Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.

Zhou Y; Peng S; Wang H; Cai X; Wang Q

doi:10.3390/genes15040468

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.

Affiliations

1. Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Authors
Zhou Y¹
Peng S¹
Wang H¹
Wang Q¹
(4 authors)
2. Shanghai R&D Centre for Standardization of Chinese Medicines, Shanghai 202103, China.
Authors
Cai X²
(1 author)

ORCIDs linked to this article

Wang Q | 0000-0001-7472-996X

Genes, 08 Apr 2024, 15(4):468
https://doi.org/10.3390/genes15040468 PMID: 38674402 PMCID: PMC11049652

Review

This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.

Free full text in Europe PMC

This article is based on a previously available preprint.

Abstract

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Free full text

Genes (Basel). 2024 Apr; 15(4): 468.

Published online 2024 Apr 8. https://doi.org/10.3390/genes15040468

PMCID: PMC11049652

PMID: 38674402

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

Yalan Zhou,¹ Siqi Peng,¹ Huizhen Wang,¹ Xinyin Cai,^2,^* and Qingzhong Wang^1,^*

Zhiqiang Li, Academic Editor

Author information Article notes Copyright and License information Disclaimer

This article has been cited by other articles in PMC.

Go to:

Associated Data

Data Availability Statement: Not applicable.

Go to:

Abstract

Keywords: personalized therapy, pharmacogenomics, personalized medicine, anticancer, natural products

Go to:

1. Introduction

In recent years, with the completion of the Human Genome Project, the development of genomics, proteomics, and imaging technology and the advent of molecularly targeted drugs, personalized medicine has come into being [1,2,3]. Personalized medicine is an emerging field, in which physicians utilize diagnostic tests to determine which medical treatments will work best for each patient or apply medical interventions to alter molecular mechanisms that impact health. By combining data from diagnostic tests with an individual medical history, circumstances, and values, medical staff can develop targeted treatment and prevention plans with their patients. According to the concept of personalized medicine, the treatment plan is selected and determined based on information about the patient’s individual genetics, environment, and lifestyle. In this way, each patient can achieve the maximum health benefits, while reducing ineffective treatments and side effects [4]. Up to 2018, 355 pharmacogenomic biomarkers and 284 drugs have been approved by the FDA. Those pharmacogenomic biomarkers approved for use in personalized medicines are usually specific genetic variants (i.e., gene tags) or abnormally expressed proteins [5]. They can help distinguish those who will or will not respond to a drug, prevent adverse drug reactions, and play an important role in optimizing drug dosing [6,7,8]. For those personalized medicines, anti-tumor drugs have a larger proportion, which mainly resulted from the discovery of oncogenic driving genes and the development of molecular targeted drugs [9]. These personalized oncology drugs mainly include receptor tyrosine kinase inhibitors, small molecule inhibitors, vaccines, antibodies/small molecule–antibody conjugates, and monoclonal antibodies [10,11,12]. Besides new personalized drugs, repositioning drugs is also an important approach to personalized drug discovery, which mainly involves developing and gene-tagging “complementary diagnostic” biomarkers for drugs already on the market [13,14,15]. In the case of the lung cancer drug ramucirumab, for example, the FDA cautions that patients treated with this drug must be those with epidermal growth factor receptor (EGFR) and ALK receptor tyrosine kinase (ALK) mutations [16,17]. The new use of old drugs can not only save a lot of time and cost while ensuring efficacy, but also reduce safety risks and speed up drug approval [18]. These drugs are mainly used in cancer, neuropsychiatric diseases, infections, cardiovascular diseases, metabolic diseases, and other areas [19,20,21,22,23]. With the advent of increasingly personalized medicine, Big Data analytics is creating a model of precision medicine that allows for a more detailed treatment plan according to the comprehensive situation of a person’s genes, lifestyle, environment, and other factors. This can not only reduce the safety risks of drugs, but also greatly improve their effectiveness [24]. Based on the theme of personalized medicine, this review mainly discusses the strategies of discovering novel drugs, chemical antitumor agents, natural antitumor products, and so on.

Go to:

2. Technical System for the Development of New Drugs and Personalized Medicine

The main goal of personalized medicine is to enable precise treatment with drugs for specific populations and types of diseases through design, screening, and optimization [25,26]. With the continuous innovation and development of life sciences and technologies, the research and development strategies for new drugs have changed in recent years from the traditional models based on a disease phenotype, local signaling pathway, and single target. These new models are based on the molecular typing of the disease, the global signaling network in the organism, and all targets [27,28,29]. They have also greatly promoted the research and development of personalized medicine. The rapid development of new omics technologies, rapid accumulation of multidimensional and large-scale omics data, advent of molecular imaging, advent of supercomputers, and continuous improvement of correlation analysis and mining algorithms have all contributed [30,31,32,33,34,35,36,37]. Next, we will focus on the emerging research in personalized medicine and the development of new key technologies for systematic deployment (See Table 1 for more supplementary information).

2.1. Artificial Intelligence Technology

Artificial intelligence is a branch of computer science that aims to simulate human thought processes, learning abilities, and knowledge reserves [38,39]. In recent decades, the improvement of computer speed and the rapid development of artificial intelligence (AI) have promoted the study of drug discovery and personalized medicine [40,41]. As one part of the development of personalized medicine, large-scale biomedical data were being used to identify the biological principles behind drugs to more accurately simulate and predict the complex effects of drug molecules in vivo [42]. In late 2016, Goldman Sachs Group published a comprehensive report on artificial intelligence in the ecology and future of AI. This report pointed out that the application of AI and powerful algorithms will help to get rid of the risks in the development of new drugs [43]. In 2021, one study reported that machine learning was able to predict the higher accuracy equal to 76% for the outcome of phase III clinical trials of anti-tumor agents for the treatment of prostate cancer [44]. In the field of disease diagnosis, a study has shown that melanoma diagnoses using machine learning can reach the level of well-experienced dermatologists [45]. In addition, Google company has developed DeepMind’s Deep Learning algorithm, which can quickly and accurately detect early signs of diseases, such as age-related macular degeneration and diabetic retinopathy, to prevent and treat them in advance [46,47]. Artificial intelligence technology, with its powerful automatic feature extraction, complex model building, and image processing, opens up new possibilities for the analysis, processing, and application of biomedical Big Data [48,49,50].

2.2. Approaches with Multidimensional Omics Data

Theoretically, large-scale integrated analysis of multidimensional omics data can provide comprehensive and complete insight into the molecular mechanisms of complex diseases and specific drugs. That is, genome-wide data encompassing the genome, transcriptome, proteome, metabolome, and other various dimensions can help to analyze the target, regulatory mechanism, and biological effect of drugs at different molecular levels [51,52]. In this way, the targeting and off-target effects of the drug can be revealed globally, and different drugs can be combined according to the different characteristics of each drug to improve efficacy and reduce side effects. Thus, personalized and accurate guidance for drug treatment of diseases is achieved [53]. Meanwhile, new potential targets, novel genetic variants, and regulatory mechanisms can be screened out from the genome-wide approaches, which will help to improve the effectiveness and successful probabilities of the new use of old drugs to a certain extent [54,55]. At present, the strategies of personalized medicine discovery based on multi-omics data are not yet mature, but they have been tentatively applied in the phase of personalized medicine discovery [56]. Recent studies have shown that by integrating multi-omics data, including SNP and copy number variants, mRNA expression profiling, and protein profiling of lung adenocarcinoma, normal tissue, and tumor xenograft models. It has been predicted that the changed proteins that cannot been found from the single-omics data are strongly correlated with cell metabolism and survival in patients with lung cancer and other cancer [57,58]. In addition, integration analysis between transcriptomic and proteomic data from 24 human tumor xenograft models of breast cancer patients has shown that proteomics can better detect dynamic changes in some proteins and protein phosphorylation, including AKT and ARAF, BRAF, and HSP90AB1 [59]. All in all, multi-omics integration can provide strong technical support for clinical personalized treatment and personalized medicine development [60].

2.3. Study on High-Throughput Targets with Chemical Proteomics Technology

The function of chemical proteomics technology is to exploit the specific interaction between the drug and the target protein. By combining various enrichment methods with high-resolution biomass spectroscopy, the target proteins and molecular regulatory mechanisms can be studied [61]. This method can identify binding targets of small-molecule compounds from complex biological samples (cells or tissues), with the advantages of high throughput and unbiasedness. Chemical proteomics technology can provide important information for further analysis of the full target spectrum of active molecules at the cellular level and evaluation of drug activity, toxicology, and indications [62,63]. Currently, chemical proteomics-based technologies are being used to identify drug targets. These primarily cover affinity-based protein profiling, activity-based protein profiling, thermal proteomic profiling, and affinity-dependent drug target stability [64]. We listed the principles, advantages, and disadvantages of those four technologies.

2.4. Computer-Aided Drug Discovery (CADD) System

CADD technology can be used in various phases of the development of new personalized medicine. These comprise drug target identification, lead discovery and optimization, therapeutic marker and predictive model discovery, drug combination and repositioning research, ADME and safety research, etc. [65,66,67,68]. CADD can reduce costs and shorten the time of research and development, while greatly improving the successful risk [69]. We will focus on the application of computational methods in target identification and lead discovery in personalized medicine. (1) Target identification: currently, there are still a large number of potential targets to be explored for effective drug development [70]. The potential targets of chemical compounds can be predicted using approaches that incorporate bioinformatics and cheminformatics at multiple levels to improve the reliability of data analysis and effectively promote the use of computers. These include chemical structure similarity searches, data mining using machine learning methods, reverse molecular docking, and algorithm-based bioactivity profiling [71,72,73]. Keiser et al. (2018) developed the similarity ensemble approach to enable large-scale searches for known drug targets by comparing the molecular fingerprint similarities of thousands of compounds [74]. In addition, the dynamic structure and thermodynamic dynamics parameters of molecules and protein can be fully obtained by simulating molecular dynamics, which is helpful in the search for new personalized drug targets [75,76]. (2) Lead compounds discovery: Molecular docking is the most commonly used method for designing drugs based on receptor structure [77]. These related software programs include Gold, AutoDock, GLIDE, etc. [78]. In addition, molecular dynamics (MD) can also be used to study the dynamic structure of proteins, such as AMBER (Amber Molecular Dynamics), GROMACS (GROningen MAchine for Chemical Simulations), and NAMD (NAnoscale Molecular Dynamics) [79]. The free energy of the binding of the protein to the ligand was calculated using MD, and then the binding mode and the binding activity of the two were predicted to screen the lead compounds [80]. For ligand-based drug design methods, compound similarity analysis, quantitative structure–activity relationship (QSAR), and pharmacophore model analysis have been successfully used for lead discovery [81,82]. For example, Mueller et al. (2012) used QSAR to identify 27 allosteric modulators of the mGlu5 receptor that can be used to treat anxiety disorders, Parkinson’s disease, and schizophrenia [83]. In addition, Ijjaali et al. (2007) performed ligand-based virtual screening of 2 million compounds and identified 16 highly active human T-type calcium channel blockers that could be used to treat epilepsy and neuropathic pain [84]. All these approaches have helped to identify the new drug design process, and they are usually combined multiple approaches to design and optimize lead structures.

