Application and Research of Natural Products in Modern Medical Treatment

 

Muhammad Akram1*, Abid Rashid2, Rida Zainab1, Umme Laila1, Muhammad Talha Khalil1, Hina Anwar1, Naveena Thotakura3, Muhammad Riaz4

 

1Department of Eastern Medicine, Government College University Faisalabad, Punjab, Pakistan

2Faculty of Medical Sciences, Government College University Faisalabad, Punjab, Pakistan

3Apollo Institute of Medical Sciences and Research, Hyderabad, Telangana, India

4Department of Allied Health Sciences, University of Sargodha, Sargosha, Pakistan

 

*Correspondence to: Muhammad Akram, PhD, Chairman, Department of Eastern Medicine, Government College University Faisalabad, Kotwali Road, Gurunanakpura, Faisalabad, Punjab 38000, Pakistan; Email: makram_0451@hotmail.com

 

DOI: 10.53964/jmpp.2023007

 

Abstract

Inventions obtained from nature and structural counterparts are widely involved in an essential function in the treatment of a disorder with drugs. Plants have been used for therapeutic purposes since the discovery of mankind. Medicines obtained from plants have been used to cure different kind of medical ailments. Instead of isolating the active ingredients, these drugs are used as compounds or concentrated plant fractions. In modern medication, though one or two active molecules must be isolated and impurities removed. However, there are a number of global health problems, such as tumours, degenerative illnesses, acquired immune deficiency syndrome, and diabetes, for which modern medication still cannot produce treatments. Sometime, The isolation of the "active compound" has yielded an ineffective chemical. Recent developments in analytical and computational approaches have paved for new ways in the development of complex natural compounds and exploit their structures to create novel drugs. In fact, when it comes to natural objects, we are living in the age of computational molecular design. The discovery of molecular targets of simple compounds and their derivatives has been aided by prognostic computational software. The goal of this study is to assess the literature on the interaction between natural products, conventional medicine, and modern medicine, as well as to investigate how natural discoveries and alternative medicines might be used for innovation of drug. This study summarises the distinctive aspects of traditional medicinal systems' assumption, application, contemporary position or condition, and up to date investigation.

 

Keywords: natural products, therapeutic, degenerative, computational software

 

1 INTRODUCTION

Natural materials, for instance animals, microbes, plants and organisms in sea, have been utilised in medication from prehistoric times to improve and cure the ailments. Fossil evidence suggests that humans have used plant-derived medicines for at least 60,000 years[1,2]. The use of natural inventions as medications must have been a significant problem for early humans. Early humans likely ate poisonous plants for nutrition, resulting in additional harmful effects including loose motion, vomiting, coma, or death. Early humans were able to assimilate information from alternative edibles and natural medicines in this manner[3]. Humans discovered fire, determined how to produce alcohol, developed urbanized beliefs, technological achievements, and as a result discovered how to develop innovative medications.

 

The age of "modern" pharmaceuticals began in the early nineteenth century. Friedrich Sertürner, a young German pharmacist, discovered the first pharmacologically active chemical morphine from the poppy plant in 1805[4,5]. As a result, a large number of active chemicals have been isolated from natural sources. Some of them stick to their original purposes, while others don't. Later, the development of synthetic processes reduced the importance of natural inventions, and there were fears that the use of a number of natural materials for therapeutic purposes would be completely prohibited. Natural discoveries, on the other hand, are critical in the development of novel pharmaceuticals, and they have been in use for a long time. Natural ingredients can significantly help some types of drugs, for instance used against cancer, high blood pressure, and medications intended to reduce the severity of migraine[6].

 

From 1981 and 2002, the use of natural inventions in the evolution of novel medications, particularly in the search for novel chemical compositions, has been a remarkable success. Medicines produced from natural products played an important role during that 22-year period. This is notably true in the field of antihypertensive medications, where natural product structures account for approximately 64% of newly synthesised pharmaceuticals[6].

 

The creation of novel drugs based only on modern technologies seems to have reached a problem. Since 1980s, the pharmaceutical industry has been inclined to produce novel drugs using high throughput production and combinatorial chemistry based remedy research; despite significant efforts in this direction, drug productivity has not met the expectations. The development of new products for some huge pharmaceutical companies is proving to be extremely difficult. Natural products in combination with innovative technologies have received increasing attention in the search for innovative drugs, such as high-throughput sequencing since the last decade.

 

Natural products and their derived mixtures have been very essential sections of pharmacopoeias over the past 50 years, despite the development of new drugs using high-throughput screening technologies and combinatorial chemistry[7,8].

 

Natural products have a chemical diversity that has evolved over millions of years, resulting in a wide variety of biological actions and drug-like properties. These products have proven a valuable source for the production of novel lead alloys. Natural products are used indefinitely to meet the requirements for successful pharmaceuticals. Moreover, they play a crucial role in the innovation of medicines for the treatment of human ailments[9].

 

Natural products have a wide spectrum of multi dimensional chemical compositions. Recently, the value of natural innovations as biological benefit modifiers has received much attention. Since then, they have been successfully used in the development of novel drugs, and their impact on chemicobiology has been considerable[10-12]. The enormous structural variety of natural products has been recognised from the standpoint of physical chemistry for over a century. Their efficiency is linked to the complexity of their well-organized three dimensional chemical and steric properties that provide numerous benefits in expressions of molecular targeting effect and selectivity. Artemisinin and its analogues are now widely used for anti-malarial therapy, a successful example of drug production from natural chemicals. This demonstrates how natural product research has significantly helped the drug development[13,14].

 

Between 1940 and 2002, approximately 54 percent of licensed anticancer treatment was developed using drugs derived from natural compounds or through information about them. For example, successful anticancer medications produced from plants include Catharanthus roseus which yield Vinca alkaloids and the Taxus baccata which yield terpene paclitaxel[15].

 

Many drugs continue to be an important part in numerous formulations of drugs and research programs due to their versatile chemical assortment and unique actions. Those natural products have evolved over time in their ability to interact with a wide range of biological targets, and have even become the most important drugs in many health care organizations[16,17]. As a result, there is great expectations for identifying predictors from plants and other natural products, which can aid drug development by providing essential information on unique chemical compositions and new modes of action.

 

With the help of technological advancement, innovative medicine from natural ingredients is proposed to fight global health concerns. Most significantly, new and creative computational and analytical approaches for identifying the chemical constituents of crude plant extracts, in order to optimize the extraction to identify the molecules, cause the intended therapeutic outcome and eliminate the obstacles. Finally, rather than focusing on single compound, additional research should be conducted on the combinatorial consequences of chemicals derived from plant fractions. Through current "-omics" platforms, researchers must study how these compounds influence genes and proteins involved in a variety of biological processes. Plant extracted compounds may now be designed and tested in drug development because of advancements in microfluidics and computer analysis. Technological advancements, for instance the production of novel analytical and bioinformatic tools, will help in the design of innovative configurations, their production, and biological testing[18,19]. Natural products provide an almost limitless supply of chemicals to aid in the development of pharmacologically significant molecular products[20-22].

