Biotechnology and Drug Development for Targeting Human Diseases E-Book
54,93 €
Mehr erfahren.
- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Recent Advances in Biotechnology
- Sprache: Englisch
- Veröffentlichungsjahr: 2024
Biotechnology and Drug Development for Targeting Human Diseases is an insightful compendium on drug development technologies for professionals and students in biotechnology and pharmacology. This book meticulously explores the intersection of biotechnology with drug development, emphasizing its crucial role in creating new therapies for human disease.
Central to the book is the innovative use of biotechnology in understanding and treating diseases. It begins with an exploration of multi-omics profiles, shedding light on disease mechanisms and drug development. Subsequent chapters explain in silico methods for drug design, the role of natural products in antimicrobial applications and wound healing, and the use of viruses as carriers in biotechnology.
Key features of this reference include a blend of theoretical knowledge and practical insights, detailed analyses of molecular docking in drug discovery, the repurposing of drugs for various diseases, and the emerging field of omics technologies in drug interaction studies. Each chapter is comprehensive, offering current information backed by extensive references, making the book both a foundational and advanced resource.
Readership
Students and professionals in the fields of biotechnology and pharmacology.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 430
Ähnliche
BENTHAM SCIENCE PUBLISHERS LTD.
End User License Agreement (for non-institutional, personal use)
This is an agreement between you and Bentham Science Publishers Ltd. Please read this License Agreement carefully before using the book/echapter/ejournal (“Work”). Your use of the Work constitutes your agreement to the terms and conditions set forth in this License Agreement. If you do not agree to these terms and conditions then you should not use the Work.
Bentham Science Publishers agrees to grant you a non-exclusive, non-transferable limited license to use the Work subject to and in accordance with the following terms and conditions. This License Agreement is for non-library, personal use only. For a library / institutional / multi user license in respect of the Work, please contact: [email protected].
Usage Rules:
All rights reserved: The Work is the subject of copyright and Bentham Science Publishers either owns the Work (and the copyright in it) or is licensed to distribute the Work. You shall not copy, reproduce, modify, remove, delete, augment, add to, publish, transmit, sell, resell, create derivative works from, or in any way exploit the Work or make the Work available for others to do any of the same, in any form or by any means, in whole or in part, in each case without the prior written permission of Bentham Science Publishers, unless stated otherwise in this License Agreement.You may download a copy of the Work on one occasion to one personal computer (including tablet, laptop, desktop, or other such devices). You may make one back-up copy of the Work to avoid losing it.The unauthorised use or distribution of copyrighted or other proprietary content is illegal and could subject you to liability for substantial money damages. You will be liable for any damage resulting from your misuse of the Work or any violation of this License Agreement, including any infringement by you of copyrights or proprietary rights.Disclaimer:
Bentham Science Publishers does not guarantee that the information in the Work is error-free, or warrant that it will meet your requirements or that access to the Work will be uninterrupted or error-free. The Work is provided "as is" without warranty of any kind, either express or implied or statutory, including, without limitation, implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the results and performance of the Work is assumed by you. No responsibility is assumed by Bentham Science Publishers, its staff, editors and/or authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products instruction, advertisements or ideas contained in the Work.
Limitation of Liability:
In no event will Bentham Science Publishers, its staff, editors and/or authors, be liable for any damages, including, without limitation, special, incidental and/or consequential damages and/or damages for lost data and/or profits arising out of (whether directly or indirectly) the use or inability to use the Work. The entire liability of Bentham Science Publishers shall be limited to the amount actually paid by you for the Work.
General:
Any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims) will be governed by and construed in accordance with the laws of Singapore. Each party agrees that the courts of the state of Singapore shall have exclusive jurisdiction to settle any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims).Your rights under this License Agreement will automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement. In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights.You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions. To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail.Bentham Science Publishers Pte. Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected]
PREFACE
Biotechnology weaves its transformational effect across the intricate fabric of life, altering our understanding of illnesses and igniting creative approaches to their treatment. Intricate biological processes and disease mechanisms have been decoded through the groundbreaking use of biotechnology methods. The potential of biotechnology in the context of drug development is considerable. "Recent advances in Biotechnology Vol. 9, Biotechnology and Drug Development for Targeting Human Diseases” is a collection of insightful discussions and in-depth analyses on the use of biotechnology in the treatment of disease.
