Features of the various types of ceramic water filter presses reviewed in this study.
\r\n\tThis book intends to cover major mineral deficiency problems such as calcium, iron, magnesium, sodium, potassium and zinc. These minerals have very important task either on intracellular or extracellular level as well as regulatory functions in maintaining body homeostasis.
\r\n\r\n\t
\r\n\tBoth macrominerals and trace minerals (microminerals) are equally important, but trace minerals are needed in smaller amounts than major minerals. The measurements of these minerals quite differ. Mineral levels depend on their uptake, metabolism, consumption, absorption, lifestyle, medical drug therapies, physical activities etc.
\r\n\tAs a self-contained collection of scholarly papers, the book will target an audience of practicing researchers, academics, PhD students and other scientists. Since it will be published as an Open Access publication, it will allow unrestricted online access to chapters with no reading or subscription fees.
",isbn:"978-1-83881-085-6",printIsbn:"978-1-83881-081-8",pdfIsbn:"978-1-83881-086-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8bc7bd085801296d26c5ea58a7154de3",bookSignature:"Dr. Gyula Mozsik and Dr. Gonzalo Díaz-Soto",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8935.jpg",keywords:"Calcium, Iron, Magnesium, Potassium, Sodium, Zinc, Diagnostic tools, Treatments, Food Fortification, Malnutrition, Metabolic Disorders, Lifestyle",numberOfDownloads:741,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 26th 2020",dateEndSecondStepPublish:"June 16th 2020",dateEndThirdStepPublish:"August 15th 2020",dateEndFourthStepPublish:"November 3rd 2020",dateEndFifthStepPublish:"January 2nd 2021",remainingDaysToSecondStep:"9 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus of Medicine at Univesity of Pecs, Hungary, and recipient of Andre Roberts award from the International Union of Pharmacology in 2014. He published 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, and edited 32 books.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.jpg",biography:"Gyula Mózsik, MD,PhD, ScD(med) is a professor emeritus of medicine at First Department of Medicine, Univesity of Pécs, Hungary. He was head of the Department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition. His research fields are biochemical and molecular pharmacological studies in gastrointestinal tract, clinical pharmacological and clinical nutritional studies, clinical genetic studies, and innovative pharmacological and nutritional (dietetical) research in new drug production and food production. He published around 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, 32 edited books. He organized 38 national and international (in Croatia ,France, Romania, Italy, U.S.A., Japan) congresses /Symposia. He received the Andre Robert’s award from the International Union of Pharmacology, Gastrointestinal Section (2014). 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Once a lead compound has been discovered from a chemical screen, a drug designer can design a series of structural analogues with improved pharmaceutical properties. The fundamental principle behind similarity-based drug discovery is the “chemical similarity principle,” which states that if two molecules share similar structures, then they will likely have similar bioactivities. While there are exceptions, correlation between chemical structure and compound activities has been well established in medicinal chemistry [3]. Consequently, determining whether two molecules are structurally similar is a prerequisite for similarity-based drug discovery. At a rudimentary level, the similarity between two ligands can be easily discerned through visual inspection by identifying common functional groups, structural motifs, or substructures. However, human intervention is often subjective and not suitable for large-scale analysis. Thus, applying computational algorithms for unbiased chemical similarity comparison and database searching is essential for a successful drug discovery campaign.
\nSeveral computational chemical similarity search algorithms have been developed [1, 4, 5]. The most commonly used approaches use chemical substructure fingerprints. Non-hashed structural fingerprints such as MACCS keys or Obabel FP3 fingerprints detect predefined substructures or functional group patterns in a molecule by mapping common chemical motifs into binary arrays known as structural keys. To compare the chemical similarity between two molecules, each molecule is converted into a binary series of 0 and 1, indicating the absence and presence of a particular substructure. On the other hand, chemical hashed fingerprints such as Daylight fingerprints or Obabel FP2 fingerprints use path information derived from molecular graphs to compare chemical structures [4]. While path-based fingerprints usually confer higher specificity, structural fingerprints can nevertheless be useful for detecting hits with distinct chemical scaffolds. Once the chemical fingerprints have been determined in a chemical search and the molecules have been converted to appropriate data representations, the next step is to evaluate the chemical similarity using a distance metric. Common distance measures include Euclidean, Manhattan, and Mahalanobis metrics, which have been widely applied in chemoinformatics and bioinformatics applications [6]. However, in the case of binary chemical fingerprints, the simplest and most direct distance measure is the Tanimoto index. Tanimoto metrics calculate the fraction of shared bits between chemical fingerprints in the range of 0–1. Although there is no universal Tanimoto index cutoff (Tc) to determine whether two molecules are sufficiently similar, a Tc value of 0.7 is a reasonable starting point for most chemical searches. Alternatively, statistical scores such as a Z-score can also be calculated based on the overall Tc score distribution [7].
\nIn addition to 2D fingerprints, 3D chemical similarity fingerprints have also been developed. 3D chemical similarity fingerprints utilize the 3D structural information of the ligands such as molecular shape, pharmacophore points, or molecular interaction fields (MIF) for structural similarity comparison. Although 3D chemical similarity comparison can often capture structural features essential for protein-ligand binding, 3D alignment algorithms often require extensive optimization procedures to maximize the overlapped volume and are computationally intensive. Alternatively, nonalignment methods based on chemical descriptors such as GETAWAY or 3D-MoRSE descriptors can also be used [8, 9]. The 3D chemical descriptor is capable of capturing 3D ligand properties from 2D information and may improve computational time. However, substantial postvalidation may be required to confirm 3D structural similarity.
\nThe application of chemical similarity searches for ligand bioactivity prediction has recently gained substantial interest [10]. Due to the high failure rate of many new chemical entities (NCE) in the late stage of clinical trials, understanding on- and off-target binding of a drug to predict mechanisms of action and adverse reactions has become crucial for drug discovery programs [11]. If the chemical structure of a compound is known, then it is possible to predict compound bioactivities based on the chemical similarity methods described previously. Drug targets can be inferred from bioactivity databases with annotated targets sharing the highest similarity to the target molecules. Many public bioactivity databases are freely available and can be applied to this application including ChEMBL, PubChem, DrugBank, and Binding Database to name a few [12–14].