 

Isolating specific components of medicinal extracts may be counterproductive since they often function in a synergistic mode to produce curative benefits. To investigate and harness such molecules that can potentially lead to breakthrough drugs, new methodologies are required. Furthermore, an organizational biology directed approach to natural product pharmacology provides a unique perspective. For successful new drug discovery, the plant species from which the natural discoveries are produced and assigned medicinal properties must be of high quality, accurate identification and reliability. Because different chemicals and quantities are found in different plant species, using a diverse or incorrect plant species can greatly influence the healing characteristics. To create an accurate identification strategy for plants and varieties from natural product, genomic technologies are critical[23].

 

For species-level identification, genomic approaches for example DNA barcoding are well organized methods that depend on variations in the sequence of short and conventional DNA segments[24]. When evaluated against current methods of physical identification and limited conventional (linguistic) names, DNA barcoding using genomics will enable a more robust and accurate identification[25]. Natural product DNA barcoding has been used in biodiversity catalogues and herbal product validation[26-29]. ITS2 or psbA-trnH order intensification was used in an integrated method for the identification of plant species, such as Amaranthus hybridus L. and crude pharmaceuticals were reported in the Japanese Pharmacopoeia[30].

 

Genomic examination can be used to identify and target natural inventions or compounds. The combination of whole genome sequencing and transcriptome analysis has opened up hitherto uncharted territory in terms of drug or chemical targeting. At the genome level, transcription features fusion sites, protein modifications, DNA structural changes, and methylation patterns may currently be investigated and evaluated[31-36]. Several investigations, have found deletions, insertions, reproductive figure anomalies, accumulation changes and translocations associated with specific malignancies, revealing new therapeutic targets in the process[37-42]. The unbiased discovery of pharmacological targets has become possible due to the advent of unique and unrivalled technologies that allow genome wide analysis. These tools, combined through the accessibility of large databases of chemicals or composites, have allowed shortening the entire drug development process, from drug design to clinical trials[43-49].

 

The use of proteomics platforms in defining the method of achievement of many natural inventions, is highly organized and complementary to transcriptomic and genomic methods. Proteomic techniques to natural product drug discovery have the ability to illuminate protein structure, function, metabolic, as well as biosynthetic pathways that depend on curative outcomes, resulting in consistent product superiority and profiling[50,51]. Quantitative protein profiling will be aided by techniques such as mass spectrometry with isotope labels and two dimensional electrophoresis, which yield quantitative statistics on equilibrium and affinity similar to that produced at the genome point. Proteomics has been effectively utilized to distinguish between two Chinese herbal medicinal species, Panax ginseng and Panax quinquefolium[52,53]. Proteomics and imaging procedures can be used to effectively analyze the metabolism of natural substances and their components, elucidating their therapeutic effects[54,55]. Proteomics is a complementary approach for illuminating multi-target outcomes of complex natural product developments, with the identification of various components and parts, natural product characterization, and eventually, a molecular research platform[56].

 

It is critical to recognize the target proteins of natural compounds before they may be employed as medicines. Several approaches, including disequilibrium chromatography, have been used to successfully identify target proteins. Natural products with higher activity have emerged from the development of methods that allow the identification of target protein without modifying the natural invention. Cellular thermal transfer assay that relies on the compensation of target proteins when they bind to their ligand, thermal proteome sketch, which depends on the mobility of target proteins at elevated temperature. The computational biology based research of target stability can approach the connectivity and medicine analogy. Natural products exhibit a wide spectrum of biological activity due to their diverse forms and complexity. This is largely due to their ability to attach to a variety of ligands. Because of its off-target effects, every possible drug needs to be screened for adverse effects. In order to discover all of its potential target proteins, composite biological material among prospective target proteins must be thoroughly analyzed. Affinity chromatography[57-63] is one of the most widely used technology for identifying target proteins and their biological activity. This is an approach that immobilises the natural invention on a physically hard substrate[64].

 

Mass spectrometry is used to determine the bound proteins. Natural products, on the other hand, can be modified to have little or no activity. The success of target detection depends on the development of unique and innovative procedures that do not require any adaptation[65,66]. Using unlabeled natural compounds, numerous approaches have recently been able to detect target proteins. The natural discovery marks protein complex reactions to thermal and proteomic manipulation is measured using these novel and improved methodologies[67-69]. Proteomic analysis can be used to discover multiple target proteins for a single natural discovery utilizing this innovative method[70,71].

 

Metabolomic sketch of natural inventions aims to recognize and quantify the entire collection of its distinctive end products of cellular regulatory process[72,73]. Discovering the chemicals of therapeutic interest from natural products using untargeted metabolomics and metabonomics methods has the impending to lead new medications for global health. Metabolomics broadly attempts to analyze the overall and forceful metabolic reaction of existing structures to an organic stimulus or genetic modification[74-77]. The identification of diagnostic compounds on herbs, for example, Hyptis suaveolens, Cassia abbreviata, Newbouldia laevis, and Panax herbs has been made possible by metabolomic sketch of natural inventions using the procedures like ultra performance liquid chromatography quadruple time of mass spectrometry (not clear)[78-80]. Metabolomics has been used for the detection of developed Panax varieties (Panax ginseng and Panax quinquefolius) using Nuclear Magnetic Resonance (NMR) dependent metabolomics, ultra performance liquid chromatography[81].

 

Automation is often linked to negative emotions, with many people connecting it with job losses and unlikely outcomes for instance robots taking over the planet. However, in the case of drug innovation automation, it has been effectively employed to speed up the process. Several pharmaceutical industries have previously been successfully implemented high-throughput assays in the drug development process[82]. The design of the majority of simulated substances, as well as their synthesis, is facilitated by computers and various applications. ADAM and EVE, which are used in target and hit finding, are examples of software used in drug design[83,84]. To eliminate troublesome false positives as well as material used during chemical design, synthesis, and biological assays, new software and equipment are being developed[85].

 

For chemical screening and synthesis, laboratories and pharmaceutical companies are designing incorporated microfluidics structures that are integrated by the ability to handle fluids and the heat required for the production, investigation and decontamination[86,87]. This has allowed various theories to be tested over the course of a few days. More advanced technologies, such as synthetic cleverness and "organ-on-chip" tools, are now fully integrated into drug innovation development, assisting researchers with drug formation and procedure optimization[88-91]. All of these tools allow for less human errors with biases throughout drug preparation and optimization, a decrease in the amount of candidate compounds and reproducibility of clinical science over in vitro analyses[92,93]. In many cases, scientific advancements with innovation have spurred false expectations and not lived up to them. Drug discovery automation and development should be rapid, yet sustainable in the long term[94,95].