The chapters in this book provide a thorough examination of the many biotechnology applications, covering topics like the use of multi-omics profiles in disease research and drug development, in silico drug design techniques, the use of viruses as carriers, and the investigation of natural products for use in wound healing and as antimicrobials. The notion of drug repurposing, the intersection of omics technologies with biotechnology in drug interaction investigations, and the most recent biotechnological discoveries in disease prevention also receive special emphasis.
Every chapter in this book has been meticulously chosen to provide thorough, up-to-date, and understandable knowledge, backed by a variety of references that let the reader dive deeper into the subject. These chapters work together to give readers a comprehensive picture of how biotechnology is fundamentally changing the field of drug research and disease treatment.
We sincerely thank the authors of each chapter for their contributions to the spirit of this book through their knowledge and thorough study. This compilation was made possible by their perceptions, know-how, and diligence.
We would like to express our sincere gratitude to our families, whose unwavering support has been essential at every stage of the process of writing this book. Their support and faith in this project have been essential.
We really hope that this book will be a helpful resource for individuals who are curious about the field of biotechnology and its applications to the treatment of diseases. This information not only sheds light on the condition of the field now, but also prepares the path for future developments in biotechnology and pharmaceutical research.
List of Contributors
Multi-omics Profiles are Applicable to Human Diseases and Drug Development
Adriana Montserrat Espinosa-González1,José del Carmen Benítez-Flores2,Juan Carlos Gómez-Verjan3,Nadia Alejandra Rivero-Segura3,Ignacio Peñalosa Castro1,Jose Cruz Rivera Cabrera4,Edgar Antonio Estrella-Parra1,*Abstract
Traditional medicine has been a reliable source for the discovery of molecules with therapeutic activity against human diseases of clinical interest. In the past, knowledge of traditional medicine was mainly transmitted orally and in writing. Recently, the advent of “multiomics” tools (transcriptomics, metabolomics, epigenomics, proteomics, and lipidomics, among others) has increased and merged our knowledge, both traditional knowledge and that gained with these new multiomics technologies. In this way, the development of medicines with these 'multiomics technologies' has allowed pharmaceutical advances in the discovery of new drugs. In addition, 'multiomics' technologies have made it possible to uncover new biological activities of drugs that are currently used in clinical therapy. In the same way, 'multiomics' has allowed for the development of 'personalized medicine', that is, a particular and specific treatment and/or diagnosis of a patient with respect to a disease. Therefore, 'multiomics' technologies have facilitated the discovery of new clinical therapeutics for disease, as well as allowing for the diagnosis and/or treatment of diseases in an individual and personalized way.
INTRODUCTION
In the past, knowledge from original peoples was transmitted only from generation to generation, but today, the knowledge is used in the development of new medicines [1]. Natural products are chemical compounds produced by living organisms, including plants, animals, and microorganisms, and have long been used in medicine and other biological applications [1]. Biological research has undergone many changes since the end of the 20th century and the beginning of the 21st century, with the publication of the complete human genome sequence by the International Genome Sequencing Consortium in 2003 being a crucial step in genetic research [2]. In a similar manner, drug development has been considered a conservative strategy with highly regulated processes. However, medicine is rapidly evolving with the help of different strategies that allow for the development of comprehensive and personalized treatments for different types of diseases and/or patients [3].