\nThe simplest approach for drug target inference is by a simple chemical similarity search where the target of a query compound is inferred from the annotated ligand sharing the highest chemical similarity (Figure 1). However, there are several limitations to this approach. First, target information for the reference molecules may be incomplete; thus, target inference from a single molecular entity can miss potential targets from molecules sharing lower chemical similarity. Likewise, pair-wise target predictions may not provide consistent predictions for a group of structurally similar ligands. Second, chemical similarity values are not effective at ranking on and off targets and do not consider the structure-activity relationship (SAR) of congeneric series. Most importantly, simple ligand-based searches cannot be applied to analyzing large numbers of ligands such as the unannotated hits from a chemical screen. To circumvent this shortcoming, we recently proposed a new network target inference approach based on chemical similarity networks called chemical similarity network analysis pull-down (CSNAP) [15].
Chemical similarity search using 2D chemical fingerprints.
CSNAP uses a network-based algorithm to predict drug targets and does not rely on absolute chemical similarity values. It utilizes a scoring function (S-score) to find the consensus targets of a ligand in its nearest neighbors in a chemical similarity network, which is similar to that used to predict protein functions in a protein-protein interaction (PPI) network [16]. CSNAP is compatible with publicly available bioactivity databases, and we routinely use the ChEMBL database, which is one of the largest bioactivity databases that contains more than 1 million compounds with known target annotations. The original CSNAP algorithm applies 2D Obabel chemical similarity fingerprints (FP2, FP3, FP4, and MACCS) for target predictions. More recently, we developed CSNAP3D that combines 2D and 3D chemical search algorithms to improve the chemical search space [17]. CSNAP3D uses a fast 2D chemical similarity search using either FP2 or FP3 fingerprints to identify sets of hit molecules from large compound databases, and hit molecules are rescored using a combination of 3D similarity descriptors based on a combination of shape and pharmacophore. From our benchmark studies, we found that the CSNAP computational framework was highly accurate and was capable of analyzing large compound sets with diverse chemical structures. Consistently, the CSNAP application has been recently extended for large-scale metabolite analysis [18]. We have made the CSNAP algorithm freely available as the CSNAP web and it can be accessed at http://services.mbi.ucla.edu/CSNAP/.
\nMathematically, a chemical similarity network can be considered as a graph G (V, E) where the vertex V represents compounds and the edge E represents chemical similarity and connects two compounds if they share a chemical similarity above an arbitrary threshold [19]. The CSNAP algorithm is performed in three steps: (1) chemical similarity database search, (2) chemical similarity network construction, and (3) drug target scoring and inference.
\nChemical similarity searching is the first step in the CSNAP algorithm (Figure 2A). The chemical similarity comparisons are performed using various 2D Obabel fingerprints including FP2, FP3, MACCS, and others. Query compounds prepared in SMILES or SDF formats are used as inputs to the CSNAP program. The compounds are searched sequentially against the ChEMBL database. To identify the ChEMBL compounds most similar to the query, the relative chemical similarity score is quantified by a Z-value relative to the distribution of the top 100 chemical similarity values. The ChEMBL compounds with a Z-score >2.5 are selected and serve as the annotated compounds for target inference in the next step.
\nTo generate chemical similarity networks, pair-wise chemical similarity values are evaluated between every pair of compounds. A network edge is established between two ligands whenever their similarity value is above a predefined threshold (>0.7) (Figure 2B). When large compound data sets are analyzed by the CSNAP algorithm, structurally diverse compounds are partitioned into subnetworks of distinct chemical scaffolds, known as “chemotypes.” The chemical similarity networks can be used to estimate the chemical diversity of input structures at this stage.
\nCSNAP infers drug targets using consensus statistics. Specifically, drug targets in the first neighbor of the query are identified and ranked based on their target annotation frequency (Figure 2C). A consensus score called an S-score is used to rank the probability that the predicted target will interact with the query ligand (Figure 2D). There are several advantages of using this network-based scoring function. First, the S-score eliminates the possibility of missing target information from the nearest neighbor and considers all possible targets within the same congeneric series. Second, since the drug target is inferred from the target consensus and is in agreement with the observed structure-activity relationship, the robustness of the prediction increases substantially. From our benchmark studies, we showed that this network-based target inference approach improves the prediction success rate over traditional approaches that use simple chemical searches [15].
\nChemical similarity network analysis pull-down (CSNAP) algorithm for large-scale drug target prediction. (A) Two-dimensional chemical search of three query ligands (green, red, and yellow) identified nine reference compounds from the bioactivity database. (B) Chemical similarity networks clustered compounds into subnetworks corresponding to three major chemotypes. Note that reference compounds interact with four distinct targets. (C) An S-score based on consensus statistics is applied to rank the most probable target based on the target annotation frequency of the first-order neighbor targets. (D) On and off targets are differentiated by ranking the predicted S-scores.
To reduce concept to practice, we constructed a CSNAP web server for large-scale target prediction and drug discovery. The CSNAP web includes a front-end graphical user interface (GUI) that provides user interaction and output visualization, while target prediction is performed at the back-end by running the CSNAP algorithm.
\nThe CSNAP web server accepts two ligand input formats: SDF and SMILES, which are two of the most commonly used molecular formats that handle large compound databases. In addition, a JME molecular editor is also included, which can be used to convert a chemical structure to a SMILES string on the fly (Figure 3A). Several chemical fingerprints are provided to perform chemical comparisons during the search and network clustering steps, including Obabel FP2, FP3, PF4, and MACCS fingerprints (Figure 3B). Obabel FP2 fingerprints use a path-based algorithm and are more specific than FP3, FP4, and MACCS that utilize a predefined set of substructures for chemical searches. On the other hand, when structural analogues are not available in the chemical database, FP3 can instead be used to search structurally distinct chemicals with similar bioactivities. To perform chemical searches, the chemical similarity cutoff needs to be defined. Here, CSNAP web supports a combination of absolute cutoff based on Tanimoto coefficient (Tc > 0.7) and relative chemical similarity cutoff based on a Z-score. From our experience, the default option using a Z-score cutoff of 2.5 will be optimal for most initial CSNAP predictions.
\nOnce the query ligands and chemical search parameters have been defined, the CSNAP algorithm will search the ChEMBL compound activity database to identify structurally similar compounds for target inference (Figure 3B). The ChEMBL database assigns targets to a compound based on the level of target specificity (confidence score). Similarly, the compounds are also classified based on the assay type from which they are derived, including biochemical, functional, or ADMET assays. These database parameters will also need to be selected to perform the CSNAP analysis.
