Katty Goossens

Ronnie Willaert
email: ronnie.willaert@vub.ac.be

Flocculation protein structure and cell-cell adhesion mechanism in Saccharomyces cerevisiae

Keywords: cell-cell adhesion, Flo1p, flocculation, FLO genes, Flo proteins, Saccharomyces cerevisiae

Cell-cell adhesion occurs in a broad spectrum of biological processes, of which yeast flocculation is an area of interest for evolutionary scientists to brewers and winemakers. The flocculation mechanism is based on a lectin-carbohydrate interaction but is not yet fully understood, although the first model dates back to the nineteen fifties. This review will update the current understanding of the complex mechanism behind yeast flocculation. Moreover, modern technologies to measure the forces involved in single carbohydrate-lectin interactions, are discussed. The Flo1 protein has been extensively described as the protein responsible for strong flocculation. Recently, more research has been directed to the detailed analysis of this flocculin. Due to the advances in the field of bioinformatics, more information about Flo1p could be obtained via structurally or functionally related proteins. Here, we review the current knowledge of the Flo1 protein, with a strong emphasis towards its structure.

The Flo adhesin family is involved in cell-cell and cell surface adhesion

A broad spectrum of biological processes requires controlled cell adhesion. In microbiology, this includes fungal, viral and bacterial infections or the formation of organised cell structures such as biofilms. Other examples are the assembly of tissues and the nervous system, cell culturing, embryonic development, cellular communication and tumor metastasis.

Cell adhesion in general is commonly defined as the binding of a cell to a substrate, which can be another cell, a surface or an organic matrix. The process is regulated by specific cell adhesion molecules.

Many fungi contain a family of cell wall glycoproteins, called "adhesins" that confer unique adhesion properties (Teunissen and Steensma 1995[START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF][START_REF] Hoyer | The ALS gene family of Candida albicans[END_REF]). These molecules are required for the interactions of fungal cells with each other (flocculation and filamentation) (Teunissen and Steensma 1995[START_REF] Lo | The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae[END_REF][START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF][START_REF] Vyas | Snf1 kinases with different beta-subunit isoforms play distinct roles in regulating haploid invasive growth[END_REF], inert surfaces such as agar and plastic [START_REF] Gaur | Expression, cloning, and characterization of a Candida albicans gene, ALA1, that confers adherence properties upon Saccharomyces cerevisiae for extracellular matrix proteins[END_REF][START_REF] Lo | The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae[END_REF][START_REF] Reynolds | Bakers' yeast, a model for fungal biofilm formation[END_REF] and mammalian tissues (Cormack et al. 1999, Li and[START_REF] Li | EAP1, a Candida albicans gene involved in binding human epithelial cells[END_REF]. They are also crucial for the formation of fungal biofilms [START_REF] Baillie | Role of dimorphism in the development of Candida albicans biofilms[END_REF][START_REF] Reynolds | Bakers' yeast, a model for fungal biofilm formation[END_REF][START_REF] Green | RT-PCR detection of Candida albicans ALS gene expression in the reconstituted human epithelium (RHE) model of oral candidiasis and in model biofilms[END_REF]).

The adhesin protein family in Saccharomyces cerevisiae strain S288C can be subdivided into two groups. The first group of proteins is encoded by genes including FLO1, FLO5, FLO9 and FLO10. These proteins are called flocculins [START_REF] Caro | In silicio identification of glycosylphosphatidylinositol-anchored plasma-membrane and cell wall proteins of Saccharomyces cerevisiae[END_REF]) because they promote cell-cell adhesion by interacting with the cell wall of adjacent cells. This binding event leads to the formation of multicellular clumps (flocs), which sediment out of solution. The FLO1, FLO5, FLO9 and FLO10 genes share considerable sequence homology. The second group of the Flo family, including Flo11p, Fig2p and Aga1p, has quite unrelated amino acid sequences. These last two proteins are induced during mating [START_REF] Roy | The AGA1 product is involved in cell surface attachment of the Saccharomyces cerevisiae cell adhesion glycoprotein a-agglutinin[END_REF][START_REF] Erdman | Pheromone-regulated genes required for yeast mating differentiation[END_REF], while Flo11p is required for diploid pseudohyphal formation and haploid invasive growth (Lambrechts et al. 1996, Lo and[START_REF] Lo | The cell surface flocculin Flo11 is required for pseudohyphae formation and invasion by Saccharomyces cerevisiae[END_REF]. In haploid invasive growth, cells adhere to the agar surface, so that they do not wash off [START_REF] Roberts | Elements of a single MAP kinase cascade in Saccharomyces cerevisiae mediate two developmental programs in the same cell type: mating and invasive growth[END_REF]. In diploid pseudohyphal growth, cells adhere to each other after division and form long chains or filaments [START_REF] Gimeno | Unipolar cell divisions in the yeast S. cerevisiae lead to filamentous growth: regulation by starvation and RAS[END_REF]. When yeast cells are grown on semi-solid (0.3% agar) medium, they form -mats‖: complex multicellular structures composed of yeast cells (Reynolds andFink 2001, Reynolds et al. 2008). Yeast mat formation, as well as the attachment of cells to plastic surfaces, requires Flo11p. These morphogenetic events (flocculation, filamentation and invasive growth) are tightly regulated [START_REF] Verstrepen | Flocculation, adhesion and biofilm formation in yeasts[END_REF][START_REF] Fichtner | Differential Flo8p-dependent regulation of FLO1 and FLO11 for cell-cell and cell-substrate adherence of S. cerevisiae S288c[END_REF][START_REF] Dietvorst | Flocculation in Saccharomyces cerevisiae is repressed by the COMPASS methylation complex during high-gravity fermentation[END_REF]. Flocculation often occurs upon depletion of sugar during late-exponential or stationary phases of growth (Stewart and Russel 1981), whereas filamentation requires starvation for nitrogen [START_REF] Gimeno | Unipolar cell divisions in the yeast S. cerevisiae lead to filamentous growth: regulation by starvation and RAS[END_REF][START_REF] Braus | Amino acid starvation and Gcn4p regulate adhesive growth and FLO11 gene expression in Saccharomyces cerevisiae[END_REF][START_REF] Granek | Environmental and genetic determinants of colony morphology in yeast[END_REF]. The FLO8 gene is a transcriptional activator of FLO1 [START_REF] Kobayashi | Molecular cloning and analysis of the dominant flocculation gene FLO8 from Saccharomyces cerevisiae[END_REF] and FLO11 [START_REF] Rupp | MAP kinase and cAMP filamentation signaling pathways converge on the unusually large promoter of the yeast FLO11 gene[END_REF]) and essential for their expression. In commonly used laboratory strains a nonsense mutation in the FLO8 gene leads to transcriptionally silencing of the FLO genes.

Moreover, it was shown that one FLO gene may compensate for another in diverse morphogenetic events [START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF].

The dominant FLO1 gene was identified and localised at the very end of the right arm of chromosome I [START_REF] Russell | Revised nomenclature of genes that control yeast flocculation[END_REF], Teunissen et al. 1993b). Flo1p is a cell wall protein responsible for flocculation of Flo1-expressing cells and its presence is directly related to the degree of flocculation [START_REF] Bidard | The Saccharomyces cerevisiae FLO1 flocculation gene encodes for a cell surface protein[END_REF][START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF][START_REF] Bony | Distribution of the flocculation protein, flop, at the cell surface during yeast growth: the availability of flop determines the flocculation level[END_REF]. The FLO5 gene is responsible for the strong flocculation of Flo5-expressing cells [START_REF] Johnston | Genetic control of flocculation[END_REF]. This gene is not allelic with the FLO1 gene but shows 96% identity [START_REF] Jin | Flocculation of Saccharomyces cerevisiae[END_REF]. It is located on the very end of chromosome VIII (Teunissen et al. 1995). When the FLO5 gene is incorporated in a non-flocculent yeast strain, this strain acquires flocculation properties [START_REF] Vezinhet | Mapping of FLO5 of Saccharomyces cerevisiae by transfer of a chromosome during cytoduction[END_REF][START_REF] Bidard | The Saccharomyces cerevisiae FLO1 flocculation gene encodes for a cell surface protein[END_REF]. Flo5p is, like Flo1p, a cell wall protein and the expression of Flo5p is correlated with the degree of flocculation [START_REF] Bony | Distribution of the flocculation protein, flop, at the cell surface during yeast growth: the availability of flop determines the flocculation level[END_REF]. Not all yeast strains contain the dominant FLO9 gene, which is located on chromosome I, but at the other end of the chromosome, when compared to FLO1 (Teunissen and Steensma 1995). This gene shows 94% identity with FLO1 and 74% identity with the FLO5 gene [START_REF] Bossier | Constitutive flocculation in Saccharomyces cerevisiae through overexpression of the GTS1 gene, coding for a 'Glo'-type Zn-finger-containing protein[END_REF]. The FLO10 gene product has 58% identity with FLO1 and is located at chromosome XI (Teunissen and Steensma 1995). Overexpressing the FLO10 gene in S. cerevisiae leads to weak flocculation compared to the overexpression of the FLO1 gene. Flo10p is also responsible for filamentation and the adhesion to agar or plastic [START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF]. The Flo11p protein has the same domain structure as the other Flo proteins, but has a totally different amino acid sequence. The protein is responsible for flocculation, pseudohyphae formation, agar invasion, adhesion to substrates as well as for filamentation [START_REF] Lambrechts | Muc1, a mucin-like protein that is regulated by Mss10, is critical for pseudohyphal differentiation in yeast[END_REF][START_REF] Lo | FLO11, a yeast gene related to the STA genes, encodes a novel cell surface flocculin[END_REF], 1998[START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF].