The omic sciences are a set of technologies used to study the global molecular components of an organism, such as genes, proteins, metabolites, and lipids. These technologies include genomics, transcriptomics, proteomics, metabolomics, and lipidomics; furthermore, these technologies have been used in a wide variety of applications, including research in biology, medicine, agriculture, and ecology [3, 4]. These “omics technologies” and advances in bioinformatics have generated new knowledge and integrated new technologies such as artificial intelligence (AI) to improve precision medicine [5]. In this “post genomics” era, research is focused on the role of genes, understanding transcriptional regulation, the biochemical roles of gene products and their interactions, and understanding how various chemicals influence metabolic behavior. These new “omics” technologies are based on global and high-throughput analytical methods, such as microarrays, 2D-gel, 2DLC/MS and mass spectrometry, which produce data on a large scale, as well as bioinformatics and computer modeling [2, 3]. In this manner, multiomics sciences are used to identify and investigate new bioactive compounds from natural products [3, 6].
Important factors for the success of precision medicine (or personalized medicine) include early clinical development, the “back translation” of knowledge via the development of drugs and the translation of omic signatures into clinically relevant biomarkers, as well as the development of precision diagnostics adapted to each patient [3]. Moreover, multiomics science permits the development of these omic technologies and their application in biomedical research and pharmaceutical products, thereby offering a broader exploration of the genome, transcriptome, and proteome and with a greater possibility of finding solutions for the discovery and validation of new drugs, evaluating their efficacy, toxicity, safety and personalized access, as well as the availability of new drugs [2].
The goal of this chapter is to describe the development of new drugs used in clinical therapy and their applicability in personalized medicine based on multiomics sciences.
Ancestral Knowledge: Traditional Medicine in the Multiomics Era
There is a growing interest in the discovery of new drugs from traditional medicine [7]. Ancestrally, knowledge has been transferred from generation to generation, although in modern times, this knowledge that is transferred orally is at risk of being lost [1], not only hindering the development of new drugs but also the discovery of new therapeutic strategies [8]. Ancestral documents such as the ‘Shenlong’s classis of materia medica’ from China describe the use of 365 drugs; moreover, in ancient Greece, Dioscorides described the use of 600 medicinal plants with therapeutic activity [9]. In medieval Europe, traditional medicine comes from the Greeks and Romans such as Hippocrates, Galen, and Dioscorides, and this knowledge was preserved by Benedictine monks through botanical gardens such as the Abbeys of Montecassino and St. Gall, respectively [10]. A convergent referent between traditional medicine and omics science occurred in Japan. In this country, Chinese medical practice was introduced in the 6th century A.D., and eventually the concept of ‘KAMPOmics’, which represented the merging of omic sciences with traditional Japanese medicine, was developed [11]. The principles of yin (cold) and yang (hot) in traditional Chinese medicine were evaluated using metabolomics on serum from fever rats administered a traditional herbal treatment. The rats had an increase in temperature following treatment with plants that stimulated heat, in contrast to their response following treatment with plants that reduce temperature; certain metabolomic markers could discriminate the samples based on the traditional herbal treatment [12]. In addition, in 2014, the Brazilian government published a book that summarized the traditional medicine of the ‘Yanomani people’, which identifies the botanical species and their preparations that are used as therapeutic material [8]. Furthermore, the ‘Herbalomic project’, which focuses on new methods to elucidate molecules, establishes libraries of plants in the context of traditional Chinese medicine [13]. Concurrently, China developed the concept of GP-TCM (Good Practice in Traditional Chinese Medicine research in the postgenomic era), which utilizes coordinated actions to regulate interdisciplinary and intersectoral activities in traditional medicine [14].
Consequently, in the 20th century and early 21st century, innovations have been made that help us to understand life [15]. Traditional natural medicines can be modernized with the use of novel high-tech methods for the development of new phytotherapeutics [1]. In this way, the FDA of the USA describes omics science as a technological tool with automated methods to analyze several types of molecules simultaneously [16], with a methodological strategy for the study, standardization and quality control of herbal formulas [17]. Accordingly, omics technology, such as genomics, transcriptomics, proteomics and metabolomics, helps us understand the pharmacologic effects of plants used in traditional medicine [8].
Consequently, there is an important relationship between ancestral knowledge in medicine and omics tools, and this relationship has led to work that brings together traditions and innovative technologies.