\nThe CSNAP output page consists of three main panels: (1) chemical similarity networks, (2) chemical structure information, and (3) ligand-target interaction fingerprint (LTIF) (Figure 3C).
\nThe chemical similarity networks panel displays the generated chemical networks using the CSNAP algorithm based on input ligands. The chemical similarity network connects query (red) and annotated ligands (gray) from the ChEMBL database, and the targets are inferred using consensus statistics. For large compound sets, the number of generated chemical clusters can be used to estimate the chemical diversity of the sets. To retrieve additional information regarding a specific ligand, the user can click on the node and the relevant information will be displayed in the chemical structure information panel.
\nThe chemical structure information panel displays the chemical information selected from the chemical similarity network panel. The panel consists of several columns that include chemical structure information (chemical ID, chemical structure, SMILES string, InChI key) and the predicted target information (target name and UniProt ID). In the ChEMBL prediction column, the predicted targets of each compound are ranked by the S-score.
\nDrug target prediction using the CSNAP web server: (A) construct a query molecule using the JME molecular editor. (B) The molecule is converted to a SMILES string and entered into the CSNAP query submission page. (C) The CSNAP output page consists of three main panels: (1) the chemical similarity network, (2) the chemical structure information, and (3) the ligand-target interaction fingerprint (LTIF).
To analyze the results from large-scale target prediction searches, the ligand-target interaction fingerprint (LTIF) is provided in the CSNAP web output. The LTIF panel displays the predicted S-score of each compound mapped against the predicted targets, and the color intensity of the LTIF heatmap is correlated with the S-score values. The LTIF can be used to infer compounds sharing similar target binding profiles, which may have similar bioactivity. By clicking on the LTIF button at the top of the LTIF panel, a separate window is created that shows the target spectrum and Gene Ontology (GO) term search derived from the LTIF analysis.
\nTo further differentiate primary targets from off-targets in the LTIF, the CSNAP web also computes a target spectrum, by summing the S-score (∑S) of all analyzed compounds for each target column (Figure 4). For a single compound analysis, the highest peak corresponds to the primary target. Similarly, for multi-ligand analysis, the highest peak corresponds to the most abundant target in the set. To determine the functional role of the predicted targets, the Gene Ontology (GO) search result is also provided (Figure 4). GO is a popular bioinformatics tool that maps genes into functions based on controlled vocabulary (GO terms) and has been widely used for pathway analysis of functional genomic data [20]. Here, CSNAP web incorporates a GO search in target predictions as a strategy for posttarget selection and validation. The GO terms can be used to further select relevant targets in a cell-based or phenotype-based screen based on the knowledge of anticipated molecular etiology including cellular components, molecular functions, and biological process. Smaller subsets of targets can then be filtered for additional experimental validation.
Posttarget validation using Gene Ontology (GO) analysis in the CSNAP web. (Top) Target spectrum. (Bottom) GO search results.
The CSNAP algorithm was validated using 206 known drugs from the directory of useful decoy (DUD) set [21]. The benchmark set included 46 angiotensin-converting enzyme (ACE), 47 cyclin-dependent kinase 2 (CDK2), 23 heat-shock protein 90 (HSP90), 34 HIV reverse-transcriptase (HIVRT), 25 HMG-CoA reductase (HMGA), and 31 poly [ADP-ribose] polymerase (PARP) inhibitors. Using the default search criteria (fingerprint: FP2, Tc = 1, Z-score = 2.5), we evaluated the ability of the CSNAP algorithm to accurately predict the designated targets of each compound based on the S-score rankings. CSNAP analysis of the 206 compounds showed that the chemical similarity network clustered the drugs into distinct subnetworks, corresponding to diverse chemical scaffolds (chemotypes) (Figure 5). For a given subnetwork, the S-score was further used to predict the drug target of each compound based on their network connectivity with the reference ligands. The prediction results were then compared with those obtained by the similarity ensemble approach (SEA) [22]. The CSNAP algorithm gave an overall 80–94% true-positive prediction rate (TPR) in comparison with SEA (63–75%) based on the top 1, top 5, and top 10 ranking of target predictions. In particular, CSNAP substantially improved the target prediction rate for promiscuous ligands such as CDK2 and ACE inhibitors (92 and 96%) compared to the SEA approach (30 and 65%) (Figure 6).
CSNAP2D clustering of 206 benchmark compounds consisting of six known drug classes from the directory of useful decoy (DUD) set.
Performance comparison between CSNAP and SEA. CSNAP achieved improved prediction accuracy (TPR) for promiscuous drug classes like ACE, CDK2, and HIVRT inhibitors.
We applied the CSNAP algorithm to predict the drug targets of a set of 212 compounds that were inhibitors of cell division [23]. CSNAP clustering of the mitotic compounds resulted into 85 chemical similarity subnetworks (Figure 7A). To identify the most common targets within the set, we applied the LTIF analysis. The target spectrum derived from the LTIF revealed four broad classes of mitotic targets including fatty acid desaturases (SCD, SCD1, and FADS2), ABL1 kinase, non-receptor-type tyrosine phosphatases (PTPN7, PTPN12, PTPN22, PTPRC, and ACP1), and beta tubulins. In particular, the target spectrum showed that beta tubulin had the largest peak height and was the most prominent protein target for the mitotic compounds. Further analysis showed that 51 compounds were associated with tubulin-targeting chemotypes. The predicted drug targets were validated by comparing siRNA-treated and drug-treated mitotic phenotypes in cell culture using immunofluorescence microscopy. In addition, in vitro tubulin polymerization assays were used to determine the effects of these compounds on microtubule formation. Among the 51 tested compounds, 31 compounds showed a perturbation of microtubule polymerization >25%, and thus, the CSNAP algorithm achieved a prediction accuracy of >70%.
Discovery of novel tubulin-targeting drugs (compounds 1–7). (A) CSNAP analysis of 212 mitotic drugs identified seven novel tubulin destabilizing compounds. (B) The compounds induced a G2/M cell cycle arrest and decreased cell viability in cell-based assays. (C) Discovery of compound 3 as the most potent compound in the series. (D) A mass spectrometry-based competition assay was used to show that compound 3 and podophyllotoxin (PPT) competed for binding to tubulin in contrast to vincristine (VCT). (E) Compound-treated (1–3) and colchicine-treated (COL) cells displayed a tubulin destabilizing effect in comparison with the tubulin stabilizing effect of taxol (TX).
