Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
Display Method:
Unlocking the potential of atractylenolide II: mitigating non-alcoholic fatty liver disease through farnesoid X receptor-endoplasmic reticulum stress interplay
Ming Gu, Zhiwei Chen, Yujun Chen, Yiping Li, Hongqing Wang, Ya-ru Feng, Peiyong Zheng, Cheng Huang
 doi: 10.1016/j.jpha.2025.101318
[Abstract](0) [PDF 11560KB](0)
Abstract:
Evidences indicate that farnesoid X receptor (FXR) activation mitigates non-alcoholic fatty liver disease (NAFLD) by reducing endoplasmic reticulum (ER) stress. However, the mechanisms underlying FXR-ER stress interactions in combating NAFLD remain obscure. Moreover, few phytochemicals have been noted to improve NAFLD through this pathway. Here, we found that FXR activation directly induces the transcription of sarco/endoplasmic reticulum Ca2+ ATPase 2 (SERCA2), which acts as an ER stress repressor. This process leads to the dephosphorylation of the eukaryotic translation initiation factor 2 subunit α (eIF2α) within hepatocytes, consequently alleviating ER stress. Furthermore, through drug binding assays, luciferase reporter gene testing, gene expression analysis and biochemical evaluation, we identified the phytochemical atractylenolide II (AT-II) as a novel FXR agonist that effectively triggers SERCA2 activation. Our results showed AT-II effectively supresses accumulation of lipids and ER stress in palmitic acid-induced hepatocytes. In in vivo experiments, we demonstrated that AT-II attenuates fatty liver in diet- or chemical-induced NAFLD mouse models. Additionally, we showed that AT-II corrects diet-induced obesity, serum dyslipidemia, metabolic complications, and insulin resistance. Mechanistically, AT-II reduces ER stress, lipogenesis and inflammation and improves hepatic insulin signaling through stimulation of the hepatic FXR-SERCA2-eIF2α axis in mice. This conclusion was further reinforced by Serca2 knockdown both in vivo and in vitro, as well as FXR silencing in hepatocytes. Our findings provide new insights into the FXR-ER stress interplay in the control of NAFLD and suggest the potential of AT-II as an FXR agonist for the treatment of NAFLD through SERCA2 activation.
ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training
Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li, Yun Tang, Guixia Liu
 doi: 10.1016/j.jpha.2025.101317
[Abstract](0) [PDF 34400KB](0)
Abstract:
Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.
DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction
Jie Yu, Cheng Shi, Yiran Zhou, Ningfeng Liu, Xiaolin Zong, Zhenming Liu, Liangren Zhang
 doi: 10.1016/j.jpha.2025.101315
[Abstract](0) [PDF 4697KB](0)
Abstract:
Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained singlecell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.
Chemical Analysis, Antihyperglycemic Properties and Enzyme Inhibition of Opuntia dillenii: A Detailed Analysis of Juice and Peel Extracts
El Hassania Loukili, Amal Elrherabi, Asmae Hbik, Amine Elbouzidi, Mohamed Taibi, Mohammed Merzouki, Mohamed Bouhrim, Abdelaaty A. Shahat, Omar M. Noman, Abdellah Azougay, Bruno Eto, Mohamed Bnouham, Belkheir Hammouti, Mohammed Ramdani
 doi: 10.1016/j.jpha.2025.101320
[Abstract](0) [PDF 8932KB](0)
Abstract:
Opuntia dillenii has been widely used in traditional medicine for various health conditions. This study examined the chemical composition of aqueous extracts from the plant's juice and peel and evaluated their effects on pancreatic α-amylase, lipase, and intestinal α-glucosidase enzymes, as well as their antihyperglycemic properties in vivo. Using High-Performance Liquid Chromatography with a Photodiode Array Detector (HPLC-DAD), significant variations in the composition were found between the plant's juice and peel, Key compounds included gallic acid, vanillic acid, p-coumaric acid, 3-hydroxy flavone, quercetin, cinnamic acid, kaempferol, and flavone. p-coumaric acid was highest in the juice (298.71±0.43 mg/100g) and peel (38.18±1.08 mg/100g), while flavone was higher in the peel (120.03±0.26 mg/100g). The extracts significantly inhibited pancreatic α-amylase and intestinal α-glucosidase in vitro, with confirmed in vivo effects reducing hyperglycemia in both healthy and diabetic rats. No toxicity was observed until the dose of 4000 mg/kg body weight. Molecular docking models showed that the plant's phytochemicals interacted with pancreatic enzymes more effectively than the drug acarbose. These findings highlight Opuntia dillenii's potential as a source of natural compounds with therapeutic properties, warranting further exploration.