Globalization of Traditional Medicine
Previously, due to the effects of globalization, plants were only used locally, and their use outside the local population was restricted. However, recently, the globalization of traditional medicines has led to self-medication in which herbal remedies such as ‘aryuveda’ and other therapies appear in supermarkets, health stores, and pharmacies, among other places of business [18].
Currently, the economic interest in herbal remedies as alternative and complementary medicines in the United States is estimated to be approximately 50-128.8 million dollars [16]. Moreover, the globalization of herbal medicine products affects the market within the USA, and there must be communication between the scientific community and industry [9]. Therefore, the pharmaceutical industry cannot ignore emerging markets in the development of new therapeutic substances because this information can be used to reduce costs and the number of obstacles preventing their approval [19]. Pharmaceutical and biotech companies often confidentially apply translational emerging safety biomarkers (ESBs) during drug development, which influences the development of new drugs [20].
Thus, the search for new therapies for various diseases has given rise to a greater diffusion of traditional medicine, even outside the place of origin, particularly in the search for molecules with therapeutic activity, as we will see later.
The Construction of New Drugs Based on the Omics Approach
The postgenomic era started with the completion of the Human Genome Project [13], and new drugs are continually being developed [21]. There have been success stories in the development of new drugs; for example, antiretroviral therapy against HIV/AIDS decreased mortality from 16.2 (1995) to 2.7% (2010), and medicines related to heart disease reduced mortality by 45% from 1999 to 2005 [19]. Moreover, between 1981 and 2014, many new drugs were introduced into the market, more than 50% of which came from natural products [8]. In this manner, in 2015, a researcher named you-you was awarded the Nobel prize for the discovery of ‘artemisinin’, a drug to fight malaria. This compound was extracted from Artemisia annuna L., which is a plant used in traditional Chinese medicine [22] that was reproduced based on a recipe from an ancient prescription handbook [16]. Other drugs used in clinical therapy, such as captopril, enalapril and lisinopril, were developed based on peptides that were isolated from the Brazilian snake Bothrops jararaca, as well as the anti-malaria drug malarone, which was a model Lapachol molecule isolated from a tree that was used in traditional Brazilian medicine [8]. Other drugs, such as pirfenidone and nintedanib, are antifibrotic agents that increase the risk of idiopathic pulmonary fibrosis but have adverse effects during treatment; the natural product galectin-3 is a promising agent with beneficial effects and is currently undergoing phase 2 clinical trials [23].
Novel technologies make the development of a new drug more efficient, but they also lead to more detailed requirements, which increase the time and economic costs required to implement the latest generation of drugs [19]. Recently, the use of omics techniques for scientific research has increased, and omics can be used to analyze most classes of biological molecules, such as DNA, RNA, proteins and metabolites [24]. Omics techniques are useful for the identification of biological targets and the elucidation of mechanisms of action in drug discovery [25]. For example, omics tools have allowed us to identify the differences in breast cancer in two female patients; the results showed that there were differences between the two individuals at multiple biological levels [26]. Omics tools can also be used to evaluate comorbidities and differences in various types of gastrointestinal tract cancers [27]. Moreover, omics tools were used to determine that histone H1 regulates chromatin compaction in humans, as well as the mechanisms of transcription and coregulation [28]. Analyses using omics have allowed us to establish the concept of 'deep phenotyping', which refers to defining the biological age and classifying the human body by groups of organs and systems, with the goal of inspecting the longevity of people [29]. Along this line, the InnoMed PredTox consortium (PredTox Project) was created to ensure safety in preclinical studies by incorporating multiomics tools from real-life data, as well as data about drug candidates from various participating companies that previously failed during nonclinical development [30].
More than half of all diagnosed lung cancer patients are in a very advanced stage or in metastasis; thus, it is necessary to determine biomarkers in the initial stage using multi-omics tools such as genomics, transcriptomics, and metabolomics, which would discriminate between malignant and benign nodules or simple injuries [31]. A certain diagnosis is necessary for a good prognosis and quality of life, as we will see later.