Go to:

3. Anti-Cancer Personalized Medicine

The main purpose of personalized therapy is to develop effective targeted drugs for different sub-phenotypic patients. The application of personalized anti-cancer drugs has been widespread due to the discovery of oncogenic-driving genetic mutations in the development of molecular targeted drugs. These personalized anticancer drugs mainly include tyrosine kinase inhibitors, small molecule inhibitors, vaccines, antibodies/small molecule antibody conjugates, and monoclonal antibodies [12,85,86]. Among these personalized medicines, a great number of drugs were found from drug repositioning, which identifies new therapeutic opportunities for existing drugs. For example, pembrolizumab, novolumab, and other FDA-approved drugs are used for personalized treatment through drug repositioning [87,88]. We have summarized the FDA-approved personalized anti-tumor drugs (see Table 2 for details). Here, we focused on personalized anti-cancer agents targeting ALK and EFGR receptors.

Table 2

FDA approved personalized anti-cancer drugs.

Seq_ID	Medicine	Personalized Tag	Approval Time	Molecular Formula	Mechanism of Action	Disease
1	Crizotinib	ALK⁺	2011		ALK inhibitor	Metastatic non-small-cell lung cancer with ALK or ROS1 positive
2	Ceritinib	ALK⁺	2014		ALK inhibitor	Non-small-cell lung cancer
3	Alectinib	ALK⁺	2015		ALK inhibitor	Non-small-cell lung cancer
4	Brigatinib	ALK⁺	2017		ALK inhibitor	Non-small-cell lung cancer
5	Lorlatinib	ALK⁺ is positive	2018		A dual-target inhibitor of ALK/ROS1	Non-small-cell lung cancer
6	Gefitinib	EGFR	2003		EGFR inhibitor	Non-small-cell lung cancer
7	Erlotinib	EGFR	2004		EGFR inhibitor	Non-small-cell lung cancer
8	Afatinib	EGFR	2013		EGFR inhibitor	Non-small-cell lung cancer
9	Osimertinib	EGFR	2015		EGFR inhibitor	Non-small-cell lung cancer
10	Pembrolizumab	PD-1	2015	C₆₅₃₄H₁₀₀₀₄N₁₇₁₆O₂₀₃₆S₄₆ _(PDB:5dk3)	PD-1 inhibitor	Non-small-cell lung cancer
11	Nivolumab	PD-1	2014	C₆₃₆₂H₉₈₆₂N₁₇₁₂O₁₉₉₅S₄₂ _(PDB:5ggr)	PD-1 inhibitor	Non-small-cell lung cancer
12	Olaparib	PARP	2014		PARP inhibitor	Ovarian cancer
13	Rucaparib	PARP	2016		PARP inhibitor	Ovarian cancer
14	Palbociclib	CDK 4/6 kinase	2015		CDK 4/6 kinase inhibitor	Breast cancer
15	Trastuzumab Deruxtecan	HER-2	2022		HER-2 inhibitor	Non-small-cell lung cancer
16	Tucatinib	HER-2	2020		HER-2 inhibitor	Colorectal cancer
17	Vemurafenib	BRAF	2011		BRAF inhibitor	Metastatic melanoma
18	Larotrectinib	Tyrosinase kinase	2018		Tyrosinase kinase inhibitor	Solid Tumors
19	Ibrutinib	Tyrosinase kinase	2013		Tyrosinase kinase inhibitor	Mixed lineage leukemia

Table 1

Screening and comparison of drug targets based on chemical proteomics.

Technology	Principle	Advantage	Disadvantage
AfBPP	Affinity of target proteins to active small molecules on stationary phases	1. No bias; 2. Systematic study of total protein; 3. It can enrich the target and is suitable for identification of low-abundance proteins.	1. A detailed understanding of the structure–activity relationship of active molecules is required; 2. Chemical derivatization of active molecules is required; 3. Targets with low abundance and low affinity are easy to be washed off; 4. Probes usually cannot enter cells.
ABPP	The target protein forms a covalent bond with a covalent small molecule.	1. No bias; 2. Systematic study of whole protein; 3. It can enrich the target and is suitable for identification of low-abundance proteins; 4. Grasp low-affinity targets; 5. Probes usually get into cells.	1. A thorough understanding of the structure–activity relationship of active molecules is required; 2. Chemical derivatization of active molecules is required; 3. Non-specific covalent binding is easy to occur.
TPP	The thermal stability of the target protein increases after binding with small molecules, and it is not easy to precipitate	1. No bias; 2. Systematic study of whole protein; 3. No derivations of small active molecules are required.	1. Limited effect on extreme conditions, such as heat insensitivity or heat-unstable proteins; 2. Further measures should be taken to reduce the complexity of samples so as to realize the identification of low-abundance proteins.
DARTS	The stability of the target protein increases after binding with small molecules and is not easily degraded by enzymes	1. No bias; 2. Systematic study of whole protein; 3. No derivations of small active molecules are required.	1. The protein that is not sensitive to enzyme digestion has limited effect; 2. Further measures should be taken to reduce the complexity of samples so as to realize the identification of low-abundance proteins.

3.1. ALK Inhibitors

Anaplastic lymphoma kinase (ALK) is an important molecular marker of non-small-cell lung cancer [89,90]. In 2007, EML-ALK, the ALK fusion gene, was found to be present in 3%–7% of patients with non-small-cell lung cancer, triggering the research and development boom of ALK personalized medicine [91]. (1) Crizotinib is an orally administered ALK inhibitor that effectively inhibits phosphorylation of the NPMALK fusion protein in human degenerative cell lymphomas Karpas-299 and SU-DHL -1 cells. Cell cycle arrest and apoptosis were significantly increased after 24- and 48-h treatment with crizotinib. In mice transplanted with a Karpas-299 tumor, oral administration of 100 mg/kg crizotinib for 15 days resulted in complete tumor regression. This is consistent with antitumor activity in vivo. (2) Ceritinib is a second-generation ALK inhibitor synthesized by Pierre-Yves Michelly’s team at Novartis based on TAE684. Cell proliferation assays showed that ceritinib significantly inhibited proliferation of Karpas-299 and BaF3 tool cells with high expression of the ALK fusion protein. A 25 mg/kg dose of ceritinib significantly inhibited the growth of subcutaneously transplanted tumors in nude mice with Karpas-299 and NCLH2228 tumor cell lines. Ceritinib has some ability to cross the blood–brain barrier, and the brain-tissue-to-plasma exposure ratio measured by isotopic labeling is approximately 15% [92,93,94]. (3) Alectimib is an orally active and highly selective inhibitor ALK. Cell-level studies have shown that alectimib can inhibit the activation of ALK and downstream STAT3 and AKT signaling pathways in NCL-H2228 non-small-cell lung cancer cells. Meanwhile, alectimib significantly inhibited the proliferation of NCI-H2228 non-small-cell lung cancer cells. In nude mice, alectimib at a dose of 6 mg/kg significantly inhibited the growth of NCI-H2228 and KARPAS-299 cell transplantation tumors. In terms of the ability to overcome mutations, 100 nM alectimib inhibited the phosphorylation of ALK in BaF3/EML4- ALK -L1196M cells [95,96]. (4) Brigatinib is a dual-target inhibitor of ALK and EGFR. Subsequent pharmacodynamic evaluation studies showed that at the molecular level, brigatinib significantly inhibited kinase activity mutated by C1156Y, F1174L, L1196M, G1202R, and R1275Q ALK and ALK, while brigatinib inhibited EGFR and other kinase activities mutated by ROS1, FLT3, and D835Y. At the cellular level, brigatinib was shown to inhibit proliferation and intracellular ALK protein phosphorylation of AlK-driven lymphoma cells Karpas-299, SU-DHL -1, L-82, and SUP -M2 and lung cancer cells H3122 and NCLH2228.The results showed that 50 mg/kg brigatinib could lead to shrinkage or even complete regression of Karpas-299 and NCI-H2228 transplanted tumors in mice. No significant reversal of tumor growth inhibition was observed 15 to 30 days after drug discontinuation. A 50 mg/kg dose of brigatinib significantly inhibited intracranial lesions and prolonged survival in mouse models of brain metastases containing transplanted NCLH2228 tumors. In mice with BaF3/EML4- ALK -G1202R grafts, 25 mg/kg and 50 mg/kg brigatinib inhibited tumors 55% and 88%, respectively [92,94,97]. (5) Lorlatinib, a third-generation ALK inhibitor, showed significant inhibitory activity against ROS1 and ALK kinases. The tumor transplantation experiment in mice showed that 25 mg/kg lorlatinib could significantly inhibit tumor growth in H1322 and H3122/EM1A-ALKLSH312/EMLA-ALKG1269A mice. Lorlatinib (10 mg/kg) can significantly inhibit transplanted tumor growth in the tissue-derived mouse model of tumor transplantation, and a tumor can also significantly decline when lorlatinib is replaced after crizotinib resistance. In an intracranial mouse transplant tumor model, lorlatinib significantly inhibited internal tumor growth and significantly prolonged mouse survival [98,99,100].

3.2. EGFR Inhibitors

EGFR is a member of the epidermal growth factor receptor family of ErbB receptor tyrosine kinases, which also includes BGFR, ErbB2 (HER2), ErbB3(HER3), and ErbB4(HER4) [101,102]. EGFR is a transmembrane receptor protein consisting of extracellular ligand-binding domains, transmembrane domains, and intracellular kinase-active domains [103]. Upon binding of the extracellular ligand-binding region to a ligand, homodimerization or heterodimerization occurs, followed by autophosphorylation in the intracellular region, to activate its kinase. Phosphorylated EGFR terminals bind to various downstream adaptor proteins and perform various physiological functions, such as maintaining cell growth and inhibiting cell apoptosis through movement. EGFR is expressed in a variety of tissue cells [104]. Under normal physiological conditions, EGFR regulates a number of biological processes, such as cell proliferation and differentiation. For example, high expression of EGFR or abnormal activation is associated with the development and progression of various tumors, such as non-small-cell lung cancer (NSCLC), metastatic colorectal cancer (mCRC), head and neck cancer (HNSCC), glioblastoma (GBM), ovarian cancer, and so on. Among these tumors, the occurrence and development of NSCLC are most closely associated with EGFR, and the molecular mechanism of EGFR driving the occurrence and development of NSCLC is also the most profound. Several EGFR inhibitors have been used to treat NSCLC. The discovery of EGFR-activated mutations as sensitive markers for small-molecule EGFR inhibitors is not only a milestone in the history of lung cancer treatment, but also a model for personalized tumor treatment [102,103,104,105].