 

Accessibility is a key factor in the automation of compound synthesis with the use of structure mass and chemical reactions resulting in a variety of side discoveries. Small amounts of starting mixtures and compressed production along with in sequence decontamination as well as analysis, have led to the discovery of unique tools to developed complex compositions that mimic the biosynthesis of the majority of biological molecules[96-98]. Significantly 3D printing may be used to create a variety of microfluidic devices that use a variety of complex and specific algorithms to track product development. Because most microfluidics devices are specifically produced for this function, 3D printing is critical for them. Although some recent automated robotic synthesis systems can be operated remotely, they still require additional resources[99,100]. Various automated composite formation methods are highly flexible, requiring only a minute number of mass structures to yield a wide range of byproducts[101].

 

Compound production using microfluidics permits for a more stable combination than batch synthesis. Drug oxidation catalyzed by cytochrome P450 can now be simulated, suggesting that chemical transformations of substances on a chip may one day replace in vitro metabolite discovery[102,103]. Drug discovery automation is being revolutionized by incorporation of microfluidics procedure examination and decontamination within the generation of molecules[104]. Microfluidics allows the use of minimal quantities of mixtures with reagents, while apparently avoiding human exposure to chemicals and toxic solutions[105-107].

 

A number of incorporated microfluidics supported synthesis along with testing platforms are currently available, integrating reagent and mixture collection with the ability to adjust depending on the available materials for downstream steps throughout chemical production and analysis. Numerous computational tools and networks (including millions of responses along with pathways for composite production) have been developed to aid in the discovery of the best and most novel method of compound synthesis[108-111]. Artificial intelligence-assisted drug design is a prerequisite for a long-term drug development process[112-115].

 

Machine-generated hypotheses can lead to the design of compounds based on multiple criteria at the same time. Negative results of biological activity and synthesis are examples of such criteria. Machine directed reference or composite design development is significantly more rapid and can create multiple designs at once. As a result, artificial intelligence facilitates machinery that aids scientists in optimized model detection[116-118].

 

Synthetic composites with configurations encouraged by natural inventions can assist to address a variety of global health issues, but in many cases, innovative synthetic composites are neglected despite their inappropriateness for drug development. The so described "rule of three" as well as "rule of five" criteria, which are frequently used for drug lead decision making, are very strict, and very few of the novel designs are successful[119]. Many of the criteria utilized in drug development are subject to human bias, limiting their especially for and usefulness, especially for natural substances[120-123]. Many pharmaceutical synthetic compounds, including numerous anticancer medicines have been generated using computer-aided designs[124-127].

 

For example, the Scaffold Hunter software was utilized to construct elementary segments of less chemically attractive molecules from complex natural compounds[128]. The biological activity of simple molecules seen through such computer software should be the similar as the mother complex. This approach has been used previously to discover pyruvate kinase inhibitors and activators. However, it's possible that simple molecules produced from natural products will have lesser activity than the parent chemical. With significant effectiveness, the PASS software has been utilized to predict the natural processes of common constituents or chemical constituents derived from the parent substance. Anti tumour properties of several marine alkaloids have been predicted using the PASS software[129-131].

 

At the early stages of drug development, new methods are being industrialized to identify candidate or lead molecule toxicity[132-135]. Drug toxicity can be detected early in the drug development process using strategies that use in silico technologies. When integrated with in vitro and in vivo biological investigation, such approaches can dramatically reduce drug discovery time and cost while also improving safety evaluation. The goal of quantitative configuration action connection models is to be aware of the link among a compound's configuration and its toxicity[136-139]. To understand the potential accumulation of a drug, for example, adsorption, circulation, metabolism, and excretion properties should be assessed[140-143].

 

Advances in modern technology[144-147] have enabled scientists to identify the degree of cancer heterogeneity with different patient responses to treatment[144-147]. Drug development, on the other hand, is focused on “one drug, one target” paradigm. Combination therapy is, without a doubt, the current gold standard. As a result, drug development must adopt a combinatorial approach, in which two or more drugs target the same pathway or work together to provide a treatment. Targeted therapies, such as kinase inhibitors, can be used with traditional chemotherapeutic drugs[148-152].

 

Over the last few years, genomics has played a role in medicine innovation; however, the scientific success of the resulting drugs has been mixed. This is owing to the fact that ailments are extremely complex. Advances in genomics technology and analytical methods have now made possible the rapid identification and elucidation of genetic variants that drive patient-specific disease symptoms[153]. Precision medicine focus on these precise symptoms in order to find a cure. At the heart of the Human Genome Project is the quest to learn how genetics influences disease. The potential for genomics to alter drug discovery has piqued the interest of oncologists and cancer specialists. The rapid development of numerous new technologies currently allowed for the examination of the genomes of both sick and healthy people. Importantly, the genome and clinical presentation of a patient can be linked[154]. Many medications are now in use as a result of research into whether specific proteins are pharmacological targets. However, in the absence of definite novel pharmacological targets, productivity in terms of drugs produced is decreased over time. The “gene to screen” strategy was founded on the realization that genes communicated within a cell are the primary contributors to the cell's overall phenotype[155,156].

 

2 CONCLUSION

The reduced speed of drug innovation success necessitates a pattern transfer in drug progression methodologies. For efficient treatment of medical conditions, innovative medication research begins with natural ingredients as inspiration. The importance of natural products in developing novel treatments to combat communicable and non-communicable diseases cannot be overstated. Technological advancements have made it possible to decipher the contours of these complex natural chemicals, perhaps leading to the development of novel medications. Natural product lead compounds have been used to isolate or synthesize a large number of blockbuster medications. Natural product drug discovery has become one of the most successful techniques for the production of new medical drugs. Novel drug detection from natural products has the prospective to boost the success rate of new curative interventions in this era of rapid advancement of science and technology. Natural product drug discovery is a critical component of addressing global health concerns and achieving health-related sustainable development objectives.

 

Acknowledgements

Not applicable.

 

Conflicts of Interest

All authors declared no conflict of interest.

 

Author Contribution

Akram M, Rashid A, Thotakura N and Riaz M contributed in study design, literature review and manuscript drafting. Zainab R, Laila U, Khalil MT, and Anwar H drafted the manuscript for possible publication of manuscript. All authors contributed to the manuscript and approved the final version.

 

References

[1] Shi Q, Li L, Huo C et al. Study on natural medicinal chemistry and new drug development. Chin Tradit Herb Drugs, 2010; 41: 1583-1589.