Omics Science in Personalized Medicine: A Gold Standard
‘Personalized medicine’ is a recent concept that investigates how differences between individuals affect the way they respond to a drug. Personalized medicine is a strategy that prevents incorrect diagnoses and applies optimal treatments for a particular disease [32], thereby providing a better patient prognosis [33]. Moreover, personalized medicine accounts for variability in the molecular, genomic, cellular, clinical, environmental and physiological dimensions [34, 35]. Tools such as multiomics and bioinformatics provide an opportunity for a good prognosis for patients with various diseases [36] and are considered the 'gold standard' [37]. These types of omics tools allowed for the development of ‘biomarkers’, which are molecules that are measured before and after exposure to a medical product and are important for the development of new drugs [37, 20].
The contributions of omics to the development of personalized medicine have been widely reported. In the treatment of asthma and COPD, pharmacological and ventilator treatments have not changed over five decades; meanwhile, personalized medicine not only includes traditional treatment but also treats symptoms and aids in the development of drugs for these conditions [38]. In patients with asthma (from medium to severe), transcriptomic and proteomic analysis was carried out on 266 people, and their profiles were compared with that of the omics database; these data were used to define a phenotype that is associated with smokers, and these essential tools allow for a more personalized treatment according to ‘your omics profile’ [39]. Cholangiocarcinoma is a rare cancer; based on omics data and biomarkers discovered from transcriptomic studies, research on the identification of candidate drugs to treat this type of rare cancer accelerated [40]. Meanwhile, the multiomics profile of 155 esophageal squamous cell carcinoma samples allowed for the accurate diagnosis of cancer patients and the prediction of therapeutic response with 85.75% sensitivity and 90% specificity; these data allowed us to distinguish the four subtypes of dominant alterations and predicting a possible personal therapy [41]. The construction of an inclusive multiomics model was used to monitor breast cancer based on the clinical data from several patients; this data was used to provide more feasible diagnoses [42]. In neurodegenerative diseases, lipid variability was observed in the plasmalemmas, as well as deregulation of lipid metabolism, particularly in the growth cones, such as the lipids lysophosphatidylserines and cardiolipins, which could be possible biomarkers in neurodegenerative diseases [43]. In addition, omics tools even made it possible to identify 83 genes that are associated with both PD and breast cancer, which allowed for the more efficient prediction of specific drugs that would be effective for these diseases [44]. In necrotizing enterocolitis, multiomics analysis allowed for the discovery of biomarkers of this disease; the biomarkers were identified using available information and processed by algorithms [33]. In Parkinson's disease (PD), multiomics tools helped develop therapeutic strategies for this condition through epigenetic analysis, as well as personalized nutrition, which contributes to this disease [45]. Furthermore, in the early diagnosis of PD, biomarkers such as α-synuclein combined with enhanced T2 star weighted angiography and microRNA-4639-5p were identified from proteomic profiles [46]. In papillary thyroid cancer, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA) and the Erb-B2, CDK1 and CDK2 receptors, as well as histone deacetylase receptors, were identified using multiomics tools; these proteins and other miRNAs were found to be biomarkers for this disease [47]. Two subtypes of lactate metabolism patterns were established in lung squamous carcinoma, and the application of a prognostic index (LMRPI) predicted the prognosis of the disease based on synergy with some anticancer therapies [48]. In addition, 1,061 biomarkers and 892 constitutive biomarkers were identified in the plasma of patients in the acute posttraumatic phase [49]. A multiomics deep learning network method was used to distinguish glioma patients with poor prognoses that are in the dire need of treatment through the construction of transcriptome, miRNA, and DNA methylation profiles, among others; in this case, omics tools helped find drug targets for different gliomas [50]. In patients with glioma, inhibitory CDH11 methylation was found to contribute to poor prognosis [50]. In patients with liver metastasis, information about the immune microenvironment of cancer cells was determined; the cells had high levels of T-cell suppression and other markers, which were useful for predicting a good prognosis [51]. In 80% of acute lymphoblastic leukemia patients, oncogenic lesions were identified, as well as nonconductive mutations at the subclonal level; this allowed researchers to infer resistance to cancer therapy, and in the future, this information could be used to establish personalized therapy for this disease [52]. Furthermore, the bone marrow microenvironment of acute myeloid leukemia patients was analyzed using the secretome/transcriptome, and the identification of deregulated genes (Tfpi, Dtk, KLKB1, and Prekallikrein) and proteins led to the conclusion that the microenvironment is active in this disease [53]. However, importantly, bioethics in studies with omics research must adhere to human rights and the principles of every person, justice, and charity [35].