EGFR inhibitors include primarily gefitinib and erlotinib [106]. They bind to the EGFR kinase region in a competitive ATP-binding manner, reversibly inhibiting EGFR kinase activity and thus blocking downstream signaling. (1) Gefitinib was approved by the FDA in 2003 for the treatment of patients with advanced NSCLC who have failed chemotherapy [107]. (2) Erlotinib was approved by the FDA in 2004 for the treatment of locally advanced or metastatic NSCLC and was subsequently approved in combination with gemcitabine for the treatment of locally advanced or metastatic pancreatic cancer [108]. The discovery of the deletion mutation in exon EGFR19 (exon19del) and the base substitution mutation L858R in exon 21 (L858R mutation), a sensitive marker, uncovered the reason why some people are sensitive to EGFR inhibitors. EGFR-sensitive mutations are located in the intracellular ATP-linked pocket kinase region. They increase the affinity between the binding pockets and ATP, leading to disruption of the EGFR self-inhibition pathway and continuous activation of downstream signaling pathways, which in turn causes carcinogenesis. The affinity of gefitinib and erlotinib for this mutant EGFR protein is stronger than that of ATP molecules, leading to more severe clinical effects in patients with these mutations. Therefore, EGFR mutation detection has been approved for first-line clinical treatment of progressive EGFR-mutated NSCLC and has become routine clinical practice in most cancer centers worldwide. Although gefitinib and erlotinib are effective in treating NSCLC with EGFR-sensitive mutations, patients develop drug resistance within an average of 9 to 14 months after treatment, severely limiting the clinical use of first-generation inhibitors [106,109,110,111]. The emergence of resistance mutations and the activation of compensatory signaling pathways are the main causes of drug resistance. The EGFR T790M mutation was the most common cause of drug resistance and accounted for more than 50% of acquired drug resistance. Therefore, the research and development of second-generation EGFR inhibitors targeting EGFR wild-type and EGFRT790M-resistant mutations has attracted much attention. (3) Afatinib, which can covalently bind the EGFR ATP binding site C797 in the pocket, has significant inhibitory activity against both EGFR WT and T79M resistant mutations. The drug was approved in 2013 by the US FDA for the treatment of advanced non-small-cell lung cancer and HER2-positive advanced breast cancer, and in 2016 for the treatment of patients with advanced lung cancer, whose disease has progressed after platinum-based chemotherapy [112]. However, afatinib has resulted in significant side effects due to its potent inhibition of wild-type EGER activity and has been unable to achieve the effective blood levels of EGFR T790M in humans. Therefore, the research and development of third-generation EGFR inhibitors that selectively inhibit resistant EGFR T7M mutations has attracted much attention [105,113]. (4) Osimertinib can covalently bind to the cysteine site of EGFR 797 and selectively inhibit EGFR-sensitive and drug-resistant mutations. Osimertinib weakly inhibits EGFR wild-type and clinically shows good efficacy and few side effects in patients with drug-resistant mutations containing EGFR T790M. Osimertinib was approved by the US FDA in November 2015 for the second-line treatment of patients with metastatic NSCLC containing EGFR T790M. In 2018, osimertinib was approved by the US FDA as a first-line treatment for patients with EGFR-sensitive, mutation-positive metastatic NSCLC. In addition, osimertinib can effectively cross the blood–brain barrier and is effective in patients with brain metastases (including meningeal metastases) from lung cancer, which is a major advantage over other small-molecule EGFR inhibitors [114,115,116].

Go to:

4. Pharmacogenetics of the Anti-Cancer Natural Products

The action of drugs on the body is generally divided into two phases, the pharmacokinetic phase and the pharmacodynamic phase, and the actual action of drugs in vivo begins with binding to targets; therefore, the pharmacodynamic phase cannot be ignored [117]. It is caused by targeting specific molecular mechanisms and signaling pathways, and at the same time, drug–target interactions are influenced by genetic variations in genes [118,119]. Numerous bioactive natural products have been discovered and isolated, but the role of genetic variations in targets has not been adequately explored. Moreover, few pharmacological targets have been clearly confirmed in comparison with metabolizing enzymes and transporters [120,121]. It is reported that in people with different pharmacological target genotypes, the drug effects of natural products differ. Some genetic variations affect drug action in other ways, such as modulating the functions of related proteins that are not the direct target proteins, enzymes, or transporters [122]. As a result, the situation is more complex, and more attention needs to be paid to drug-related pathways. For some natural products, there are defined targets, but there are still a large number of natural products that are linked not only to their direct targets, but also to other indirect reactions. However, evidence of direct interactions has been found to be difficult to obtain, making the study of the molecular targets of natural products very challenging. The interactions between natural products and target sites are mainly described in the following examples using tumors (see details in Table 3).

Table 3

Pharmacogenetics in the pharmacological targets and pathways of natural products.

Seq_ID	Natural Products	Main Sources	Related Gene	Disease
1	Trabectedin	Ecteinascidia turbinata	BRCA1, BRCA2	Soft tissue sarcoma, Breast cancer
2	Vincristine	Catharanthus roseus	CYP3A enzymes, ABC transporters	Leukemias, Lymphomas, Brain tumors, Solid tumors
5	Paclitaxel	Taxus baccata Linn	ABCB1 G2677T/A mutation	Ovarian cancer
3	Gigantol	Dendrobium draconis	CD133, ALDH1A1	Non-small-cell lung cancer
6	Chrysotoxine	Dendrobium pulchellum	ABCG2	Lung cancer

Currently, genome-wide association studies (GWASs) and other techniques have found many compounds with anti-tumor activities, including many natural products with anti-tumor activities through various mechanisms, such as cytotoxicity [123,124]. This biological action leads to a variety of biological responses, such as inhibition of mitosis, DNA damage, DNA synthesis, and repair damage [125]. The cytotoxic pathways may be related to the efficacy and adverse effects of natural tumor products.

Trabectedin is a marine-derived natural product originally isolated from the marine ascidian Ecteinascidia turbinate. Trabectedin has a complex mechanism of action that affects important cell biological processes in tumor cells and is the first marine anticancer agent approved for patients with soft tissue sarcoma (STS) in the European Union [126]. The DNA damage induced by the drug is largely caused by the nucleotide excision repair protein (NER) and an arginine residue in Rad13 (Arg961). Trabectedin can kill cells by poisoning the DNA NER machinery and DNA repair pathways of homologous recombination [126]. Trabectedin has a unique, multilayered mechanism involving transcriptional regulation and DNA repair systems. In addition, transcription-coupled nucleotide excision repair and homologous recombination repair (HRR) are the main features of its antiproliferative activity. It also has the ability to modulate the tumor microenvironment, which can alter the function and expression of DNA repair genes, such as BRCA1 and BRCA2 [127,128]. BRCA proteins play a critical role in DNA repair, as they are essential for the repair of double-strand breaks. Cancers that have a mutation in the BRCA1 or BRCA2 genes that reduce protein activity, as in ovarian and breast cancers, may increase the activity of drugs that exert their cytotoxicity via DNA double-strand breaks [129,130]. As for BRCA2 mutations, trabectedin showed higher antitumor activity in relapsed metastatic breast cancer patients with germline BRCA2 mutations than in those with BRCA1 mutations. Loss of the wild-type BRCA2 allele in the tumor results in an excellent early, complete metabolic response due to a somatic aberration that likely leads to deregulation of the cellular HR function responsible for increased sensitivity to trabectedin [128]. These reports demonstrate the importance of BRCA1/2 mutations in the administration of trabectedin for the treatment of tumors with defective DNA damage repair.

Vincristine is isolated from the plant Catharanthus roseus and is a drug widely used in cancer treatment. It can be used to treat leukemias, lymphomas, brain tumors, and also solid tumors [131]. The mechanism of tumor restriction is due to its interference with microtubules in the mitotic spindle [132]. In the pharmacokinetics of vincristine, CYP3A enzymes and ABC transporters may play an important role. As with other substrates of CYP3A enzymes, genetic variants of CYP3A also lead to its adverse effects, such as vincristine-induced peripheral neuropathy [133]. There are studies confirming that active CYP3A5 expressors have a lower risk of VIPN than nonexpressers [134,135]. However, a lower risk of VIPN has been observed in some children with the CYP3A5*3 genotype. This is an inverse conclusion when toxicity is accumulated to the highest concentration without CYP3A5 enzyme activity [136]. We need to pay more attention to the neurotoxicity caused by genetic variations of vincristine and perform clinical optimization of its metabolism.

Gigantol, a bibenzyl phenolic compound derived from several medicinal orchids, has been shown to inhibit proliferation, migration, EMT, and the cancer stem cell (CSC) phenotype in lung cancer cells [137,138,139]. At non-toxic doses (below 20 μM), gigantol isolated from Dendrobium draconis could suppress tumor spheroid formation and decrease lung CSC marker proteins, including CD133 and ALDH1A1, in non-small-cell lung cancer NCI-H460 cells. Additionally, gigantol inhibited cancer stem-cell-like phenotypes through down-regulation of the AKT signaling pathway, which leads to reduced levels of Oct4 and Nanog [137].

Paclitaxel (PTX) is one of the natural broad-spectrum antitumor drugs used as first-line chemotherapy in ovarian cancer therapy [140]. The efficacy of paclitaxel is associated with ABCB1 G2677T/A mutation. ABCB1, also known as MDR1, is the efflux pump of cells. After paclitaxel enters human tumor cells, it is pumped out of the cells by ABCB1 [141]. The mutation at position 2677 reduces the transport capacity of ABCB1, allowing the drug to accumulate in tumor cells and achieve a good therapeutic effect. Studies have shown that in patients with ovarian cancer, the G2677T/A mutation has a good effect on paclitaxel [142].

Chrysotoxine, a bibenzyl compound isolated from stems of Dendrobium pulchellum, has been reported to sensitize anoikis and inhibit metastasis of lung cancer cells in an anchorage-independent fashion. Bhummaphan et al. (2019) investigated the suppressive effect of chrysotoxin on CSC-rich populations of H460 and H23 cells and primary CSCs in three-dimensional (3D) cultures. The result showed that non-toxic doses (≤20 μM) of chrysotoxin inhibited CSC-like phenotypes and decreased CSC markers CD133, CD44, ABCG2, and ALDH1A1, which was mediated by a Src-AKT-Sox2-dependent mechanism [143].

