The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure-performance relationships (QSPR) for rapid and accurate in silico BCS classification. The present study aims to develop a web platform for high-throughput BCS classification based on high-performance ML models. Initially, four data sets of BCS-related molecular properties: log S, log P, log D, and log P app were curated. Subsequently, 6 ML algorithms or deep learning frameworks were employed to construct models, with diverse molecular representations ranging from one-dimensional molecular fingerprints, descriptors, and molecular graphs to three-dimensional molecular spatial coordinates. By comparing different combinations of molecular representations and learning algorithms, LightGBM exhibited excellent performance in solubility prediction, with an R 2 of 0.84; AttentiveFP outperformed others in permeability prediction, with R 2 values of 0.96 and 0.76 for log P and log D, respectively; and XGBoost was the most accurate for log P app prediction, with an R 2 of 0.71. When externally validated on a marketed drug BCS category data set, the best-performing models achieved classification accuracies of over 77 and 73% for solubility and permeability, respectively. Finally, the well-trained models were embedded into the first ML-based BCS class prediction web platform (x f), enabling pharmaceutical scientists to quickly determine the BCS category of candidate drugs, which will aid in the high-throughput BCS assessment for candidate drugs during the preformulation stage, thereby promoting reduced risk and enhanced efficiency in drug development and regulation.

Although biopharmaceuticals constitute around 10% of the drug landscape, eight of the ten top-selling products were biopharmaceuticals in 2023. This study did a comprehensive analysis of the FDA's Purple Book database. Firstly, our research uncovered market trends and provided insights into biologics distributions. According to the investigation, although biotechnology has advanced and legislative shifts have made the approval process faster, there are still challenges to overcome, such as molecular instability and formulation design. Moreover, our research comprehensively analyzed biological formulations, pointing out significant strategies regarding administration routes, dosage forms, product packaging, and excipients. In conjunction with biologics, the widespread integration of innovative delivery strategies will be implemented to confront the evolving challenges in healthcare and meet an expanding array of treatment needs.

AIM:This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm. BACKGROUND:In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market. OBJECTIVE:To collect data from the database and apply machine learning to build the model. METHODS:LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies. RESULTS:The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries. CONCLUSION:In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.

Chemodynamic therapy (CDT) eradicates tumors by intratumoral catalytic chemical reaction and subsequently disrupts redox homeostasis, which shows tumor specific reactive oxygen species (ROS)-mediated therapy. However, insufficient ROS generation and high levels of glutathione (GSH) in cancer cells have limited the therapeutic efficacy of CDT. Herein, we constructed a multifunctional oxidative stress nanoamplifier with ROS amplification and GSH exhaustion for enhanced CDT. Such a sandwich-like nanoamplifier comprised layer-by-layer artesunate (AS) and calcium carbonate coatings on the surface of manganese dioxide (MnO2) nanoparticles. The nanoamplifier was disassembled under an acidic environment once accumulated into tumor sites, and subsequently released AS to replenish the intratumoral peroxide pool for ROS amplification. Besides being an AS carrier, MnO2 exhausted GSH to yield Mn2+ ions that catalyzed the overexpression of H2O2 in the tumor, further intensifying the oxidative stress and facilitating cancer cell death. Taken together, our findings not only provide a paradigm for fabricating intratumoral catalytic nanomaterials, but also present a new ROS enhancement strategy to improve anti-tumor efficacy. Our multifunctional oxidative stress nanoamplifier might broaden the future of CDT.

Zebrafish is an essential model organism for studying cardiovascular diseases, given its advantages of fast proliferation and high gene homology with humans. Zebrafish embryos/larvae are valuable experimental models used in toxicology studies to analyze drug toxicity, including hepatoxicity, nephrotoxicity and cardiotoxicity, as well as for drug discovery and drug safety screening in the preclinical stage. Heart rate (HR) serves as a functional endpoint in studies of cardiotoxicity, while heart rate variability (HRV) serves as an indicator of cardiac arrhythmia. Cardiotoxicity is a major cause of early and late termination of drug trials, so a more comprehensive understanding of zebrafish HR and HRV is important. This review summarized HR and HRV in a specific range of applications and fields, focusing on zebrafish heartbeat detection procedures, signal analysis technology and well-established commercial software, such as LabVIEW, Rvlpulse, and ZebraLab. We also compared HR detection algorithms and electrocardiography (ECG)-based methods of heart signal extraction. The relationship between HR and HRV was also systematically analyzed; HR was shown to have an inverse correlation with HRV. Applications to drug testing are also highlighted in this review. Furthermore, HR and HRV were shown to be regulated by the automatic nervous system; their connections with ECG measurements are also summarized herein.

Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R2 = 0.880 vs. R2 = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.

Eleven undescribed tetracyclic triterpenoids, meliazedarachins A-K, along with twenty-six known compounds were isolated from the fruits of Melia azedarach L.. Their structures were determined by HRESIMS, UV, IR, NMR, X-ray diffraction, electronic circular dichroism (ECD) spectra, and the modified Mosher's method. The cytotoxic activities of all the isolates were measured. Meliazedarachin K and mesendanin N showed cytotoxicity against five human cancer cell lines with IC50 values ranging from 9.02 to 31.31 μM. Meliazedarachin K showed significant cytotoxicity against HCT116 cell line with IC50 value of 9.02 ± 0.84 μM. 21α-methylmelianodiol showed significant cytotoxicity against HCT116 and RKO cell lines with IC50 values of 10.16 ± 1.22 and 8.57 ± 0.80 μM, respectively.

Small interfering RNA (siRNA) gene silencing therapy has great potential for treating multiple diseases. The lipid nanoparticle (LNP) technology for siRNA delivery succussed in clinical treatment. However, the formulation design of siRNA-LNP still faces enormous challenges. Current research aims to develop an integrated computer methodology for the rational design of siRNA-LNP formulations. The machine learning (ML) algorithm lightGBM was built to predict the knockdown efficiency of siRNA-LNP in vitro and in vivo delivery and reached good accuracy with 80% and 78.89% in the validation set. Further siRNA experiments well validated the ML model. Moreover, molecular dynamic (MD) simulation was utilized to investigate the molecular structure of siRNA-LNP. In conclusion, a novel integrated computer methodology based on ML, experimental, and MD simulation was successfully developed for siRNA-LNP formulation design.

Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.

Three new biflavonoids, umcephabiovins C - E (1 - 3), along with fourteen known compounds were isolated from the twigs and leaves of Cephalotaxus oliveri. Their structures and configurations were elucidated by UV, IR, NMR, ECD, and HR-ESI-MS spectra. Compounds 1 - 3 exhibited significant alpha-glucosidase inhibitory activity with IC50 values of 7.05 +/- 2.66, 24.45 +/- 4.73, and 1.84 +/- 1.14 mu M, respectively. Compound 11 showed moderate cytotoxicity against the BaF3/T315I cell line. [GRAPHICS] .

Background and purpose: Asiatic acid is one of the active compounds isolated from Centella asiatica and has been used to treat many diseases, including hypertension, pulmonary fibrosis, and cancer. It exhibits anticancer effects in many cancers, such as ovarian, lung and colon cancer; however, its anticancer effects in breast cancer and the underlying mechanism are not fully understood. Chemoresistance is often induced after the use of chemotherapy, and it is a challenging problem in cancer therapy. The effects of asiatic acid on chemoresistance in breast cancer have never been studied. Therefore, the aim of the present study was to examine the anticancer effects of asiatic acid in doxorubicin-resistant breast cancer MCF-7 cells. Methods: The cells were incubated with asiatic acid at 0-160 mu M for 2-24 h. Cell viability and cytotoxicity were evaluated by 3-[4, 5-dimethyl-2-thiazolyl]-2,5-diphenyltetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays. Florescent images were taken using a confocal microscope. P-gp function and apoptosis assays were performed using flow cytometry. Caspase activity was measured with the Caspase-Glo (TM) Assay System. The phosphorylation and expression of relevant proteins were assessed by western blots. Molecular docking was performed and scored by AutoDock. Cellular thermal shift assay (CETSA) was applied for experimental valuation. Results: Our data demonstrated that asiatic acid induced cell death in multiple ways, including reactive oxygen species production, adenosine triphosphate (ATP) content reduction, and adaptive immunity balance via intrinsic apoptosis, AMP-activated protein kinase (AMPK), programmed death-ligand 1 (PD-L1), and indirect nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kappa B) transcriptional pathways, using experimental validation and in silico analysis. Moreover, asiatic acid also enhanced the sensitivity of doxorubicinresistant MCF-7 cells to doxorubicin by improving P-glycoprotein (P-gp) function. Conclusions: This study provides evidence that asiatic acid has strong anticancer effects to reverse multidrug resistance and could be developed as a promising adjuvant drug for the treatment of chemoresistant cancer.

With the increasing demand for traditional Chinese medicine (TCM) in Portuguese-speaking countries (PSC), local regulatory systems and relevant legislation are still insufficient and lagging, even blank in some of them. This kind of unbalanced pace either makes users of TCM exposed in potential risk or eventually obstructs the long-term development of TCM in PSC. Despite existing tremendous studies on the internationalization of TCM, there are few studies specific to PSC. Thus, by a comprehensive desk review and typical case study, this article aims to summarize current situation of TCM in PSC by a cross-regional comparison, to identify various critical challenges, and further to provide an insightful reference to impel the development of TCM in PSC.

