High-throughput Screening Strategy Research Articles

Comprehensive Understanding of Laboratory Evolution for Food Enzymes: From Design to Screening Innovations.

In the field of food processing, enzymes play a pivotal role in improving product quality and flavor, and extending shelf life. However, the exposure of traditional food enzymes to high temperatures during processing often leads to a decrease in activity or even inactivation, limiting the effectiveness of their application under high-temperature conditions. Therefore, the modification of thermostability and activity of enzymes to adapt to extreme conditions through protein engineering has become a key way to improve the efficiency and economic benefits of industrial production. Directed evolution and semirational design strategies in the laboratory have proven to be broadly applicable frameworks for biochemical researchers in the food field, including those who are beginners. In this review, we systematically summarize semirational design strategies and high-throughput screening strategies, and introduce some intuitive computer simulation software to improve the thermostability and enzyme activity of food enzymes. The application of these strategies and techniques provides a comprehensive guide for the optimization of food enzymes. In addition, the latest hot topics of genetically engineered food enzymes in the field of application are discussed.

Computational Insight into Transition Metal Atoms Anchored on B2C3P as Single‐Atom Electrocatalysts for Nitrogen Reduction Reaction

AbstractNH3 is not only an important chemical raw material but also a high‐energy storage chemical with zero carbon. Electrocatalytic nitrogen reduction reaction (NRR), which can be driven by clean electric energy under ambient conditions, has become a promising technology for NH3 synthesis due to its environmentally friendly properties. Because of the limitations of low yield and high overpotential, efficient catalysts are urgently needed to solve this problem. In this study, based on density functional theory method and high throughput screening strategy, the NRR was investigated on transition metal single atom anchored to 2D B2C3P surface (TM@B2C3P) as single‐atom catalysts (SACs). The results showed that V@B2C3P and Ti@B2C3P have good catalytic properties, and the limiting potentials were −0.10 and −0.24 V, respectively. Furthermore, the charge density difference and crystal orbital Hamilton population calculations demonstrated that the high catalytic activity can be attributed to the obvious charge transfer between TM@B2C3P and the adsorption intermediates. It is hoped that this work can play a certain role in exploring the application of SACs in NRR.

High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

Abstract2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS2, as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm2/Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.

Novel Drug-like HsrA Inhibitors Exhibit Potent Narrow-Spectrum Antimicrobial Activities against Helicobacter pylori.

Helicobacter pylori infection constitutes a silent pandemic of global concern. In the last decades, the alarming increase in multidrug resistance evolved by this pathogen has led to a marked drop in the eradication rates of traditional therapies worldwide. By using a high-throughput screening strategy, in combination with in vitro DNA binding assays and antibacterial activity testing, we identified a battery of novel drug-like HsrA inhibitors with MIC values ranging from 0.031 to 4 mg/L against several antibiotic-resistant strains of H. pylori, and minor effects against both Gram-negative and Gram-positive species of human microbiota. The most potent anti-H. pylori candidate demonstrated a high therapeutic index, an additive effect in combination with metronidazole and clarithromycin as well as a strong antimicrobial action against Campylobacter jejuni, another clinically relevant pathogen of phylum Campylobacterota. Transcriptomic analysis suggests that the in vivo inhibition of HsrA triggers lethal global disturbances in H. pylori physiology including the arrest of protein biosynthesis, malfunction of respiratory chain, detriment in ATP generation, and oxidative stress. The novel drug-like HsrA inhibitors described here constitute valuable candidates to a new family of narrow-spectrum antibiotics that allow overcoming the current resistome, protecting from dysbiosis, and increasing therapeutic options for novel personalized treatments against H. pylori.

High-Throughput screening of TM1TM2/N6–1 (TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Mo) as effective electrocatalysts for nitrogen fixation

The electrochemical nitrogen reduction reaction (NRR) is an emerging nitrogen fixation method in recent years. Double-atom catalysts compensate for the disadvantage of single-atom catalysts having only one active site, improving catalytic reaction activity. Herein, using a four-step high-throughput screening strategy with density functional theory (DFT) calculations, 45 homonuclear and heteronuclear catalysts, namely, transition metal dimers anchored on nitrogen-doped graphene (TM1TM2/N6–1, TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Mo), were systematically studied to evaluate their catalytic performance for NRR. Our results showed that 16 catalysts stand out, among them, ScMo/N6–1 has an ultralow limiting potential of -0.09 V and a Faradaic efficiency of 100 %. This work provides a group of promising candidates and a reasonable high-throughput design strategy for NRR catalysts.