Go to:

5. The Challenges of Personalized Therapy

Although a significant number of gene variants’ specific anti-tumor drugs have been approved by the FDA, the percentage of personalized medicines is less than 10% of all the FDA-approved drugs [144]. The development of personalized medicines still confronts some challenges: (1) Interpretation of genetic variations: thousands of mutant genes are scattered throughout the genome of cancer cells, and there are hundreds of different mutant genes associated with cancer [145]. According to current clinical statistical data on personalized tumor treatment, only 30–50% of patients can link the tumor to the corresponding mutations. Moreover, only 3–13% of patients can choose personalized therapy through individual genomic analysis [146]. At present, genome sequencing technology, especially single-cell sequencing and omics analysis, has been rapidly developed. We are able to obtain whole genome data in a relatively short time and at low cost [147,148,149]. Therefore, people need to not only interpret gene functions, but also decipher the functional effects of gene mutations. (2) Dynamic molecular changes of cancer: the pathogenic genes in cancer patients can usually evolve and escape the therapeutic effect of drugs on the lesion through genetic mutation, which is referred to as secondary drug resistance. After secondary drug resistance, current diagnostic and treatment methods may no longer be applicable in the original disease state. Due to our limited understanding of complex signal transduction pathways, the development of personalized treatment options based on basic research cannot resolve the new disease molecular state with some variants and mutants [150]. Therefore, we have to confront significant challenges in monitoring the molecular typing of patients, identifying and defining the occurrence of drug resistance, and finding new treatment options for the protective mechanism of tumors. (3) Combination of drugs: In developing personalized treatment, it is rare for a single drug to act on all the gene mutations causing a patient’s disease. Therefore, it is more beneficial to use a combination drug to treat diseases with multiple genes [151]. Currently, drug combinations are commonly used for cancer, infectious diseases, cardiovascular diseases, and other areas to obtain reasonable strategies for the development of drug combinations and prediction methods for different molecular disease types in order to enhance the synergistic effect between drugs and reduce the occurrence of side effects [152,153,154].

Go to:

6. Conclusions and Future Perspectives

Personalized medicine is changing the way diseases are diagnosed and treated [155]. The development of modern medicine, especially the advances in drug discovery and development based on new technologies and genomics, provides a solid foundation for personalized treatment. With the continuous advances in science and technology, personalized drug matching, combination therapy, and natural product-based therapy have gradually entered the practice and provide better treatment options for cancer patients. Advances in genomics allow us to better understand the genetic basis of disease and identify individuals most likely to benefit from specific treatments. Pharmacogenomics has proven essential for developing personalized treatment options for patients [6]. By identifying genetic variants that affect drug metabolism, efficacy, and toxicity, clinicians can tailor drug therapy to each patient’s unique genetic makeup, thereby improving clinical care, reducing adverse drug events, and optimizing drug dosing and selection. The development of new technologies has provided the basis for deeper evaluation of genomic data and identification of new therapeutic targets [156]. The development of new technologies and a better understanding of the underlying biology of the disease have enabled the development of more effective targeted therapies [157]. Currently, some chemotherapeutic agents, such as pembrolizumab and nivolumab, are used for the individualized treatment of tumors. Meanwhile, studies have shown that natural products, such as vinblastine, have antitumor effects, in addition to synthetic drugs [158]. Previous studies have confirmed that some natural products are related to pharmacogenomics, which can affect enzymes and transporters of drug metabolism. It is suggested that natural products and traditional Chinese medicine may be potential sources for personalized treatment [159].

In general, the development of personalized therapy has far-reaching prospects, but the challenges cannot be ignored. In the future, we must not only continue to advance research, but also strengthen collaboration and holistic thinking in various aspects. In this way, we can better put personalized treatment into practice and improve patients’ quality of life and the effect of treatment. The integration of pharmacogenomics and other cutting-edge technologies is providing new insights into disease mechanisms and leading to the development of personalized therapeutic strategies. As our understanding of the genetic and molecular mechanisms of disease increases, further breakthroughs in personalized medicine can be expected. These include, in particular, the identification of new drug targets and the development of more effective therapies based on natural products and traditional medicine.

Go to:

Abbreviations

EGFR, Epidermal Growth Factor Receptor; ALK, ALK receptor tyrosine kinase; AI, Artificial Intelligence; CADD, Computer-Aided Drug Discovery; MD, Molecular Dynamics; AMBER, Amber Molecular Dynamics; GROMACS, GROningen MAchine for Chemical Simulations; NAMD, NAnoscale Molecular Dynamics; QSAR, Quantitative Structure–activity Relationship; NSCLC, Non-small-Cell Lung Cancer; mCRC, Metastatic Colorectal Cancer; HNSCC, Head and Neck Cancer; GBM, Glioblastoma; GWAS, Genome-wide Association Studies; STS, Soft Tissue Sarcoma; NER, Nucleotide Excision Repair; HRR, Homologous Recombination Repair; CSC, Cancer Stem Cell; PTX, Paclitaxel.

Go to:

Funding Statement

We acknowledge the financial support from the National Natural Science Foundation of China (31871281), the Scientific Research Foundation for Advanced Talents of Shanghai University of Traditional Chinese Medicine, and the Shanghai Municipal Health Commission (2020XGKY12).

Go to:

Author Contributions

Q.W. and X.C. designed and revised the manuscript. Y.Z., S.P. and H.W. reviewed the literature and drafted the original manuscript. All authors have read and agreed to the published version of the manuscript.

Go to:

Institutional Review Board Statement

Not applicable.

Go to:

Informed Consent Statement

Not applicable.

Go to:

Data Availability Statement

Not applicable.

Go to:

Conflicts of Interest

The authors declare no conflict of interest.

Go to:

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Go to:

References

1. Su M., Zhang Z., Zhou L., Han C., Huang C., Nice E.C. Proteomics, personalized medicine and cancer. Cancers. 2021;13:2512. 10.3390/cancers13112512. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

2. Chen R., Snyder M. Promise of personalized omics to precision medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 2013;5:73–82. 10.1002/wsbm.1198. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

3. Carrasco-Ramiro F., Peiró-Pastor R., Aguado B. Human genomics projects and precision medicine. Gene Ther. 2017;24:551–561. 10.1038/gt.2017.77. [Abstract] [CrossRef] [Google Scholar]

4. Jain K.K. Personalized medicine. Curr. Opin. Mol. Ther. 2002;4:548–558. [Abstract] [Google Scholar]

5. Najjar S., Allison K.H. Updates on breast biomarkers. Virchows Arch. 2022;480:163–176. 10.1007/s00428-022-03267-x. [Abstract] [CrossRef] [Google Scholar]

6. Cecchin E., Stocco G. Pharmacogenomics and Personalized Medicine. Genes. 2020;11:679. 10.3390/genes11060679. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

7. Chang C.J., Chen C.B., Hung S.I., Ji C., Chung W.H. Pharmacogenetic Testing for Prevention of Severe Cutaneous Adverse Drug Reactions. Front. Pharm. 2020;11:969. 10.3389/fphar.2020.00969. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

8. Zastrozhin M.S., Sorokin A.S., Agibalova T.V., Grishina E.A., Antonenko A., Rozochkin I.N., Duzhev D.V., Skryabin V.Y., Galaktionova T.E., Barna I.V., et al. Using a personalized clinical decision support system for bromdihydrochlorphenylbenzodiazepine dosing in patients with anxiety disorders based on the pharmacogenomic markers. Hum. Psychopharmacol. 2018;33:e2677. 10.1002/hup.2677. [Abstract] [CrossRef] [Google Scholar]

9. Workman P. Pharmacogenomics in cancer drug discovery and development: Inhibitors of the Hsp90 molecular chaperone. Cancer Detect. Prev. 2002;26:405–410. 10.1016/S0361-090X(02)00126-5. [Abstract] [CrossRef] [Google Scholar]

10. Letai A., Bhola P., Welm A.L. Functional precision oncology: Testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell. 2022;40:26–35. 10.1016/j.ccell.2021.12.004. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

11. Shirley M. Bruton Tyrosine Kinase Inhibitors in B-Cell Malignancies: Their Use and Differential Features. Target. Oncol. 2022;17:69–84. 10.1007/s11523-021-00857-8. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

12. Zhong L., Li Y., Xiong L., Wang W., Wu M., Yuan T., Yang W., Tian C., Miao Z., Wang T., et al. Small molecules in targeted cancer therapy: Advances, challenges, and future perspectives. Signal. Transduct. Target. 2021;6:201. 10.1038/s41392-021-00572-w. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

13. Pushpakom S., Iorio F., Eyers P.A., Escott K.J., Hopper S., Wells A., Doig A., Guilliams T., Latimer J., McNamee C., et al. Drug repurposing: Progress, challenges and recommendations. Nat. Rev. Drug. Discov. 2019;18:41–58. 10.1038/nrd.2018.168. [Abstract] [CrossRef] [Google Scholar]

14. Grover A., Sharma P.C. Development and use of molecular markers: Past and present. Crit. Rev. Biotechnol. 2016;36:290–302. 10.3109/07388551.2014.959891. [Abstract] [CrossRef] [Google Scholar]

15. Yu T.W., Lane H.Y., Lin C.H. Novel Therapeutic Approaches for Alzheimer’s Disease: An Updated Review. Int. J. Mol. Sci. 2021;22:8208. 10.3390/ijms22158208. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

16. Nakagawa K., Garon E.B., Seto T., Nishio M., Ponce Aix S., Paz-Ares L., Chiu C.H., Park K., Novello S., Nadal E., et al. Ramucirumab plus erlotinib in patients with untreated, EGFR-mutated, advanced non-small-cell lung cancer (RELAY): A randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2019;20:1655–1669. 10.1016/S1470-2045(19)30634-5. [Abstract] [CrossRef] [Google Scholar]

17. Casak S.J., Fashoyin-Aje I., Lemery S.J., Zhang L., Jin R., Li H., Zhao L., Zhao H., Zhang H., Chen H., et al. FDA Approval Summary: Ramucirumab for Gastric Cancer. Clin. Cancer Res. 2015;21:3372–3376. 10.1158/1078-0432.CCR-15-0600. [Abstract] [CrossRef] [Google Scholar]

18. Domingos S., André V., Quaresma S., Martins I.C., Minas da Piedade M.F., Duarte M.T. New forms of old drugs: Improving without changing. J. Pharm. Pharm. 2015;67:830–846. 10.1111/jphp.12384. [Abstract] [CrossRef] [Google Scholar]

19. Vlahopoulos S., Critselis E., Voutsas I.F., Perez S.A., Moschovi M., Baxevanis C.N., Chrousos G.P. New use for old drugs? Prospective targets of chloroquines in cancer therapy. Curr. Drug. Targets. 2014;15:843–851. 10.2174/1389450115666140714121514. [Abstract] [CrossRef] [Google Scholar]

20. Joyce C.R. New drugs for mental diseases? New diseases for old drugs? J. R. Soc. Med. 1987;80:406–409. 10.1177/014107688708000705. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