SITA: Predicting Site-specific Immunogenicity for Therapeutic Antibodies
Yewei Cun, Hao Ding, Tiantian Mao, Yuan Wang, Caicui Wang, Jiajun Li, Zihao Li, Mengdie Hu, Zhiwei Cao, Tianyi Qiu
 doi: 10.1016/j.jpha.2025.101316
[Abstract](0) [PDF 33468KB](0)
Abstract:
Antibody (Ab) humanization is critical to reduce immunogenicity and enhance efficacy in the preclinical phase of the development of therapeutic Abs originated from animal models. Computational suggestions have long been desired, but available tools focused on immunogenicity calculation of whole Ab sequences and sequence segments, missing the individual residue sites. This study introduces SITA, a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody, but also individual residues, based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures. A transfer-learning-inspired framework was purposely adopted to overcome the scarcity of Ab-Ab structural complexes. On an independent testing dataset derived from 13 antibodyantibody structural complexes, SITA successfully predicted the epitope sites for Ab-Ab structures with a ROC-AUC of 0.85 and a PR-AUC of 0.305 at the residue level. Furthermore, the SITA score can significantly distinguish immunogenicity levels of whole human Abs, therapeutic Abs and nonhuman-derived Abs. More importantly, analysis of an additional 25 therapeutic Abs revealed that over 70% of them were detected with decreased immunogenicity after modification compared to their parent variants. Among these, nearly 66% Abs successfully identified actual modification sites from the top five sites with the highest SITA scores, suggesting the ability of SITA scores for guide the humanization of antibody. Overall, these findings highlight the potential of SITA in optimizing immunogenicity assessments during the process of therapeutic antibody design.
Decoding protein dynamics with limited proteolysis coupled to mass spectrometry: A comprehensive review
Zilu Zhao, Xue Zhang, Xin Dong, Zhanying Hong
 doi: 10.1016/j.jpha.2025.101319
[Abstract](0) [PDF 19280KB](0)
Abstract:
Proteins are indispensable to all biological systems and drive life processes through activities that are intricately linked to their three-dimensional structures. Traditional proteomics often provides static snapshots of protein expression, leaving unanswered questions about how proteins respond to stimuli and affect cellular functions. Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful technique for exploring protein structure and function under near-natural conditions. Studies have revealed that LiP-MS is invaluable for structural and functional proteomics because it offers novel insights into protein dynamics. In this review, we summarise the current applications of LiP-MS in diverse areas such as the discovery and identification of drug targets, metabolite action mechanisms, proteome dynamics, protein interactions, and disease biomarkers. We also address the critical challenges in ongoing research and discuss their broader implications for advancing our understanding of protein biology and drug discovery. LiP-MS holds significant promise for accelerating biomarker and therapeutic target development as well as advancing molecular biology research in animals, plants, and microorganisms.