Medicines have never been more personalized than they are now. Coupled with technological development, personalized medicine allows for a better patient prognosis, but this has also necessitated the search for new drugs as an omics approach.
Challenges in the Discovery of New Drugs
The development of drugs and techniques in some areas of health has lagged behind for years [38]. Thus, novel technologies make the development of new drugs more efficient, but it has led to more detailed requirements and an increase in the time and economic cost required to implement the latest generation of drugs [19]. The pharmaceutical industry and the scientific community have worked jointly through the use of omic tools to develop and discover new drugs with lower cost and time requirements for their application [16]. In this manner, omics tools such as genomics, proteomics and metabolomics can lead to the discovery of active molecules [54]. Additionally, multiomics tools such as gene-centric multichannel (GCMC) have been used to predict cancer drug response, and these models determined the efficacy of 265 drugs used for cancer therapy [55]. Additionally, genome-wide association studies (GWASs) allow for the identification of variants and associated loci in various diseases, thereby providing information for drug development [42]. In contrast, the use of omics tools to understand the mechanisms of idiosyncratic drug-induced hepatotoxicity demonstrated that idiosyncratic drugs induce an increase in intercellular ceramides, which changes the expression of genes by inducing inflammation and ER stress [56].
Recently, there has been much interest in studying drugs already established as therapy for other diseases. Currently, the pharmaceutical industry is looking for molecules that interact simultaneously and specifically with multiple therapeutic targets, a term called ‘compound promiscuity’ [57]. Therefore, ‘empagliflozin’, which is used in diabetes and patients with obesity, was explored using omics tools; the results showed that this drug modulates the microbiota and the metabolism of tryptophan, making it a promising drug against obesity based on the host-microbe interaction [58]. ‘Capreomycin’ is a drug used to treat tuberculosis; multiomics analysis showed mutations in tlYA in some drug-resistant strains, as well as dysregulation of lipid and fatty acid metabolism. This result will allow for the readjustment of therapeutic treatments for tuberculosis [59]. Another drug, triclosan, was evaluated by metabolomic analysis and the results showed that it induces hepatoxicity and enterotoxicity [60]. Likewise, cyclosporin-A induces cholestiasis [21] and mitochondrial damage by activating Nrf2 and ATF4 [61]. Likewise, in breast cancer, growth differentiation factor 10 (GDF10) is associated with the progression of breast cancer and is a promising target for the development of drugs [62]. Additionally, through the proteomic analysis of at least 949 cancer cell lines from 28 different types of tissue, the synergy of various drugs in cancer therapy was analyzed. As there were only 1500 proteins with potential predictive power for this disease, a proteomic pan cancer map was developed [63]. In invasive breast carcinoma (BRCA), multiomics approaches have been used to identify potential autophagy regulators, such as SF3B3, TRAPPC10, SIRT3, MTERFD1, and FBXO5, with SF3B3 and SIRT3 being new targets for drug development [6]. In glioblastoma, the FN1 biomarker was discovered and found to have many implications in this disease; the FN1 molecule is a marker of a good prognosis in the initial stages of this disease [64]. The mechanism of action of the new antimalarial compound JPC-3210 (2-aminomethylphenol), which is in the final stages of preclinical development prior to testing in humans through proteomic, metabolomic and peptidomic analysis, was elucidated, and the mechanism included the inhibition of hemoglobin and the deregulation of DNA replication and the translation of Plasmodium falciparum proteins [65]. In atherosclerosis, the interactome between the intestinal microbiota and antibiotics induces a loss of intestinal diversity, decreases tryptophan abundance, and alters lipid metabolism [66]. Additionally, omics technologies have allowed for advances in studies on the treatment of osteoarthritis, which has permitted the development of new drugs for the disease [67].
