect upon either MAO-A or MAO-B. In addition, these data suggest that the zMAO binding site is significantly different from those of both MAO-A and MAO-B from mammals.
Since neither the MAO-A nor MAO-B structure can be chosen a priori as a better template for modeling zMAO, we decided to build two different models using each isoform of human MAO as templates. The MAO-A (Protein Data Bank, PDB code: 2BXS) and MAO-B (PDB code: 2BYB) crystal structures at 3.15 Å and 2.2 Å resolution respectively (De Colibus et al., 2005) were employed. The amino acid sequence and crystal structure of each protein were extracted from the National Center for Biotechnology Information (NCBI) and PDB databases. Sequence alignments were prepared separately. Models were built using standard parameters and the outcomes were ranked on the basis of the internal scoring function of the program MODELLER9v6 (Sali & Blundell, 1993). The best model obtained in each case (using MAO-A or MAO-B as template) was submitted to the H++ server (Gordon et al., 2005; http://biophysics.cs.vt.edu/H++) to compute pKa values of ionizable groups and to add missing hydrogen atoms according to the specified pH of the environment. Each structure selected was inserted into a POPC membrane, TIP3 solvated and ions were added creating an overall neutral system simulating approximately 0.2 M NaCl. The ions were equally distributed in a water box. The final system was subjected to a molecular dynamics (MD) simulation for 5 ns using NAMD 2.6 (Phillips et al., 2005). The NPT ensemble was used to perform MD calculations. Periodic boundary conditions were applied to the system in the three coordinate directions. A pressure of 1 atm was used and temperature was kept at 310 K. The simulation time was sufficient to obtain an equilibrated system (RMSD < 2 Å). Stereochemical and energy quality of the homology models were evaluated using the PROSAII server (Wiederstain & Sippl 2007) and Procheck (Laskowski et al., 1993)
Dockings of (S)-MeONIPA in the zMAO models, as well as in the human MAO-A and MAO-B structures were done using the AutoDock 4.0 suite (Morris et al., 1998). MeONIPA was selected for this study since it was the most potent zMAO inhibitor of the series evaluated and because it also inhibited both human MAO-A and MAO-B at low concentrations. The choice of the (S)-isomer for MeONIPA docking experiments was done on the basis that (S)-amphetamine derivatives (which are always dextrorotatory) are usually the eutomers at MAO (Hurtado-Guzmán et al., 2003). All other docking conditions were as previously reported (Fierro et al., 2007; Vilches-Herrera et al., 2009). Briefly, the grid maps were calculated using the autogrid4 option and were centered on the putative ligand-binding site. The volumes chosen for the grid maps were made up of 40 × 40 × 40 points, with a grid-point spacing of 0.375 Å. The autotors option was used to define the rotating bond in the ligand. The docked compound complexes were built using the lowest docked-energy binding positions. MeONIPA was built using Gaussian03 (Frisch et al., 2004) and the partial charges were corrected using ESP methodology.
Figure 4 depicts the global zMAO models obtained using human MAO-A (left) and human MAO-B (right) as templates. As expected, the overall structure of zMAO was similar to those of the human enzymes. The presumed ligand binding site appears lined by a series of hydrophobic residues and the isoalloxazine ring of the flavin cofactor (top inset Fig. 4). Amino acids forming the binding site of zMAO and human MAO-A and -B are shown in insets of Figure 4.
Cartoons of zMAO models obtained using human MAO-A (left) or human MAO-B (right). Insets show the main amino acids of the active sites of zMAO (top), human MAO-A (left) and human MAO-B (right). Amino acids in white, green or blue indicate apolar, polar or positively charged residues respectively.
As shown in Figure 5, docking experiments revealed that in both zMAO models, MeONIPA exhibits a binding mode where the aromatic ring is oriented almost perpendicularly to the isoalloxazine ring of FAD, with the methoxyl group pointing to the binding site entrance, whereas the aminopropyl chain points toward the isoalloxazine ring and appears positioned close to two tyrosine residues which, together with the isoalloxazine ring, form the so-called aromatic cage (Figs. 5 A and 5B). Interestingly, docking of MeONIPA in both human MAO-A and MAO-B, yielded binding modes where the inhibitor molecule adopted an almost opposite orientation to those observed in zMAO models. Thus, the most energetically favorable conformations of MeONIPA were those in which the amino group points away from the flavin ring, whereas the methoxyl group is located between the corresponding tyrosine residues (Figs. 5 C and 5D). These results suggest that the different inhibitory potencies of MeONIPA (and likely other inhibitors) toward zebrafish and human MAOs, might be attributed to the differential binding modes exhibited by the drug. Similar conclusions attempting to explain why MAO inhibitors show differential inhibition properties upon MAO from different species have been reached in previous studies (Fierro et al., 2007; Nandingama et al., 2002). Moreover, our findings suggest that, even in the cases where similar potencies are detected, the mechanism of enzyme inhibition for a given drug might be different in zebrafish and human MAOs.