[2] Fabricant DS, Farnsworth NR. The value of plants used in traditional medicine for drug discovery. Environ Health Perspect, 2001; 109: 69-75. DOI: 10.1289/ehp.01109s169

[3] Gao X, Zhang T, Zhang J et al. Chinese Materia Medica. China Press of traditional Chinese Medicine, 2007; 323.

[4] Joo YE. Natural product-derived drugs for the treatment of inflammatory bowel diseases. Intest Res. 2014; 12: 103-109. DOI: 10.5217/ir.2014.12.2.103

[5] Hamilton GR, Baskett TF. In the arms of morpheus: The development of morphine for postoperative pain relief. Can J Anaesth, 2000; 47: 367-374. DOI: 10.1007/BF03020955

[6] Newman DJ, Cragg GM, Snader KM. Natural products as sources of new drugs over the period 1981-2002. J Nat Prod, 2003; 66: 1022-1037. DOI: 10.1021/np030096l

[7] Ngo LT, Okogun JI, Folk WR. 21st century natural product research and drug development and traditional medicines. Nat Prod Rep, 2013; 30: 584-592. DOI: 10.1039/c3np20120a

[8] Zhu F, Ma X, Qin C et al. Drug discovery prospect from untapped species: Indications from approved natural product drugs. Plos One, 2012; 7: e39782. DOI: 10.1371/journal.pone.0039782

[9] Galm U, Shen B. Natural product drug discovery: The times have never been better. Chem Biol, 2007; 14: 1098-1104. DOI: 10.1016/j.chembiol.2007.10.004

[10] Hong JY. Natural product diversity and its role in chemical biology and drug discovery. Curr Opin Chem Biol, 2011; 15: 350-354. DOI: 10.1016/j.cbpa.2011.03.004

[11] Rosén J, Gottfries J, Muresan S et al. Novel chemical space exploration via natural products. J Med Chem, 2009; 52: 1953-1962. DOI: 10.1021/jm801514w

[12] Butler MS. Natural products to drugs: Natural product-derived compounds in clinical trials. Nat Prod Rep, 2008; 25, 475-516. DOI: 10.1039/b514294f

[13] Muschietti L, Vila R, Filho VC et al. Tropical protozoan diseases: Natural product drug discovery and development. Evid Based Complement Altern Med, 2013; 2013. DOI: 10.1155/2013/404250

[14] Cragg GM, Newman DJ. Natural products: A continuing source of novel drug leads. Biochim Biophys Acta, 2013; 1830: 3670-3695. DOI: 10.1016/j.bbagen.2013.02.008

[15] Li-Weber M. New therapeutic aspects of flavones: The anticancer properties of Scutellaria and its main active constituents Wogonin, Baicalein and Baicalin. Cancer Treat Rev, 2009; 35: 57-68. DOI: 10.1016/j.ctrv.2008.09.005

[16] Winter JM, Tang Y. Synthetic biological approaches to natural product biosynthesis. Curr Opin Biotechnol, 2012; 23: 736-743. DOI: 10.1016/j.copbio.2011.12.016

[17] Li JW, Vederas JC. Drug discovery and natural products: End of an era or an endless frontier? Science, 2009; 325: 161-165. DOI: 10.1126/science.1168243

[18] Medema MH, Fischbach MA. Computational approaches to natural product discovery. Nat Chem Biol, 2015; 11: 639-648. DOI: 10.1038/nchembio.1884

[19] Kim E, Moore BS, Yoon YJ. Reinvigorating natural product combinatorial biosynthesis with synthetic biology. Nat Chem Biol, 2015; 11: 649-659. DOI: 10.1038/nchembio.1893

[20] Akbulut Y, Gaunt HJ, Muraki K et al. (-)-Englerin A is a potent and selective activator of TRPC4 and TRPC5 calcium channels. Angew Chem Int Ed Engl, 2015; 54: 3787-3791. DOI: 10.1002/anie.201411511

[21] Ludlow MJ, Gaunt HJ, Rubaiy HN et al. (-)-Englerin A-evoked cytotoxicity is mediated by Na+ influx and counteracted by Na+/K+-ATPase*. J Biol Chem, 2017; 292: 723-731. DOI: 10.1074/jbc.M116.755678

[22] Muraki K, Ohnishi K, Takezawa A et al. Na+ entry through heteromeric TRPC4/C1 channels mediates (-)Englerin A-induced cytotoxicity in synovial sarcoma cells. Sci Rep, 2017; 7: 16988. DOI: 10.1038/s41598-017-17303-3

[23] Buriani A, Garcia-Bermejo ML, Bosisio E et al. Omic techniques in systems biology approaches to traditional Chinese medicine research: Present and future. J Ethnopharmacol, 2012; 140: 535-544. DOI: 10.1016/j.jep.2012.01.055

[24] Ganie SH, Upadhyay P, Das S et al. Authentication of medicinal plants by DNA markers. Plant Gene, 2015; 4: 83-99. DOI: 10.1016/j.plgene.2015.10.002

[25] Ghorbani A, Saeedi Y, Boer HJ. Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran. Plos One, 2017; 12: e0175722. DOI: 10.1371/journal.pone.0175722

[26] Thompson KA, Newmaster SG. Molecular taxonomic tools provide more accurate estimates of species richness at less cost than traditional morphology-based taxonomic practices in a vegetation survey. Biodivers Conserv, 2014; 23: 1411-1424. DOI: 10.1007/s10531-014-0672-z

[27] Cao M, Wang J, Yao L et al. Authentication of animal signatures in traditional Chinese medicine of Lingyang Qingfei Wan using routine molecular diagnostic assays. Mol Biol Rep, 2014; 41: 2485-2491. DOI: 10.1007/s11033-014-3105-x

[28] Newmaster SG, Grguric M, Shanmughanandhan D et al. DNA barcoding detects contamination and substitution in North American herbal products. BMC Med, 2013; 11: 222. DOI: 10.1186/1741-7015-11-222

[29] Mishra P, Kumar A, Nagireddy A et al. DNA barcoding: An efficient tool to overcome authentication challenges in the herbal market. Plant Biotechnol J, 2016; 14: 8-21. DOI: 10.1111/pbi.12419

[30] Chen X, Xiang L, Shi L et al. Identification of crude drugs in the Japanese pharmacopoeia using a DNA barcoding system. Sci Rep, 2017; 7: 42325. DOI: 10.1038/srep42325

[31] Jones MJ, Goodman SJ, Kobor MS. DNA methylation and healthy human aging. Aging Cell, 2015; 14: 924-932. DOI: 10.1111/acel.12349

[32] Kelly TK, Liu Y, Lay FD et al. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res, 2012; 22: 2497-2506. DOI: 10.1101/gr.143008.112

[33] Nordlund J, Backlin CL, Wahlberg P et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol, 2013; 14: r105. DOI: 10.1186/gb-2013-14-9-r105