21. Cortez-Maya S., Moreno-Herrera A., Palos I., Rivera G. Old Antiprotozoal Drugs: Are They Still Viable Options for Parasitic Infections or New Options for Other Diseases? Curr. Med. Chem. 2020;27:5403–5428. 10.2174/0929867326666190628163633. [Abstract] [CrossRef] [Google Scholar]

22. Arbel Y., Abuzeid W., Rosenson R.S., Weisman A., Farkouh M.E. Old Drugs for New Indications in Cardiovascular Medicine. Cardiovasc. Drugs Ther. 2018;32:223–232. 10.1007/s10557-018-6785-y. [Abstract] [CrossRef] [Google Scholar]

23. Kaiser D., Oetjen E. Something old, something new and something very old: Drugs for treating type 2 diabetes. Br. J. Pharm. 2014;171:2940–2950. 10.1111/bph.12624. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

24. Janiaud P., Serghiou S., Ioannidis J.P.A. New clinical trial designs in the era of precision medicine: An overview of definitions, strengths, weaknesses, and current use in oncology. Cancer Treat. Rev. 2019;73:20–30. 10.1016/j.ctrv.2018.12.003. [Abstract] [CrossRef] [Google Scholar]

25. Di Sanzo M., Cipolloni L., Borro M., La Russa R., Santurro A., Scopetti M., Simmaco M., Frati P. Clinical Applications of Personalized Medicine: A New Paradigm and Challenge. Curr. Pharm. Biotechnol. 2017;18:194–203. 10.2174/1389201018666170224105600. [Abstract] [CrossRef] [Google Scholar]

26. Jørgensen J.T. Oncology drug-companion diagnostic combinations. Cancer Treat. Res. Commun. 2021;29:100492. 10.1016/j.ctarc.2021.100492. [Abstract] [CrossRef] [Google Scholar]

27. Zwart H., Landeweerd L., Lemmens P. Continental philosophical perspectives on life sciences and emerging technologies. Life Sci. Soc. Policy. 2016;12:8. 10.1186/s40504-016-0041-7. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

28. Hartl D., de Luca V., Kostikova A., Laramie J., Kennedy S., Ferrero E., Siegel R., Fink M., Ahmed S., Millholland J., et al. Translational precision medicine: An industry perspective. J. Transl. Med. 2021;19:245. 10.1186/s12967-021-02910-6. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

29. Lian M.A., Ke-Bin W., Sheng-Xian W.U. Research and development of new traditional Chinese medicine drugs for certain syndromes based on “theoretical innovation” Zhongguo Zhong Yao Za Zhi. 2020;45:5048–5056. 10.19540/j.cnki.cjcmm.20200229.501. [Abstract] [CrossRef] [Google Scholar]

30. Jiang Z., Zhou X., Li R., Michal J.J., Zhang S., Dodson M.V., Zhang Z., Harland R.M. Whole transcriptome analysis with sequencing: Methods, challenges and potential solutions. Cell Mol. Life Sci. 2015;72:3425–3439. 10.1007/s00018-015-1934-y. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

31. Sherman R.M., Salzberg S.L. Pan-genomics in the human genome era. Nat. Rev. Genet. 2020;21:243–254. 10.1038/s41576-020-0210-7. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

32. Wang Y., Yang W. Proteome-Scale Analysis of Protein S-Acylation Comes of Age. J. Proteome Res. 2021;20:14–26. 10.1021/acs.jproteome.0c00409. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

33. Balashova E.E., Trifonova O.P., Maslov D.L., Lichtenberg S.R., Lokhov P.G., Archakov A.I. Metabolome profiling in the study of aging processes. Biomed. Khim. 2022;68:321–338. 10.18097/pbmc20226805321. [Abstract] [CrossRef] [Google Scholar]

34. Li X., Ma J., Leng L., Han M., Li M., He F., Zhu Y. MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis. Front. Genet. 2022;13:806842. 10.3389/fgene.2022.806842. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

35. Farber G., Boczar K.E., Wiefels C.C., Zelt J.G.E., Guler E.C., deKemp R.A., Beanlands R.S., Rotstein B.H. The Future of Cardiac Molecular Imaging. Semin. Nucl. Med. 2020;50:367–385. 10.1053/j.semnuclmed.2020.02.005. [Abstract] [CrossRef] [Google Scholar]

36. Bagchi A. Latest trends in structure based drug design with protein targets. Adv. Protein Chem. Struct. Biol. 2020;121:1–23. 10.1016/bs.apcsb.2019.11.008. [Abstract] [CrossRef] [Google Scholar]

37. Joshi R.R., Samant V.V. Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight. J. Mol. Model. 2007;13:275–282. 10.1007/s00894-006-0141-z. [Abstract] [CrossRef] [Google Scholar]

38. Ramesh A.N., Kambhampati C., Monson J.R., Drew P.J. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004;86:334–338. 10.1308/147870804290. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

39. Carrera-Rivera A., Ochoa W., Larrinaga F., Lasa G. How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX. 2022;9:101895. 10.1016/j.mex.2022.101895. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

40. Rasteau S., Ernenwein D., Savoldelli C., Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. J. Stomatol. Oral. Maxillofac. Surg. 2022;123:276–282. 10.1016/j.jormas.2022.01.010. [Abstract] [CrossRef] [Google Scholar]

41. Wang A., Xiu X., Liu S., Qian Q., Wu S. Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov . Int. J. Env. Res. Public. Health. 2022;19:13691. 10.3390/ijerph192013691. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

42. Wang L., Alexander C.A. Big data analytics in medical engineering and healthcare: Methods, advances and challenges. J. Med. Eng. Technol. 2020;44:267–283. 10.1080/03091902.2020.1769758. [Abstract] [CrossRef] [Google Scholar]

43. Dallas S., Sensenhauser C., Batheja A., Singer M., Markowska M., Zakszewski C., Mamidi R.N., McMillia M., Han C., Zhou H., et al. De-risking bio-therapeutics for possible drug interactions using cryopreserved human hepatocytes. Curr. Drug. Metab. 2012;13:923–929. 10.2174/138920012802138589. [Abstract] [CrossRef] [Google Scholar]

44. Lee Y.W., Choi J.W., Shin E.H. Machine learning model for predicting malaria using clinical information. Comput. Biol. Med. 2021;129:104151. 10.1016/j.compbiomed.2020.104151. [Abstract] [CrossRef] [Google Scholar]

45. Chan S., Reddy V., Myers B., Thibodeaux Q., Brownstone N., Liao W. Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations. Dermatol. Ther. 2020;10:365–386. 10.1007/s13555-020-00372-0. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

46. Jiang Y., Yang M., Wang S., Li X., Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun. 2020;40:154–166. 10.1002/cac2.12012. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

47. Dong L., He W., Zhang R., Ge Z., Wang Y.X., Zhou J., Xu J., Shao L., Wang Q., Yan Y., et al. Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases. JAMA Netw. Open. 2022;5:e229960. 10.1001/jamanetworkopen.2022.9960. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

48. Yu K.H., Beam A.L., Kohane I.S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2018;2:719–731. 10.1038/s41551-018-0305-z. [Abstract] [CrossRef] [Google Scholar]

49. Ji Y., Liu S., Hong X., Lu Y., Wu X., Li K., Li K., Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front. Cell Dev. Biol. 2022;10:1107689. 10.3389/fcell.2022.1107689. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

50. Song Q., Li X. Application and development of voice analysis and endoscopic technology combined with artificial intelligence in the diagnosis and treatment of throat disease. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2022;36:647–650. 10.13201/j.issn.2096-7993.2022.08.017. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

51. Ye Y., Zhang Z., Liu Y., Diao L., Han L. A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine. Trends Genet. 2020;36:318–336. 10.1016/j.tig.2020.01.009. [Abstract] [CrossRef] [Google Scholar]

52. Chong J., Wishart D.S., Xia J. Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. Curr. Protoc. Bioinform. 2019;68:e86. 10.1002/cpbi.86. [Abstract] [CrossRef] [Google Scholar]

53. Nygaard H.B. Targeting Fyn Kinase in Alzheimer’s Disease. Biol. Psychiatry. 2018;83:369–376. 10.1016/j.biopsych.2017.06.004. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

54. Wang M., Li A., Sekiya M., Beckmann N.D., Quan X., Schrode N., Fernando M.B., Yu A., Zhu L., Cao J., et al. Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron. 2021;109:257–272.e214. 10.1016/j.neuron.2020.11.002. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

55. Parker G., Brotchie H. Do the old psychostimulant drugs have a role in managing treatment-resistant depression? Acta Psychiatr. Scand. 2010;121:308–314. 10.1111/j.1600-0447.2009.01434.x. [Abstract] [CrossRef] [Google Scholar]

56. Whirl-Carrillo M., McDonagh E.M., Hebert J.M., Gong L., Sangkuhl K., Thorn C.F., Altman R.B., Klein T.E. Pharmacogenomics knowledge for personalized medicine. Clin. Pharm. 2012;92:414–417. 10.1038/clpt.2012.96. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

57. Qu J., Kalyani F.S., Liu L., Cheng T., Chen L. Tumor organoids: Synergistic applications, current challenges, and future prospects in cancer therapy. Cancer Commun. 2021;41:1331–1353. 10.1002/cac2.12224. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

58. Park M., Kim D., Moon K., Park T. Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components. Int. J. Mol. Sci. 2020;21:8202. 10.3390/ijms21218202. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

59. Ding Z., Wang N., Ji N., Chen Z.S. Proteomics technologies for cancer liquid biopsies. Mol. Cancer. 2022;21:53. 10.1186/s12943-022-01526-8. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

60. Battaglini D., Al-Husinat L., Normando A.G., Leme A.P., Franchini K., Morales M., Pelosi P., Rocco P.R. Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes. Respir. Res. 2022;23:318. 10.1186/s12931-022-02233-0. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

61. Spradlin J.N., Zhang E., Nomura D.K. Reimagining Druggability Using Chemoproteomic Platforms. Acc. Chem. Res. 2021;54:1801–1813. 10.1021/acs.accounts.1c00065. [Abstract] [CrossRef] [Google Scholar]

62. Kanduc D. The role of proteomics in defining autoimmunity. Expert. Rev. Proteom. 2021;18:177–184. 10.1080/14789450.2021.1914595. [Abstract] [CrossRef] [Google Scholar]

63. Elyada E., Bolisetty M., Laise P., Flynn W.F., Courtois E.T., Burkhart R.A., Teinor J.A., Belleau P., Biffi G., Lucito M.S., et al. Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts. Cancer Discov. 2019;9:1102–1123. 10.1158/2159-8290.CD-19-0094. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

64. Pang Z., Schafroth M.A., Ogasawara D., Wang Y., Nudell V., Lal N.K., Yang D., Wang K., Herbst D.M., Ha J., et al. In situ identification of cellular drug targets in mammalian tissue. Cell. 2022;185:1793–1805.e1717. 10.1016/j.cell.2022.03.040. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