BACKGROUND:Coronavirus Disease 2019 (COVID-19) is an unprecedented disaster for people around the world. Many studies have shown that traditional Chinese medicine (TCM) are effective in treating COVID-19. However, it is difficult to find the most effective combination herbal pair among numerous herbs, as well as identifying its potential mechanisms. Herbal pair is the main form of a combination of TCM herbs, which is widely used for the treatment of diseases. It can also help us to better understand the compatibility of TCM prescriptions, thus improving the curative effects. The purpose of this article is to explore the compatibility of TCM prescriptions and identify the most important herbal pair for the treatment of COVID-19, and then analyze the active components and potential mechanisms of this herbal pair.METHODS:We first systematically sorted the TCM prescriptions recommended by the leading experts for treating COVID-19, and the specific herbs contained in these prescriptions across different stages of the disease. Next, the association rule approach was employed to examine the distribution and compatibility among these TCM prescriptions, and then identify the most important herbal pair. On this basis, we further investigated the active ingredients and potential targets in the selected herbal pair by a network pharmacology approach, and analyzed the potential mechanisms against COVID-19. Finally, the main active compounds in the herbal pair were selected for molecular docking with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) 3CLpro and angiotensin converting enzyme II (ACE2) for further verification.RESULT:We obtained 32 association rules for the herbal combinations in the selection of TCM treatment for COVID-19. The results showed that the combination of Amygdalus Communis Vas (ACV) and Ephedra sinica Stapf (ESS) had the highest confidence degree and lift value, as well as high support degree, which can be used in almost all the stages of COVID-19, so ACV and ESS (AE) were selected as the most important herbal pair. There were 26 active ingredients and 44 potential targets, which might be related to the herbal pair of AE against COVID-19. The main active ingredients of AE against COVID-19 were quercetin, kaempferol, luteolin, while the potential targets were Interleukin 6 (IL-6), Mitogen-activated Protein Kinase 1 (MAPK)1, MAPK8, Interleukin-1β (IL-1β), and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) p65 subunit (RELA). The protein-protein interaction (PPI) cluster demonstrated that IL-6 was the seed in the cluster, which plays an important role in connecting other nodes in the PPI network. The potential pathways mainly involved tumor necrosis factor (TNF), Toll-like receptor (TLR), hypoxia-inducible factor-1 (HIF-1), and nucleotide-binding oligomerization domain (NOD)-like receptor (NLRs). The molecular docking results showed that the main active ingredients of AE have good affinity with SARS-COV-2 3CLpro and ACE2, which are consistent with the above analysis.CONCLUSIONS:There were 32 association rules in the TCM prescriptions recommended by experts for COVID-19. The combination of ACV and EAS was the most important herbal pair for the treatment of COVID-19. AE might have therapeutic effects against COVID-19 by affecting the inflammatory and immune responses, cell apoptosis, hypoxia damage and other pathological processes through multiple components, targets and pathways.

Toosendan Fructus with various pharmaceutical activities is a good source for the finding of new bioactive components, especially limonoids inside have been reported to have anticancer and antifeedant activities. To find more potential new bioactive compounds, the mass spectrometric characteristics of nimbolinin-type limonoids were first investigated. Utilizing these characteristics, totally 60 nimbolinins, including 33 new ones and at least 10 bioactive compounds, were identified by ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Furthermore, based on UHPLC-Q-TOF/MS and statistical analysis, 9 limonoids were identified to be the differential components between Toosendan Fructus and Azedarach Fructus. Particularly, nimbolinin A and toosendanin (TSN) with higher content in Azedarach Fructus and Toosendan Fructus respectively should be good markers. Finally, an UHPLC-triple quadrupole mass spectrometry (UHPLC-QQQ/MS) quantification approach for nimbolinin A and TSN was developed for their quality control. These results provided the basis for drug development and quality control of Toosendan Fructus and Azedarach Fructus.

Nine new limonoids, meliazedarines A-I (1-9), seven known analogues (10-16), and five known triterpenoids (17-21) were isolated from the fruits of Melia azedarach. Their structures were determined by analysis of 1D and 2D NMR, HRESIMS, X-ray diffraction, and electronic circular dichroism (ECD) data. Compound 7 showed significant cytotoxicity against the HCT116 cell line with IC50 values of 0.3 ± 0.1 μM.