Export
Review papers
Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions
Boyang Wang, Tingyu Zhang, Qingyuan Liu, Chayanis Sutcharitchan, Ziyi Zhou, Dingfan Zhang, Shao Li
2025, 15(3): 101144.   doi: 10.1016/j.jpha.2024.101144
Abstract(112) HTML Full Text PDF(9)
Abstract:

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

New uses of halofuginone to treat cancer
Runan Zuo, Xinyi Guo, Xinhao Song, Xiuge Gao, Junren Zhang, Shanxiang Jiang, Vojtech Adam, Kamil Kuca, Wenda Wu, Dawei Guo
2025, 15(3): 101080.   doi: 10.1016/j.jpha.2024.101080
Abstract(103) HTML Full Text PDF(2)
Abstract:

The small-molecule alkaloid halofuginone (HF) is obtained from febrifugine. Recent studies on HF have aroused widespread attention owing to its universal range of noteworthy biological activities and therapeutic functions, which range from parasite infections and fibrosis to autoimmune diseases. In particular, HF is believed to play an excellent anticancer role by suppressing the proliferation, adhesion, metastasis, and invasion of cancers. This review supports the goal of demonstrating various anticancer effects and molecular mechanisms of HF. In the studies covered in this review, the anticancer molecular mechanisms of HF mainly included transforming growth factor-β (TGF-β)/Smad-3/nuclear factor erythroid 2-related factor 2 (Nrf2), serine/threonine kinase proteins (Akt)/mechanistic target of rapamycin complex 1(mTORC1)/wingless/integrated (Wnt)/β-catenin, the exosomal microRNA-31 (miR-31)/histone deacetylase 2 (HDAC2) signaling pathway, and the interaction of the extracellular matrix (ECM) and immune cells. Notably, HF, as a novel type of adenosine triphosphate (ATP)-dependent inhibitor that is often combined with prolyl transfer RNA synthetase (ProRS) and amino acid starvation therapy (AAS) to suppress the formation of ribosome, further exerts a significant effect on the tumor microenvironment (TME). Additionally, the combination of HF with other drugs or therapies obtained universal attention. Our results showed that HF has significant potential for clinical cancer treatment.

The role of mitochondria transfer in cancer biological behavior, the immune system and therapeutic resistance
Xintong Lyu, Yangyang Yu, Yuanjun Jiang, Zhiyuan Li, Qiao Qiao
2025, 15(3): 101141.   doi: 10.1016/j.jpha.2024.101141
Abstract(79) HTML Full Text PDF(4)
Abstract:

Mitochondria play a crucial role as organelles, managing several physiological processes such as redox balance, cell metabolism, and energy synthesis. Initially, the assumption was that mitochondria primarily resided in the host cells and could exclusively transmit from oocytes to offspring by a mechanism known as vertical inheritance of mitochondria. Recent scholarly works, however, suggest that certain cell types transmit their mitochondria to other developmental cell types via a mechanism referred to as intercellular or horizontal mitochondrial transfer. This review details the process of which mitochondria are transferred across cells and explains the impact of mitochondrial transfer between cells on the efficacy and functionality of cancer cells in various cancer forms. Specifically, we review the role of mitochondria transfer in regulating cellular metabolism restoration, excess reactive oxygen species (ROS) generation, proliferation, invasion, metastasis, mitophagy activation, mitochondrial DNA (mtDNA) inheritance, immune system modulation and therapeutic resistance in cancer. Additionally, we highlight the possibility of using intercellular mitochondria transfer as a therapeutic approach to treat cancer and enhance the efficacy of cancer treatments.