Using a negative selection strategy, we identified seven novel tubulin-targeting agents that were active in our tubulin polymerization assay but had not been associated with known tubulin chemotypes (Figure 7A). The seven compounds were analogues of phenyl-sulfanyl-thiazol-acetamide scaffolds that exhibit various degrees of tubulin destabilizing effects through a mechanism similar to that of the tubulin destabilizing agent colchicine. The most potent compound, compound 3, in the series exhibited a cytotoxic effect in the nano-molar range (EC50: G2/M = 33 nM; EC50: cell death = 60 nM) when evaluated in cell viability and G2/M arrest assays (Figure 7B, C) [15]. The predicted mechanism was validated using a mass spectrometry-based competition assay where both the selected analogues and podophyllotoxin, a known colchicine site binder, competed for binding to tubulin in contrast to the negative control vincristine that interacted with a distant site in beta tubulin (Figure 7D) [24]. Likewise, both compound-treated and colchicine-treated cells displayed a tubulin destabilizing phenotype, characterized by rapid shortening of microtubule length and the disappearance of microtubule polymer mass (Figure 7E).
\nBinding mechanism of a novel tubulin destabilizing chemotype (compounds 1–7). (A) Pharmacophore alignment between compound 1 and colchicine (COL) showed a consensus pharmacophore. (B) Docking of compound 1 in the colchicine site using the tubulin crystal structure (PDB code: 1SA0) revealed colchicine-like interactions with critical residues (Met259, Cys241). (C) Docking of the seven analogues into the colchicine site showed similar interactions.
To investigate how the novel antimitotics interacted with beta tubulin, we performed structural alignments between compound 6 and colchicine and identified a consensus pharmacophore between the two molecules (Figure 8A). Further docking of compound 6 into the colchicine binding site also showed that both compound 6 and colchicine interacted with common residues, including the 2 and 10 methoxy groups and 9-keto group that interacted with Met259 and Cys241 of beta tubulin, respectively (Figure 8B). Similarly, all seven analogues docked into the same site through similar interactions. Interestingly, the elucidated binding modes could be used to explain the observed SAR. For example, the increased potency of compound 7 and 8 in comparison with 6 could be attributed to the hydrophilic interactions between the N-propyl and N-phenyl groups with Leu248 and Lys352 within the subpockets of the colchicine binding site (Figure 8C).
\nChemical similarity searches based on 2D chemical structures have several limitations. First, compounds with distinct scaffolds can exhibit similar bioactivity due to “scaffold hopping” by interacting with a common receptor [25, 26]. Second, although 2D fingerprints based on substructure or fragment searches have the potential to detect scaffold hopping, the scaffold enrichment rate is low. Furthermore, 2D searches do not capture essential features of protein-ligand interactions in three-dimensional space. Consequently, 3D chemical searches based on the three-dimensional information of the ligands will offer additional opportunities to discover novel compounds.
\nThe most common approach to compare ligand similarity in 3D is by shape superposition, which maximizes the Gaussian volume overlap between two ligands [27]. Alternatively, ligand alignments that use molecular interaction field (MIF) or pharmacophore have also been proposed [28, 29]. These approaches take into account the shared chemical features arranged in three-dimensional space. To identify the optimal 3D chemical descriptors, we performed an unbiased screen of diverse 3D chemical descriptors based on molecular shapes or pharmacophores. Using 206 benchmark compounds from the DUD set, we tested the ability of each 3D descriptor to enrich class-specific scaffolds ranked by respective similarity scores. The lowest energy conformer of each ligand was generated using the MOE program. The results showed that 3D chemical descriptors using a combination of shape and pharmacophore features achieved the highest enrichment rate and ligand alignment accuracy compared to those based on shape or pharmacophore alone. This observation agrees with our current understanding that shape complementary and chemical matching are essential for the protein-ligand binding process.
\nWe subsequently developed a 3D chemical similarity search method called “ShapeAlign” that utilized two open-source softwares: “Shape-it” and “Align-it” [30]. Similar to the combo score implemented in the ROCS program, the ShapeAlign algorithm also used a combination of shape and pharmacophore for 3D chemical searches. However, ShapeAlign incorporated a 2D fingerprint similarity score as an integral part of the searching process. Given a ligand with a pre-generated 3D conformation, the ShapeAlign algorithm first detects ligands from the chemical database with the highest shape matching evaluated by a shape Tanimoto index. The hit molecules are then aligned and rescored according to the degree of pharmacophore matching using the Align-it program.
\nWe incorporated the “ShapeAlign” algorithm into our CSNAP program called “CSNAP3D” to cluster chemical structures and predict drug targets based on 3D ligand similarity. To evaluate CSNAP3D performance, we assessed the average true-positive rate (TPR) and false-positive rate (FPR) of predicting drug targets for the 206 benchmark compounds. The result showed that CSNAP3D achieved a TPR of >95% at 0.85 Tanimoto cutoff in comparison with other 2D target prediction approaches including CSNAP2D, SEA, and PASS approaches [17]. A comparison of CSNAP3D and CSNAP2D generated networks showed that diverse 2D scaffold subnetworks were clustered into smaller subsets of 3D chemical networks, suggesting that CSNAP3D could be used to identify scaffold hopping ligands not identifiable by conventional 2D methods (Figure 9).
CSNAP3D clustered 34 distinct HIVRT NNRTI chemotypes into a shape-based chemical similarity network. The figure shows that many NNRTIs are scaffold hopping ligands to a common nucleotidyltransferase binding site. The 3D alignment between ligands was based on molecular shape and pharmacophore points (HD: hydrogen donor, HA: hydrogen acceptor, AR: aromatic, LP: lipophilic).