Comparison of the binding modes of MeONIPA into zMAO (A and B), human MAO-A (C) and human MAO-B (D) active sites. Figures 5 A and 5Bshow the docking poses of MeONIPA into zMAO models obtained using human MAO-A and human MAO-B respectively. Main active site amino acid residues and FAD are rendered as stick models.
The structures of human and rat MAO-A co-crystallized with clorgyline (PDB codes: 2BXS and 1O5W respectively) and human MAO-B co-crystallized with l-deprenyl (PDB code: 2BYB) were employed. Furthermore, structures of zMAO models and human MAO-A and MAO-B obtained after docking of MeONIPA (see previous section), were used in additional comparisons.
The PocketMatch algorithm was selected for this study due to its relatively low computational complexity and high performance. All aspects involved in binding site comparisons followed the procedure published in the original article describing the algorithm (Yeturu & Chandra, 2008). Briefly, each binding site was considered as that determined by the residues for which one or more atoms surround either a crystallographic or a docked ligand at a given distance (4 Å by default; in some cases distances from 3 Å to 10 Å from the ligand were considered; see following section). Each residue was classified into one of 5 groups, taken into account its chemical properties. Then, each residue was represented as a set of three points corresponding to the coordinates of the C-Alpha, the C-Beta and the Centroid Atom of the side chain. Distances between every three points of each residue in the binding sites were measured. All distances computed were sorted in ascending order and stored in sets of distances organized by type of pairs of points and type of pairs of tags. The sorted and organized distances were aligned and compared using a threshold of 0.5 Å, which was established considering the natural dynamics of biological systems. The similarity between sites, referred to as the PMScore, was measured by scoring the alignment of the pair of sites under comparison. Thus, the PMScore represents the percentage of the number of “matches” calculated over the maximal number of distances computed for each binding site. A PMScore of 0.5 (50 %) or higher was considered as indicative of similarity between binding sites.
Initially, we compared human and rat MAO-A. The amino acid sequence in the active sites of both proteins is identical, and therefore we expected to find a high degree of similarity. Surprisingly, a PMScore value of 0.27 was obtained after comparing the residues located at 4 Å from the ligand (clorgyline in both proteins), which is the PocketMatch default condition. It should be considered that PMScores > 0.5 are indicative of binding site similarity, whereas values below 0.5 indicate lack of similarity. It should also be noted that, as shown in the original report by Yeturu & Chandra (2008), a distance of 4 Å from the ligand was clearly suitable to find similarities between a series of structurally related and unrelated proteins. Therefore, it was rather intriguing that such a low PMScore should be obtained, suggesting the existence of relevant differences between rat and human MAO-A binding sites, most likely in the form in which residues in close proximity to the ligand are arranged. Such a conformational difference has been revealed by the crystal structures of both proteins, which show that the cavity-shaping loop 210–216 and specifically residues Gln215 and Glu216 are differentially oriented in human and rat MAO-A (De Colibus et al., 2005). This differential arrangement determines a larger volume of the active site of human MAO-A (550 Å3) as compared to that of rat MAO-A (450 Å3). Thus, our results confirm that rat and human MAOs are not as similar as could be inferred from the analysis of their amino acid sequences, and highlight the sensitivity of PocketMatch to determine subtle differences between highly related proteins.
Despite these considerations, we developed a script that allows the automatic evaluation of PMScores considering distances from 3 Å to 10 Å from the ligand, with the hope that such an analysis could yield further information regarding the similarity of the binding sites of MAOs. Thus, we were able to build “similarity profiles”, which graphically show at what distance from the ligand (if any) the binding sites begin to be similar. Figure 6 shows the similarity profile after comparing rat and human MAO-A.