Furthermore, the study of natural products for the development of new pharmaceuticals is continuous. Thus, the use of traditional Chinese medicine with multiomics tools has allowed for the identification of biomarkers such as ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4, which are differentially expressed in different human cancer cell lines [68]. Additionally, the natural compound luteolin-7-O-a-L-rhamnoside is a potential ‘promiscuous enzyme inhibitor’ of tyrosinase, hyaluronidase and alpha amylase, and is implicated in some chronic diseases [57]. In addition, metabolomic and proteomic analyses allowed us to determine the profile of molecules in ischemic stroke and their interaction with a decoction of Chinese medicinal plants; in this study, researchers determined the neuroprotective effects of molecules such as scutellarin, quercetin 3-O- glucuronide, ginsenoside Rb1, schizandrol A and 3,5-diCQA, which activate the NF-kB signaling pathway [69]. The decoction used in traditional Chinese medicine was from the ‘Qing dynasty’, and it improved brain function in a model of cerebral ischemia. Using proteomics, metabolomics and transcriptome methods, 15 targets, such as Aprt, Pde1b, Gpd1, Glb1, HEXA and HEXB, were found to reverse the adverse effects of cerebral ischemia [70]. In chronic obstructive pulmonary disease, a traditional Chinese medicine decoction (bufei Jianoi granules) reduced the duration of acute exacerbation of the disease; proteomics, metabolomics and bioinformatics analyses showed that natural products such as pachymic acid, shionone, peiminine and astragaloside A activated the EGFR, ERK1, PAI-1, and p53 signaling pathways, making them promising agents for the development of new drugs [71]. Through transcriptomic profiling, the alkaloid roemerin was found to have activity against Bacillus subtilis; roemerin accumulated in cells and generated oxidative stress and ROS [25]. Moreover, integrative omics studies identified 29 compounds from plants, such as luteolin, apigenin, and thujone, among others, that were bioactive against non-small cell lung cancer and aided in the discovery of differences in disease types and the prediction of potential therapeutic strategies [72].
Hence, the information obtained by clinical analyses with recent technology has led to the discovery of new molecules with therapeutic activity against particular diseases.
Bioinformatics in Omics: An Accumulation of Experiences in Synergy
None of the recent medical advances would be contextualized if there was no technological support and without the development of bioinformatics. The current technologies have allowed for the development of phage-nomic, epigenetic, proteomic, and metabolomic data, although assembling such information is a challenge [73]. Currently, there is an effort to combine omics data with clinical data to create several databases and computer programs [34]. ‘IntelliOmics’ is a term that allows for the complete analysis of raw data files until a diagnostic report is obtained, which can be associated with the treatment recommendation [74]. ‘Automics’ allows for the integration of omics tools into algorithmic models through the construction of unique omics models for each data, which are then combined in a deep learning mathematical program [73].
In 2014, the National Cancer Institute allowed access to the Cancer Genome Atlas (TCGA) database, which was created using various omics tools derived from the analysis of cancer patients [26]. Through bioinformatics analysis, which used data from the Cancer Genome Atlas Database and Molecular Signatures Database (508 patients), transcriptomics and genomics helped to better predict the prognosis of invasive ductal carcinoma of the breast [75]. The cancer therapeutics database response portal allowed for the accurate prediction of drug response in different tissues with cancer and correlations between their genomic and molecular characteristics in response to various drugs were assessed [76]. The myocardial infarction knowledge base (MIKB) is a database that includes 1,782 omics factors, 28 MI subtypes, and 2,347 omics factor-MI interactions, as well as 1,253 genes and 6 chromosomal alterations collected from 2,647 research articles [77]. Additionally, for the discovery of drugs, GWAS meta-analysis has been used in combination with genomic studies, as in the case of thromboembolism, in which the target molecules are interleukin-4 and interleukin-13 [78]. The Omics and Multidimensional Spatial (OMS) method is a method that has been used to evaluate the clinical metadata of different patients who present with therapeutically resistant metastasis; this data has allowed for the identification of new therapeutic vulnerabilities that can lead to more effective treatments for cancer [79]. ‘SUMO’ is a computational program that refines factorization and multi-patient similarities to identify similar molecular subtypes of patients with any disease by reducing noise and improving incomplete data [80]. Thus, in patients diagnosed with lower-grade glioma, ‘SUMO’ was used to determined that non-CpG island methylation is associated with the gene CLCF1, and which is a biomarker of glioma [80]. In HIV-1 infection, using ‘SWATH-MS’ analyses and proteomic data, three factors (LAG-3, CD147, CD231) were found to be altered in several infected cell lines, thereby confirming that there is a universal antigen because of the variability in biomarkers between the different clones [81]. The samples of 4,277 healthy subjects were collected to discriminate the basal levels of optimal health using algorithms [36]. Moreover, docking analyses revealed that 5 drugs could be helpful in the treatment of papillary thyroid cancer disease [47].