As further validation, we presented a case study of predicting targets for a set of HIVRT inhibitors using the CSNAP3D algorithm. HIVRT inhibitors can be classified as nucleoside-based analogues (NRTIs) or non-nucleoside-based analogues (NNRTIs) [31]. In particular, NNRTIs have been difficult drug classes for computational dug target prediction due to the chemical diversity of the drug classes where many compounds are scaffold hopping ligands that bind to a common nucleotidyltransferase binding site. Although 3D ligand-based target predictions that use either the alignment or nonalignment methods have been attempted, many of these approaches yielded low predictability. Here, we applied CSNAP3D to predict the drug targets of 34 structurally diverse HIVRT NNRTIs and compared the prediction results with the CSNAP2D approach (Figure 9). Initial 2D chemical similarity network analysis clustered the 34 NNRTIs into 20 structurally diverse chemical similarity scaffolds. Further LTIF analysis, by mapping target prediction S-scores to the heatmap, showed that more than 20 compounds did not have a prediction. The NNRTIs were similarly analyzed by the CSNAP3D program using the ShapeAlign algorithm. In contrast, all the 34 ligands were clustered by CSNAP3D into a single shape-based chemical similarity network, suggesting that many NNRTIs are scaffold hopping ligands to a common binding site. LTIF analysis showed that 33 of NNRTIs were correctly predicted, thus achieving a TPR of >97%. In particular, 3D chemical similarity networks correctly identify three FDA-approved NNRTIs, namely efavirenz, nevirapine, and tivirapine whose structure alignment agreed with previous crystal structures and SAR studies (Figure 9). In addition, several novel scaffold hopping pairs were also identified (Figure 9).
Taxol (paclitaxel) is a well-known anticancer natural product derived from the Yew tree, whose antiproliferative effect was first discovered in 1960s from an NCI anticancer drug screening campaign [32]. Taxol has since been found to be effective for treating a wide range of cancers including ovarian, breast, lung, bladder, prostate, melanoma, esophageal, and other solid tumors. However, the efficacy of taxol has been limited by severe side effects, toxicity, and synthetic feasibility. Thus, identification of low-weight taxol mimetics with more tolerable drug profiles is critical. While several taxol mimetics have been discovered including Synstab B and GS-164, both discovered by chemical screening, their binding mechanisms have remained undetermined [33, 34].
\nHere, we sought a rational approach to discover taxol mimetics using the CSNAP3D algorithm based on our existing structural knowledge of the original taxol molecule. CSNAP3D analysis of the 212 mitotic compounds from a chemical screen identified 42 potential taxol mimetics linked to 30 taxol structural conformers. Seven predicted taxol mimetics were found to be true positives with a >25% fold change in optical density when tested in tubulin polymerization assays in vitro and four compounds shared a consensus chemotype by co-localizing within one chemical similarity subnetwork. The structural alignment of the four selected molecules with taxol showed that they shared a similar T-shape conformation despite a simpler scaffold (Figure 10A). Docking studies showed that the increase in microtubule polymerization activity could be attributed to the phenyl moiety of these ligands, which was capable of forming a pi–pi stacking interaction with the critical residue His229 within the taxane site (Figure 10B). Three of the compounds demonstrated cytotoxic and antimitotic effects in cell culture with a potency <5 µM. Similarly, all the compounds displayed a similar tubulin stabilizing phenotype, characterized by microtubule aster formation in immunofluorescence microscopy studies (Figure 10C).
Structure-based discovery of taxol mimetics. (A) CSNAP3D analysis of 212 mitotic compounds from a cell-based screen identified four low molecular weight taxol (TX) mimetic analogues. (B) Compound 8 (8) demonstrated a fast tubulin polymerization rate at 50 µM similar to taxol (Tax) at 5 μM in comparison with colchicine (Col). (C) Compound 8 displayed a tubulin stabilizing phenotype, characterized by microtubule aster formation in immunofluorescence microscopy studies.
Chemical similarity is an important concept in medicinal chemistry and drug discovery to identify similar compounds with improved bioactivities. Here, we have expanded on this concept to chemical similarity network theory, where descriptive network statistics and graph topology can be applied to large-scale analysis of chemical diversity, bioactivities, and target identification. To demonstrate the utility of this approach, we have implemented the CSNAP algorithm, which can be used for large-scale compound analysis and target predictions. Analogous to protein function prediction in PPI networks, we applied consensus statistics to identify the common targets of each query ligand. We showed that this scoring function outperforms several target prediction methods based on simple chemical similarity searches. To address the challenge of scaffold hopping, where structurally diverse ligands can potentially interact with a common receptor, we developed the CSNAP3D algorithm as a CSNAP extension. CSNAP3D searches chemical structure using the “ShapeAlign” protocol, which utilizes a combination of shape and pharmacophore descriptors. We found that CSNAP3D improves target prediction, particularly for challenging drug classes such as HIVRT NNRTIs that showed high structural diversity and are scaffold hopping ligands. Finally, we successfully applied CSNAP3D to rationally discover low molecular weight taxol mimetics, which exhibit a taxol-like anticancer mechanism and potentially possess improved transport and pharmacokinetic properties than its natural counterpart.
\nThe current CSNAP framework can be extended in several directions. For instance, consensus scoring can be expanded by considering higher-order neighbors, which has been demonstrated to improve prediction accuracy in PPI networks. Similarly, graph theoretical approaches based on maximum network flow and other global optimization approaches can be applied for target assignments [35]. To improve posttarget validation, high throughput functional genomics data can be incorporated to aid in the identification of critical targets relevant to a disease pathway. One example is multiplayer network approaches that integrate drug, target, and annotation interaction networks to enhance target predictions and validations [36]. While CSNAP3D has substantially improved the predictability of CSNAP2D, the algorithm is limited to receptors with bound ligands and the ligand alignment is based on the lowest energy conformer. This shortcoming can be circumvented by considering multi-conformer networks that correlate ligand conformation with target specificities. Likewise, pseudo-ligands generated as the mirror image of an orphan receptor can be considered for receptor deorphanization.
\nIn conclusion, chemical similarity networks are an emerging field in ligand-based drug discovery where the collective properties of a ligand can be easily dissected using descriptive network statistics and graph topology. Here, we presented a new network-based approach for drug discovery and target identification called chemical similarity network analysis pull-down (CSNAP) and a new CSNAP framework called CSNAP3D. The CSNAP computational framework represents a new concept in computational drug discovery with practical application in target identification and drug discovery. We anticipate that the CSNAP approach will stimulate further work in systems and network-based drug discovery that will aid in the discovery of novel drugs for the treatment of cancer and other important diseases.