Generally, flocculation can be governed by the different FLO genes except by FLO11. But, when overexpressed, many of these proteins can substitute for each other in diverse morphogenic events: flocculation, mating, haploid invasion and filamentation [START_REF] Guo | A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating[END_REF]. Indeed, yeast cells expressing Flo11p showed flocculation behaviour [START_REF] Douglas | Expression and characterization of the flocculin Flo11/Muc1, a Saccharomyces cerevisiae mannoprotein with homotypic properties of adhesion[END_REF]) but the flocculation ability of yeast cells carrying the FLO11 gene depends on the strain in which this gene is expressed. The yeast strain S. cerevisiae var. diastaticus exhibits Flo11 dependent flocculation and biofilm formation but did not invade agar or form pseudohyphae, whereas the strain with the ∑1278b background required Flo11p to form pseudohyphae, invade agar, adhere to plastic and develop biofilms, but did not flocculate [START_REF] Douglas | Expression and characterization of the flocculin Flo11/Muc1, a Saccharomyces cerevisiae mannoprotein with homotypic properties of adhesion[END_REF]). These results clearly show that adhesin properties are influenced by the yeast strain in which they are studied. Specific glycosylation of the adhesin, or auxiliary factors influencing adhesin conformation on the cell wall may thus be responsible for differences in biophysical properties. This has also been investigated by [START_REF] Govender | FLO gene-dependent phenotypes in industrial wine yeast strains[END_REF]. They explain that the adhesion phenotype after expressing individual FLO genes is different depending on the yeast strain in which they are expressed. This is due to the high allele heterogeneity of the FLO genes and to the fact that those genes are subjected to epigenetic regulation. So it can be concluded that care has to be taken when extrapolating data obtained in laboratory strains to industrial strains and that the optimization of the flocculation pattern of individual commercial strains will have to be based on a strain-by-strain approach [START_REF] Govender | FLO gene-dependent phenotypes in industrial wine yeast strains[END_REF].

Flo protein cell-cell adhesion mechanism

Flocculation was first defined as the phenomenon wherein yeast cells adhere in clumps, and sediment rapidly from the medium in which they are suspended [START_REF] Stewart | Effect of some monovalent and divalent metal ions on the flocculation of brewer's yeast strains[END_REF]. Further refinement led to the present definition:

-flocculation is the asexual, reversible and calcium-dependent aggregation of yeast cells to form flocs containing thousands of cells that rapidly sediment to the bottom of the liquid growth substrate‖ [START_REF] Stratford | Evidence for two mechanisms of flocculation in Saccharomyces cerevisiae[END_REF][START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF].

Other yeast strains, called ale yeasts, show a biofilm at the air-liquid interface at the end of the fermentation. The model for this biofilm formation is based on an increase in cell surface hydrophobicity and the fact that the multicellular aggregates effectively entrap carbon dioxide [START_REF] Zara | FLO11-Based Model for Air-Liquid Interfacial Biofilm Formation by Saccharomyces cerevisiae[END_REF]. The flocculation behaviour is of great importance for yeast cells as it is a way to escape from harsh conditions in the growth medium. It was suggested that cells in the middle of the floc could lyse and act as a source of new nutrients for the other cells (Stewart andRussell 1981, Iserentant 1996).

Therefore, flocculation enhances the survival rate of yeast cells in starvation conditions.

In an attempt to elucidate the phenomenon of flocculation, [START_REF] Eddy | Part of the yeast surface apparently involved in flocculation[END_REF] proposed the lectin hypothesis. In the presence of calcium, flocculins are able to bind highly branched mannose polymers located in the cell wall of adjacent cells (Eddy andRudin 1958, Miki et al. 1982). Moreover, it was shown that flocculation could be reversibly inhibited by the presence of sugars [START_REF] Taylor | Aromatic compounds and sugars in flocculation of Saccharomyces cerevisiae[END_REF][START_REF] Miki | Flocculation in Saccharomyces cerevisiae: mechanism of cell-cell interaction[END_REF][START_REF] Nishihara | Essential roles of cell surface protein and carbohydrate components in flocculation of a brewer's yeast[END_REF][START_REF] Stratford | Yeast flocculation: Flo1 and NewFlo phenotypes and receptor structure[END_REF], which is validating the hypothesis. This flocculation model is now widely accepted.

The flocculation phenotypes of yeast strains can be divided into two groups: the Flo1-phenotype and the NewFlophenotype [START_REF] Stratford | Evidence for two mechanisms of flocculation in Saccharomyces cerevisiae[END_REF]Assinder 1991, Sieiro et al. 1995). This partition is based on the type of carbohydrate that inhibits flocculation. Flocculation of the Flo1-phenotype can be inhibited by mannose, but not by glucose, maltose, sucrose or galactose. Therefore, Flo1p is considered as the most specific adhesion protein of the Flo-family [START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF][START_REF] Govender | Controlled expression of the dominant flocculation genes FLO1, FLO5, and FLO11 in Saccharomyces cerevisiae[END_REF][START_REF] Van Mulders | Phenotypic diversity of Flo protein family-mediated adhesion in Saccharomyces cerevisiae[END_REF]. Flocculation of the NewFlo-phenotype can be inhibited by mannose, glucose, maltose and sucrose, but not by galactose. This flocculation phenotype is governed by a Flo1p homologue, named Lg-Flo1p. Considering the current research about the capacity of different sugars to inhibit the flocculation, the clear distinction of two flocculation phenotypes is questioned. Not all flocculation phenotypes can be classified in either the Flo-phenotype or the NewFlo-phenotype. For example, the expression of FLO5, FLO9 or FLO10 does not lead to a specific flocculation phenotype making them less selective adhesins. They were strongly inhibited by mannose but also weakly inhibited by a range of sugars [START_REF] Van Mulders | Phenotypic diversity of Flo protein family-mediated adhesion in Saccharomyces cerevisiae[END_REF]. Although, based on the sequence homology with Flo1p, it was found that Flo5p and Flo9p were exclusively mannose-binding flocculins (Teunissen and Steensma 1995). More detailed research about the inhibition of flocculation by sugars has been performed. Miki et al. have quantified the inhibition of flocculation (1980). Up to 500 mM D-mannose was required to completely inhibit flocculation. Later, the sugars were ranked according to their inhibition capacity [START_REF] Smit | Flocculence of Saccharomyces cerevisiae cells is induced by nutrient limitation, with cell surface hydrophobicity as a major determinant[END_REF]. It was shown that α-D-mannose could disrupt the flocs at a concentration of only 25 mM. This experiment has been repeated recently using a yeast strain overexpressing Flo1p [START_REF] Van Mulders | Phenotypic diversity of Flo protein family-mediated adhesion in Saccharomyces cerevisiae[END_REF]. It was observed that the flocculation mediated by Flo1p was only inhibited by mannose at the high concentration of 1 M.