Similarity profile between rat and human MAO-A, both co-crystalized with clorgyline, as calculated using PocketMatch. The horizontal black line indicates PMScore = 0.5. The vertical black line indicates the distance from the ligand where the PMScore begins to be consistently greater than 0.5. Each point corresponds to the PMScore.
As can be seen, PMScores greater than 0.5 appeared at 4.5 Å and were consistently observed at longer distances from the ligand. Since most amino acids located at 4.5 Å from the ligand line the binding site (see Figure 8A and 8B), these results indicate that, beyond the shape differences revealed by crystal structures and detected by PocketMatch, the binding sites of MAO-A from rat and human are quite similar.
In contrast, when binding sites of human MAO-A and MAO-B were compared, PMScores indicating similarity (> 0.5) were only found at distances higher than 6.4 Å from the ligand (Fig. 7).
Similarity profile between human MAO-A (co-crystalized with clorgyline) and human MAO-B (co-crystalized with deprenyl), as calculated using PocketMatch. The horizontal black line indicates PMScore = 0.5. The vertical black line indicates the distance from the ligand where the PMScore begins to be consistently greater than 0.5. Each point corresponds to the PMScore.
As shown in Figures 8C and 8D, at a distance of 6.4 Å from the ligand, several amino acids considered in the similarity determination are located outside the binding site.
Binding site residues surrounding the inhibitors clorgyline (blue) and deprenyl (pink) bound to human MAO-A (HMAO-A), rat MAO-A (RMAO-A) or human MAO-B (HMAO-B). Figures 8A and 8B show the residues located at 4.5 Å from the ligand, while figures 8C and 8D show the residues located at 6.5 Å from the ligand
Therefore, the similarity profile shown in Figure 7 indicates that human MAO-A and MAO-B binding sites are less similar than those of rat and human MAO-A. It also shows that, although showing differences at their binding sites, human MAO-A and MAO-B exhibit a high degree of global structural similarity (all PMScores obtained at distances longer than 6.5 Å were well over 0.5). Though both findings might be considered obvious from the analysis of each protein sequence and function, they confirm the suitability of PocketMatch to find and predict such characteristics, an aspect that could be particularly useful when comparing proteins from which less functional information is available. In addition, our results suggest that in some cases the determination of similarity profiles can be more informative than point comparisons.
Figures 9 and 10 show the similarity profiles after comparing the homology models of zMAO with those of human MAO-A and MAO-B, respectively. As mentioned, in all cases, MeONIPA docked in each MAO structure was used as ligand.
Similarity profile between zMAO (in this case the model corresponds to that based on human MAO-A) and human MAO-A, as calculated using PocketMatch. In both proteins, docked MeONIPA was used as ligand. The horizontal black line indicates PMScore = 0.5. The vertical black line indicates the distance from the ligand where the PMScore begins to be consistently greater than 0.5. Each point corresponds to the PMScore.
Similarity profile between zMAO (in this case the model corresponds to that based on human MAO-B) and human MAO-B, as calculated using PocketMatch. In both proteins, docked MeONIPA was used as ligand. The horizontal black line indicates PMScore = 0.5. The vertical black line indicates the distance from the ligand where the PMScore begins to be consistently greater than 0.5. Each point corresponds to the PMScore.
As shown in Figures 9 and 10, PMScores indicative of similarity between the binding sites of zMAO and human MAO-A or MAO-B (i.e., PMScore > 0.5) were consistently seen at distances higher than 6 Å from the ligand. It should be noted that comparable values were obtained even though the zMAO model was built using either human MAO-A or MAO-B as templates, and regardless of which human enzyme was used for the comparison. These results suggest that the zMAO binding site is as different from those of both human isoforms as the binding site of MAO-A differs from that of MAO-B. In addition, the similarity profiles of zMAO against both human proteins indicate that global structural similarity is found across these species, while the main differences are found at their binding sites. Since, to perform the similarity determination, PocketMatch considers both the shape and the chemical nature of the residues forming the site (Yeturu & Chandra, 2008), these two factors are likely involved in the differences detected between the MAO isoforms. Considering the sequence identity between zebrafish and human enzymes, one may predict that conformational differences are more important when comparing zMAO and human MAO-A, while the chemical features of the residues are more relevant to the differences between zMAO and human MAO-B. Nevertheless, further analyses are necessary to determine the relative contribution of each aspect to the differences found.