\nWater is most essential for sustaining life and enhancing the quality of life, but it can transmit diseases. When adequate access to clean, safe water is lacking, incidences of waterborne diseases become rampant [1, 2]. Unsafe drinking water is one of the major causes of diarrhoeal diseases, which are known to be a leading cause of mortality globally especially in children aged five and below [3]. The 2015 WHO/UNICEF Joint Monitoring Programme (JMP) update reports that 69% of Nigeria’s population use improved drinking water sources, which are presumed to be safe [4]. However, due to non-functionality, unsustainability, and lack of proper maintenance of most improved water sources, they are often of non-satisfactory quality [1, 5]. Therefore, the reality is that a lesser percentage of Nigerians than presented actually have access to safe drinking water. Furthermore, even where there is access to safe water, because most of these water sources are not located on premises or piped directly into the houses, there is the risk of contamination in the process of collection, transportation, and storage, thereby leaving the initially safe water unsafe at the point of consumption [2, 6]. It is therefore essential to ensure water is safe for drinking at the point of consumption. Point-of-use water treatment implies any water treatment system that purifies water at the point of consumption and it involves effective treatment and safe storage. It has been identified as an important public health intervention which serves to reduce the faecal-oral transmission of diarrhoeal diseases [7].
Recent studies on point-of-use household water treatment systems, suggest that ceramic water filters are the most sustainable and lowest cost options for water purification in developing countries [8]. The essential raw materials, basically clay and combustible bio-wastes, required to make this technology available and accessible in Nigeria are locally available in large quantities. However, there is a wide knowledge gap in the exploration and development of the technology of manufacturing ceramic water filters in the country. As much as there exists a need for household water treatment method such as the ceramic water filters, not many manufacturers engage in the production of ceramic filters. The springing forth of many peri-urban settlements in many Nigerian cities like Akure leaves the nation fraught with an urgent need to explore innovative solutions to put an end in sight to the prevalent water-related health challenges.
While household water treatment and safe storage systems have been considered as effective, low-cost alternatives and a reliable means of achieving safe water at point of use, having shown to significantly reduce diarrhoeal prevalence [6, 7, 9]; very few potters engage in the making of the ceramic water filters. In Nigeria, there are two factories that currently produce ceramic water filters, although production is fraught with many challenges such as understanding the technology behind the working of the filtration system. The major challenge however, to the establishment of a ceramic water filter production facility is the acquisition of the filter press machine.
The ceramic filter press machine is the priority piece of equipment required in the production process of ceramic water filters [10, 11]. The filter press machine, which is mostly hydraulic operated, is used to form the filters into its shape by the application of pressure to the clay mixture in-between a set of moulds. This method of forming is most suitable for making ceramic water filters because a non-plastic material mix is desired and therefore can be only formed successfully by semi-dry pressing techniques. This all-important equipment for the production of ceramic water filters is quite expensive to purchase, with very high shipping and importation costs and tariffs.
Personal communications in a pilot study with operators of ceramic water filter factories in Nigeria reveals that the cost of acquisition of a piece of filter press machine with its corresponding aluminium moulds ranged from $3000 to $3500 (USD). This is also confirmed by other researchers [10], stating that the cost of this press is estimated at over $3000 and therefore is considered a fundamental limiting factor to production of ceramic water filters to meet demands in areas where it is needed. While the Resource Development International - Cambodia (RDIC) approximated the cost at $2300, excluding shipping and handling costs [12]. This is too high an investment cost for a start-up ceramic/pottery business to bear considering the economic conditions in the country. Therefore the only feasible option to the making of ceramic water filters in Nigeria, to improve access to safe drinking water at the point-of-use, is to resort to the design and fabrication of a filter press machine using locally available materials.
The Potters Without borders (PWB) is one of the organizations that have carried out research on ceramic water filters and design of hydraulic filter press machine [10]. The PWB filter press machine design was adopted for this study, whose objective was to design and fabricate a hydraulic filter press unit using locally sourced materials with a view to promote the affordability and availability of this technology for the manufacture of ceramic water filters, consequently increasing access to safe drinking water in Nigeria.
At its inception by Fernando Mazariegos, the ceramic pot water filter was shaped by hand on the potters’ wheel. But in the 1980s, the Central American Institute of Industrial Research and Technology (ICAITI) introduced the use of hydraulic presses in the shaping of ceramic water filters resulting in more efficient ceramic water filter production and performance [10]. However, other literature [13] reports that the first press and the first set of moulds were developed to standardize the shape of the ceramic water filter (see Figure 1).
Ron Rivera working on the first ceramic filter press [13].
While the Potters Without Borders (PWB) press design is the most commonly used, other attempts have been made to explore different press designs to improve the workings and efficiency of the presses in the production of ceramic water filters and to meet the specific socio-economic needs of varying localities. The PWB filter press design operates with a 20-ton hydraulic jack and a hand lever for lifting and lowering the H-slide to which the male mould is attached. It produces the flat-bottomed ceramic water filters, using a set of aluminium moulds.
A recent study [14] on a multi-component water treatment, reported that they created a simple plastic press mould to shape the ceramic component of their water filtration system with the aim to improve efficiency and allow for easy replication. (see Figure 2).
Modelled diagram of the press mould and product [14].
Another study [10] designed a low-cost filter press with the goal of less than $200 in cost, less manpower requirement and shorter manufacture time. Their work concentrated on designing and prototyping a low-cost, filter press using locally-sourced materials. They attempted to achieve a lower filter formation pressure as a key requirement to reducing the cost, considering that using a 2-ton car jack instead of the 20-ton hydraulic jack used by PWB would greatly reduce cost. The press was designed for the round-bottom filters and adopted an inverted design in which the car jack was mounted to the frame headstock and the female mould was suspended on the underside of the jack elevator while the male mould sat on the base [10]. For the moulds, they improvised with the use of inexpensive aluminium bowls (see Figure 3).
A low-cost filter press prototype [10].
A group of researchers [11] in their study described the use of a 30-ton manually operated hydraulic press developed and manufactured by MEC Ltd., India. The press makes use of a screw system to lower and lift the male mould which is attached to the die screw connector plate, while the female mould sits on a base plate which is attached to the hydraulic jack (see Figure 4). This press produces the flat-bottomed, frustum shaped filters of 23 cm height with 25.5 cm base diameter.
Filter press operated with screw and hydraulic system [11].
The Ceramic Filter Manufacturing Manual [15] developed by Pure Home Water, reported two types of press designs for shaping ceramic water filters; the Potters for Peace (PfP) press and the Mani press. The PfP press design as described in the text is a portable press that uses a 20-ton hydraulic jack with a removable female mould while the male mould is attached to a moveable shaft on the frame. It operates a crankshaft system which allows for the lifting and lowering of the shaft that holds the male mould. The hydraulic jack is positioned above the male mould after it has been lowered into the female mould which contains the clay (see Figure 5).
Operating the portable PfP press with crank system [15].