65. Ambure P., Roy K. CADD Modeling of Multi-Target Drugs Against Alzheimer’s Disease. Curr. Drug. Targets. 2017;18:522–533. 10.2174/1389450116666150907104855. [Abstract] [CrossRef] [Google Scholar]

66. Lomenick B., Hao R., Jonai N., Chin R.M., Aghajan M., Warburton S., Wang J., Wu R.P., Gomez F., Loo J.A., et al. Target identification using drug affinity responsive target stability (DARTS) Proc. Natl. Acad. Sci. USA. 2009;106:21984–21989. 10.1073/pnas.0910040106. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

67. Li G., Lin P., Wang K., Gu C.C., Kusari S. Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms. Trends Cancer. 2022;8:65–80. 10.1016/j.trecan.2021.10.002. [Abstract] [CrossRef] [Google Scholar]

68. Wong M., Badri A., Gasparis C., Belfort G., Koffas M. Modular optimization in metabolic engineering. Crit. Rev. Biochem. Mol. Biol. 2021;56:587–602. 10.1080/10409238.2021.1937928. [Abstract] [CrossRef] [Google Scholar]

69. Gomeni R., Bani M., D’Angeli C., Corsi M., Bye A. Computer-assisted drug development (CADD): An emerging technology for designing first-time-in-man and proof-of-concept studies from preclinical experiments. Eur. J. Pharm. Sci. 2001;13:261–270. 10.1016/S0928-0987(01)00111-7. [Abstract] [CrossRef] [Google Scholar]

70. Vintonyak V.V., Antonchick A.P., Rauh D., Waldmann H. The therapeutic potential of phosphatase inhibitors. Curr. Opin. Chem. Biol. 2009;13:272–283. 10.1016/j.cbpa.2009.03.021. [Abstract] [CrossRef] [Google Scholar]

71. Avram S., Mernea M., Limban C., Borcan F., Chifiriuc C. Potential Therapeutic Approaches to Alzheimer’s Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools. Curr. Neuropharmacol. 2020;18:696–719. 10.2174/1570159X18666191230120053. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

72. Akalin P.K. Introduction to bioinformatics. Mol. Nutr. Food Res. 2006;50:610–619. 10.1002/mnfr.200500273. [Abstract] [CrossRef] [Google Scholar]

73. Wishart D.S. Introduction to cheminformatics. Curr. Protoc. Bioinform. 2007;14:14.1.1–14.1.21. 10.1002/0471250953.bi1401s18. [Abstract] [CrossRef] [Google Scholar]

74. Irwin J.J., Gaskins G., Sterling T., Mysinger M.M., Keiser M.J. Predicted Biological Activity of Purchasable Chemical Space. J. Chem. Inf. Model. 2018;58:148–164. 10.1021/acs.jcim.7b00316. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

75. Mitusińska K., Raczyńska A., Bzówka M., Bagrowska W., Góra A. Applications of water molecules for analysis of macromolecule properties. Comput. Struct. Biotechnol. J. 2020;18:355–365. 10.1016/j.csbj.2020.02.001. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

76. Zobdeh F., Ben Kraiem A., Attwood M.M., Chubarev V.N., Tarasov V.V., Schiöth H.B., Mwinyi J. Pharmacological treatment of migraine: Drug classes, mechanisms of action, clinical trials and new treatments. Br. J. Pharm. 2021;178:4588–4607. 10.1111/bph.15657. [Abstract] [CrossRef] [Google Scholar]

77. Pinzi L., Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int. J. Mol. Sci. 2019;20:4331. 10.3390/ijms20184331. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

78. Reddy K.K., Rathore R.S., Srujana P., Burri R.R., Reddy C.R., Sumakanth M., Reddanna P., Reddy M.R. Performance Evaluation of Docking Programs- Glide, GOLD, AutoDock & SurflexDock, Using Free Energy Perturbation Reference Data: A Case Study of Fructose-1, 6-bisphosphatase-AMP Analogs. Mini Rev. Med. Chem. 2020;20:1179–1187. 10.2174/1389557520666200526183353. [Abstract] [CrossRef] [Google Scholar]

79. Collier T.A., Piggot T.J., Allison J.R. Molecular Dynamics Simulation of Proteins. Methods Mol. Biol. 2020;2073:311–327. 10.1007/978-1-4939-9869-2_17. [Abstract] [CrossRef] [Google Scholar]

80. Wang L., Chambers J., Abel R. Protein-Ligand Binding Free Energy Calculations with FEP. Methods Mol. Biol. 2019;2022:201–232. 10.1007/978-1-4939-9608-7_9. [Abstract] [CrossRef] [Google Scholar]

81. Gupta R., Srivastava D., Sahu M., Tiwari S., Ambasta R.K., Kumar P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers. 2021;25:1315–1360. 10.1007/s11030-021-10217-3. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

82. Hessler G., Baringhaus K.H. Artificial Intelligence in Drug Design. Molecules. 2018;23:2520. 10.3390/molecules23102520. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

83. Mueller R., Dawson E.S., Meiler J., Rodriguez A.L., Chauder B.A., Bates B.S., Felts A.S., Lamb J.P., Menon U.N., Jadhav S.B., et al. Discovery of 2-(2-benzoxazoyl amino)-4-aryl-5-cyanopyrimidine as negative allosteric modulators (NAMs) of metabotropic glutamate receptor 5 (mGlu₅): From an artificial neural network virtual screen to an in vivo tool compound. ChemMedChem. 2012;7:406–414. 10.1002/cmdc.201100510. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

84. Ijjaali I., Barrere C., Nargeot J., Petitet F., Bourinet E. Ligand-based virtual screening to identify new T-type calcium channel blockers. Channels. 2007;1:300–304. 10.4161/chan.4999. [Abstract] [CrossRef] [Google Scholar]

85. Dembic Z. Antitumor Drugs and Their Targets. Molecules. 2020;25:5776. 10.3390/molecules25235776. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

86. Jain K.K. Personalized Immuno-Oncology. Med. Princ. Pr. 2021;30:1–16. 10.1159/000511107. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

87. Awad M.M., Govindan R., Balogh K.N., Spigel D.R., Garon E.B., Bushway M.E., Poran A., Sheen J.H., Kohler V., Esaulova E., et al. Personalized neoantigen vaccine NEO-PV-01 with chemotherapy and anti-PD-1 as first-line treatment for non-squamous non-small cell lung cancer. Cancer Cell. 2022;40:1010–1026.e1011. 10.1016/j.ccell.2022.08.003. [Abstract] [CrossRef] [Google Scholar]

88. Reijers I.L.M., Menzies A.M., van Akkooi A.C.J., Versluis J.M., van den Heuvel N.M.J., Saw R.P.M., Pennington T.E., Kapiteijn E., van der Veldt A.A.M., Suijkerbuijk K.P.M., et al. Personalized response-directed surgery and adjuvant therapy after neoadjuvant ipilimumab and nivolumab in high-risk stage III melanoma: The PRADO trial. Nat. Med. 2022;28:1178–1188. 10.1038/s41591-022-01851-x. [Abstract] [CrossRef] [Google Scholar]

89. Du X., Shao Y., Qin H.F., Tai Y.H., Gao H.J. ALK-rearrangement in non-small-cell lung cancer (NSCLC) Thorac. Cancer. 2018;9:423–430. 10.1111/1759-7714.12613. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

90. Golding B., Luu A., Jones R., Viloria-Petit A.M. The function and therapeutic targeting of anaplastic lymphoma kinase (ALK) in non-small cell lung cancer (NSCLC) Mol. Cancer. 2018;17:52. 10.1186/s12943-018-0810-4. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

91. Lin J.J., Riely G.J., Shaw A.T. Targeting ALK: Precision Medicine Takes on Drug Resistance. Cancer Discov. 2017;7:137–155. 10.1158/2159-8290.CD-16-1123. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

92. Camidge D.R., Kim H.R., Ahn M.J., Yang J.C., Han J.Y., Lee J.S., Hochmair M.J., Li J.Y., Chang G.C., Lee K.H., et al. Brigatinib versus Crizotinib in ALK-Positive Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2018;379:2027–2039. 10.1056/NEJMoa1810171. [Abstract] [CrossRef] [Google Scholar]

93. Horn L., Wang Z., Wu G., Poddubskaya E., Mok T., Reck M., Wakelee H., Chiappori A.A., Lee D.H., Breder V., et al. Ensartinib vs Crizotinib for Patients with Anaplastic Lymphoma Kinase-Positive Non-Small Cell Lung Cancer: A Randomized Clinical Trial. JAMA Oncol. 2021;7:1617–1625. 10.1001/jamaoncol.2021.3523. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

94. Camidge D.R., Kim H.R., Ahn M.J., Yang J.C.H., Han J.Y., Hochmair M.J., Lee K.H., Delmonte A., García Campelo M.R., Kim D.W., et al. Brigatinib Versus Crizotinib in Advanced ALK Inhibitor-Naive ALK-Positive Non-Small Cell Lung Cancer: Second Interim Analysis of the Phase III ALTA-1L Trial. J. Clin. Oncol. 2020;38:3592–3603. 10.1200/JCO.20.00505. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

95. Popat S., Liu G., Lu S., Song G., Ma X., Yang J.C. Brigatinib vs alectinib in crizotinib-resistant advanced anaplastic lymphoma kinase-positive non-small-cell lung cancer (ALTA-3) Future Oncol. 2021;17:4237–4247. 10.2217/fon-2021-0608. [Abstract] [CrossRef] [Google Scholar]

96. Taniguchi K., Konishi H., Yoshinaga A., Tsugane M., Takahashi H., Nishisaka F., Shishido Y., Asai A. Efficacy of combination treatment using YHO-1701, an orally active STAT3 inhibitor, with molecular-targeted agents on cancer cell lines. Sci. Rep. 2021;11:6685. 10.1038/s41598-021-86021-8. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

97. Camidge D.R., Kim H.R., Ahn M.J., Yang J.C.H., Han J.Y., Hochmair M.J., Lee K.H., Delmonte A., Garcia Campelo M.R., Kim D.W., et al. Brigatinib Versus Crizotinib in ALK Inhibitor-Naive Advanced ALK-Positive NSCLC: Final Results of Phase 3 ALTA-1L Trial. J. Thorac. Oncol. 2021;16:2091–2108. 10.1016/j.jtho.2021.07.035. [Abstract] [CrossRef] [Google Scholar]

98. Cooper A.J., Sequist L.V., Lin J.J. Third-generation EGFR and ALK inhibitors: Mechanisms of resistance and management. Nat. Rev. Clin. Oncol. 2022;19:499–514. 10.1038/s41571-022-00639-9. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

