| Literature DB >> 31656120 |
Surendra Sarsaiya1,2, Jingshan Shi1, Jishuang Chen2,3.
Abstract
The bioengineering tools have significant advantages through less time-consuming and utilized as a promising stage for the production of pharmaceutical bioproducts under the single platform. This review highlighted the advantages and current improvement in the plant, animal and microbial bioengineering tools and outlines feasible approaches by biological and process's bioengineering levels for advancing the economic feasibility of pharmaceutical's production. The critical analysis results revealed that system biology and synthetic biology along with advanced bioengineering tools like transcriptome, proteome, metabolome and nano bioengineering tools have shown a promising impact on the development of pharmaceutical's bioproducts. Tools to overcome and resolve the accompanying encounters of pharmaceutical's production that include nano bioengineering tools are also discussed. As a summary and prospect, it also gives new insight into the challenges and possible breakthrough of the development of pharmaceutical's bioproducts through bioengineering tools.Entities:
Keywords: Bioengineered tools; bioproducts; genomics; metabolomics; pharmaceuticals; proteomics
Mesh:
Substances:
Year: 2019 PMID: 31656120 PMCID: PMC6844412 DOI: 10.1080/21655979.2019.1682108
Source DB: PubMed Journal: Bioengineered ISSN: 2165-5979 Impact factor: 3.269
Figure 1.Diverse approaches of the bioengineered tools for the pharmaceutical’s development.
Several active databases record for systems biology investigation with their corresponding web links.
| Databases class | Common name | Key features | Databases URL | Access on | References |
|---|---|---|---|---|---|
| Genomic Databases | DDBJ | Collecting nucleotide sequence data; sharing and analysis services for data; Analytical services by supercomputer system and their recent modifications | 2019 | [ | |
| EMBL | Provide Nucleotide Sequence Database; preferred web-based submission for analysis; EBI’s Sequence Retrieval System (SRS); integrating and linking the main nucleotide and protein databases plus many specialized databases; Common tools EMBL Nucleotide Sequence Database and SWISS-PROT. | 2019 | [ | ||
| Entrezpy | It is a Python library; automates the data from the Entrez databases at NCBI, entrezpy 2.0.1; Entrezpy is implemented in Python 3 (≥ 3.6) | 2019 | [ | ||
| GOLD | Access to information regarding genome and metagenome sequencing projects, and their associated metadata; GOLD Release v.7 | 2019 | [ | ||
| Microarray Databases | GEO | Functional genomics data repository; Accepted array- and sequence-based data; download experiments and curated gene expression profiles. | 2019 | [ | |
| ArrayExpress | Functional Genomics Data stores from high-throughput functional genomics experiments | 2019 | [ | ||
| Proteomic Databases | ExPASy | It is expert Protein Analysis System; Provided databases on proteomics, genomics, phylogeny, systems biology, population genetics, transcriptomics; databases include SWISS-PROT and TrEMBL, SWISS-2DPAGE, PROSITE, ENZYME and the SWISS-MODEL repository | 2019 | [ | |
| MIPS | It is a Mammalian Protein-Protein Interaction Database; functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences | 2019 | [ | ||
| STRING | Predicted functional associations between proteins; provides a high-level view of functional linkage, facilitating the analysis of modularity in biological processes. | 2019 | [ | ||
| Metabolic Pathway Databases | ENZYME | Repository of information relative to the nomenclature of enzymes | 2019 | [ | |
| BRENDA | Database on functional and molecular information of enzymes; Important tool for biochemical and medical research; tool for enzyme mechanisms, metabolic pathways, the evolution of metabolism. | 2019 | [ | ||
| BioCyc | Pathway/Genome Databases (PGDBs), plus software tools; Computationally predicted metabolic pathways and operons | 2019 | [ | ||
| Biocarta | maps of metabolic and signaling pathways | 2019 | [ | ||
| KEGG | Databases of high-level functions and utilities of the biological system; provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. | 2019 | [ | ||
| MetaCyc | Database of experimentally elucidated metabolic pathways; | 2019 | [ | ||
| PathDB | Provide specific metabolic information | 2019 | [ | ||
| Program Packages | INSILICO Discovery | Advanced computational tool for network oriented ‘in silico’ analysis and design of cellular properties. | 2019 | [ | |
| FluxAnalyzer | Analysis of structure, pathways and flux distributions in metabolic networks | 2019 | [ | ||
| Flux Balance Analysis | Metabolic information of the target organism and represents a knowledge database; resource for information (query tool), high-throughput data mapping (context for content), and a starting point for mathematical models | 2019 | [ |
(Adopted from Sangwan et al., 2018)
Figure 2.Available bioengineered tools and techniques for the production of pharmaceuticals.