In summary, results from biochemical evaluation, molecular simulation and similarity detection studies presented here add novel evidence to the notion that even though zMAO exhibits some functional and structural properties overlapping those of MAO-A and -B, the zebrafish protein behaves quite distinctively from its mammalian counterparts. Therefore, although still an attractive model for drug discovery, in our opinion zebrafish is not a useful model for the identification of novel MAO inhibitors aimed for use in humans.
We thank Dr. K. Yeturu and Prof. N. Chandra for their valuable comments regarding Pocket Match results and functioning. We also thank Prof. Bruce K. Cassels for critical reading of the manuscript. This work was funded by MSI Grant P05/001-F, PBCT grant PDA-23 to AF and FONDECYT Grants 110-85002 to AF, 110-0542 to PI-V and 109-0037 to MR-P. D.E.E. acknowledges research support from the National Institutes of Health GM 29433
Tuberculosis (TB) is a disease, which requires more than just biomedical treatment. WHO-recommended standard TB treatment requires a minimum duration of 6 months. The patients have to regularly take treatment without interruption to get a cure. However, discontinuation of treatment because of loss to follow-up (LTFU) is a significant problem, especially among patients suffering from multidrug-resistant tuberculosis (MDR-TB), requiring urgent attention. The proportions of LTFU and its associated factors differ among various countries. A clear understanding of these underlying causes is essential for the success and effectiveness of the National Tuberculosis Program (NTP) of every nation. Hence, appropriate measures targeting LTFU are needed to achieve the goals of the NTP.
In 2012, a large group of researchers from Africa, Asia, America, Europe, and the Pacific suggested that the term ‘defaulter’ is inappropriate for the patient [1]. Instead, they recommended using the term ‘person lost to follow-up’ to become more patient-centered. In 2013, the WHO decided to use the term ‘loss to follow-up’ instead of ‘defaulter’ for reporting treatment outcomes because the former is less judgmental [2]. They defined LTFU as “A TB patient who did not start treatment or whose treatment was interrupted for 2 consecutive months or more.” Since then, several papers have started reporting according to this new term and definition [3, 4, 5, 6, 7, 8, 9, 10].
The patients who were LTFU have not completed the treatment regime. This can cause serious public health problems because these patients are at higher risk of drug resistance [11]. They continue to spread the potentially resistant bacilli to the public, infecting the public. This has been proved in a Bayesian mapping where LTFU has served as an important indicator for the distribution of TB patients [12]. Therefore, LTFU should be one of our primary concerns in the battle against TB.
Even just a single case of LTFU could cause an outbreak of TB, as observed in countries with low incidence such as Norway [13, 14], USA [15], and Austria [16]. In such outbreaks, the index cases are mostly immigrants, spreading the infection to their families, friends, and other social networks. To further visualize this problem, we need to look into the proportion of LTFU among different countries in the world.
The proportion of LTFU varies considerably among different countries, different types of TB, and different patient populations. It has been studied extensively and was found to be ranging from 2.5 to 44.9% [17, 18, 19, 20, 21, 22, 23]. A very high proportion (44.9%) of the patients were LTFU in rural northern Mozambique revealing that LTFU is a very serious problem [19]. In addition, systematic reviews and meta-analyses have estimated the mean proportion of multidrug-resistant TB patients who were LTFU. A 2009 systematic review of MDR-TB patients has found that this proportion is 12% [24]. Another 2009 systematic review also found a similar proportion of 13% [25]. However, a 2012 individual patient data meta-analysis found a higher proportion of 23% [26]. A rough literature review has revealed that the proportion of MDR TB patients who were LTFU ranges from 2.2 to 47% [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]. The figures vary vastly among different years, countries, and institutions, suggesting that the underlying factors responsible for these variations should be studied carefully.
However, few studies have reported on the proportion of LTFU among patients with extra-pulmonary TB. According to a French study, this proportion was 25% among lymph node TB patients [44]. Another study from Gabon reported that the proportion among cervical lymph node TB patients was 24.3% [45]. In India, among the miliary tuberculosis patients presenting with neurological manifestations, the proportion was 10% [46]. However, in Saudi Arabia, the proportion among CNS tuberculoma patients was reported to be 25.8% [47].
Another area of interest is latent TB since developed countries such as the USA and the UK are giving much attention to latent TB and its LTFU rate. Studies from the USA reported proportions ranging from 12 to 35.6% [48, 49]. In the UK, this proportion is 22.8% [50], and in Switzerland, 11% [51].