99. Shaw A.T., Solomon B.J., Besse B., Bauer T.M., Lin C.C., Soo R.A., Riely G.J., Ou S.I., Clancy J.S., Li S., et al. ALK Resistance Mutations and Efficacy of Lorlatinib in Advanced Anaplastic Lymphoma Kinase-Positive Non-Small-Cell Lung Cancer. J. Clin. Oncol. 2019;37:1370–1379. 10.1200/JCO.18.02236. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

100. Baba K., Goto Y. Lorlatinib as a treatment for ALK-positive lung cancer. Future Oncol. 2022;18:2745–2766. 10.2217/fon-2022-0184. [Abstract] [CrossRef] [Google Scholar]

101. Hyman D.M., Piha-Paul S.A., Won H., Rodon J., Saura C., Shapiro G.I., Juric D., Quinn D.I., Moreno V., Doger B., et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature. 2018;554:189–194. 10.1038/nature25475. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

102. Harrison P.T., Vyse S., Huang P.H. Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer. Semin. Cancer Biol. 2020;61:167–179. 10.1016/j.semcancer.2019.09.015. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

103. Sigismund S., Avanzato D., Lanzetti L. Emerging functions of the EGFR in cancer. Mol. Oncol. 2018;12:3–20. 10.1002/1878-0261.12155. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

104. Voldborg B.R., Damstrup L., Spang-Thomsen M., Poulsen H.S. Epidermal growth factor receptor (EGFR) and EGFR mutations, function and possible role in clinical trials. Ann. Oncol. 1997;8:1197–1206. 10.1023/A:1008209720526. [Abstract] [CrossRef] [Google Scholar]

105. Wu S.G., Shih J.Y. Management of acquired resistance to EGFR TKI-targeted therapy in advanced non-small cell lung cancer. Mol. Cancer. 2018;17:38. 10.1186/s12943-018-0777-1. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

106. Yang Z., Hackshaw A., Feng Q., Fu X., Zhang Y., Mao C., Tang J. Comparison of gefitinib, erlotinib and afatinib in non-small cell lung cancer: A meta-analysis. Int. J. Cancer. 2017;140:2805–2819. 10.1002/ijc.30691. [Abstract] [CrossRef] [Google Scholar]

107. Cheng H., Liu H., Du Q., Zhang H., Zhang X., Wang Y., Shao J., Yang F., Zhang B., Shi J., et al. Efficacy and safety of domestic and imported gefitinib in patients with advanced non-small cell lung cancer. Ann. Palliat. Med. 2021;10:10–15. 10.21037/apm-20-2140. [Abstract] [CrossRef] [Google Scholar]

108. Çoban Ö., Değim Z. Development of Nanocochleates Containing Erlotinib HCl and Dexketoprofen Trometamol and Evaluation of In Vitro Characteristic Properties. Turk. J. Pharm. Sci. 2018;15:16–21. 10.4274/tjps.83803. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

109. Lynch T.J., Bell D.W., Sordella R., Gurubhagavatula S., Okimoto R.A., Brannigan B.W., Harris P.L., Haserlat S.M., Supko J.G., Haluska F.G., et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 2004;350:2129–2139. 10.1056/NEJMoa040938. [Abstract] [CrossRef] [Google Scholar]

110. Zhong W.Z., Wang Q., Mao W.M., Xu S.T., Wu L., Wei Y.C., Liu Y.Y., Chen C., Cheng Y., Yin R., et al. Gefitinib Versus Vinorelbine Plus Cisplatin as Adjuvant Treatment for Stage II-IIIA (N1-N2) EGFR-Mutant NSCLC: Final Overall Survival Analysis of CTONG1104 Phase III Trial. J. Clin. Oncol. 2021;39:713–722. 10.1200/JCO.20.01820. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

111. Fu Y., Zhang Y., Lei Z., Liu T., Cai T., Wang A., Du W., Zeng Y., Zhu J., Liu Z., et al. Abnormally activated OPN/integrin αVβ3/FAK signalling is responsible for EGFR-TKI resistance in EGFR mutant non-small-cell lung cancer. J. Hematol. Oncol. 2020;13:169. 10.1186/s13045-020-01009-7. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

112. Harvey R.D., Adams V.R., Beardslee T., Medina P. Afatinib for the treatment of EGFR mutation-positive NSCLC: A review of clinical findings. J. Oncol. Pharm. Pr. 2020;26:1461–1474. 10.1177/1078155220931926. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

113. Passaro A., Mok T., Peters S., Popat S., Ahn M.J., de Marinis F. Recent Advances on the Role of EGFR Tyrosine Kinase Inhibitors in the Management of NSCLC With Uncommon, Non Exon 20 Insertions, EGFR Mutations. J. Thorac. Oncol. 2021;16:764–773. 10.1016/j.jtho.2020.12.002. [Abstract] [CrossRef] [Google Scholar]

114. Leonetti A., Sharma S., Minari R., Perego P., Giovannetti E., Tiseo M. Resistance mechanisms to osimertinib in EGFR-mutated non-small cell lung cancer. Br. J. Cancer. 2019;121:725–737. 10.1038/s41416-019-0573-8. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

115. Ramalingam S.S., Vansteenkiste J., Planchard D., Cho B.C., Gray J.E., Ohe Y., Zhou C., Reungwetwattana T., Cheng Y., Chewaskulyong B., et al. Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC. N. Engl. J. Med. 2020;382:41–50. 10.1056/NEJMoa1913662. [Abstract] [CrossRef] [Google Scholar]

116. Remon J., Steuer C.E., Ramalingam S.S., Felip E. Osimertinib and other third-generation EGFR TKI in EGFR-mutant NSCLC patients. Ann. Oncol. 2018;29:i20–i27. 10.1093/annonc/mdx704. [Abstract] [CrossRef] [Google Scholar]

117. Fan J., de Lannoy I.A. Pharmacokinetics. Biochem. Pharm. 2014;87:93–120. 10.1016/j.bcp.2013.09.007. [Abstract] [CrossRef] [Google Scholar]

118. Evans W.E., McLeod H.L. Pharmacogenomics--drug disposition, drug targets, and side effects. N. Engl. J. Med. 2003;348:538–549. 10.1056/NEJMra020526. [Abstract] [CrossRef] [Google Scholar]

119. Mougey E.B., Lang J.E., Wen X., Lima J.J. Effect of citrus juice and SLCO2B1 genotype on the pharmacokinetics of montelukast. J. Clin. Pharm. 2011;51:751–760. 10.1177/0091270010374472. [Abstract] [CrossRef] [Google Scholar]

120. Harvey A.L., Edrada-Ebel R., Quinn R.J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug. Discov. 2015;14:111–129. 10.1038/nrd4510. [Abstract] [CrossRef] [Google Scholar]

121. Xu L., Li Y., Dai Y., Peng J. Natural products for the treatment of type 2 diabetes mellitus: Pharmacology and mechanisms. Pharm. Res. 2018;130:451–465. 10.1016/j.phrs.2018.01.015. [Abstract] [CrossRef] [Google Scholar]

122. Rodrigues T., Reker D., Schneider P., Schneider G. Counting on natural products for drug design. Nat. Chem. 2016;8:531–541. 10.1038/nchem.2479. [Abstract] [CrossRef] [Google Scholar]

123. Liu X., Simon J.M., Xie H., Hu L., Wang J., Zurlo G., Fan C., Ptacek T.S., Herring L., Tan X., et al. Genome-wide Screening Identifies SFMBT1 as an Oncogenic Driver in Cancer with VHL Loss. Mol. Cell. 2020;77:1294–1306.e1295. 10.1016/j.molcel.2020.01.009. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

124. Pan H., Xue C., Auerbach B.J., Fan J., Bashore A.C., Cui J., Yang D.Y., Trignano S.B., Liu W., Shi J., et al. Single-Cell Genomics Reveals a Novel Cell State During Smooth Muscle Cell Phenotypic Switching and Potential Therapeutic Targets for Atherosclerosis in Mouse and Human. Circulation. 2020;142:2060–2075. 10.1161/CIRCULATIONAHA.120.048378. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

125. Warren N.J.H., Eastman A. Comparison of the different mechanisms of cytotoxicity induced by checkpoint kinase I inhibitors when used as single agents or in combination with DNA damage. Oncogene. 2020;39:1389–1401. 10.1038/s41388-019-1079-9. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

126. Cuevas C., Francesch A. Development of Yondelis (trabectedin, ET-743). A semisynthetic process solves the supply problem. Nat. Prod. Rep. 2009;26:322–337. 10.1039/b808331m. [Abstract] [CrossRef] [Google Scholar]

127. Italiano A., Touati N., Litière S., Collin F., Pourquier P., Gronchi A. Prospective assessment of the predictive value of the BRCA1 gene status in sarcoma patients treated with trabectedin: An updated analysis of the EORTC 62091 trial. Cancer Med. 2018;7:1575–1577. 10.1002/cam4.1403. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

128. Monk B.J., Lorusso D., Italiano A., Kaye S.B., Aracil M., Tanović A., D’Incalci M. Trabectedin as a chemotherapy option for patients with BRCA deficiency. Cancer Treat. Rev. 2016;50:175–182. 10.1016/j.ctrv.2016.09.009. [Abstract] [CrossRef] [Google Scholar]

129. Risdon E.N., Chau C.H., Price D.K., Sartor O., Figg W.D. PARP Inhibitors and Prostate Cancer: To Infinity and Beyond BRCA. Oncologist. 2021;26:e115–e129. 10.1634/theoncologist.2020-0697. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

130. Yoshida K., Miki Y. Role of BRCA1 and BRCA2 as regulators of DNA repair, transcription, and cell cycle in response to DNA damage. Cancer Sci. 2004;95:866–871. 10.1111/j.1349-7006.2004.tb02195.x. [Abstract] [CrossRef] [Google Scholar]

131. Evans D.G. Neurofibromatosis 2. In: Adam M.P., Mirzaa G.M., Pagon R.A., Wallace S.E., Bean L.J.H., Gripp K.W., Amemiya A., editors. GeneReviews(®) University of Washington; Seattle, WA, USA: 1993. University of Washington, Seattle Copyright © 1993–2023. [Google Scholar]

132. Van der Valk M.J.M., Hilling D.E., Bastiaannet E., Meershoek-Klein Kranenbarg E., Beets G.L., Figueiredo N.L., Habr-Gama A., Perez R.O., Renehan A.G., van de Velde C.J.H. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): An international multicentre registry study. Lancet. 2018;391:2537–2545. 10.1016/s0140-6736(18)31078-x. [Abstract] [CrossRef] [Google Scholar]

133. Skiles J.L., Chiang C., Li C.H., Martin S., Smith E.L., Olbara G., Jones D.R., Vik T.A., Mostert S., Abbink F., et al. CYP3A5 genotype and its impact on vincristine pharmacokinetics and development of neuropathy in Kenyan children with cancer. Pediatr. Blood Cancer. 2018;65:26854. 10.1002/pbc.26854. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