Unlocking the dual role of autophagy: A new strategy for treating lung cancer
Fei Tang, Jing-Nan Zhang, Xiao-Lan Zhao, Li-Yue Xu, Hui Ao, Cheng Peng
2025, 15(3): 101098.   doi: 10.1016/j.jpha.2024.101098
Abstract(120) HTML Full Text PDF(17)
Abstract:

Lung cancer exhibits the highest incidence and mortality rates among cancers globally, with a five-year overall survival rate alarmingly below 20%. Targeting autophagy, though a controversial therapeutic strategy, is extensively employed in clinical practice. Current research is actively pursuing various therapeutic strategies using small molecules to exploit the dual function of autophagy. Nevertheless, the pivotal question of enhancing or inhibiting autophagy in cancer therapy merits further attention. This review aims to provide a comprehensive overview of the mechanisms of autophagy in lung cancer. It also explores recent advances in targeting cytotoxic autophagy and inhibiting protective autophagy with small molecules to induce cell death in lung cancer cells. Notably, most autophagy-targeting drugs, primarily natural small molecules, have demonstrated that activating cytotoxic autophagy effectively induces cell death in lung cancer, as opposed to inhibiting protective autophagy. These insights contribute to identifying druggable targets and drug candidates for potential autophagy-related lung cancer therapies, offering promising approaches to combat this disease.

View All >
Molecular immune pathogenesis and diagnosis of COVID-19
Xiaowei Li, Manman Geng, Yizhao Peng, Liesu Meng, Shemin Lu
2020, 10(2): 102-108.  
[Abstract](1969) [PDF 2284KB](24)
摘要:
Coronavirus disease 2019 (COVID-19) is a kind of viral pneumonia which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence of SARS-CoV-2 has been marked as the third introduction of a highly pathogenic coronavirus into the human population after the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coro-navirus (MERS-CoV) in the twenty-first century. In this minireview, we provide a brief introduction of the general features of SARS-CoV-2 and discuss current knowledge of molecular immune pathogenesis, diagnosis and treatment of COVID-19 on the base of the present understanding of SARS-CoV and MERS-CoV infections, which may be helpful in offering novel insights and potential therapeutic targets for combating the SARS-CoV-2 infection.
Structural basis of SARS-CoV-23CLpro and anti-COVID-19 drug discovery from medicinal plants
Muhammad Tahir ul Qamar, Safar M.Alqahtani, Mubarak A.Alamri, Ling-Ling Chen
2020, 10(4): 313-319.  
[Abstract](9919) [PDF 5841KB](65)
摘要:
The recent pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 has raised global health concerns. The viral 3-chymotrypsin-like cysteine protease (3CLpro) enzyme controls coronavirus replication and is essential for its life cycle. 3CLpro is a proven drug discovery target in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Recent studies revealed that the genome sequence of SARS-CoV-2 is very similar to that of SARS-CoV. Therefore, herein, we analysed the 3CLpro sequence, constructed its 3D homology model, and screened it against a medicinal plant library containing 32,297 potential anti-viral phytochemicals/traditional Chinese medicinal compounds. Our analyses revealed that the top nine hits might serve as potential anti- SARS-CoV-2 lead molecules for further optimisation and drug development process to combat COVID-19.
Recent advances and perspectives of nucleic acid detection for coronavirus
Minzhe Shen, Ying Zhou, Jiawei Ye, Abdu Ahmed Abdullah AL-maskri, Yu Kang, Su Zeng, Sheng Cai
2020, 10(2): 97-101.  
[Abstract](1834) [PDF 2697KB](21)
摘要:
The recent pneumonia outbreak caused by a novel coronavirus (SARS-CoV-2) is posing a great threat to global public health. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. It is important to establish a quick standard diagnostic test for the detection of the infectious disease (COVID-19) to prevent subsequent secondary spread. Polymerase chain reaction (PCR) is regarded as a gold standard test for the molecular diagnosis of viral and bacterial infections with high sensitivity and specificity. Isothermal nucleic acid amplification is considered to be a highly promising candidate method due to its fundamental advantage in quick procedure time at constant temperature without thermocycler opera-tion. A variety of improved or new approaches also have been developed. This review summarizes the currently available detection methods for coronavirus nucleic acid. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coro-navirus infection.
Application of microfluidic chip technology in pharmaceutical analysis:A review
Ping Cui, Sicen Wang
2019, 9(4): 238-247.  