More detailed research regarding the flocculation of yeast cells has been performed recently. A high concentration of flocculins is found on the yeast cell surface [START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF]. Where they can all interact with mannan chains of other cells, leading to multipoint attachments between yeast cells. When one interaction is in the range of millimolar affinities, it becomes micromolar affinity with two attachments. In this way the interaction strength of flocculation is increased dramatically [START_REF] Dranginis | A biochemical guide to yeast adhesins: glycoproteins for social and antisocial occasions[END_REF]). The first and only quantitative measurement of the affinity between a Flo protein and a carbohydrate was done for the interaction of Lg-Flo1p to mannose. The value for the dissociation constant was calculated to be 0.77 mM [START_REF] Groes | Purification, crystallization and preliminary X-ray diffraction analysis of the carbohydrate-binding domain of flocculin, a cell-adhesion molecule from Saccharomyces carlsbergensis[END_REF]. Compared to the affinity of other lectin carbohydrate interactions, this is indeed a very low value. This two-way interaction has been confirmed by looking carefully at the cells part of a floc resulting from multiple stresses. While analysing the flocs, it was observed that most of the cells were FLO1-expressing cells. In other words, FLO1-expressing cells preferentially aggregate with other FLO1 expressers [START_REF] Smukalla | FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast[END_REF].

This was explained by the fact that the two-way interaction, which occurs when two FLO1-expressers are binding, is stronger than the one-way interaction of a FLO1-expresser with a non-FLO1-expresser. A more striking observation has been described about the Flo11 proteins. When secreted Flo11p was covalently attached to microscopic beads, it conferred the ability to specifically bind to S. cerevisiae var. diastaticus, but not to ∑1278b cells, which do not flocculate.

They were not able to bind cells from another yeast strain expressing Flo11p or cells with a deletion of FLO11 [START_REF] Douglas | Expression and characterization of the flocculin Flo11/Muc1, a Saccharomyces cerevisiae mannoprotein with homotypic properties of adhesion[END_REF]). This Flo11p -Flo11p interaction points to a homotypic adhesive mechanism. A further quantitative investigation on the molecular level of the self-binding properties of Flo11p and the other Flo proteins is necessary to complete the flocculation mechanism.

Structural features of S. cerevisiae flocculins

The member proteins of the adhesin family have a modular configuration that consists of three domains (N-terminal, central and C-terminal domain) and an amino-terminal secretory sequence that must be removed when the protein moves to the plasma membrane through the secretory pathway [START_REF] Hoyer | Identification of Candida albicans ALS2 and ALS4 and localization of als proteins to the fungal cell surface[END_REF][START_REF] Hoyer | The ALS gene family of Candida albicans[END_REF][START_REF] Verstrepen | Origins of variation in the fungal cell surface[END_REF]. Until now, no atomic structure of any Flo protein has been determined. However, the N-terminal part of a homolog of Flo1p (Lg-Flo1p) has been crystallised and diffracted to high-resolution using X-ray radiation [START_REF] Groes | Purification, crystallization and preliminary X-ray diffraction analysis of the carbohydrate-binding domain of flocculin, a cell-adhesion molecule from Saccharomyces carlsbergensis[END_REF], but the structure has not been solved yet. However, some structural information about the Flo-proteins has already been obtained from structural bioinformatics. The prediction of the secondary structure of Flo1p shows that the protein is mainly composed of β-sheets alternated with coils [START_REF] Nishikawa | Predicting protein secondary structure based on amino acid sequence[END_REF]. α-Helices are only found at the very end of the N-and C-terminal parts of the protein. The study of the hydrophobicity of the Flo1p protein showed that the N-and C-terminal parts of the protein were more hydrophobic than the middle part [START_REF] Kyte | A simple method for displaying the hydropathic character of a protein[END_REF]. An overview of the general gene pattern of the Flo family in strain S288C is represented in Fig. 1a. The length of each domain varies according to each FLO gene, which is visualised in Fig. 1b and described in Table 1. The Flo1p has been studied most extensively of all Flo proteins and will be discussed in more detail.

Modular configuration of the Flo proteins

The C-terminal domain contains a glycosylphosphatidylinositol (GPI)-attachment site. The adhesins are covalently linked to the β-1,6-glucans of the yeast cell wall through the GPI-remnant (De [START_REF] Nobel | Is there a role for GPIs in yeast cell-wall assembly?[END_REF][START_REF] Caro | In silicio identification of glycosylphosphatidylinositol-anchored plasma-membrane and cell wall proteins of Saccharomyces cerevisiae[END_REF], Hamada et al. 1998a, Hamada et al. 1998b[START_REF] Groot | Genome-wide identification of fungal GPI proteins[END_REF].

The central domain of Flo1p contains many tandem repeats and 46% of the amino acids are serine and threonine residues (Teunissen et al. 1993a[START_REF] Caro | In silicio identification of glycosylphosphatidylinositol-anchored plasma-membrane and cell wall proteins of Saccharomyces cerevisiae[END_REF]) (see Fig. 2). These amino acids are prone to extensive O-glycosylation during the post-translational modification of the protein and the O-linked oligosaccharide side-chains then enable the flocculins to attain a long, semi-rigid rod-like structure [START_REF] Jentoft | Why are proteins O-glycosylated?[END_REF]) that might be stabilised by Ca 2+ ions [START_REF] Verstrepen | Flocculation, adhesion and biofilm formation in yeasts[END_REF]. The proline residues, which are common in this region, may also prevent the central domain to form a compact domain [START_REF] Dranginis | A biochemical guide to yeast adhesins: glycoproteins for social and antisocial occasions[END_REF]. All together, this leads to the general idea that the protein is attached to the cell wall and sticks out, reaching for mannose chains to interact with. Along the amino acid sequence, many Asn-Xaa-Thr/Ser sequences are found, where Xaa represents any amino acid except proline. This is the consensus sequence for N-glycosylation [START_REF] Kornfeld | Assembly of asparagine-linked oligosaccharides[END_REF], hence, both O-glycosylation and N-glycosylation are present [START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF]).

The N-terminal domain is the lectin domain, which interacts with mannose residues. This interaction is responsible for flocculation. This was discovered by expressing a truncated form of Flo1p (deletion of amino acids 50 to 278). It was observed that this truncated protein could not trigger flocculation [START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF]). These results are in agreement with those of Kobayashi and co-workers (1998), who also reported that the N-terminal region of Flo1p contains the sugar recognition domain. Indeed, the replacement of the N-terminus of the Flo1 protein by the corresponding region of the Lg-Flo1 protein, caused conversion of the Flo1 flocculation phenotype to the NewFlo flocculation phenotype [START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF]).

The Flo protein at molecular level Iterative database searches using a domain insert sequence from bacterial β-glucosidases revealed the presence of a conserved domain, called the PA14 domain. It is shared by a wide variety of bacterial and eukaryotic proteins, which include many glycosidases, glycosyltransferases, proteases, amidases, bacterial toxins such as anthrax protective antigen (PA), and also yeast adhesins [START_REF] Rigden | The PA14 domain, a conserved all-beta domain in bacterial toxins, enzymes, adhesins and signaling molecules[END_REF]). This conserved domain is named PA14 after its location in the PA 20 pro-peptide of the anthrax toxin protective antigen. The crystal structure of the anthrax toxin protective antigen (shown in Fig. 3a) indicates that the PA14 domain consists of a series of antiparallel β-strands. These correspond roughly to the conserved regions of the domain and are separated by loops that coincide with the variable region [START_REF] Petosa | Crystal structure of the anthrax toxin protective antigen[END_REF].

Most of the experimentally characterised PA14-containing proteins are involved in carbohydrate binding and/or metabolism. As mentioned before, in the S. cerevisiae flocculins, carbohydrate binding is associated with the N-terminal third of the protein. For Flo1p, this part has been assigned as a new domain (PF07691) in the Pfam database [START_REF] Finn | The Pfam protein families database[END_REF]; http://pfam.sanger.ac.uk), covering residues 86-249, which overlaps the PA14 domain. The flocculin N-terminal domain might, therefore, be considered as one of the many PA14 domain variants. Based on the sequence alignment of the N-terminal domain of Flo1p and the anthrax toxin protective antigen, a model was proposed for the structure of N-Flo1p, considering amino acids 85 to 261 (Fig. 3b).

The amino acids that are responsible for the carbohydrate recognition in Flo1p and Lg-Flo1p, have also been described. It was suggested that the tryptophan residue on position 228 is involved in mannose-recognition in the Flo1 flocculation phenotype. This tryptophan 228 must be replaced by leucine to produce a flocculation phenotype inhibited by glucose. The following model for sugar recognition was proposed: tryptophan 228 in Flo1p recognises the C2-hydroxyl group of mannose but does not recognise the C2-hydroxyl group of glucose. In the Lg-Flo1 protein, a leucine instead of a tryptophan at position 228 is required for the NewFlo flocculation phenotype. Also position 226 affects the flocculation phenotype: for NewFlo-type flocculation a glycine or arginine is needed at that position. Generally, the carbohydrate binding activity of Flo1p can be attributed to the VSWGT motif, a pentapeptide encompassing amino acids 226 to 230 [START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF].