[34] Su J, Wang Y, Xing X et al. Genome-wide analysis of DNA methylation in bovine placentas. BMC Genom, 2014; 15: 12. DOI: 10.1186/1471-2164-15-12

[35] Zykovich A, Hubbard A, Flynn JM et al. Genome-wide DNA methylation changes with age in disease-free human skeletal muscle. Aging Cell, 2014; 13: 360-366. DOI: 10.1111/acel.12180

[36] Barbosa S, Carreira S, Bailey D et al. Phosphorylation and SCF-mediated degradation regulate CREB-H transcription of metabolic targets. Mol Biol Cell, 2015, 26, 2939-2954. DOI: 10.1091/mbc.E15-04-0247

[37] Bose P, Vachhani P, Cortes JE. Treatment of relapsed/refractory acute myeloid leukemia. Curr Treat Options Oncol, 2017; 18: 17. DOI: 10.1007/s11864-017-0456-2

[38] Ley TJ, Miller C, Ding L et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med, 2013; 368: 2059-2074. DOI: 10.1056/NEJMoa1301689

[39] Brat DJ, Verhaak RG, Aldape KD et al. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med, 2015; 372: 2481-2498. DOI: 10.1056/NEJMoa1402121

[40] Eckel-Passow JE, Lachance DH, Molinaro AM et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med, 2015; 372: 2499-2508. DOI: 10.1056/NEJMoa1407279

[41] Mwapagha LM, Tiffin N, Parker MI. Delineation of the HPV11e6 and HPV18e6 pathways in initiating cellular transformation. Front Oncol, 2017; 7: 258. DOI: 10.3389/fonc.2017.00258

[42] Vogelsang M, Wang Y, Veber N et al. The cumulative effects of polymorphisms in the DNA mismatch repair genes and tobacco smoking in oesophageal cancer risk. Plos One, 2012; 7: e36962. DOI: 10.1371/journal.pone.0036962

[43] Fishilevich S, Nudel R, Rappaport N et al. Genehancer: Genome-wide integration of enhancers and target genes in genecards. Database, 2017; 2017. DOI: 10.1093/database/bax028

[44] Guo X, Long J, Zeng C et al. Fine-scale mapping of the 4q24 locus identifies two independent loci associated with breast cancer risk. Cancer Epidemiol Biomark Prev, 2015; 24: 1680-1691. DOI: 10.1158/1055-9965.EPI-15-0363

[45] Ombrello MJ, Sikora KA, Kastner DL. Genetics, genomics, and their relevance to pathology and therapy. Best Pract Res Clin Rheumatol, 2014; 28: 175-189. DOI: 10.1016/j.berh.2014.05.001

[46] Simmonds P, Loomis E, Curry E. DNA methylation-based chromatin compartments and CHIP-seq profiles reveal transcriptional drivers of prostate carcinogenesis. Genome Med, 2017; 9: 54. DOI: 10.1186/s13073-017-0443-z

[47] Yang TY, Hsu LI, Chiu AW et al. Comparison of genome-wide DNA methylation in urothelial carcinomas of patients with and without arsenic exposure. Environ Res, 2014; 128: 57-63. DOI: 10.1016/j.envres.2013.10.006

[48] Mehta G, Jalan R, Mookerjee RP. Cracking the encode: From transcription to therapeutics. Hepatology, 2013; 57: 2532-2535. DOI: 10.1002/hep.26449

[49] Tragante V, Moore JH, Asselbergs FW. The encode project and perspectives on pathways. Genet Epidemiol, 2014; 38: 275-280. DOI: 10.1002/gepi.21802

[50] Bumpus SB, Evans BS, Thomas PM et al. A proteomics approach to discovery of natural products and their biosynthetic pathways. Nat Biotechnol, 2009; 27: 951-956. DOI: 10.1038/nbt.1565

[51] Martínez-Esteso MJ, Martínez-Márquez A, Sellés-Marchart S et al. The role of proteomics in progressing insights into plant secondary metabolism. Front Plant Sci, 2015; 6: 504. DOI: 10.3389/fpls.2015.00504

[52] Lum JH, Fung KL, Cheung PY et al. Proteome of oriental ginseng Panax ginseng C.A. Meyer and the potential to use it as an identification tool. Proteomics, 2002; 2: 1123-1130. DOI: 10.1002/1615-9861(200209)2:9<1123::AID-PROT1123>3.0.CO;2-S

[53] Kim SW, Lee SH, Min CW et al. Ginseng (Panax sp.) proteomics: An update. Appl Biol Chem, 2017; 60: 311-320. DOI: 10.1007/s13765-017-0283-y

[54] Li ZH, Alex D, Siu SO et al. Combined in vivo imaging and omics approaches reveal metabolism of icaritin and its glycosides in zebrafish larvae. Mol BioSyst, 2011; 7: 2128-2138. DOI: 10.1039/c1mb00001b

[55] Hung MW, Zhang ZJ, Li S et al. From omics to drug metabolism and high content screen of natural product in zebrafish: A new model for discovery of neuroactive compound. Evid-Based Complement Altern Med, 2012; 2012: 605303. DOI: 10.1155/2012/605303

[56] Lao Y, Wang X, Xu N et al. Application of proteomics to determine the mechanism of action of traditional Chinese medicine remedies. J Ethnopharmacol, 2014; 155: 1-8. DOI: 10.1016/j.jep.2014.05.022

[57] Guan D, Chen Z. Challenges and recent advances in affinity purification of tag-free proteins. Biotechnol Lett, 2014; 36: 1391-1406. DOI: 10.1007/s10529-014-1509-2

[58] Novick D, Rubinstein M. Ligand affinity chromatography, an indispensable method for the purification of soluble cytokine receptors and binding proteins. Methods Mol Biol, 2012; 820: 195-214. DOI: 10.1007/978-1-61779-439-1_12

[59] Pfaunmiller EL, Paulemond ML, Dupper CM et al. Affinity monolith chromatography: A review of principles and recent analytical applications. Anal Bioanal Chem, 2013; 405: 2133-2145. DOI: 10.1007/s00216-012-6568-4

[60] Rix U, Gridling M, Superti-Furga G. Compound immobilization and drug-affinity chromatography. Methods Mol Biol, 2012; 803: 25-38. DOI: 10.1007/978-1-61779-364-6_3

[61] Wang H, Chu Z, Chen C et al. Recombinant passenger proteins can be conveniently purified by one-step affinity chromatography. Plos One, 2015; 10: e0143598. DOI: 10.1371/journal.pone.0143598

[62] Zhang X, Wang T, Zhang H et al. Profiling of drug binding proteins by monolithic affinity chromatography in combination with liquid chromatography-tandem mass spectrometry. J Chromatogr A, 2014; 1359: 84-90. DOI: 10.1016/j.chroma.2014.07.020

[63] Schenone M, Dancik V, Wagner BK et al. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol, 2013; 9: 232-240. DOI: 10.1038/nchembio.1199

[64] McFedries A, Schwaid A, Saghatelian A. Methods for the elucidation of protein-small molecule interactions. Chem Biol, 2013; 20: 667-673. DOI: 10.1016/j.chembiol.2013.04.008

[65] Rix U, Superti-Furga G. Target profiling of small molecules by chemical proteomics. Nat Chem Biol, 2009; 5: 616-624.