High-throughput screening identifies ibuprofen as an sEV PD-L1 inhibitor for synergistic cancer immunotherapy

Programmed death-ligand 1 (PD-L1) on tumor-derived small extracellular vesicles (sEVs) limits therapeutic effectiveness by interacting with the PD-1 receptor on host immune cells. Targeting the secretion of sEV PD-L1 has emerged as a promising strategy to enhance immunotherapy. However, the lack of small-molecule inhibitors poses a challenge for clinical translation. In this study, we developed a target and phenotype dual-driven high-throughput screening (TAP-HTS) strategy that combined virtual screening with nanoflow-based experimental verification. We identified ibuprofen (IBP) as a novel inhibitor that effectively targeted sEV PD-L1 secretion. IBP disrupted the biogenesis and secretion of PD-L1+ sEVs in tumor cells by physically interacting with a critical regulator of sEV biogenesis, hepatocyte growth factor-regulated tyrosine kinase substrate (HRS). Notably, the mechanism of action of IBP is distinct from its commonly known targets, cyclooxygenases (COX1 or COX2). Administration of IBP stimulated antitumor immunity and enhanced the efficacy of anti-PD-1 therapy in melanoma and oral squamous cell carcinoma (OSCC) mouse models. To address potential adverse effects, we further developed an IBP gel for topical application, which demonstrated remarkable therapeutic efficacy when combined with anti-PD-1 treatment. The discovery of this specific small inhibitor provides a promising avenue for establishing durable, systemic antitumor immunity.

High-throughput characterization of the influence of Streptococcus sanguinis genes on the interaction between Streptococcus sanguinis and Porphyromonas gingivalis.

Porphyromonas gingivalis is a keystone pathogen in periodontitis, and Streptococcus sanguinis is an abundant oral commensal bacterium associated with periodontal health. However, the interaction between P. gingivalis and S. sanguinis remains obscure. Here, we established a strategy for high-throughput measurement of the cell number of P. gingivalis in the coculture with S. sanguinis by detecting the concentration of hydrogen sulfate. The interaction between P. gingivalis and over 2000 S. sanguinis single-gene mutants was characterized using this strategy, and several interaction-associated genes in S. sanguinis were determined by detecting more P. gingivalis cells in the coculture with matched S. sanguinis mutants. Three S. sanguinis interaction-associated genes were predicted to be responsible for cysteine metabolism, and the supplementation of exogenous L-cysteine promoted the cell number of P. gingivalis in the coculture with S. sanguinis. Thus, exogenous L-cysteine and the compromised cysteine metabolism in S. sanguinis enhanced the growth of P. gingivalis in the existence of S. sanguinis. Additionally, the interaction between P. gingivalis and other Streptococcus spp. was examined, and S. pneumoniae was the only streptococci that had no inhibition on the cell number of P. gingivalis. In total, this study established a new strategy for high-throughput screening of the interaction between Streptococcus and P. gingivalis and discovered a set of genes in S. sanguinis that impacted the interaction. The influence of exogenous L-cysteine on the interaction between P. gingivalis and S. sanguinis in the oral cavity needs further investigation.

Activation preference: A new descriptor to predict non-radical oxidation pathways

The peroxymonosulfate (PMS) activation was influenced strongly by active sites on a catalyst surface. To some extent, biochar characters, such as structure, elemental composition, and specific surface area (SSA), can reflect the composition of active sites. However, the understanding of active centers lacked accuracy in the structure–activity relationships of biochar-based catalysts (BBC) due to the consideration of one single feature. In this study, XGBoost (XGB), random forest regression (RF), and supporting vector regression (SVR) models were employed to construct descriptors to predict non-radical (NR) contribution in BBC/PMS systems. Consequently, the XGB model provided the best prediction with an R2 value of 0.81. Thereby, activation preference (γ), as a characteristic descriptor of BBC, was developed using the XGB model. Interestingly, the descriptor fully considered carbon content (C%), oxygen content (O%), defects (ID/IG), and SSA. Besides, O% played the most critical role in predicting NR contribution. Noticeably, the NR oxidation pathway could be promoted within the range of 50–60 % for C%, 30–50 % for O%, 0.4–1.2 for ID/IG, and 579–1259 m2/g for SSA in the BBC/PMS system. Herein, machine learning (ML) was applied to develop a comprehensive descriptor, further providing an innovative strategy for high-throughput screening of BBCs to activate PMS efficiently. The comprehensive descriptor offered a new perspective for elucidating the structure–activity relationship of BBC, facilitating the application of BBC/PMS systems in water treatment.

A High-Throughput Screening Strategy for Synthesizing Molecularly Imprinted Polymer Nanoparticles Selectively Targeting Tumors.

Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12m) are identified. These "bionic antibodies" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5cellsmL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Inhibitors of the Structural and Nonstructural Proteins of Alphaviruses.