Recently, an EYDGA pentapeptide motif belonging to the PA14 domain was identified. The pentapeptide is involved in sugar recognition in the N-terminal domain of the Epa1p (epithelial adhesin) from Candida glabrata. This can be compared to the VSWGT pentapeptide in Flo1p [START_REF] Zupancic | Glycan microarray analysis of Candida glabrata adhesin ligand specificity[END_REF]. A multiple sequence alignment of various PA14 domains showed convincingly that the VSWGT motif of Flo1p and the EYDGA motif of Epa1p are found in precisely the same position, within a hypervariable region of PA14 [START_REF] Rigden | The PA14 domain, a conserved all-beta domain in bacterial toxins, enzymes, adhesins and signaling molecules[END_REF]). The VSWGT/KVLAR motif of Flo1p/Lg-Flo1p, and the EYDGA motif of Epa1p, correspond to a surface loop between two β-strands (strands 9 and 10) in the anthrax toxin PA domain structure [START_REF] Petosa | Crystal structure of the anthrax toxin protective antigen[END_REF]) (see the blue loop in Fig. 3b).

The FLO genes posses internal tandem repeats located in the central region, that function as interchangeable modules [START_REF] Watari | Molecular cloning and analysis of the yeast flocculation gene FLO1[END_REF]. During DNA replication, the presence of internal tandem repeats is responsible for recombination events. Therefore, yeast cells show a highly variable adhesion phenotype as they are able to quickly adapt their adhesion properties to new environments. The removal or addition of repeat units results in a longer or shorter adhesin. This is immediately reflected in its flocculation behaviour: a longer adhesin showing a stronger flocculation phenotype. This can be explained by the fact that the N-terminal domain should be exposed clearly to the neighbouring cells to allow flocculation [START_REF] Frieman | Modular domain structure in the Candida glabrata adhesin Epa1p, a beta1,6 glucan-cross-linked cell wall protein[END_REF], 2004[START_REF] Verstrepen | Intragenic tandem repeats generate functional variability[END_REF]. Surprisingly, Liu et al. suggest that the deletion of tandem repeats can cause the conversion from Flo1-to NewFlo1-phenotype (Liu et al. 2007a, b). Moreover, for the Flo11 protein, the length of the gene affects the biofilm-forming ability of the cells expressing Flo11p. This was demonstrated by cloning two alleles of the FLO11 gene with different sizes (3.1 and 5 kb) into S. cerevisiae strain BY4742 and analysing the corresponding phenotype [START_REF] Zara | FLO11 gene length and transcriptional level affect biofilm forming ability of wild flor strains of Saccharomyces cerevisiae[END_REF]). This is not in agreement with what is known so far about the specificity of the flocculin, which is believed to be on the N-terminal domain only [START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF][START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF][START_REF] Van Mulders | Phenotypic diversity of Flo protein family-mediated adhesion in Saccharomyces cerevisiae[END_REF]. Not only Flo proteins, but several other yeast adhesins such as Als and Epa proteins from C. albicans and C. glabrata respectively, contain tandem repeat (TR) regions. So far, their structures are unknown, but recently ab initio modeling with either Rosetta or LINUS generated consistent structures of three-stranded anti-parallel βsheet domains for the TR regions of Als5p (Fig. 3c) [START_REF] Frank | Structure and function of glycosylated tandem repeats from Candida albicans ALS adhesins[END_REF].

Searching the pool of all known yeast adhesins using the TANGO program (http://tango.crg.es/), many adhesins were found to contain sequences with high β-aggregation potential, including Flo1p and Flo11p (Ramsook et al. 2009). When a peptide from the central domain of Flo1p (aa 305 to 315) was suspended in neutral buffer, it readily formed amyloids. It was proposed that amyloid formation plays a role in cell adhesion by increasing the avidity of the adhesins by -bundling‖ the adhesins. Therefore, amyloid formation can greatly increase the intercellular binding strength by increasing avidity [START_REF] Otoo | Candida albicans Als adhesins have conserved amyloid-forming sequences[END_REF][START_REF] Frank | Structure and function of glycosylated tandem repeats from Candida albicans ALS adhesins[END_REF], Ramsook et al. 2010).

Glycosylation of Flo proteins

Flo proteins are heavily O-and N-glycosylated (Straver et al. 1994a[START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF]. Glycosylation is an enzymedirected and site-specific process, which is required for proper functioning of the protein. It is a post-translational modification and the protein is glycosylated on its way through the endoplasmic reticulum and the Golgi apparatus. Nglycosylation is the addition of oligosaccharides to the amide group of asparagines and results in highly branched and extended high mannose structures [START_REF] Dean | Asparagine-linked glycosylation in the yeast Golgi[END_REF]. O-glycosylation occurs at a later stage during protein processing and short linear chains up to five mannose residues are added to the β-hydroxyl group of serine and threonine residues [START_REF] Tanner | Protein glycosylation in yeast[END_REF][START_REF] Aikawa | Asparagine-linked glycosylation in yeast, Saccharomyces cerevisiae: Basic mechanism of core glycosylation and effects on biochemical properties[END_REF][START_REF] Goto | Protein O-glycosylation in fungi: diverse structures and multiple functions[END_REF]. Proteins of diverse yeast species contain other O-and N-linked oligosaccharide structures (glycans). The differences found in various yeast species, has been reviewed [START_REF] Gemmill | Overview of N-and O-linked oligosaccharide structures found in various yeast species[END_REF]. Sugar analysis of a homolog of Flo1p revealed the presence of 64% carbohydrate, in the molar ratio of 60:27:1, which corresponds to mannose, glucose and N-acetylglucosamine respectively. However, the exact glycan profile of cell wall Flo proteins is not yet known (Straver et al. 1994b[START_REF] Bony | Localization and cell surface anchoring of the Saccharomyces cerevisiae flocculation protein Flo1p[END_REF][START_REF] Douglas | Expression and characterization of the flocculin Flo11/Muc1, a Saccharomyces cerevisiae mannoprotein with homotypic properties of adhesion[END_REF]).

The role of direct carbohydrate-carbohydrate interactions has not yet been explored in yeast flocculation. Recently, it was shown that these interactions can be important for cell adhesion phenomena since glycans occur on the outermost cell periphery and therefore are likely involved in the first intercellular contact (Bucior andBurger 2004, Bucior et al. 2009). Carbohydrate chains offer a rich supply of potential low-affinity binding sites, arranged in a polyvalent array that may create a flexible and versatile carbohydrate-carbohydrate recognition system [START_REF] Misevic | Reconstitution of high cell binding affinity of a marine sponge aggregation factor by cross-linking of small low affinity fragments into a large polyvalent polymer[END_REF]. This then allows cells to explore surrounding surfaces, and to form or reinforce interactions before subsequent steps in the cell that are mediated by tight and stable covalent bonds [START_REF] Mallinson | From weak interactions to covalent bonds: a continuum in the complexes of 1,8-bis(dimethylamino)naphthalene[END_REF]. The interaction between individual carbohydrates is fairly weak, and biologically relevant high affinities are achieved by the organisation of cell surface proteoglycans, glycoproteins or glycolipids into clusters or superstructures [START_REF] Hakomori | Structure, organization, and function of glycosphingolipids in membrane[END_REF][START_REF] Yoneda | Regulation of cytoskeletal organization by syndecan transmembrane proteoglycans[END_REF][START_REF] Todeschini | Functional role of glycosphingolipids and gangliosides in control of cell adhesion, motility, and growth, through glycosynaptic microdomains[END_REF]. These provide avidities that can attach cells to each other under physiological conditions. Carbohydrate self-recognition takes place through surfaces determined by carbohydrate epitopes and is based on non-covalent bonds: van der Waals contacts, hydrogen bonds, electrostatic forces and interactions with cations [START_REF] Spillmann | Carbohydrate-carbohydrate interactions in adhesion[END_REF].