Attention should also be paid toward LTFU among certain special populations. The proportion of LTFU among childhood TB patients ranges from 4 to 37% [52, 53, 54, 55, 56, 57]. Among the children with drug-resistant TB, it ranges from 5 to 19.09% [58, 59, 60]. These figures are much similar to those of the adult population. On the other hand, researchers from Côte d’Ivoire found out that the proportion of LTFU was rising among the elderly TB patients [61]. This is an area that researchers should explore more in the future.
We should not forget about our fellow healthcare workers since LTFU could lead to serious problems in the healthcare service setting. They are expected to have low rates of LTFU because of the medical knowledge they possess. Fortunately, a study from Morocco confirmed that the proportion of LTFU among healthcare workers in the public sector was only 0.8% [62]. However, many studies need to be done to explore this area of study.
Other populations of interest are prisoners and migrants. Northern Ethiopian prisons reported a low LTFU proportion of only 2.5% [63], which is an excellent result. In contrast, among the Ugandan prison inmates, 43% were LTFU and the odds are greater among the transferred prisoners [64]. On the other hand, researchers from the USA found out that 25.8% of the cases in a public health intervention were LTFU, and they were mainly undocumented migrants [65]. In such countries, as discussed above, even a single case of LTFU can cause an outbreak of TB. The same problem is arising in Australia where all of the detained illegal foreign fishermen were LTFU [66]. They concluded that
“Treatment completion in illegal foreign fishermen may be as low as zero; deporting fishermen before curative treatment is completed undermines TB control efforts and may lead to an emergence of drug resistance and an increased burden of active TB disease in our region.”
This is an area of concern that needs urgent measures. On the other hand, the International Organization of Migration is achieving great results among Vietnamese immigrants [67]. Only 7% of the MDR-TB patients from these migrants were LTFU. It is likely that such ‘international intergovernmental’ effort is necessary to tackle the problem of LTFU among the migrants since individual governments are facing difficulties handling this problem.
Individual factors play a role in the process of being LTFU from treatment. Sometimes, the results may contradict between different studies, probably due to the cultural, social, and other variations of the study settings.
Among the various sociodemographic characteristics, age is a recognized factor associated with LTFU. Studies from India, Brazil, and China revealed that elderly patients have higher LTFU [4, 68, 69, 70], whereas studies from Norway, Botswana, and South Africa suggested that adolescents have significant risk [8, 30, 71]. One study from the UK even suggested a wider range of age of 15–44 years as a high-risk group for LTFU [11]. Regarding gender, studies uniformly suggest that higher LTFU was found in males, as seen in Kenya, Ethiopia, Georgia, and Uzbekistan [7, 18, 41, 72].
Residence plays a role in the mechanism of LTFU. In Pakistan, the rural residence is associated with LTFU [73], whereas in Uzbekistan, the urban residence is associated with LTFU [18]. This may be caused by access to the treatment center since being far from the treatment center is also associated with LTFU [74]. Transportation should be improved to increase accessibility toward the treatment center. Alternatively, they could be built in the hard-to-reach areas. Both approaches include challenges, and ultimately, these challenges may be what cause LTFU. Further discussion regarding different providers will be given in the next section.
Education plays a role in the development of LTFU. Brazilian researchers have found out that less than 8 years of schooling increases the risk of LTFU [4]. In addition, scarce TB knowledge is a risk factor for LTFU [75], and better TB knowledge a protective factor [5]. Therefore, health education and proper counseling should always be at the heart of every anti-TB treatment program.
Financial factors should also be considered while giving treatment, and programs without such considerations will likely to result in high LTFU. A study from Uzbekistan found that joblessness contributes toward LTFU [18]. This is confirmed by a study from China which found that pre-school children, unemployed laborers, and retirees have a higher rate of LTFU [76]. Patients with low income have financial constraints to complete treatment leading to LTFU as seen in India [77], a lower middle-income country. A similar phenomenon has been observed in South Korea, a high-income country [78]. Even in the USA, it was found that homelessness is associated with LTFU [79, 80], which might be due to low income. Therefore, regardless of the country, patients with low income still have barriers against treatment completion.