The Mani press has both its male and female moulds attached; while the male mould is attached to an extendable table, the female mould is attached to the press frame. It uses an 8-ton hydraulic jack and works with a pulley system that operates with a hand crank for lifting and lowering the female mould (see Figure 6).
The Mani press [15].
The mould in the PfP press described in the Ceramic Filter Manufacturing Manual [15] is made of nylon while the material used to make the Mani press moulds was not stated in their report but can be made of concrete or metal. It, however, concluded that the Mani press delivered greater advantage and ease in use than the portable PfP press. The RDIC manual [12] describes a fully automated hydraulic system-operated ceramic water filter press. It uses a set of metal moulds, most likely aluminium. The male mould is attached to the frame headstock while the female mould is attached to a moveable shaft which is controlled by the hydraulic system which works with the use of an electric motor (see Figure 7). This action controls the press and the release of the clay filter mix in between the moulds.
An electric motor driven hydraulic press [12].
The features of the various designs of ceramic water filter presses reviewed in the course of this study are presented in Table 1.
Ref. no | Description | Type of filters | Position of moulds | Mould material | Mould moving mechanism | Mode of operation |
---|---|---|---|---|---|---|
14 | Hand press mould | Ceramic filter component | — | Plastic | — | Hand/manual |
10 | Low-cost filter press | Round bottom | Inverted; female above | Aluminium bowls | — | 2-ton hydraulic car jack |
11 | MEC India manufactured press | Flat bottom | Upright; male above | — | Hand-operated screw system | 30-ton hydraulic jack |
15 | PfP portable press | Flat bottom | Upright; removable female mould positioned below | Nylon | Hand-operated crank system | 20-ton hydraulic jack |
15 | Mani press | Round bottom | Inverted; male attached to extendable surface | — | Crank-operated pulley system | 8-ton hydraulic jack |
12 | RDI-C | Flat bottom | Upright; male above | Metal | — | Automated hydraulic system |
10 | PWB | Flat bottom | Upright; male above | Aluminium | Hand-operated lever | 20-ton hydraulic jack |
Features of the various types of ceramic water filter presses reviewed in this study.
After a review of the designs of ceramic water filter presses as discussed hitherto, the PWB ceramic water filter press design was adopted based on the following considerations:
A non-electrically operated press was desired to overcome the challenge of poor electricity supply within the country;
The use of a fully manual system was also not desirable because it will increase the time taken to press one filter; therefore a hydraulic press mechanism was desired;
A lever was preferred for the lowering and lifting of the moulds, to the crank (as in the portable PfP press [15]) and the screw (as in [11]) because it makes the filter pressing more cumbersome and time consuming;
The moulds were preferred fitted to the press frame to overcome the challenge of misalignment of moulds, possible in removable moulds, and as well, the inconvenience and health hazard of lifting heavy moulds in each process of filter pressing (as in the portable PfP press [15]).
Based on these specific requirements, the Potters Without Borders (PWB) ceramic water filter press design was adapted for manufacture in Akure, Nigeria. The PWB ceramic water filter press is said to have several benefits with respect to design and operation. Its high-strength (20-ton) design allows the pressing of flat-bottom filters [10] while creating stability and preventing deformation in the shaped filters. The flat-bottom filters are said to provide more surface area and therefore higher flow rates [10]. Some of the adjustments made to the PWB filter press design, included the replacement of the hydraulic car jack with a locally fabricated industrial hydraulic jack, as well as the design and manufacture of the press mould to fit locally available wide-rimmed plastic containers to meet the water needs in larger households.
Flow chart of steps taken in fabricating the ceramic water filter press.
It became expedient to fabricate a hydraulic press machine to facilitate the shaping of the ceramic water filters by the press cast method. This is the most suitable method of forming the ceramic filters because the mix is highly non-plastic and hence cannot withstand other ceramic forming techniques besides slip casting which is not very feasible at the desired dimensions of ceramic water filters.
For this study to ensure the economic feasibility, sustainability and hence the scalability of the manufacture of the ceramic water filter press in Nigeria, it was important to set a cost limit for fabricating the press; and this was set at 350000 naira (approximately $1000). This was done considering the issue of low access to capital for start-ups, which is common in the country. This study, however, intends to encourage local potters to venture into the production of ceramic water filters by alleviating some of the cost-related challenges of setting up a filter production unit.
All the materials and manpower used in fabricating this press were sourced from within the country. The hydraulic press machine typically consists of two parts; the moulds and the frame which holds the moulds and the hydraulic component. The procedures engaged in the making of both parts are discussed further.
The mould for the filter press machine was designed and made using aluminium as material, which was shaped using the sand casting method. The processes involved in the making of the filter mould include; generating a CAD drawing (see Figure 8), detailing the dimensions of the moulds; and the making of a wooden mould patterns (see Figures 9 and 10) from which sand moulds were derived.
CAD drawing for moulds (material: Aluminium).
Wooden patterns for the mould.
Top view of wooden patterns for the mould.
The mould design was generated during the course of the study using dimensions which were estimated by the researcher to produce a ceramic water filter that would fit into commonly available wide-rimmed large plastic containers. The size of the container was used as mark up for the determination of the dimensions of the moulds. The core and drag mould components were designed to give a pressed ceramic filter product of 30 mm thickness all round; this is to accommodate the high shrinkage possible in most plastic ball clays available for use in South West Nigeria; as well as to allow for longer contact time with silver for the inactivation of pathogens in water and greater possibility of trapping the pathogens as they travel through the filter walls. With this design sketch, a wooden pattern made of cut out pieces of 2-inch plywood held together with resin bond, was derived. The pattern is highly essential to the process because the sand moulds which was used for casting the metal form is taken from it. So it is important to ensure correctness of dimensions and form in the wooden pattern.
The process of making of the sand moulds included filling up firmly, a square-shaped wooden frame in which the wooden pattern has been placed with fine sand (see Figure 11); after which the pattern is taken out and the sand is smoothened out using a metal spoon (see Figures 12–14). The metal cast was then taken from the prepared sand mould.
Filling the frame with sand.
Pattern taken out.
Smoothening the sand mould.
Finished sand mould.
Pieces of waste aluminium collected from the local scrap market were charged into the rotary furnace and melted (see Figure 15) at temperatures between 600 and 700°C. The crucible bearing the molten aluminium was removed from the furnace using a pair of furnace tongs (see Figure 16) and the crucible holding the molten metal was set in a 2-man carrier rod (see Figure 17).