Carbohydrate self-interactions have been recently well described for sponge proteoglycans. Dissociated sponge cells from two different species have the capacity to recognise specifically their own species and reaggregate through cellsurface proteoglycans, termed aggregation factors (AFs) [START_REF] Fernàndez-Busquets | Circular proteoglycans from sponges: first members of the spongican family[END_REF]. In a calcium-independent process the AFs adhere to the cell surface, and in a calcium-dependent process they exhibit AF self-association [START_REF] Jarchow | Supramolecular structure of a new family of circular proteoglycans mediating cell adhesion in sponges[END_REF][START_REF] Haseley | Carbohydrate self-recognition mediates marine sponge cellular adhesion[END_REF][START_REF] Fernàndez-Busquets | Circular proteoglycans from sponges: first members of the spongican family[END_REF][START_REF] Carvalho De Souza | Adhesion forces in the self-recognition of oligosaccharide epitopes of the proteoglycan aggregation factor of the marine sponge Microciona prolifera[END_REF]. AFs are large molecules (2 x 10 4 to 2 x 10 7 Da) that are composed of 30-60% carbohydrates. Atomic Force Microscope (AFM) visualisation has revealed either a linear or a sunburst-like core structure with 20-25 radiating arms [START_REF] Dammer | Binding strength between cell adhesion proteoglycans measured by atomic force microscopy[END_REF]. Single-molecule force spectroscopy measurements reported equally strong adhesion forces between glycan molecules (190-310 pN) as between proteins in antibody-antigen interactions (244 pN) (Bucior et al. 2004). This observation suggested the existence of intermolecular carbohydrate adhesion domains [START_REF] Garcia-Manyes | Proteoglycan mechanics studied by single-molecule force spectroscopy of allotypic cell adhesion glycans[END_REF].

Cell-cell adhesion biophysics

Molecular and genetic approaches have identified various cell adhesion molecules (CAMs) with their ligand specificities, and have determined the processes in which they are involved. However, the molecular mechanisms by which CAMs regulate different types of adhesion are open debates [START_REF] Morgan | Synergistic control of cell adhesion by integrins and syndecans[END_REF][START_REF] Ludwig | Probing cellular microenvironments and tissue remodeling by atomic force microscopy[END_REF]). To understand cell adhesion, the vast amount of qualitative data that is available must be augmented with quantitative data of the biophysics of adhesion. Historically, the strength of cell adhesion to a substrate has been studied using simple washing assays [START_REF] Klebe | Isolation of a collagen-dependent cell attachment factor[END_REF]. Washing assays have proven to be versatile and useful in identifying CAMs, important extra-cellular-matrix components and other proteins that are involved in various forms of cell adhesion. To estimate the force to which cells are subjected, various assays that are based on the regulated flow of media have been implemented, including flow chamber methods [START_REF] Kaplanski | Granulocyteendothelium initial adhesion. Analysis of transient binding events mediated by E-selectin in a laminar shear flow[END_REF]). However, these assays only give estimates of adhesion forces, since the shear force that is exerted on the cells depends on parameters such as cell size, cell shape and how the cell is attached to the substrate. Recently, single-cell and single-molecule techniques have been developed to obtain more controlled and quantitative measurements of adhesion strength.

Single-cell force spectroscopy assays on living cells have been applied to measure the strength of cell adhesion down to single-molecule levels [START_REF] Helenius | Single-cell force spectroscopy[END_REF]. A living cell can be attached to a tipless cantilever of an AFM and the interacting partner (molecule or cell) on a substrate-coated surface. Alternatively, the living cell can be fixed on a surface and the tip functionalised with the interacting molecule. AFM force spectroscopy with a single cantilever-bound cell can be used to investigate cell-cell and cell matrix interactions. The approach and withdrawal of this cell to and from its surface can be precisely controlled by parameters such as applied force, contact time and pulling speed by benefiting from the AFM's high-force sensitivity and spatial resolution. The data collected in these experiments include information on repulsive forces before contact, cell deformability, maximum unbinding forces, individual unbinding events, and the total work required to remove a cell from the surface (Table 2). Force spectroscopy can identify cell subpopulations and characterise the regulation of cell adhesion events with single-molecule resolution [START_REF] Taubenberger | Revealing early steps of alpha2beta1 integrin-mediated adhesion to collagen type I by using single-cell force spectroscopy[END_REF]). Analysis of discrete cell adhesion forces demonstrated a dynamic increase of adhesion over time.

Single S. cerevisiae cells have been attached to a tipless AFM cantilever, and used as a living single-cell probe to perform single-cell force spectroscopy [START_REF] Kang | Bioinspired single bacterial cell force spectroscopy[END_REF]. The contributions of several galectin family members in cell-substratum adhesion of Madin-Darby canine kidney cells have been studied using quantitative single cell AFM force spectroscopy [START_REF] Friedrichs | Contributions of galectin-3 and -9 to epithelial cell adhesion analyzed by single cell force spectroscopy[END_REF]) (Table 2). Optical tweezers have been used to orient uropathogenic Escherichia coli (which present a FimH lectin at the tip of their type 1 pilus) relative to a mannose-presenting surface, and thus, limit the number of points of attachment [START_REF] Liang | Measuring the forces involved in polyvalent adhesion of uropathogenic Escherichia coli to mannose-presenting surfaces[END_REF] (Table 2). It was possible to quantify the forces required to break a single interaction between pilus and mannose groups.

The Flo proteins Flo1p, Lg-Flo1p, Flo5p, Flo9p and Flo10p are lectins, since they have an affinity towards specific sugar moieties. Recently, AFM force spectroscopy has been used to directly measure the forces involved in single carbohydrate-lectin interactions (Table 3). AFM force spectroscopy has been used to determine the unbinding force of oligoglucose carbohydrates and Lg-Flo1p present on the cell wall of industrial brewer's yeast strains (Touhami et al. 2003a). AFM force probes functionalised with carboxymethyl-amylose were used to record force-distance curves on living cells. Flocculation cells showed adhesion forces of 121 ± 53 pN. Unbinding forces of other lectin-carbohydrate interactions range from 30 pN to 200 pN (Table 2 and3).

Medical and industrial significance of S. cerevisiae flocculation phenotype

In hazardous environmental conditions, S. cerevisiae cells possess the remarkable properties to adhere to other cells or to substrates like agar or plastics. Adhesion to surfaces is a mechanism that may lead to biofilm formation. It is often used as a model to study biofilm formation of pathogenic yeasts, responsible for 41% of the mortality rate in hospitals [START_REF] Wisplinghoff | Nosocomial bloodstream infections in US hospitals: analysis of 24179 cases from a prospective nationwide surveillance study[END_REF]. Biofilms give protection to yeast cells, e.g. by conferring resistance to antifungal drugs.

Adhesion to abiotic surfaces as catheters or prostheses can serve as a reservoir of pathogenic cells ready to gain access to the bloodstream of patients [START_REF] Jabra-Rizk | Cell surface hydrophobicityassociated adherence of Candida dubliniensis to human buccal epithelial cells[END_REF][START_REF] Donlan | Biofilms: survival mechanisms of clinically relevant microorganisms[END_REF][START_REF] Jabra-Rizk | Fungal biofilms and drug resistance[END_REF][START_REF] Kojic | Candida infections of medical devices[END_REF]. A detailed study of this mechanism is necessary in the search for an adequate antifungal treatment.

The flocculation phenomenon is exploited in the brewery industry as an easy, convenient and cost-effective way to separate the aggregated yeast cells from the beer at the end of the primary fermentation. The timing of flocculation is crucial for brewers as the quality of beer highly depends on it. When cells start to flocculate too early, the fermentation will be incomplete with undesirable aromas and too many residual sugars. On the other hand, when the flocculation is delayed, problems can arise during beer filtration (Willaert 2007a). A good understanding of the underlying mechanism and the factors affecting flocculation is of crucial importance for the performance and control of the brewing process [START_REF] Verstrepen | Yeast flocculation: what brewers should know[END_REF]. Another application is the self-aggregation of yeast cells, which can be classified as a type of cell immobilisation (Willaert andBaron 1996, Willaert 2007b). Flocculent yeast strains can be selected to obtain a high cell density during the beer fermentation process [START_REF] Nedovic | Beer production using immobilised cells[END_REF][START_REF] Willaert | Primary beer fermentation by immobilised yeast -a review on flavour formation and control strategies[END_REF][START_REF] Van Mulders | Phenotypic diversity of Flo protein family-mediated adhesion in Saccharomyces cerevisiae[END_REF].

Consequently, the volumetric productivity can be increased considerably.

Altogether, elucidating the structure and the physical binding properties of adhesins will allow controlling yeast adhesion more efficiently in medical and biotechnological applications.

Tables Table 1: Overview of the lengths of the important domains for each of the 6 Flo proteins; according the Pfam database [START_REF] Finn | The Pfam protein families database[END_REF]; http://pfam.sanger.ac.uk). Numbers refer to the amino acid residues in the primary sequence.