134. Maki K., Harada T., Akiyama Y., Ogasawara H., Kishi F. Giant cell tumor of the rib. Intern. Med. 2007;46:1151–1152. 10.2169/internalmedicine.46.0203. [Abstract] [CrossRef] [Google Scholar]

135. Egbelakin A., Ferguson M.J., MacGill E.A., Lehmann A.S., Topletz A.R., Quinney S.K., Li L., McCammack K.C., Hall S.D., Renbarger J.L. Increased risk of vincristine neurotoxicity associated with low CYP3A5 expression genotype in children with acute lymphoblastic leukemia. Pediatr. Blood Cancer. 2011;56:361–367. 10.1002/pbc.22845. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

136. Uittenboogaard A., Neutel C.L.G., Ket J.C.F., Njuguna F., Huitema A.D.R., Kaspers G.J.L., van de Velde M.E. Pharmacogenomics of Vincristine-Induced Peripheral Neuropathy in Children with Cancer: A Systematic Review and Meta-Analysis. Cancers. 2022;14:612. 10.3390/cancers14030612. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

137. Losuwannarak N., Maiuthed A., Kitkumthorn N., Leelahavanichkul A., Roytrakul S., Chanvorachote P. Gigantol Targets Cancer Stem Cells and Destabilizes Tumors via the Suppression of the PI3K/AKT and JAK/STAT Pathways in Ectopic Lung Cancer Xenografts. Cancers. 2019;11:2032. 10.3390/cancers11122032. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

138. Bhummaphan N., Chanvorachote P. Gigantol Suppresses Cancer Stem Cell-Like Phenotypes in Lung Cancer Cells. Evid. Based Complement. Altern. Med. 2015;2015:836564. 10.1155/2015/836564. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

139. Losuwannarak N., Roytrakul S., Chanvorachote P. Gigantol Targets MYC for Ubiquitin-proteasomal Degradation and Suppresses Lung Cancer Cell Growth. Cancer Genom. Proteom. 2020;17:781–793. 10.21873/cgp.20232. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

140. Verweij J., Clavel M., Chevalier B. Paclitaxel (Taxol) and docetaxel (Taxotere): Not simply two of a kind. Ann. Oncol. 1994;5:495–505. 10.1093/oxfordjournals.annonc.a058903. [Abstract] [CrossRef] [Google Scholar]

141. McCorkle J.R., Gorski J.W., Liu J., Riggs M.B., McDowell A.B., Lin N., Wang C., Ueland F.R., Kolesar J.M. Lapatinib and poziotinib overcome ABCB1-mediated paclitaxel resistance in ovarian cancer. PLoS ONE. 2021;16:e0254205. 10.1371/journal.pone.0254205. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

142. Van Eerden R.A.G., van Doorn L., de Man F.M., Heersche N., Doukas M., van den Bosch T.P.P., Oomen-de Hoop E., de Bruijn P., Bins S., Ibrahim E., et al. Tissue Type Differences in ABCB1 Expression and Paclitaxel Tissue Pharmacokinetics in Patients With Esophageal Cancer. Front. Pharm. 2021;12:759146. 10.3389/fphar.2021.759146. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

143. Bhummaphan N., Petpiroon N., Prakhongcheep O., Sritularak B., Chanvorachote P. Lusianthridin targeting of lung cancer stem cells via Src-STAT3 suppression. Phytomedicine. 2019;62:152932. 10.1016/j.phymed.2019.152932. [Abstract] [CrossRef] [Google Scholar]

144. Wu Q., Qian W., Sun X., Jiang S. Small-molecule inhibitors, immune checkpoint inhibitors, and more: FDA-approved novel therapeutic drugs for solid tumors from 1991 to 2021. J. Hematol. Oncol. 2022;15:143. 10.1186/s13045-022-01362-9. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

145. Nam A.S., Chaligne R., Landau D.A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat. Rev. Genet. 2021;22:3–18. 10.1038/s41576-020-0265-5. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

146. Jardim D.L., Goodman A., de Melo Gagliato D., Kurzrock R. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell. 2021;39:154–173. 10.1016/j.ccell.2020.10.001. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

147. Pareek C.S., Smoczynski R., Tretyn A. Sequencing technologies and genome sequencing. J. Appl. Genet. 2011;52:413–435. 10.1007/s13353-011-0057-x. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

148. Reuter J.A., Spacek D.V., Snyder M.P. High-throughput sequencing technologies. Mol. Cell. 2015;58:586–597. 10.1016/j.molcel.2015.05.004. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

149. Hwang B., Lee J.H., Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 2018;50:1–14. 10.1038/s12276-018-0071-8. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

150. Ngamcherdtrakul W., Yantasee W. siRNA therapeutics for breast cancer: Recent efforts in targeting metastasis, drug resistance, and immune evasion. Transl. Res. 2019;214:105–120. 10.1016/j.trsl.2019.08.005. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

151. Mu Q., Yu J., McConnachie L.A., Kraft J.C., Gao Y., Gulati G.K., Ho R.J.Y. Translation of combination nanodrugs into nanomedicines: Lessons learned and future outlook. J. Drug. Target. 2018;26:435–447. 10.1080/1061186X.2017.1419363. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

152. Stuver R., Shah G.L., Korde N.S., Roeker L.E., Mato A.R., Batlevi C.L., Chung D.J., Doddi S., Falchi L., Gyurkocza B., et al. Activity of AZD7442 (tixagevimab-cilgavimab) against Omicron SARS-CoV-2 in patients with hematologic malignancies. Cancer Cell. 2022;40:590–591. 10.1016/j.ccell.2022.05.007. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

153. Cilento M.E., Ong Y.T., Tedbury P.R., Sarafianos S.G. Drug Interactions in Lenacapavir-Based Long-Acting Antiviral Combinations. Viruses. 2022;14:1202. 10.3390/v14061202. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

154. Liang Z., He Y., Hu X. Cardio-Oncology: Mechanisms, Drug Combinations, and Reverse Cardio-Oncology. Int. J. Mol. Sci. 2022;23:10617. 10.3390/ijms231810617. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

155. McDonald E.S., Clark A.S., Tchou J., Zhang P., Freedman G.M. Clinical Diagnosis and Management of Breast Cancer. J. Nucl. Med. 2016;57:9s–16s. 10.2967/jnumed.115.157834. [Abstract] [CrossRef] [Google Scholar]

156. Ho D., Quake S.R., McCabe E.R.B., Chng W.J., Chow E.K., Ding X., Gelb B.D., Ginsburg G.S., Hassenstab J., Ho C.M., et al. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol. 2020;38:497–518. 10.1016/j.tibtech.2019.12.021. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

157. Litman T. Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases. Apmis. 2019;127:386–424. 10.1111/apm.12934. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

158. Botella P., Rivero-Buceta E. Safe approaches for camptothecin delivery: Structural analogues and nanomedicines. J. Control. Release. 2017;247:28–54. 10.1016/j.jconrel.2016.12.023. [Abstract] [CrossRef] [Google Scholar]

159. Thomford N.E., Senthebane D.A., Rowe A., Munro D., Seele P., Maroyi A., Dzobo K. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery. Int. J. Mol. Sci. 2018;19:1578. 10.3390/ijms19061578. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

Articles from Genes are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

Full text links

Read article at publisher's site: https://doi.org/10.3390/genes15040468

Read article for free, from open access legal sources, via Unpaywall: https://www.mdpi.com/2073-4425/15/4/468/pdf?version=1712561230

Citations & impact

Impact metrics

Citations

Jump to Citations

Article citations

A comprehensive review on lncRNA LOXL1-AS1: molecular mechanistic pathways of lncRNA LOXL1-AS1 in tumorigenicity of cancer cells.
Yousefnia S
Front Oncol, 14:1384342, 29 Jul 2024
Cited by: 0 articles | PMID: 39136001 | PMCID: PMC11317273
Review
This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
Free full text in Europe PMC
A Systematic Review of Cardio-Metabolic Properties of Lonicera caerulea L.
Bora L, Lombrea A, Batrina SL, Buda VO, Esanu OM, Pasca O, Dehelean CA, Dinu S, Diaconeasa Z, Danciu C
Antioxidants (Basel), 13(6):694, 05 Jun 2024
Cited by: 0 articles | PMID: 38929133
Review

Data

Data behind the article

This data has been text mined from the article, or deposited into data resources.

Protein structures in PDBe (2)

(1 citation) PDBe - 5ggr
View structure
(1 citation) PDBe - 5dk3
View structure

Funding

Funders who supported this work.

National Natural Science Foundation of China (1)

Grant ID: 31871281
15 publications

Scientific Research Foundation for Advanced Talents of Shanghai University of Traditional Chinese Medicine

Shanghai Municipal Health Commission (1)

Grant ID: 2020XGKY12
4 publications

Search life-sciences literature (45,104,145 articles, preprints and more)

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.

Author information

Affiliations

Authors

Authors

ORCIDs linked to this article

Abstract

Free full text

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products

Yalan Zhou

Siqi Peng

Huizhen Wang

Xinyin Cai

Qingzhong Wang

Associated Data

Abstract

1. Introduction

2. Technical System for the Development of New Drugs and Personalized Medicine

2.1. Artificial Intelligence Technology

2.2. Approaches with Multidimensional Omics Data

2.3. Study on High-Throughput Targets with Chemical Proteomics Technology

2.4. Computer-Aided Drug Discovery (CADD) System

3. Anti-Cancer Personalized Medicine

Table 2

Table 1

3.1. ALK Inhibitors

3.2. EGFR Inhibitors

4. Pharmacogenetics of the Anti-Cancer Natural Products

Table 3

5. The Challenges of Personalized Therapy

6. Conclusions and Future Perspectives

Abbreviations

Funding Statement

Author Contributions

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Footnotes

References

Full text links

Citations & impact

Impact metrics

Article citations

A comprehensive review on lncRNA LOXL1-AS1: molecular mechanistic pathways of lncRNA LOXL1-AS1 in tumorigenicity of cancer cells.

A Systematic Review of Cardio-Metabolic Properties of Lonicera caerulea L.

Data

Data behind the article

Protein structures in PDBe (2)

Similar Articles

Pharmacogenomics: Driving Personalized Medicine.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine.

Quantitation of Targetable Somatic Mutations Among Patients Evaluated by a Personalized Medicine Clinical Service: Considerations for Off-Label Drug Use.

Pharmacogenetics and pharmacogenomics as tools in cancer therapy.

Funding

National Natural Science Foundation of China (1)﻿

Scientific Research Foundation for Advanced Talents of Shanghai University of Traditional Chinese Medicine

Shanghai Municipal Health Commission (1)﻿

Partnerships & funding

National Natural Science Foundation of China (1)

Shanghai Municipal Health Commission (1)