[66] Lee H, Lee JW. Target identification for biologically active small molecules using chemical biology approaches. Arch Pharm Res, 2016; 39: 1193-1201. DOI: 10.1007/s12272-016-0791-z

[67] Franken H, Mathieson T, Childs D et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat Protoc, 2015; 10: 1567-1593. DOI: 10.1038/nprot.2015.101

[68] Jafari R, Almqvist H, Axelsson H et al. The cellular thermal shift assay for evaluating drug target interactions in cells. Nat Protoc, 2014; 9: 2100-2122. DOI: 10.1038/nprot.2014.138

[69] Lomenick B, Hao R, Jonai N et al. Target identification using drug affinity responsive target stability (darts). Proc Natl Acad Sci, 2009; 106: 21984-21989. DOI: 10.1073/pnas.0910040106

[70] Chang J, Kim Y, Kwon HJ. Advances in identification and validation of protein targets of natural products without chemical modification. Nat Prod Rep, 2016; 33: 719-730. DOI: 10.1039/C5NP00107B

[71] Schirle M, Bantscheff M, Kuster B. Mass spectrometry-based proteomics in preclinical drug discovery. Chem Biol, 2012; 19: 72-84. DOI: 10.1016/j.chembiol.2012.01.002

[72] Liu X, Locasale JW. Metabolomics: A primer. Trends Biochem Sci, 2017; 42: 274-284. DOI: 10.1016/j.tibs.2017.01.004

[73] Clish CB. Metabolomics: An emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud, 2015; 1: a000588. DOI: 10.1101/mcs.a000588

[74] Nicholson JK, Lindon JC. Systems biology: Metabonomics. Nature, 2008; 455: 1054-1056. DOI: 10.1038/4551054a

[75] Perez-Pinera P, Ousterout DG, Gersbach CA. Advances in targeted genome editing. Curr Opin Chem Biol, 2012; 16: 268-277. DOI: 10.1016/j.cbpa.2012.06.007

[76] Siminovitch L. Genetic manipulation: Now is the time to consider controls. Sci Forum, 1973; 6: 7-11.

[77] Yarmush ML, Banta S. Metabolic engineering: Advances in modeling and intervention in health and disease. Ann Rev Biomed Eng, 2003; 5: 349-381. DOI: 10.1146/annurev.bioeng.5.031003.163247

[78] Yan T, Fu Q, Wang J et al. UPLC-MS/MS determination of ephedrine, methylephedrine, amygdalin and glycyrrhizic acid in beagle plasma and its application to a pharmacokinetic study after oral administration of Ma Huang Tang. Drug Test Anal, 2015; 7: 158-163. DOI: 10.1002/dta.1635

[79] Ekow Thomford N, Dzobo K, Adu F et al. Bush mint (Hyptis suaveolens) and spreading hogweed (Boerhavia diffusa) medicinal plant extracts differentially affect activities of CYP1A2, CYP2D6 and CYP3A4 enzymes. J Ethnopharmacol, 2018; 211: 58-69. DOI: 10.1016/j.jep.2017.09.023

[80] Xie G, Plumb R, Su M et al. Ultra-performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research. J Sep Sci, 2008; 31: 1015-1026. DOI: 10.1002/jssc.200700650

[81] Park HW, In G, Kim JH et al. Metabolomic approach for discrimination of processed ginseng genus (Panax ginseng and Panax quinquefolius) using UPLC-QTOF MS. J Ginseng Res, 2014; 38: 59-65. DOI: 10.1016/j.jgr.2013.11.011

[82] Chapman T. Lab automation and robotics: Automation on the move. Nature, 2003; 421: 661-666. DOI: 10.1038/421661a

[83] King RD, Rowland J, Oliver SG et al. The automation of science. Science, 2009; 324: 85-89. DOI: 10.1126/science.1165620

[84] Sparkes A, Aubrey W, Byrne E et al. Towards robot scientists for autonomous scientific discovery. Autom Exp, 2010; 2: 1. DOI: 10.1186/1759-4499-2-1

[85] Meanwell NA. Improving drug design: An update on recent applications of efficiency metrics, strategies for replacing problematic elements, and compounds in non-traditional drug space. Chem Res Toxicol, 2016; 29: 564-616. DOI: 10.1021/acs.chemrestox.6b00043

[86] MacConnell AB, Price AK, Paegel BM. An integrated microfluidic processor for DNA-encoded combinatorial library functional screening. ACS Comb Sci, 2017; 19: 181-192. DOI: 10.1021/acscombsci.6b00192

[87] Baranczak A, Tu NP, Marjanovic J et al. Integrated platform for expedited synthesis-purification-testing of small molecule libraries. ACS Med Chem Lett, 2017; 8: 461-465. DOI: 10.1021/acsmedchemlett.7b00054

[88] Gupta A, Muller AT, Huisman BJH et al. Generative recurrent networks for de novo drug design. Mol Inform, 2017. DOI: 10.1002/minf.201700111

[89] Merk D, Friedrich L, Grisoni F et al. De novo design of bioactive small molecules by artificial intelligence. Mol Inform, 2018. DOI: 10.1002/minf.201700153

[90] Zhang L, Tan J, Han D et al. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov Today, 2017; 22: 1680-1685. DOI: 10.1016/j.drudis.2017.08.010

[91] Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des, 2007; 13: 1497-1508. DOI: 10.2174/138161207780765954

[92] Esch EW, Bahinski A, Huh D. Organs-on-chips at the frontiers of drug discovery. Nat Rev Drug Discov, 2015; 14: 248-260. DOI: 10.1038/nrd4539

[93] Eglen RM, Randle DH. Drug discovery goes three-dimensional: Goodbye to flat high-throughput screening? Assay Drug Dev Technol, 2015; 13: 262-265. DOI: 10.1089/adt.2015.647

[94] Ozdemir V, Hekim N. Birth of industry 5.0: Making sense of big data with artificial intelligence, “the internet of things” and next-generation technology policy. Omics, 2018; 22: 65-76. DOI: 10.1089/omi.2017.0194

[95] Ozdemir V, Patrinos GP. David bowie and the art of slow innovation: A fast-second winner strategy for biotechnology and precision medicine global development. Omics, 2017; 21: 633-637. DOI: 10.1089/omi.2017.0148