Protein

Total length Signal sequence N-terminal domain PA14 Transmembrane domain for each Flo protein from the Pfam database [START_REF] Finn | The Pfam protein families database[END_REF]http://pfam.sanger.ac.uk). Table 1 gives the length of the domains for each specific protein. which is underlined (Ramsook 2009). In the N-terminal domain, it was shown that the amino acids ranging from 197 to 240 are important for binding to sugar [START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF]. Therefore, this region was zoomed in at the amino acid level. The pentapeptide involved in glycan specificity is shown in bold [START_REF] Zupancic | Glycan microarray analysis of Candida glabrata adhesin ligand specificity[END_REF]) and the amino acids contributing to sugar recognition have bigger size [START_REF] Kobayashi | Region of FLO1 proteins responsible for sugar recognition[END_REF].

Fig. 1 |

 1 Fig. 1 | (a) General domain structure of the flocculins from S. cerevisiae strain S288C, as explained in the text. The Nterminal domain contains the PA14 domain which is responsible for the interaction with the sugar. (b) Domain structure

Fig. 2 |

 2 Fig. 2 | Schematic representation of Flo1p updated from Watari and co workers (1994), indicating the 3 domains and the signal sequence (in grey) as well as the beginning and the end of each domain marked as numbers on the line above the figure. The percentage of the serine (Ser) and threonine (Thr) content is given for each domain. The internal repeats and the PA14 domain are shown in different colors and their length is marked as numbers on the line under the domains. The amino acid sequence of the 135 nt repeat is shown. This repeat contains a sequence with high β-aggregation potential,

Fig. 3 |

 3 Fig. 3 | (a)Structure of the anthrax toxin protective antigen, revealing 5 domains (PDB 1ACC). The PA14 domain (amino acids 14-150) is part of domain 1 shown in blue. PA14 domain is part of domain 1 and is composed of beta sheets[START_REF] Petosa | Crystal structure of the anthrax toxin protective antigen[END_REF]. (b) Model proposed for the tertiary structure of the N-terminal domain of Flo1p based on a sequence alignment with the anthrax toxin protective antigen, using the Phyre server[START_REF] Kelley | Protein structure prediction on the Web: a case study using the Phyre server[END_REF]. Only amino acids 85-261 from N-Flo1p were considered. The pentapeptide responsible for the binding event as described byKobayashi and co workers (1998), is shown in blue. (c) The tandem repeat region comprises multiple amino acid repeats that are thought to be arranged in antiparallel-sheets. A model was made with ROSETTA and LINUS and this consistently predicted independently folded three-stranded antiparallel-sheet domains for each repeat of the Als5 protein(Frank et al. 2009).

  Figure 1:

Figure 3 :

 3 Figure 3:

Table 2 :

 2 Carbohydrate-lectin interactions studied by single-cell (SCFS) and single-molecule (SMFS) force spectroscopy.

	Cell type	Lectin	Carbohydrate	SCFS	SMFS	SCFS	Reference
				rupture	rupture	Technique
				force (pN) force (pN)
	CHO	SGLT1	Glucose	51	-	AFM	Puntheeranurak et al.,
		(SLC5A1)					(2006, 2007)
	E. coli	FimH	Mannose			Optical Liang (2000)
						tweezers
	NIH3T3	Concanavalin A Surface-expressed	86	-	AFM	Chen (2000)
			mannose residues
	NIH3T3	Concanavalin A Not specified/known 80	95	AFM	Baumgart and
							Offenhäusser (2003)
	PMN	E-selectin	Not specified/known 140	-	AFM	Hanley (2004)
	PMN	L-selectin	Not specified/known 80	-	AFM	Hanley (2004)
	PMN and	P-selectin
	LS174T
	Red blood cells Helix pomatia Saccharides from	65	-	AFM	Grandbois et al. (2000)
		lectin	blood types O and A
	S. cerevisiae	Concanavalin A Mannan	75-200	-	AFM	Gad et al. (1997)
	S. cerevisiae	Concanavalin A Mannan/mannose	117 ± 41	-	AFM	Touhami et al. (2003b)
	S. cerevisiae	Lg-Flo1p	CM-amylose	121 ± 53	-	AFM	Touhami et al. (2003b)
	Madin-Darby	integrin	collagen-I	86.42	-	AFM	Friedrichs et al. (2007)
	canine kidney		coated substrate
	cells
	Madin-Darby	galectin-3	laminin-111	60.02	-	AFM	Friedrichs et al. (2007)
	canine kidney	and -9	coated substrate
	cells

Acknowledgements

This work is supported by the Belgian Federal Science Policy Office (Belspo) and European Space Agency (ESA) PRODEX program, the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen), and the Research Council of the Vrije Universiteit Brussel.


PharmD Efthymios Manolis
email: efthymios.manolis@ema.europa.eu

MSc Tariq Eldirdiry

Osman Bsc

MD Ralf Herold

PhD Franz Koenig

PhD Paolo Tomasi

PhD Spiros Vamvakas

MD Agnes Saint

Raymond Md

Role of Modelling and Simulation in Paediatric Investigation Plans

Keywords: Clinical Trials, pharmacokinetics < Drugs, pharmacodynamics < modelling, simulation, children, pharmacotherapy, research

Ethical and practical constraints encourage the optimal use of resources in paediatric drug development.

Modelling and simulation has emerged as a promising methodology acknowledged by industry, academia and regulators. We previously proposed a paradigm in paediatric drug development whereby modelling and simulation is used as a decision tool, for study optimisation and/or as a data analysis tool. Three and a

half years since the Paediatric Regulation came into force in 2007, the European Medicines Agency has gained substantial experience in the use of modelling and simulation in paediatric drug development. In this review we present examples on how the proposed paradigm applies in real case scenarios of planned pharmaceutical developments. We also report the results of a paediatric database search to further "validate" the paradigm. There were 47 out of 210 positive Paediatric Investigation Plan (PIP) opinions that made reference to modelling and simulation (data included all positive opinions issued up to January 2010). This reflects a major shift in regulatory thinking. The ratio of PIPs with modelling and simulation rose to two in five based on the summary reports. Population pharmacokinetic (POP-PK) and pharmacodynamics (POP-PD) and physiological based pharmacokinetic models (PBPK) are widely used by industry and endorsed or even imposed by regulators as a way to circumvent some difficulties in developing medicinal products in children. The knowledge of the effects of age and size on PK is improving and models are widely employed to make optimal use of this knowledge but less is known about the effects of size and maturation on pharmacodynamics (PD), disease progression and safety.

Extrapolation of efficacy from different age groups is often used in paediatric medicinal development as another means to alleviate the burden of clinical trials in children and this can be aided by modelling and simulation to supplement clinical data. The regulatory assessment is finally judged on clinical grounds such as feasibility, ethical issues, prioritisation of studies, and unmet medical need. The regulators are eager to expand the use of modelling and simulation to elucidate safety issues, to evaluate the effects of disease (eg. renal or hepatic dysfunction) and to qualify mechanistic models that could help shift the current medicinal development paradigm. The objective of the Paediatric Regulation ( 1 ) (EC 1901(EC / 2006) is to improve the health of children in Europe by facilitating the development and availability of medicines for children from birth to less than 18 years, ensuring that medicines for use in children are of high quality, ethically researched and authorised appropriately, and improving the availability of information on the use of medicines for children. It is expected that these objectives can be met without subjecting children to unnecessary trials or delaying the authorisation of medicinal products for use in adults.

In order to meet the objectives of the Paediatric Regulation there is a need for techniques that make optimal use of the limited opportunities for clinical research with a limited number of children.

Methodologies such as adaptive designs, Bayesian statistics, meta-analytic approaches, development of biomarkers and modelling and simulation are recognised by drug developers and regulators as promising techniques for increasing the quality of data and analyses in small populations ( 2 3 4 5 6 7 8 9 10 11 ).

In this paper we focus on modelling and simulation which is gaining momentum in paediatric medicinal development ( 12 ). Modelling is the science of using mathematical language to describe and quantify a system. Simulation refers to the use of these models to make quantitative predictions. Modelling and simulation methodology is based on the concept of data collection (in vitro, literature, preclinical, clinical), assumption testing (attempt to elucidate the data on the basis of mathematical/statistical models), learning (retaining assumptions that are informative of the test system), prediction (simulating outcomes on the underlying assumptions that can be used to predict and optimise future experimental outcome) and confirmation of model assumptions (when compared with external data).

The properties that make models particularly useful in drug development are summarised in Table 1. The main benefit of modelling and simulation for paediatric medicines is that it enables the use of all the available information to make decisions and optimise the conduct and analysis of clinical trials in children because there are limited opportunities to collect data in children due to ethical and practical constraints.