[Abstract](406) [PDF 5845KB](19)
摘要:
The development of pharmaceutical analytical methods represents one of the most significant aspects of drug development. Recent advances in microfabrication and microfluidics could provide new approaches for drug analysis, including drug screening, active testing and the study of metabolism. Microfluidic chip technologies, such as lab-on-a-chip technology, three-dimensional (3D) cell culture, organs-on-chip and droplet techniques, have all been developed rapidly. Microfluidic chips coupled with various kinds of detection techniques are suitable for the high-throughput screening, detection and mechanistic study of drugs. This review highlights the latest (2010–2018) microfluidic technology for drug analysis and dis-cusses the potential future development in this field.
Research advances in the detection of miRNA
Jiawei Ye, Mingcheng Xu, Xueke Tian, Sheng Cai, Su Zeng
2019, 9(4): 217-226.  
[Abstract](762) [PDF 6429KB](20)
摘要:
MicroRNAs (miRNAs) are a family of endogenous, small (approximately 22 nucleotides in length), noncoding, functional RNAs. With the development of molecular biology, the research of miRNA bio-logical function has attracted significant interest, as abnormal miRNA expression is identified to contribute to serious human diseases such as cancers. Traditional methods for miRNA detection do not meet current demands. In particular, nanomaterial-based methods, nucleic acid amplification-based methods such as rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), strand-displacement amplification (SDA) and some enzyme-free amplifications have been employed widely for the highly sensitive detection of miRNA. MiRNA functional research and clinical diagnostics have been accelerated by these new techniques. Herein, we summarize and discuss the recent progress in the development of miRNA detection methods and new applications. This review will provide guidelines for the development of follow-up miRNA detection methods with high sensitivity and spec-ificity, and applicability to disease diagnosis and therapy.
Carbon nanotubes:Evaluation of toxicity at biointerfaces
Debashish Mohanta, Soma Patnaik, Sanchit Sood, Nilanjan Das
2019, 9(5): 293-300.  
[Abstract](629) [PDF 3216KB](15)
摘要:
Carbon nanotubes (CNTs) are a class of carbon allotropes with interesting properties that make them productive materials for usage in various disciplines of nanotechnology such as in electronics equip-ments, optics and therapeutics. They exhibit distinguished properties viz., strength, and high electrical and heat conductivity. Their uniqueness can be attributed due to the bonding pattern present between the atoms which are very strong and also exhibit high extreme aspect ratios. CNTs are classified as single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) on the basis of number of sidewalls present and the way they are arranged spatially. Application of CNTs to improve the performance of many products, especially in healthcare, has led to an occupational and public exposure to these nanomaterials. Hence, it becomes a major concern to analyze the issues pertaining to the toxicity of CNTs and find the best suitable ways to counter those challenges. This review summarizes the toxicity issues of CNTs in vitro and in vivo in different organ systems (bio interphases) of the body that result in cellular toxicity.
Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase
Muhammad Usman Mirza, Matheus Froeyen
2020, 10(4): 320-328.  
[Abstract](580) [PDF 19436KB](12)
摘要:
Recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health safety. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are under way to test possible therapies. During this urgent situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated the highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach has facilitated the identifi-cation of potential binding modes and favourable molecular interaction profile of corresponding com-pounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural infor-mation obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.
Nanodiamonds with powerful ability for drug delivery and biomedical applications: Recent updates on in vivo study and patents
Swati Chauhan, Neha Jain, Upendra Nagaich
2020, 10(1): 1-12.  
[Abstract](365) [PDF 2643KB](8)
摘要:
Nanodiamonds are novel nanosized carbon building blocks possessing varied fascinating mechanical, chemical, optical and biological properties, making them significant active moiety carriers for biomedical application. These are known as the most'captivating' crystals attributed to their chemical inertness and unique properties posing them useful for variety of applications in biomedical era. Alongside, it becomes increasingly important to find, ascertain and circumvent the negative aspects associated with nano-diamonds. Surface modification or functionalization with biological molecules plays a significant role in managing the toxic behavior since nanodiamonds have tailorable surface chemistry. To take advantage of nanodiamond potential in drug delivery, focus has to be laid on its purity, surface chemistry and other considerations which may directly or indirectly affect drug adsorption on nanodiamond and drug release in biological environment. This review emphasizes on the basic properties, synthesis techniques, surface modification techniques, toxicity issues and biomedical applications of nanodiamonds. For the devel-opment of nanodiamonds as an effective dosage form, researchers are still engaged in the in-depth study of nanodiamonds and their effect on life interfaces.