The Alphavirus genus includes viruses that cause encephalitis due to neuroinvasion and viruses that cause arthritis due to acute and chronic inflammation. There is no approved therapeutic for alphavirus infections, but significant efforts are ongoing, more so in recent years, to develop vaccines and therapeutics for alphavirus infections. This review article highlights some of the major advances made so far to identify small molecules that can selectively target the structural and the nonstructural proteins in alphaviruses with the expectation that persistent investigation of an increasingly expanding chemical space through a variety of structure-based design and high-throughput screening strategies will yield candidate drugs for clinical studies. While most of the works discussed are still in the early discovery to lead optimization stages, promising avenues remain for drug development against this family of viruses.

A high-throughput assay identifies molecules with antimicrobial activity against persister cells.

Introduction. Persister cells are transiently non-growing antibiotic-tolerant bacteria that cause infection relapse, and there is no effective antibiotic therapy to tackle these infections.Gap statement. High-throughput assays in drug discovery are biased towards detecting drugs that inhibit bacterial growth rather than killing non-growing bacteria. A new and simple assay to discover such drugs is needed.Aim. This study aims to develop a simple and high-throughput assay to identify compounds with antimicrobial activity against persister cells and use it to identify molecular motifs with such activity.Methodology. We quantified Staphylococcus aureus persister cells by enumeration of colony forming units after 24 h ciprofloxacin treatment. We first quantified how the cell concentration, antibiotic concentration, growth phase and presence/absence of nutrients during antibiotic exposure affected the fraction of persister cells in a population. After optimizing these parameters, we screened the antimicrobial activity of compound fragments to identify molecular structures that have activity against persister cells.Results. Exponential- and stationary-phase cultures transferred to nutrient-rich media displayed a bi-phasic time-kill curve and contained 0.001-0.07% persister cells. A short rifampicin treatment resulted in 100% persister cells for 7 h, after which cells resumed activity and became susceptible. Stationary-phase cultures displayed a low but constant death rate but ultimately resulted in similarly low survival rates as the exponential-phase cultures after 24 h ciprofloxacin treatment. The persister phenotype was only maintained in most of the population for 24 h if cells were transferred to a carbon-free minimal medium before exposure to ciprofloxacin. Keeping cells starved enabled the generation of high concentrations of S. aureus cells that tolerate 50× MIC ciprofloxacin, and we used this protocol for rapid screening for biocidal antibiotics. We identified seven compounds from four structural clusters with activity against antibiotic-tolerant S. aureus. Two compounds were moderately cytotoxic, and the rest were highly cytotoxic.Conclusion. Transferring a stationary-phase culture to a carbon-free minimal medium for antimicrobial testing is a simple strategy for high-throughput screening for new antibiotics that kill persister cells. We identified molecule fragments with such activity, but further screening is needed to identify motifs with lower general cytotoxicity.

Thermal shift assay (TSA)-based drug screening strategy for rapid discovery of inhibitors against the Nsp13 helicase of SARS-CoV-2

The pandemic of coronavirus disease 2019 (COVID-19) is no longer a global health emergency, but variants of the causative agent (severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2) are still circulating today. However, only three partially effective antiviral drugs have been approved or authorized by the United States Food and Drug Administration (FDA) to treat COVID-19 (i.e., remdesivir, Paxlovid and molnupiravir). This study focuses on the discovery of small molecular inhibitors targeting Nsp13, which is an RNA helicase essential to SARS-CoV-2 replication and transcription. A cost-effective thermal shift assay (TSA) was employed in primary high-throughput screening (HTS) of 1970 compounds (10 μM), by which 26 (1.32%) hits showed high binding affinity to the recombinant Nsp13 (ΔTm > 3 °C). Among them, seven and four compounds displayed low micromolar inhibitory kinetics on the ATPase (ATP hydrolysis) and helicase (RNA unwinding) activities of Nsp13, respectively. The seven ATPase inhibitors include benidipine (IC50 = 1.07 μM), butylparaben (IC50 = 2.87 μM), diclofenac (IC50 = 6.55 μM), dyclonine HCl (IC50 = 6.54 μM), methyl salicylate (IC50 = 4.53 μM), olsalazine (IC50 = 4.91 μM) and verteporfin (IC50 = 5.01 μM). The four helicase inhibitors include curcumin (IC50 = 12.23 μM), efaproxiral (IC50 = 12.47 μM), fenretinide (IC50 = 13.63 μM) and wedelolactone (IC50 = 14.91 μM). These biochemically validated hits are worth being pursued further for antiviral efficacies and for potential development of leads. The cost-effectively TSA-based HTS strategy can be employed to screen larger compound libraries and adapted to the drug discovery for other protein targets.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

Discovery of novel natural-product-derived mutant isocitrate dehydrogenases 1 inhibitors: Structure-based virtual screening, biological evaluation and structure-activity relationship study