[96] Besnard J, Ruda GF, Setola V et al. Automated design of ligands to polypharmacological profiles. Nature, 2012; 492: 215-220. DOI: 10.1038/nature11691

[97] Koppitz M, Eis K. Automated medicinal chemistry. Drug Discov Today, 2006; 11: 561-568. DOI: 10.1016/j.drudis.2006.04.005

[98] Sutherland JD, Tu NP, Nemcek TA et al. An automated synthesis-purification-sample-management platform for the accelerated generation of pharmaceutical candidates. J Lab Autom, 2014; 19: 176-182. DOI: 10.1177/2211068213516325

[99] Godfrey AG, Masquelin T, Hemmerle H. A remote-controlled adaptive medchem lab: An innovative approach to enable drug discovery in the 21st century. Drug Discov Today, 2013; 18: 795-802. DOI: 10.1016/j.drudis.2013.03.001

[100] Nicolaou CA, Watson IA, Hu H et al. The proximal lilly collection: Mapping, exploring and exploiting feasible chemical space. J Chem Inf Model, 2016; 56: 1253-1266. DOI: 10.1021/acs.jcim.6b00173

[101] Li J, Ballmer SG, Gillis EP et al. Synthesis of many different types of organic small molecules using one automated process. Science, 2015; 347: 1221-1226. DOI: 10.1126/science.aaa5414

[102] Stalder R, Roth GP. Preparative microfluidic electrosynthesis of drug metabolites. ACS Med Chem Lett, 2013; 4: 1119-1123. DOI: 10.1021/ml400316p

[103] Genovino J, Sames D, Hamann LG et al. Accessing drug metabolites via transition-metal catalyzed c-h oxidation: The liver as synthetic inspiration. Angew Chem Int Ed Engl, 2016; 55: 14218-14238. DOI: 10.1002/anie.201602644

[104] LaPorte TL, Wang C. Continuous processes for the production of pharmaceutical intermediates and active pharmaceutical ingredients. Curr Opin Drug Discov Dev, 2007; 10: 738-745.

[105] Chin P, Barney WS, Pindzola BA. Microstructured reactors as tools for the intensification of pharmaceutical reactions and processes. Curr Opin Drug Discov Dev, 2009; 12: 848-861.

[106] Saaby S, Knudsen KR, Ladlow M et al. The use of a continuous flow-reactor employing a mixed hydrogen-liquid flow stream for the efficient reduction of imines to amines. Chem Commun, 2005; 2909-2911. DOI: 10.1039/b504854k

[107] Brzozowski M, O’Brien M, Ley SV et al. Flow chemistry: Intelligent processing of gas-liquid transformations using a tube-in-tube reactor. Acc Chem Res, 2015; 48: 349-362. DOI: 10.1021/ar500359m

[108] Kayala MA, Azencott CA, Chen JH et al. Learning to predict chemical reactions. J Chem Inf Model, 2011; 51: 2209-2222. DOI: 10.1021/ci200207y

[109] Reynolds CR, Muggleton SH, Sternberg MJ. Incorporating virtual reactions into a logic-based ligand-based virtual screening method to discover new leads. Mol Inform, 2015; 34: 615-625. DOI: 10.1002/minf.201400162

[110] Kowalik M, Gothard CM, Drews AM et al. Parallel optimization of synthetic pathways within the network of organic chemistry. Angew Chem Int Ed Engl, 2012; 51: 7928-7932. DOI: 10.1002/anie.201202209

[111] Szymkuc S, Gajewska EP, Klucznik T et al. Computer-assisted synthetic planning: The end of the beginning. Angew Chem Int Ed Engl, 2016; 55: 5904-5937. DOI: 10.1002/anie.201506101

[112] Baker M. Europe bets on drug discovery. Nature, 2013; 494: 20. DOI: 10.1038/494020a

[113] Lopez-Rubio E, Elizondo DA, Grootveld M et al. Computational intelligence techniques in medicine. Comput Math Methods Med, 2015; 2015: 196976. DOI: 10.1155/2015/196976

[114] Montanez-Godinez N, Martinez-Olguin AC, Deeb O et al. QSAR/QSPR as an application of artificial neural networks. Methods Mol Biol, 2015; 1260: 319-333. DOI: 10.1007/978-1-4939-2239-0_19

[115] Wesolowski M, Suchacz B. Artificial neural networks: Theoretical background and pharmaceutical applications: A review. J AOAC Int, 2012; 95: 652-668. DOI: 10.5740/jaoacint.SGE_Wesolowski_ANN

[116] Baskin II, Winkler D, Tetko IV. A renaissance of neural networks in drug discovery. Expert Opin Drug Discov, 2016; 11: 785-795. DOI: 10.1080/17460441.2016.1201262

[117] Nikolsky Y, Nikolskaya T, Bugrim A. Biological networks and analysis of experimental data in drug discovery. Drug Discov Today, 2005; 10: 653-662. DOI: 10.1016/S1359-6446(05)03420-3

[118] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015; 521: 436-444. DOI: 10.1038/nature14539

[119] Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods, 2000; 44: 235-249. DOI: 10.1016/S1056-8719(00)00107-6

[120] Congreve M, Carr R, Murray C et al. A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today, 2003; 8: 876-877. DOI: 10.1016/S1359-6446(03)02831-9

[121] Molle IV, Thomann A, Buckley DL et al. Dissecting fragment-based lead discovery at the von Hippel-Lindau protein: Hypoxia inducible factor 1alpha protein-protein interface. Chem Biol, 2012; 19: 1300-1312. DOI: 10.1016/j.chembiol.2012.08.015

[122] Zuegg J, Cooper MA. Drug-likeness and increased hydrophobicity of commercially available compound libraries for drug screening. Curr Top Med Chem, 2012; 12: 1500-1513. DOI: 10.2174/156802612802652466

[123] Ntie-Kang F, Lifongo LL, Judson PN et al. How “drug-like” are naturally occurring anti-cancer compounds? J Mol Model, 2014; 20: 2069. DOI: 10.1007/s00894-014-2069-z

[124] Grabowski K, Baringhaus KH, Schneider G. Scaffold diversity of natural products: Inspiration for combinatorial library design. Nat Prod Rep, 2008; 25: 892-904. DOI: 10.1039/b715668p

[125] Elumalai N, Berg A, Natarajan K et al. Nanomolar inhibitors of the transcription factor STAT5b with high selectivity over STAT5a. Angew Chem Int Ed Engl, 2015; 54: 4758-4763. DOI: 10.1002/anie.201410672

[126] Bon RS, Waldmann H. Bioactivity-guided navigation of chemical space. Acc Chem Res, 2010; 43: 1103-1114. DOI: 10.1021/ar100014h