Identification and characterization of phenolics and terpenoids from ethanolic extracts of Phyllanthus species by HPLC-ESI-QTOF-MS/MS
Sunil Kumar, Awantika Singh, Brijesh Kumar
2017, 7(4): 214-222.  
[Abstract](900) [PDF 3923KB](443)
Abstract:
Phyllanthus species plants are a rich source of phenolics and widely used due to their medicinal properties. A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed using high-pressure liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (HPLC-ESI-QTOF-MS/MS) for the identification and characterization of quercetin, kaempferol, ellagic acid and their derivatives in ethanolic extracts of Phyllanthus species. The chromatographic separation was carried out on Thermo Betasil C8 column (250 mm×4.5 mm, 5 μm) using 0.1% formic acid in water and 0.1% formic acid in methanol as the mobile phase. The identification of diagnostic fragment ions and optimization of collision energies were carried out using 21 reference standards. Totally 51 compounds were identified which include 21 compounds identified and characterized unambiguously by comparison with their authentic standards and the remaining 30 were tentatively identified and characterized in ethanolic extracts of P. emblica, P. fraternus, P. amarus and P. niruri.
Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
Xi Chen, Zhao Yang, Yang Xu, Zhe Liu, Yanfang Liu, Yuntao Dai, Shilin Chen
2023, 13(2): 142-155.   doi: 10.1016/j.jpha.2022.11.011
[Abstract](2422) [PDF 1336KB](1201)
Abstract:
Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the “single standard to determine multiple components (SSDMC)” approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA.
Preface for Special Issue: Single-Cell and Spatially Resolved Omics
2023, 13(7): 689-690.   doi: 10.1016/j.jpha.2023.07.005
[Abstract](222) [PDF 229KB](110)
Abstract:
Potential of RP-UHPLC-DAD-MS for the qualitative and quantitative analysis of sofosbuvir in film coated tablets and profiling degradants
María del Mar Contreras, Aránzazu Morales-Soto, Antonio Segura-Carretero, Javier Valverde
2017, 7(4): 208-213.  
[Abstract](202) [PDF 2055KB](96)
Abstract:
Sofosbuvir is one of the new direct-acting antiviral drugs against hepatitis C virus (HCV) infection. This drug has recently been launched into the market, and generic versions of the medication are expected to be produced by local drug producers in some countries. Therefore, new methods are required to control sofosbuvir in pharmaceuticals. In the present study, a new method based on reversed phase (RP)-ultra-high performance liquid chromatography (UHPLC) coupled to diode array detection (DAD) and mass spectrometry (MS) was developed to facilitate the qualitative and quantitative analysis of sofosbuvir in film coated tablets. A wavelength of 260 nm was selected to perform a cost-effective quantification and the method showed adequate linearity, with an R2 value of 0.9998, and acceptable values of accuracy (75%–102%) and precision (residual standard deviation < 5%). The detection and quantification limits were 0.07 μg/mL and 0.36 μg/mL, respectively. Furthermore, the use of high-resolution MS enabled us to ensure the specificity, check impurities and better sensitivity. Therefore, this methodology promises to be suitable not only for the routine analysis of sofosbuvir in pharmaceutical dosage forms, but also for potential degradants.