Process of melting the scrap aluminium in a rotary furnace.
Removing molten aluminium from the furnace using a pair of tongs.
Crucible set in the carrier rod in readiness for casting.
It is important to remove dross and check for unmolten particles of other metals before casting (see Figure 18). The molten metal is then poured into the sand moulds by means of crucible tongs and carrier rod (see Figures 19 and 20).
Stoking the molten metal to remove dross and other particles.
Pouring in the molten metal into the sand mould using furnace tongs.
Casting process using the crucible carrier.
In the process of pouring in the molten material, it is important to poke at it using a metal rod to aid the removal of any air bubbles that may have been trapped in while pouring (see Figure 21). The metal cast is afterwards left to cool for about 24 hours before it is removed from the mould (see Figure 22). The surface finish of the cast aluminium mould is mostly dull, lacks lustre and sometimes presents tiny holes as seen in Figure 23. Polishing the metal is therefore important to give a more usable finish to the cast aluminium moulds (see Figure 24).
Poking the poured-in metal to remove trapped air.
Cooling.
Cast aluminium moulds.
Polished aluminium mould.
The last phase in the making of the mould was the machining and polishing of the cast. Aluminium was the material used to make the moulds in this study. This is because aluminium is a non-rust metal and it is more affordable than stainless steel and can easily be machined because it is a relatively soft metal. Aluminium is also a very available material in most scrap markets across the country, and hence easy to access for this purpose. The machining or polishing of the moulds was carried out using a horizontal lathe machine in a privately-owned engineering workshop.
The frame of the hydraulic press machine was made from cast iron and steel parts. The design for the frame was adapted from the Potters Without borders (PWB) ceramic water filter press design (see Figure 25). The PWB filter press design incorporates the use of a removable car jack as its hydraulic mechanism. The design for this study has incorporated a hydraulic controller system which is comprised of a box, an industrial jack to drive the pressing mechanism which is expected to be more durable than the car jack over time and continued use; and a pressure gauge to measure the pressure applied in the pressing of each filter to enhance consistency in production.
PWB design of press machine [10].
The metal parts for the frame were sourced from Akure and Ibadan in South-west Nigeria. Cast iron was the major material from which the parts of the frame were made. Some parts were also of made of steel. The long metal parts were cut into dimensions (see Figures 26 and 27) and holes were drilled through them to enable assembly of the frame using nuts and bolts. Bolting was preferred to welding in the assembly of the machine parts, to allow room for adjustments and for easy movement and transportation of the machine. The cutting and welding of the frame was followed by the mounting of the moulds. The male component of the mould was bolted onto a metal plate which is welded to the headstock of the frame, and the female component was fitted via bolting onto the moveable H-slide (see Figure 28). The lever system which is used to control the lifting of the H-slide bearing the female mould during pressing and release of the moulds, was subsequently fixed in place (see Figure 29) and test run to assess the mould alignment (see Figure 30). The hydraulic jack was thereafter installed and tested in operation with the lever as shown in Figure 31. Finally, the hydraulic control box was installed and connected to the jack and the entire frame was sprayed with paint to improve its aesthetic and prevent rusting (see Figure 32). The making of the frame and the hydraulic control box, as well as the assembly of the moulds was done at Danzaki Engineering Services, a privately-owned mechanical engineering workshop in Akure, Nigeria.
Cut out metal parts for the frame.
Metal parts of frame in mock assembly.
Press frame with moulds mounted.
Installation of the lever mechanism.
Testing the installed lever and jack.
Press with hydraulic system installed.
Finished ceramic water filter press.
The outcome of the study showed the local availability of the required skills and material resources to locally manufacture a ceramic water filter hydraulic press machine in Nigeria. The total cost of the local production of the press though slightly above the set target, is approximated at $1000 USD and is about one-thirds of the cost of acquiring a press of similar specifications of foreign origin without the attending shipping and clearing costs.
The manufactured ceramic water filter press was effective in the shaping of ceramic water filters as indicated in the evenness in form and thickness of the filters pressed during a test run of the filter press (see Figure 33).
Freshly pressed ceramic water filter using the fabricated press.
The technical specifications of the ceramic water filters produced from the manufactured filter press are outlined as having an inner height of 15 cm and inner diameter of 28.5 cm; with an estimated volume capacity of 12 L. This is specified to fit into a 30-L capacity bucket with a rim diameter of 30 cm. Shrinkage allowance of 10% was estimated and factored into the design to ensure the resulting filters fit onto the desired bucket.
However, there were a few limitations to the study as outlined thus: At the size required for the set of moulds, it was difficult to find a lathe machine of a size that could hold the cast moulds for machining. Therefore, alternative materials may be explored besides aluminium, especially such materials as would not require machining/polishing. Also, there were issues surrounding the dimensions presented in the CAD sketch as generated by a draughtsman, this resulted in error in the moulds cast. This was, however, corrected by altering the dimensions of the mould during the process of machining in order to achieve even thickness around the product; and this action reduced the size of the mould and hence the resulting filter is shorter than other filters available.
This book chapter documents the procedure and results obtained in a study carried out to explore the local manufacturing of a ceramic filter press in order to prove the viability and cost efficiency of producing it locally as compared with the cost of acquiring the imported presses. This is in a view to encourage the set-up of more ceramic water filter producing factories in Nigeria, thereby bringing closer home the technology that would make clean, safe water more accessible and available to communities and households across the country.
The study indicates that ceramic water filter presses with hydraulic components as well as its corresponding set of moulds can be successfully and inexpensively manufactured in Nigeria, using all materials and skills sourced locally from within the country.
The authors would like to acknowledge the Management of the Federal University of Technology, Akure and TETFund for providing funding for this work under the IBR grant with reference number, VCPU/TETFund/155.
We appreciate Engr. A. Smart and Engr. Idowu of EMDI, Akure, for analyzing the possible designs for the hydraulic press system with us at the commencement of this study; Mr. Yekin Obe and staff of Foundry Department, FIIRO, Lagos, for their assistance with the casting of the filter moulds; and Mr. J. O. Oke and Mr. M. Familusi of the Industrial Design Department, FUTA for their assistance in the entire course of the study and specifically for test running the equipment after its manufacture. Our appreciation also goes to Robert Pillers for reviewing the filter press in progress and making useful inputs that led to some adjustments.
We would like to declare that there is no conflict of interest.
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