[127] Renner S, Otterlo WA, Seoane MD et al. Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol, 2009; 5: 585-592. DOI: 10.1038/nchembio.188

[128] Wetzel S, Klein K, Renner S et al. Interactive exploration of chemical space with scaffold hunter. Nat Chem Biol, 2009; 5: 581-583. DOI: 10.1038/nchembio.187

[129] Rodrigues T, Reker D, Schneider P et al. Counting on natural products for drug design. Nat Chem, 2016; 8: 531-541. DOI: https://doi.org/10.1038/nchem.2479

[130] Lagunin A, Stepanchikova A, Filimonov D et al. Pass: Prediction of activity spectra for biologically active substances. Bioinformatics, 2000; 16: 747-748. DOI: 10.1093/bioinformatics/16.8.747

[131] Stepanchikova AV, Lagunin AA, Filimonov DA et al. Prediction of biological activity spectra for substances: Evaluation on the diverse sets of drug-like structures. Curr Med Chem, 2003; 10: 225-233. DOI: 10.2174/0929867033368510

[132] DiMasi JA, Feldman L, Seckler A et al. Trends in risks associated with new drug development: Success rates for investigational drugs. Clin Pharmacol Ther, 2010; 87: 272-277. DOI: 10.1038/clpt.2009.295

[133] DiMasi JA, Reichert JM, Feldman L et al. Clinical approval success rates for investigational cancer drugs. Clin Pharmacol Ther, 2013; 94: 329-335. DOI: 10.1038/clpt.2013.117

[134] Hay M, Thomas DW, Craighead JL et al. Clinical development success rates for investigational drugs. Nat Biotechnol, 2014; 32: 40-51. DOI: 10.1038/nbt.2786

[135] Loong HH, Siu LL. Selecting the best drugs for phase I clinical development and beyond. ASCO, 2013; 469-473. DOI: 10.14694/EdBook_AM.2013.33.469

[136] Chavan S, Nicholls IA, Karlsson BC et al. Towards global qsar model building for acute toxicity: Munro database case study. Int J Mol Sci, 2014; 15: 18162-18174. DOI: 10.3390/ijms151018162

[137] Cherkasov A, Muratov EN, Fourches D et al. Qsar modeling: Where have you been? Where are you going to? J Med Chem, 2014; 57: 4977-5010. DOI: 10.1021/jm4004285

[138] Devillers J. Methods for building QSARs. Methods Mol Biol, 2013; 930: 3-27. DOI: 10.1007/978-1-62703-059-5_1

[139] Sullivan KM, Manuppello JR, Willett CE. Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing. SAR QSAR Environ Res, 2014; 25: 357-365. DOI: 10.1080/1062936X.2014.907203

[140] Kirchmair J, Goller AH, Lang D et al. Predicting drug metabolism: Experiment and/or computation? Nat Rev Drug Discov, 2015; 14: 387-404. DOI: 10.1038/nrd4581

[141] Mukherjee G, Gupta PL, Jayaram B. Predicting the binding modes and sites of metabolism of xenobiotics. Mol BioSyst, 2015; 11: 1914-1924. DOI: 10.1039/C5MB00118H

[142] Xiao X, Min JL, Lin WZ et al. Idrug-target: Predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J Biomol Struct Dyn, 2015; 33: 2221-2233. DOI: 10.1080/07391102.2014.998710

[143] Zhang W, Liu F, Luo L et al. Predicting drug side effects by multi-label learning and ensemble learning. BMC Bioinform, 2015; 16: 365. DOI: 10.1186/s12859-015-0774-y

[144] Bastian L, Hof J, Pfau M et al. Synergistic activity of bortezomib and hdaci in preclinical models of b-cell precursor acute lymphoblastic leukemia via modulation of p53, PI3k/AKT, and NF-κB. Clin Cancer Res, 2013; 19: 1445-1457. DOI: 10.1158/1078-0432.ccr-12-1511

[145] Stanciu-Herrera C, Morgan C, Herrera L. Anti-cd19 and anti-cd22 monoclonal antibodies increase the effectiveness of chemotherapy in pre-b acute lymphoblastic leukemia cell lines. Leukemia Res, 2008; 32: 625-632. DOI: 10.1016/j.leukres.2007.07.002

[146] Dzobo K, Senthebane DA, Rowe A et al. Cancer stem cell hypothesis for therapeutic innovation in clinical oncology? Taking the root out, not chopping the leaf. Omics, 2016; 20: 681-691. DOI: 10.1089/omi.2016.0152

[147] Dzobo K, Senthebane DA, Thomford NE et al. Not everyone fits the mold: Intratumor and intertumor heterogeneity and innovative cancer drug design and development. Omics, 2018; 22: 17-34. DOI: 10.1089/omi.2017.0174

[148] Baselga J, Cortes J, Kim SB et al. Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. N Engl J Med, 2012; 366: 109-119. DOI: 10.1056/NEJMoa1113216

[149] Kawajiri H, Takashima T, Kashiwagi S et al. Pertuzumab in combination with trastuzumab and docetaxel for HER2-positive metastatic breast cancer. Expert Rev Anticancer Ther, 2015; 15: 17-26. DOI: 10.1586/14737140.2015.992418

[150] Swain SM, Baselga J, Kim SB et al. Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. N Engl J Med, 2015; 372: 724-734. DOI: 10.1056/NEJMoa1413513

[151] Swain SM, Baselga J, Miles D et al. Incidence of central nervous system metastases in patients with HER2-positive metastatic breast cancer treated with pertuzumab, trastuzumab, and docetaxel: Results from the randomized phase iii study cleopatra. Ann Oncol, 2014; 25: 1116-1121. DOI: 10.1093/annonc/mdu133

[152] Swain SM, Kim SB, Cortes J et al. Pertuzumab, trastuzumab, and docetaxel for HER2-positive metastatic breast cancer (cleopatra study): Overall survival results from a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol, 2013; 14: 461-471. DOI: 10.1016/S1470-2045(13)70130-X

[153] Ozdemir V, Patrinos GP. David bowie and the art of slow innovation: A fast-second winner strategy for biotechnology and precision medicine global development. Omics, 2017; 21: 633-637. DOI: 10.1089/omi.2017.0148

[154] National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): National Academies Press (US); 2011. DOI: 10.17226/13284

[155] Debouck C. Integrating genomics across drug discovery and development. Toxicol Lett, 2009; 186: 9-12. DOI: 10.1016/j.toxlet.2008.09.011

[156] Debouck C, Metcalf B. The impact of genomics on drug discovery. Ann Rev Pharmacol Toxicol, 2000; 40: 193-207. DOI: 10.1146/annurev.pharmtox.40.1.193

 

Copyright © 2023 The Author(s). This open-access article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, sharing, adaptation, distribution, and reproduction in any medium, provided the original work is properly cited.