In neurodegenerative diseases, samples of patients with amyotrophic lateral sclerosis were analyzed using omics databases and computational methods, and different phenotypes and deregulated pathways of the disease were discovered [82]. ‘CPAS’ is a series of algorithms that allow for multiomics analysis of copy number variation (CNV) genes, allowing for the identification of biological pathways that would be undetectable by simple omics analysis [83]. The SIT-DIMS analysis platform, through the use of algorithms and drug libraries of cancer patients, can establish phenotypic databases and quantify synergy for the discovery of combinatorial strategies [84]. With the multiomics integration program ‘MASPD’ and proteomic data, the proteins present in the microcellular domains were identified, as well as their gene expression, in patients with schizophrenia [85]. ‘OmicView’ is a visualization platform that allows for the identification of biomarkers that interact with any drug [86]. ‘MOGSA’ is another computational method for the analysis of data from only a single omics method, and this program integrates multiple experimental data types [87]. ‘iProFun’ is a multiomics computer program that allows for the analysis of altered and methylated DNA in tumor samples. In very aggressive ovarian cancer, 600 genes with methylation and copy number alterations were identified, and the AKT1 oncogene was found to interact as a node in the cancer process; thus, this type of analysis provides biological information for the development of drugs [88]. Through machine learning and omics tools, biomarkers of clinical interest in cell carcinoma, such as ATP4B, AC144831.1 and Tfcp211, were identified [89]. ClusterProfiler 4.0 is a bioconductor package that supports omics data from thousands of organisms based on internal ontologies and pathways derived from online databases [90]. ‘Panomicon’ is a web based platform that performs multiomics analysis, and improves the storage and management of omic data, as well as their visualization and the interactions of the different omic tools [91]. Likewise, through the use of libraries such as UALKAN, KM plotter, and others, the analysis of invasive breast carcinoma tissues showed that the TP53 gene is mutated in 30% of samples, with overexpression of this gene slowing pharmacological effectiveness, making it a potential biomarker for the development of drugs for anticancer therapy [92]. The databases ‘R-ODAF’ [93] and ‘TRANSFAC’ [94] are used to process old data by taking data from old microarrays and from other state-of-the-art platforms to increase the certainty of transcriptomic analysis results.
Therefore, the development of new drugs and personalized medicine could not be conceived of without the development of bioinformatics, which acts in synergy in vivo and in silico.
In summary, the synergy between ancestral knowledge, omics science, the development of new drugs, bioinformatics/databases, personalized medicine, and the original people of this ancestral knowledge of traditional medicine (Fig. 1) is essential to understanding the different areas of knowledge in the search for promising drugs for clinical therapy.
Fig. (1)) Interaction and synergy between traditional medicine, omics sciences, bioinformatics/databases, and personalized medicine in the search for new drugs.CONCLUSION
Many strategies can be used to achieve only one objective. Ancient knowledge in synergy with new technologies, particularly based on traditional medicines, omics tools, bioinformatics/machine learning, and the subsequent development of drugs with therapeutic activity, together can be used for the successful development of medicines, especially in the era of developing new drugs via a multiomics approach.