LTFU is also associated with alcohol abuse, tobacco use, smoking, and illicit drug use. Association between alcoholism and LTFU was observed in India [77], Philippines [5], and Congo [74], tobacco use in Georgia [41], smoking in Brazil [75], and illicit drug use in Norway [30], Georgia [41], and the UK [81]. Therefore, before initiating treatment, personal history should be carefully taken to find out these risk factors, and special attention should be given to such patients.
There are also certain disease-specific factors that are associated with LTFU. Those who were previously LTFU tend to be LTFU again. This was confirmed by studies conducted in Brazil [4], Kenya [7], Uzbekistan [18], and Korea [78]. Caution should be taken while planning treatment for such patients. Studies from Nigeria and Ethiopia both point out that smear-negative TB patients were more likely to be LTFU [72, 82]. However, the opposite was observed in the UK where smear-positive pulmonary TB patients were more likely to be LTFU [11]. Researchers also found that patients with extrapulmonary TB were more likely to be LTFU [71, 83]. Co-morbid diseases such as diabetes mellitus and human immunodeficiency virus (HIV) infection also cause hindrance against TB treatment conditions [7, 71, 84].
The treatment providers should give support to the patients since a perceived lack of provider support is a barrier to regular follow-up [77], and receiving any type of assistance and support from the providers can protect against LTFU [5]. They need to build up trust [5] from the patients. An intervention program targeting these factors will be described later in the chapter. Lastly, the timing of the treatment services should be flexible according to the needs of the patients [77], but this may not be an easy task to implement.
The timing of the treatment is important since those who initiate the treatment late (beyond and within 30 days of onset) are more likely to be LTFU [85]. Those who initiate it late may not have enough motivation, will, or knowledge to continue taking treatment until they are cured. Moreover, the timing of treatment interruption is found to be the most important during the intensive phase [7]. This stage should be particularly targeted while conducting interventions against LTFU.
Different providers have different abilities to retain the patients. In Korea, patients treated by a non-pulmonologist were found to be more likely to default from TB treatment [78]. In Myanmar, patients treated by private practitioners were more likely to be LTFU [86]. An interesting situation was observed in Nigeria where patients treated at private, not-for-profit (PNFP) DOT facilities were more likely to be LTFU [87]. The researchers concluded that “Patients managed at PFP [private, for-profit] DOT facilities were probably richer, had better education, nutrition, and knowledge of TB than patients managed at PNFP DOT facilities…” Indeed, the factors causing LTFU are not simple, and they are correlated with each other. Therefore, intervention should be addressed not only on a single problem but also targeted toward the patient as a whole. Furthermore, the provider should also be consistent throughout the different stages of treatment since different providers in the intensive phase and continuation phase are associated with LTFU [88].
Studies from the USA and India have found that drug side effects are associated with LTFU [49, 77]. The researchers from the Philippines take one step further regarding this concept, stating ‘patients’ self-rating of the severity’ as an associated factor [5]. Indeed, some side effects, such as hepatitis, of the anti-tuberculosis drugs are already severe. However, some side effects, such as vomiting, might need self-rating since different patients may perceive differently. It would be interesting to research which kind of patient rates which side effect as severe.
Factors such as migration and social stigma also contribute toward LTFU. LTFU is common among the migrant population particularly in developed countries where there is an inward movement of people from the developing countries. Studies from the UK had repeatedly revealed this association [11, 50, 86, 89]. Researchers from the USA also found that birth outside the USA or Canada is associated with LTFU [80]. Higher LTFU among migrants has also been observed in Asian countries such as South Korea and China [70, 76, 90].
In countries where TB is a social stigma, treatment is very difficult and sensitive [77]. The patients may not want the health workers to give counseling. They do want to take treatment since the news of having TB may spread to the community, causing discrimination. In such places, secret treatment sessions should be initiated to control LTFU rates. In contrast, in Korea, the absence of TB stigma is associated with LTFU [78]. The authors wrote “TB stigma might motivate patients to receive TB treatment, thus increasing adherence to TB treatment.” Therefore, before starting the TB treatment program, it is important to make community observations first to find out whether TB sigma can cause or prevent LTFU.
In theory, interpersonal factors such as family dynamics, household role, peer influence, and partner and family relationships were thought to influence LTFU [5]. However, to our knowledge, none of the studies to date supports the association of LTFU with these factors.