There is a risk that existing information is not applicable to a new population, but this can be identified and addressed early in development by appropriate methodological steps ( 13 14 15 ). A prerequisite for any analysis is data from properly conducted and well designed, recorded and reported clinical trials as described in Good Clinical Practice (GCP) Guidance( 16 ). We previously described scenarios for the use of modelling and simulation in paediatric medicines development ( 17 ). Modelling and simulation was identified as a tool to navigate in the paediatric decision tree and as a study optimisation and data analysis tool. The objective of this work is to "validate" the proposed paradigm through a retrospectively analysis of all positive opinions and corresponding summary reports for Paediatric Investigation Plans (PIPs) from 2007 to 2010. The terms used in the data mining and analysis exercise are defined in Table 2 and in Figures 1 &2.

Role of Models in Paediatric Investigation Plans

Models can facilitate decision making and navigation through the different paediatric scenarios (decision tool, Figure 2) by parameterising and visualizing data. Disease models can help disentangle the different parameters that affect disease progression and responses to interventions, and can facilitate the decision on extrapolation. PK/PD models can help better compare the concentration response relationship between different age groups. Finally, models can link PD measures with clinical outcome. The PD parameter may then be used as a surrogate means to assess efficacy in different age groups.

Information available from adults and children can be used along with appropriate scaling factors to simulate paediatric data. Different study designs can be evaluated and optimised, and the optimal dose can be defined based on prior information in adults or children (study optimisation tool). When paediatric data are available, models can be used to infer results from limited sample sizes which minimises the experimental burden in children (data analysis tool). This enshrines important principles of research ethics in that as few children as possible should be exposed to risk (for example of a new drug), and invasive procedures such as blood sampling. Blood sampling should be performed in the least distressing way and using the fewest possible number of samples. This is particularly important in children who hate needles!

The basic element of a paediatric development plan is whether or not extrapolation of efficacy is possible from adults or paediatric groups; safety cannot be extrapolated. The key questions for the extrapolation is the similarity of the disease, the similarity of response and the availability of PD measurements considered surrogates for efficacy. Models are useful tools but in principle the main weight of evidence on which extrapolation is based comes from clinical data. Also the regulatory criteria for accepting extrapolation and/or modelling as pivotal evidence may take into account factors other than the ones mentioned in the decision tree (e.g. like feasibility of trials, ethical constraints and unmet medical need). A rule of thumb would be to follow the decision tree and propose the use of modelling and simulation when designing the development plan in children, but in the context of the specific product and clinical condition.

Examples of modelling and simulation use in Paediatric Investigation Plans

Example 1: Extrapolation from older to younger patients

At one end of the spectrum, the PIP case of an antiretroviral (HIV) drug (Drug Z) is presented. For HIV drugs PK and PD activity (viral load, CD4+ counts) are well recognised parameters for extrapolating efficacy data from adults to children ( 18). Factors that favour the extrapolation are the understanding of the target (HIV virus), the pharmacological effect and the disease "similarity" across different ages. In this context, modelling and simulation plays an important role in optimising the safety-PK/PD studies to support extrapolation and the analyses of the data generated in the studies. o How to make optimal use of the previous knowledge with the product? Sponsor's proposal:

The sponsor proposes single arm trials to evaluate the safety, tolerability and antiviral activity of drug Z using a staggered approach from older to younger children. PK and safety data from older groups will be available before progression to younger age groups. Efficacy seen in adults will be extrapolated to children on the basis of PK, PD (viral load, CD4+counts) and safety. For dose calculation the sponsor is targeting the same PK exposure showing activity in adults. Taking into account the differences in PK between adults and children and between different paediatric age groups the dose is adjusted to the paediatric population. Initially, the paediatric dose in the older age group (6-17 y) was defined by allometric scaling ( 19) based on the adult dose. Following the availability of PK data in the 6-17 age group modelling and simulation is to be used to describe exposure as a function of age and weight and to propose dose recommendations for the subsequent 2-6 years, 1-2 years and 2 months-1 year age groups.

The PK model will be updated as PK data from each group becomes available. The population PK model will be also used for power calculation and sampling optimisation of the PK trials in children. The optimisation of studies will be based on precision and bias of mean clearance. The model assumptions and dose recommendations will be verified by PK/PD data in children. Population PK/PD models will be used to analyse PK & PD data across different age subgroups.

PIP discussions:

The role of modelling and simulation was acknowledged by the Paediatric Committee and the extrapolation was agreed on the basis of PK, viral load and CD4+ counts.

Lessons learnt:

Modelling and simulation was agreed in the PIP opinion as a study optimisation and analysis tool. The sponsor guaranteed that model assumptions and doses would be confirmed by external paediatric data; this analytical process raised the weight of modelling and simulation in the regulatory assessment. In cases where similar disease progression and response to intervention is expected between adults and paediatric groups and there are good biomarkers for activity, PK-PD is a well-accepted surrogate for efficacy (Scenario 2).

Example 2: Extrapolation from another drug with similar mechanism of action authorised in adults and children, and from adult data with the new drug.

At the other end of the spectrum are drugs for the treatment of epilepsy( 20 ), which constitute a vast ensemble of diverse clinical sub entities that differ by age of onset, type of seizure, aetiological background, resulting handicap, prognosis and response to treatment. We present how adult data from new Clinical trials simulation will further optimise the design of the planned paediatric studies.

PIP discussions:

The critical role of modelling and simulation is particularly noted and appreciated. For partial-onset seizures (adjunctive treatment) extrapolation of efficacy from adults could be possible. However further investigations are required in paediatric epilepsy syndromes where extrapolation is not possible (Scenario 4).

Lessons learned:

State of the art modelling and simulation was proposed by the sponsor. Modelling and simulation was agreed in the PIP opinion as a study optimisation and analysis tool. All assumptions will be confirmed by observed external data providing extra assurance on the use of modelling and simulation. Of key importance is that modelling and simulation is a learning process that can be applied across different product developments.

Paediatric Investigation Plan database search to validate the drug development paradigm

In an effort to further "validate" the paradigm proposed, all positive PIP opinions, from July 2007 -January 2010 were manually screened for any reference to modelling and simulation. The non-confidential parts of the opinions are publicly accessible in the European Medicines Agency website( 21 ). Also the summary reports corresponding to positive opinions were checked for consistency and completeness of the information. The data were analysed based on the terms specified in Table 2 and Figures 1 &2. There were 47 out of 210 positive opinions that made explicit reference to modelling and simulation, which means that sponsors are also bound to the elements of the opinion with respect to M&S. Additional search in the summary reports (non-binding) of the positive opinions identified that opinions report approximately half of the M&S approaches used in PIPs. We classified the 47 opinions that made explicit reference to modelling and simulation by model type used according to the definitions in Table 2. Figure 3 represents the positive opinions by model type. Population PK models represented the majority of the models referenced in the positive opinions while exposure/Dose-Response models were rare. Modelling and simulation was mainly used for dose prediction, study optimisation and data analysis rather than for navigation through the paediatric decision tree.

Discussion

There is an increasing role of modelling and simulation in drug development and a commitment from regulators to adopt this technique to alleviate the burden of clinical experimentation in children. Whether the difference between opinions and summary reports is related to the relative weight of modelling and simulation in the PIP (only the minimum binding requirements are included in the opinion) or to other factors needs to be further clarified. Nevertheless, reference to modelling and simulation in the opinions constitutes a major evolution in regulatory thinking in that the PDCO binds the sponsor to conduct and report modelling and simulation to fulfil specific objectives. To further standardise the reporting and evaluation of modelling and simulation in PIPs, a structured and explicit presentation of models and simulations in PIP submissions is recommended.

Medicine developers and regulators are more familiar with the use of population PK and PBPK models; important research has been performed and is ongoing on the characterisation of the parameters that account for differences in PK between age groups. Further consolidation of the approach is however needed from both sponsors and regulators. The effect of age on PD targets is less understood and there is a lack of mechanistic models and biomarkers for PD responses. Disease models could not be identified in our dataset. This was not unexpected because these models are in their infancy for children. We anticipate that as the knowledge in the paediatric field accumulates, disease models will become more abundant in the literature and used in the PIPs more frequently. Safety models were also not identified. The lack of safety models in the pre-authorisation phase is mainly due to the fact that it is difficult to predict drug safety based on the pharmacological and toxicological properties of the medicinal products. The safety models available in the literature consist of statistical non-mechanistic models based on large postmarketing datasets. Further advancement of disease and safety models will probably stem from progress in the field of systems biology 22 . Current research in areas such as genomics, transcriptomics, metabolomics, proteomics, using high throughput techniques, generate enormous quantities of new data that can be used to develop and validate models and biomarkers. Links between such data and models with medicinal development and clinical practice is challenging, although some of these methods have already found applications: e.g. investigation of the effect of genetic polymorphism of cytochromes on the efficacy and safety of medicinal products 23 , determination of genetic variants that inform on the positive or negative disease prognosis and treatment outcome. Ultimately these platforms could shift the focus of medicinal development from disease -age specific endpoints to biological system specific safety and efficacy biomarkers that could be qualified along with mathematical functions as surrogates for clinical efficacy and safety. Even if this area is in its infancy, the European Medicines Agency has created a forum for such discussions with the qualification of novel methodologies procedure ( 24 ).