Bioengineered web-based tools for pharmaceuticals developments.
| Tool name | Data input | Specificity | Outcomes | Source Package or network system | Access update | Web address | References |
|---|---|---|---|---|---|---|---|
| Cogena | Drugs can be predicted based on the gene expression of disease-related data; various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. | Co-expressed gene-set enrichment analysis with drug repurposing | Discovery of smaller scale, but highly correlated cellular events | cogena_1.18.0.tar.gz | 2019 | [ | |
| BioSilico | LIGAND, ENZYME, EcoCyc and MetaCyc are integrated in a systematic; | Search and analysis of metabolic pathways | Efficiently retrieve the relevant information on enzymes, biochemical compounds and reactions | LIGAND, ENZYME, EcoCyc and MetaCyc | 2019 | [ | |
| DIGEP-Pred | 3D structure of a drug | Drug repositioning, resistance, toxicity and drug-drug interactions | Prediction of Activity Spectra for Substances (PASS) software | 2019 | [ | ||
| Galahad | Drug’s mechanism of action based on gene expression changes | Gene-expression based drug repurposing | Identification of candidate targets, elucidation of mode of action and understanding of off-target effects | Network-based analysis software | 2019 | [ | |
| Gene2drug | Large collections of transcriptional responses and measured based on molecular effects | Pathway-based rational drug repositioning | Therapeutic target gene, a prioritization of potential effective drugs | Network-based analysis software; R package will launch soon | 2019 | [ | |
| ksRepo | Use any data inputs for computational drug repositioning; Gene-expression based drug repurposing | Gene expression profiles | Predict repositioning oportunities | Package Version: 0.1.1 | 2019 | [ | |
| MANTRA | Mode of Action of novel drugs; identification of known and approved candidates for ‘drug repositioning’ | Gene-expression based drug repurposing | Exploiting similarities between drug-induced transcriptional profiles | Mantra 2.0 | 2019 | [ | |
| DRRS | Drug-drug, disease-disease | Network-based | Identifying novel treatments for diseases in drug discovery | DRRS_W, DRRS_L | 2019 | [ | |
| ProphNet | Executing a Randon Walk with Restarts algorithm on a set of networks (a disease network, a gene network and a protein domain network). | Network disease-gene prioritization tool | Given a set of diseases of interest; obtain chromosomal regions associated with genetic conditions | Network-based analysis software | 2019 | [ | |
| ChemMine | Cheminformatics and data mining tools | Small molecule data analysis | Useful for chemical genomics and drug discovery | R library ChemmineR | 2019 | [ | |
| C-SPADE | Analyzing compounds’ multi-targeting activities | Prediction and | Relationships between the compounds’ structural similarities and phenotypic responses through compound centric bioactivity clustering | Open-source web-tool | 2019 | [ | |
| BalestraWeb | Model of drug-target interactions providing only a drug identifier, providing only a target identifier, providing both a drug and a target identifier | Prediction of drug target | Identify most similar drugs or most similar targets based on their interaction patterns | DrugBank’s, NumPy’s, Flask, Python, Data-Driven Documents, Cytoscape | 2019 | [ | |
| DINIES | Predict potential interactions between drug molecules and target proteins, based on drug data and omics-scale protein data | Prediction of drug target | Drug/target names or any drug-target interaction data | DINIES Simple | 2019 | [ | |
| DT-Web, DT-Hybrid | Bipartite network projection implementing resources transfer within the network; | Prediction of drug target | Periodically synchronized with Drug Bank and Pathway Commons | Network-based analysis software | 2019 | [ | |
| Polypharmacology browser (PPB) | Multi-fingerprint approach | Prediction of drug target | Keywords (e.g., | ChEMBL Database | 2019 | [ | |
| LiMTox | LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies | Text-mining based toxicity prediction | Integrates a range of text mining, named entity recognition and information extraction components | Network-based analysis software | 2019 | [ | |
| BiGG Models | Required metabolite, reaction, gene, organism, or genome-scale model information | Prediction and | Building, viewing, and sharing visualizations of biological pathways | Version 1.5: Introducing Recon3D | 2019 | [ | |
| MetaFluxNet | Quantitatively analyzing the metabolic fluxes | understand the metabolic status and to design the metabolic engineering strategies. | Program package | 2019 | [ | ||
| PITCh | PITCh systems are the alternative methods of CRISPR-Cas9- and TALEN-mediated knock-in | Automatically designs sgRNA, microhomologies, and primers for constructing a donor vector for PITCh knock-in and for genotyping. | These systems enable seamless gene knock-in with extremely short homologous sequences (10–40 bp); Currently been applied in cultured cells (HEK293T, HeLa, HCT116, CHO), silkworms, frogs, zebrafish, and mice. | PITCh designer 2.0 | 2019 | [ |
Figure 3.Challenges of nano bioengineered tools and their bioproducts.
Figure 4.The flowchart of life cycle assessment (LCA) of pharmaceuticals bioproducts produced by bioengineered tools.