Based on the factors associated with LTFU, Rodrigo et al. have developed a scoring instrument to predict the probability of LTFU (Table 1) [91]. According to their original paper, “Scores of 0, 1, 2, 3, 4 and 5 points were associated with a lost to follow-up probability of 2.2% 5.4% 9.9%, 16.4%, 15%, and 28%, respectively.” Incorporating the instrument in the process of history taking could help the healthcare providers in identifying patients who have the potential to be LTFU. Further interventions should be carried out to prevent these patients from becoming LTFU. Similar scoring systems could be developed in different regions, since there are always country-specific variations.
LTFU risk | Score |
---|---|
Immigration | 1 |
Living alone | 1 |
Living in an institution | 2 |
Previous anti-TB treatment | 2 |
Poor patient understanding | 2 |
Intravenous drug use (IDU) | 4 |
Unknown IDU status | 1 |
A predictive scoring instrument for tuberculosis lost to follow-up outcome [86].
Indeed, DOT is a part of the WHO-recommended ‘Directly Observed Treatment Short Course’ (DOTS) strategy. Although it cannot be denied that this strategy has saved the lives of millions of TB patients, the strategy itself is not flawless. Several authors have questioned the effectiveness of DOT as summarized in a review article by Otu [92]. The 2015 Cochrane systematic review and meta-analysis on DOT compared it with self-administered treatment, and the authors concluded that “TB cure and treatment completion were low with self-administered therapy in these trials, and direct observation did not substantially improve this” [93]. They called for complementary and alternative strategies in addition to DOT. Since DOT is a well-known and well-documented intervention in the field of TB, we felt that it need not be described in further detail in this chapter. Some interventions that have the potential to correct the weaknesses of DOT will be discussed below.
Recently, mHealth has emerged as a popular choice for health programs around the world. The Global Observatory for eHealth (GOe) has defined mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” [94]. Among these mHealth initiatives, appointment reminders and treatment compliance initiatives are of interest in reducing the rate of LTFU. However, there are limited interventional studies evaluating the effectiveness of these interventions in reducing the risk of LTFU.
In 2017, Hermans et al. have evaluated a text message service in the Infectious Diseases Institute (IDI) in Kampala, Uganda [95]. In this quasi-experimental study, appointment reminders were sent the day before the appointment, and adherence reminders were sent on days 2, 7, and 11 after the appointment. A total of 96% of the participants rated the messages as being helpful, and qualitative results also confirm these findings. However, data analysis has revealed that there was no statistically significant difference in the risk of LTFU between the intervention and control group. The lack of statistical significance may be due to the small sample size. Therefore, further studies with larger sample sizes are needed to further evaluate the program.
eCompliance is a biometric-based program, developed by Operation ASHA (OpASHA) [96], an Indian not-for-profit organization founded in 2006. The system is similar to mHealth in using text message alerts to inform the missed dose. However, the unique fingerprint verification system for the patient and the health worker takes mHealth to the next level. The OpASHA website explains the working mechanism of eCompliance as follows.
“During each patient visit, the patient and healthcare worker simultaneously scan their finger in the system, the medication is dispensed, and the treatment is recorded in the system’s database. If a patient misses a dose, an SMS message alert is sent to the patient, healthcare worker and supervisor. The healthcare worker is then responsible to meet the patient within 24–48 hours to administer and record the treatment.”
This system can be used to reduce the risk of LTFU since the data from OpASHA stated that the LTFU rate is less than 4% using their system [96].
This claim by OpASHA has been put to test in Uganda by Snidal et al. in 2012 [97]. Community health workers (CHWs) were selected and trained to use the system. The intervention was conducted at the Millennium Villages Project (MVP) cluster in Ruhiira, Uganda. The patients were followed-up by CHWs until the end of the treatment period. The proportion of LTFU is surprisingly 0% in the intervention group, which is a significant reduction compared to the control group, yielding an excellent result. However, since this study suffers from a limited sample size, a large-scale interventional study is still necessary to confirm the results. Local adaptation to the software is available from OpASHA, and they should be incorporated into local national tuberculosis programs to lower the proportions of LTFU.
An innovative community-based intervention to improve TB treatment outcomes was conducted in Sidama zone, Ethiopia [98, 99]. The core health workers mainly responsible for delivering the intervention to the grass-root level were called the health extension workers (HEWs). The HEWs were trained and salaried female health workers from the respective intervention regions. Active case finding and sputum smear preparation were conducted by the HEWs. The supervisors process the smears and initiate anti-TB treatment. Again, HEWs provide treatment support which includes provision and monitoring of treatment. Evaluation of the program over 4.5 years revealed that the proportion of patients lost to follow-up decreased significantly up to 3% [99]. The authors concluded that
“We have thus demonstrated that bringing simple services that detect disease and provide treatment support close to where patients live is critical to increase access to TB diagnosis and treatment adherence and minimise the number of patients LTFU.”