The use of modelling and simulation as a tool to navigate through the different paediatric decision tree scenarios was not identified in the search performed. The authors believe that the lack of disease and mechanistic response models is one explanation. These models are needed to provide the quantitative platform to answer the key question on the similarity of disease and disease progression between different age groups (Figure 1: Question 1 & 2). Models are used to compare concentration response between age groups in order to inform dose finding but not to define an extrapolation strategy (Figure 1: Question 3). This is probably due to the fact that when designing the PIP there are not enough PK/PD data from the product in the paediatric population to enable model building for robust and sensitive PK/PD comparisons.

The lack of PD and efficacy data in paediatric populations is also the reason for not identifying models used to quantify the relationship between PD biomarkers and efficacy in order to decide on the need to conduct PK/PD or efficacy trials (Decision tool: Question 4). This is because navigation through the decision tree is based on clinical practice and quantification of the differences between age groups is of secondary importance. By implementing models in drug development and accumulating knowledge in databases and models this paradigm might also be shifted from experience based to a more integrative and quantitative approach of all data sources available.

The paradigm proposed in our previous publication is applicable to real case PIPs. Modelling and simulation is gaining weight in PIPs and is well recognised by industry and regulators as a study optimisation and data analysis tool. Modelling and simulation facilitates the extrapolation of efficacy between different age groups but the critical data (PK, PD, safety and efficacy) generally come from well conducted clinical studies following largely the principles described in the paediatric decision tree. The weight of modelling and simulation in the regulatory assessment is ultimately judged on clinical grounds (e.g. feasibility, ethical issues, prioritisation of studies, unmet medical need); but there has been a major shift in regulatory thinking where modelling and simulation had traditionally been seen as exploratory analysis, performed under the responsibility of the sponsor. The regulators are eager to expand use of modelling and simulation to elucidate safety issues, to evaluate the effects of disease (eg. renal or hepatic dysfunction) and to qualify mechanistic models that could help shift the current medicinal development paradigm. Parameterisation of data, in a way that it can be easily understood and reported. A dataset includes intrinsically all the information but cannot be used as such in order to enhance the understanding of a drug and make decisions. Usually some king of statistical analysis is required.

F

Models can explain the different sources of variability and describe the data mechanistically.

Graphical display of data, which makes decision making easier and information visible also to non pharmacometricians.

Possibility to simulate clinical trials based on different scenarios (set of assumptions). The simulations can help decision making and optimise future trials.

Models can be used to drive inference minimising the number of individuals and samples needed in clinical trials. For example sparse sampling using population PK models is well established in paediatric trials and alleviates the burden of full sampling in children. It should be highlighted that although the use of M&S for data analysis is not controversial in the case of exploratory analyses, it is disputable from a regulatory perspective when used for pivotal efficacy claims. Each scenario requires a thorough understanding of the pathophysiology of the disease, the developmental differences between adults and children that could have an impact on PK, PD safety and efficacy and of the mechanism of action of the drug. Any doubt will make extrapolation of the efficacy questionable.

Scenario 1: Extrapolation of efficacy is possible between adults and children based on achieving similar PK between the two populations. In practice we assume similar therapeutic effect with the same PK exposure.

Scenario 2: Extrapolation of efficacy is possible between adults and children based on achieving similar PD between the two populations. In practice we assume similar therapeutic effect with the same PD value.

Scenario 3: Extrapolation of efficacy is not possible between adults and children even if disease progression and response to intervention can be considered similar. The reason for this is that there is not a PK or PD measurement that can be used as a surrogate for efficacy.

Scenario 4: Extrapolation of efficacy is not possible because the disease and response is not considered similar between adults and children.

The scenarios are described for extrapolation between adults and children but can be also applicable for extrapolation between different paediatric age groups.

oo

  Treatment of HIV-1 infection in antiretroviral treatment-experienced adolescents and children. Data available at the time of PIP submission: o Adult treatment efficacy and safety data. o PK data in children (older age group 6-17 yrs). Challenges: Paediatric dose? o What kind of data for an indication in children?

o

  be used to optimise the design of clinical paediatric trials for drug N supported by information on a similar product A authorised in both adults and children. New Drug (N) Target Indication: o Partial-onset seizures (adjunctive treatment). Data available at the time of PIP submission: o New Drug (N) o Adult Phase II data in partial-onset seizures (adjunctive treatment). o Existing Approved Drug (A) o Adult efficacy and safety data in partial-onset seizures partial-onset seizures (adjunctive treatment). o Children efficacy and safety data in partial-onset seizures (adjunctive treatment). o Same mechanism of action between A and N. o Different Potency between A and N. Challenges: Paediatric dose? o What kind of data are required for partial-onset seizure indication in children (adjunctive treatment)? o How to make optimal use of the previous knowledge with the new drug N in adults and similar product A? Sponsor's proposal: Dose-ranging studies and placebo-controlled safety and efficacy trials in children with refractory partialonset seizures are planned to support the indication. Interrelation and the extrapolation of the disposition properties and of the exposure-response relationship between adults and children are investigated using a sophisticated modelling and simulation approach. A dose-response (reduction in seizure frequency) model is used to predict the effective dose range in children using seizure data with drug A in adults and children, of new drug N in adults with partial-onset seizures. This model is validated by concordance of predicted and observed effective doses of drug A in children. A PBPK model is used to predict the PK of the new drug N in children. This model is validated by concordance of observations and predictions for drug A in adults and children and for new drug N in adults. A population PK model is used to analyse the PK data for drug N in children when available and confirm the PBPK model predictions.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: M&S role in PIPs. 247x174mm (96 x 96 DPI)

Table 1 :

 1 Properties that make models particularly useful in drug development Possibility to compile data from different sources (in vitro, in vivo, literature, in house, different population, different medicinal product with similar mechanism of action).

Table 2 :

 2 DefinitionsTerms used in the document related to the paediatric regulation: PDCO: The Paediatric Committee (PDCO), a multidisciplinary scientific expert committee, was established at the European Medicines Agency in 2007. It is primarily responsible for the assessment and agreement of PIPs and waivers.

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Table 3 :

 3 Paediatric decision tree scenarios

PIP:

The Paediatric investigation plan, including information on the quality, preclinical and clinical development, as proposed by the sponsor. The plan is discussed modified and agreed or refused by the PDCO.

Summary report ( 18): The report generated by the European Medicines Agency on the evaluation of the PIP application. The report includes comments on the PIP and a scientific discussion on the different issues. The summary report is not binding to the sponsor.

Positive opinion: An opinion from the paediatric committee that agrees on a PIP. The opinion on a PIP includes the minimum requirements (key binding elements) to demonstrate the quality, safety and efficacy of the medicinal product in the paediatric population. The opinion is binding to the sponsor.

Models definition based on Manolis and Pons (2009):

PBPK models are mechanistic models mathematically transcribing anatomical, physiological, physical, and chemical descriptions of the phenomena involved in the complex absorption, distribution, metabolism and excretion (ADME) processes.

PBPK-PD: PBPK models incorporating also a pharmacodynamic component.

POP-PK: Data-driven compartmental models that describe the dose-concentration relationship by combining structural, statistical and random components to address different sources of variability.

POP-PK/PD: POP-PK models including also a link between exposure and PD effects.

Disease models: (Mechanistic) models describing the natural course of the disease.

Response models: Response to intervention defined as a change in clinical endpoints vs. dose or PK exposure.

Kinetic (K)-PD models: PD models developed for the description of drug action kinetics in the absence of drug concentration measurements.

Toxicity/adverse events models (safety models): Describe the safety of the medicinal product as a function of the PK exposure or dose.

Other terms:

Biomarkers: Measurable characteristics that are indicators of normal biologic processes, pathogenic processes, and/or response to therapeutic or other interventions (adapted from ICH Topic E15( 19)).

Surrogate endpoint:

Biomarker intended to substitute for a clinical endpoint.

Extrapolation is the use of data (in vitro, in silico, PK, PD, safety, efficacy) acquired in one population and/or experimental setting to make inference about another population of interest.