Natural product virtual-interact-phenotypic target characterization: A novel approach demonstrated with Salvia miltiorrhiza extract
Rui Xu, Hengyuan Yu, Yichen Wang, Boyu Li, Yong Chen, Xuesong Liu, Tengfei Xu
2025, 15(2): 101101.   doi: 10.1016/j.jpha.2024.101101
[Abstract](610) [PDF 6500KB](289)
Abstract:

Natural products (NPs) have historically been a fundamental source for drug discovery. Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents, and corresponding targets. In the present study, an innovative natural product virtual screening-interaction-phenotype (NP-VIP) strategy that integrates virtual screening, chemical proteomics, and metabolomics to identify and validate the bioactive targets of NPs. This approach reduces false positive results and enhances the efficiency of target identification. Salvia miltiorrhiza (SM), a herb with recognized therapeutic potential against ischemic stroke (IS), was used to illustrate the workflow. Utilizing virtual screening, chemical proteomics, and metabolomics, potential therapeutic targets for SM in the IS treatment were identified, totaling 29, 100, and 78, respectively. Further analysis via the NP-VIP strategy highlighted five high-confidence targets, including poly [ADP-ribose] polymerase 1 (PARP1), signal transducer and activator of transcription 3 (STAT3), amyloid precursor protein (APP), glutamate-ammonia ligase (GLUL), and glutamate decarboxylase 67 (GAD67). These targets were subsequently validated and found to play critical roles in the neuroprotective effects of SM. The study not only underscores the importance of SM in treating IS but also sets a precedent for NP research, proposing a comprehensive approach that could be adapted for broader pharmacological explorations.

Editorial Board
2021, 11(6).  
[Abstract](306) [PDF 64KB](149)
Abstract:
Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application
Dan Li, Zhou Fang, Qiang Shi, Nicholas Zhang, Binsheng Gong, Weida Tong, Ahmet F. Coskun, Joshua Xu
2023, 13(7): 691-693.   doi: 10.1016/j.jpha.2023.06.013
[Abstract](749) [PDF 707KB](361)
Abstract:
Single-cell analyses reveal cannabidiol rewires tumor microenvironment via inhibiting alternative activation of macrophage and synergizes with anti-PD-1 in colon cancer
Xiaofan Sun, Lisha Zhou, Yi Wang, Guoliang Deng, Xinran Cao, Bowen Ke, Xiaoqi Wu, Yanhong Gu, Haibo Cheng, Qiang Xu, Qianming Du, Hongqi Chen, Yang Sun
2023, 13(7): 726-744.   doi: 10.1016/j.jpha.2023.04.013
[Abstract](638) [PDF 9014KB](315)
Abstract:
Colorectal tumors often create an immunosuppressive microenvironment that prevents them from responding to immunotherapy. Cannabidiol (CBD) is a non-psychoactive natural active ingredient from the cannabis plant that has various pharmacological effects, including neuroprotective, antiemetic, anti-inflammatory, and antineoplastic activities. This study aimed to elucidate the specific anticancer mechanism of CBD by single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) technologies. Here, we report that CBD inhibits colorectal cancer progression by modulating the suppressive tumor microenvironment (TME). Our single-cell transcriptome and ATAC sequencing results showed that CBD suppressed M2-like macrophages and promoted M1-like macrophages in tumors both in strength and quantity. Furthermore, CBD significantly enhanced the interaction between M1-like macrophages and tumor cells and restored the intrinsic anti-tumor properties of macrophages, thereby preventing tumor progression. Mechanistically, CBD altered the metabolic pattern of macrophages and related anti-tumor signaling pathways. We found that CBD inhibited the alternative activation of macrophages and shifted the metabolic process from oxidative phosphorylation and fatty acid oxidation to glycolysis by inhibiting the phosphatidylinositol 3-kinase-protein kinase B signaling pathway and related downstream target genes. Furthermore, CBD-mediated macrophage plasticity enhanced the response to anti-programmed cell death protein-1 (PD-1) immunotherapy in xenografted mice. Taken together, we provide new insights into the anti-tumor effects of CBD.