Therefore, such community-based programs should be implemented in modified forms in different countries around the world to reduce the proportion of LTFU. Another important thing to note is that both this program and eCompliance mentioned above employed ‘task shifting’ toward basic health workers (CHWs and HEWs) to support TB treatment at the grass-root level, not the experts.
In 2013, a novel social support program was developed in India by forming groups called “treatment support group (TSG)” [100].
“A TSG is a non-statutory body of socially responsible citizens and volunteers to provide social support to each needy TB patient safeguarding his dignity and confidentiality by ensuring access to information, free and quality services and social welfare programs, empowering the patient for making decision to complete the treatment successfully.”
A TSG supports the various needs of the patient so that they can complete the anti-TB treatment without any worries. The package includes transportation service, treatment counseling, emotional and spiritual support, and providing accommodation for homeless TB patients. After the program was implemented, the rate of LTFU fell until it strikes zero in the latest cohorts. It is because it tackles the social dimension associated with LTFU. This is one program that the interviewed patients from Ethiopia, who were LTFU, had hoped for [101].
In some countries, under certain circumstances, law enforcement is controversially used to solve the problem of LTFU. Usually, the patients who were LTFU were isolated in hospitals, but in some countries, they were isolated in prisons. Usually, this method was used against patients who were homeless and had a history of alcohol abuse [102]. When all the other methods fail, the medical officer, with the power given by the health laws, has to conduct a short-term incarceration of the patients who were LTFU.
Detention of patients includes ethical and human right problems. The controversy surrounding this issue has been discussed in detail in a review article by Mburu et al. [103]. They discussed that the primary reason for detention is to protect public health, according to the Siracusa Principles adopted by the UN Economic and Social Council. However, they argued that this conflicts with the international human right laws and the 1979 Alma-Ata Declaration.
“…incarceration and detention approaches curtail the rights to health, informed consent, privacy, freedom from non-consensual treatment, freedom from inhumane and degrading treatment, and freedom of movement of people lost to follow-up. Detention could also worsen social inequalities and lead to a paradoxical increase in TB incidence.”
In the light of this information, the interventions which tackle the risk factors associated with LTFU are far superior to detention, which provides just a temporary solution to the problem, not a permanent one.
Another form of federal public health intervention is used in the USA to solve the problem of LTFU among the migrants [65]. These tools called the Do Not Board (DNB) and Border Lookout (BL) list are managed by the Department of Homeland Security (DHS) according to requests from the Centers for Disease Control and Prevention (CDC) Travel Restriction and Intervention expert workgroup. They are designed to detect land border travelers who were LTFU from TB treatment. State health departments and local health jurisdictions supply the list of patients and were reviewed under the following criteria:
“(1) infectiousness or potential infectiousness with a communicable disease that would pose a public health threat if the individual travelled internationally;
(2) the person is unaware of his/her diagnosis, fails to adhere to public health recommendations, including treatment, or public health authorities are unable to locate the person; and
(3) the person poses a risk to travel internationally or on a commercial flight” [65].
Analysis revealed that most of the patients from this list were successfully treated but most of the migrants remain LTFU, suggesting that some improvement to the program is still needed to handle this problem.
LTFU from treatment is a serious problem that cannot be ignored. Throughout this chapter, the consequences of LTFU, the magnitude of this problem in different countries, and the underlying factors have been discussed. Various researchers have designed potentially powerful interventions to tackle LTFU. But, we still need further evidence and actions to be able to successfully lower the number of patients that are LTFU. With these points in mind, it is suggested that an ambitious approach should be taken to reduce the number of LTFU patients up to 0%.
I would like to thank Dr. Pa Pa Soe, associate professor, Department of Preventive and Social Medicine, University of Medicine 1, Yangon, for her invaluable advice on writing this book chapter. I am also truly grateful to Dr. Kyaw Khan Zaw, Technical Support Officer, Population Services International, Yangon, Myanmar for reviewing the chapter and giving helpful comments.
None declared.
IntechOpen publishes different types of publications
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