Mutations in IDH1 are commonly observed across various cancers, causing the conversion of α-KG to 2-HG. Elevated levels of 2-HG disrupt histone and DNA demethylation processes, promoting tumor development. Consequently, there is substantial interest in developing small molecule inhibitors targeting the mutant enzymes. Herein, we report a structure-based high-throughput virtual screening strategy using a natural products library, followed by hit-to-lead optimization. Through this process, we discover a potent compound, named 11s, which exhibited significant inhibition to IDH1 R132H and IDH1 R132C with IC50 values of 124.4 and 95.7 nM, respectively. Furthermore, 11s effectively reduced 2-HG formation, with EC50 values of 182 nM in U87 R132H cell, and 84 nM in HT-1080 cell. In addition, 11s significantly reduced U87 R132H and HT-1080 cell proliferation with GC50 values of 3.48 and 1.38 μM, respectively. PK-PD experiments further confirmed that compound 11s significantly decreased 2-HG formation in an HT-1080 xenograft mouse model, resulting in notable suppression of tumor growth without apparent loss in body weight.

High-Throughput Screening Strategy for Electrocatalysts for Selective Catalytic Oxidation of Formaldehyde to Formic Acid.

Electrocatalytic oxidation of formaldehyde (FOR) is an effective way to prevent the damage caused by formaldehyde and produce high-value products. A screening strategy of a single-layer MnO2-supported transition metal catalyst for the selective oxidation of formaldehyde to formic acid was designed by high-throughput density functional calculation. N-MnO2@Cu and MnO2@Cu are predicted to be potential FOR electrocatalysts with potential-limiting steps (PDS) of 0.008 and -0.009 eV, respectively. Electronic structure analysis of single-atom catalysts (SACs) shows that single-layer MnO2 can regulate the spin density of loaded transition metal and thus regulate the adsorption of HCHO (Ead), and Ead is volcanically distributed with the magnetic moment descriptor -|mM - mH|. In addition, the formula quantifies Ead and |mM - mH| to construct a volcano-type descriptor α describing the PDS [ΔG(*CHO)]. Other electronic and structural properties of SACs and α are used as input features for the GBR method to construct machine learning models predicting the PDS (R2 = 0.97). This study hopes to provide some insights into FOR electrocatalysts.

Comprehensive analysis of the editing window of C-to-T TALE base editors

One of the most recent advances in the genome editing field has been the addition of “TALE Base Editors”, an innovative platform for cell therapy that relies on the deamination of cytidines within double strand DNA, leading to the formation of an uracil (U) intermediate. These molecular tools are fusions of transcription activator-like effector domains (TALE) for specific DNA sequence binding, split-DddA deaminase halves that will, upon catalytic domain reconstitution, initiate the conversion of a cytosine (C) to a thymine (T), and an uracil glycosylase inhibitor (UGI). We developed a high throughput screening strategy capable to probe key editing parameters in a precisely defined genomic context in cellulo, excluding or minimizing biases arising from different microenvironmental and/or epigenetic contexts. Here we aimed to further explore how target composition and TALEB architecture will impact the editing outcomes. We demonstrated how the nature of the linker between TALE array and split DddAtox head allows us to fine tune the editing window, also controlling possible bystander activity. Furthermore, we showed that both the TALEB architecture and spacer length separating the two TALE DNA binding regions impact the target TC editing dependence by the surrounding bases, leading to more restrictive or permissive editing profiles.

High-throughput composition screening of high-entropy rare-earth monosilicates for superior CMAS corrosion resistance up to 1873 K

Superior calcium-magnesium-aluminum-silicate (CMAS) corrosion resistance is the key to the applications of high-entropy rare-earth monosilicates (HEREMs) as environmental barrier coatings (EBCs) for next-generation gas turbine engines. Here, we report a high-throughput composition screening strategy to achieve superior CMAS corrosion resistance in HEREMs for the first time. Specifically, we first fabricate ten kinds of (Ho0.25Yb0.25Lu0.25X0.25)2SiO5 (X = Sc, Tm, Er, Y, Dy, Gd, Eu, Sm, Nd, and La) HEREM samples using a high-throughput pressure-less sintering technique, and subsequently screen their CMAS corrosion resistance at 1673 K via a high-throughput testing apparatus. Among all the as-fabricated samples, the HEREM-Er samples are found to have the best CMAS corrosion resistance, even up to 1873 K, which outperforms the EBC materials that have been reported. Such superior CMAS corrosion resistance of the HEREM-Er samples is primarily attributed to the formation of a dense apatite barrier layer, which results from its decreased melting point, as confirmed by first-principles calculations and molecular dynamic simulations. Our work provides a guide for designing exceptional anti-CMAS corrosion high-entropy rare-earth silicates.

Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis.

The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry.

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

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

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.