Three constructed wetland systems were established to treat saline sewage via high-porosity ceramsite, activated carbon, and low-porosity sand: A (ceramsite + activated carbon + sand), B (sand + activated carbon + ceramsite), and C (sand). The distribution of dissolved oxygen in these systems varied with different filling methods with the best removal efficiency of ammonium nitrogen and total nitrogen observed in system B (97.4 and 96.2%, respectively). The 16S rDNA amplicon sequencing results showed that all the systems had a high abundance of salt-tolerant denitrifiers, and the filling method significantly impacted denitrifying bacteria (e.g., Vibrio and Planctomyces) in the substrate. System B had more diverse dissolved oxygen conditions than system A and showcased aerobic nitrificationdenitrification and anaerobic ammonium oxidation pathways. Therefore, the use of substrates with different porosities can improve the dissolved oxygen supply and enhance nitrogen removal efficiency in constructed wetlands.

Epilepsy is a chronic neurological disorder, characterized by recurrent, spontaneous, and transient seizures, and affects more than 70 million people worldwide. Although two dozen antiepileptic drugs (AEDs) are approved and available in the market, seizures remain poorly controlled in one-third of epileptic patients who are suffering from drug resistance or various adverse effects. Recently, the xanthone skeleton has been regarded as an attractive scaffold for the discovery and development of emerging anticonvulsants. We had isolated several dihydroxanthone derivatives previously, including oliganthin H, oliganthin I, and oliganthin N, whose structures were similar and delicately elucidated by spectrum analysis or X-ray crystallographic data, from extracts of leaves of Garcinia oligantha. These xanthone analogues were evaluated for anticonvulsant activity, and a novel xanthone, oliganthin H, has been identified as a sound and effective natural inhibitor of convulsions in zebrafish in vivo. A preliminary structure-activity relationship analysis on the relationship between structures of the xanthone analogues and their activities was also conducted. Oliganthin H significantly suppressed convulsant behavior and reduced to about 25% and 50% of PTZ-induced activity, in 12.5 and 25 μM treatment groups (P < 0.01 and 0.001), respectively. Meanwhile, it reduced seizure activity, velocity, seizure duration, and number of bursts in zebrafish larvae (P < 0.05). Pretreatment of oliganthin H significantly restored aberrant induction of gene expressions including npas4a, c-fos, pyya, and bdnf, as well as gabra1, gad1, glsa, and glula, upon PTZ treatment. In addition, in silico analysis revealed the stability of the oliganthin H-GABAA receptor complex and their detailed binding pattern. Therefore, direct interactions with the GABAA receptor and involvement of downstream GABA-glutamate pathways were possible mechanisms of the anticonvulsant action of oliganthin H. Our findings present the anticonvulsant activity of oliganthin H, provide a novel scaffold for further modifications, and highlight the xanthone skeleton as an attractive and reliable resource for the development of emerging AEDs.

Abstract Background Traditional Chinese medicine (TCM) encompasses numerous herbal formulas which play critical therapeutic roles through “multi-components, multi-targets and multi-pathways” mechanisms. Exploring the interaction among these mechanisms can certainly help to depict the core therapeutic function of herbal formulas. Xiaoyao decoction (XYD) is one of the most well-known traditional Chinese medicine formulas which has been widely applied to treat various diseases. In this study, taking XYD as an example, we proposed a network pharmacology-based method to identify the main therapeutic targets of this herbal concoctions. Methods Chemical data of XYD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID) and Compound Reference Database (CRD) and screened oral bioavailability attributes from SwissADME using Veber’s filter. Targets of sample chemicals were identified using the online tool similarity ensemble approach (SEA), and pathways were enriched using STRING database. On the basis of targets–pathways interactions from the enrichment, a “targets–pathways–targets” (TPT) network was constructed. In the TPT network, the importance of each target was calculated by the declining value of network efficiency, which represents the influential strength of a specific set-off target on the whole network. Network-based predictive results were statistically validated with existing experimental evidence. Results The TPT network was comprised of 279 nodes and 6549 edges. The declining value of network efficiency of the sample targets was significantly correlated with their involvement frequency in existing studies of XYD using Spearman’s test (p < 0.001). The top 10% of candidate targets, such as AKT1, PIK3R1, NFKB1 and RELA, etc., were chosen as XYD’s main therapeutic targets, which further show pharmacological functions synergistically through 11 main pathways. These pathways are responsible for endocrine, nutritional or metabolic diseases, neoplasms and diseases of the nervous system, etc. Conclusions The network pharmacology-based approach in the present study shows promising potential for identifying the main therapeutic targets from TCM formulas. This study provides valuable information for TCM researchers and clinicians for better understanding the main therapeutic targets and therapeutic roles of herbal decoctions in clinical settings.

Background and Aim. The progression of liver fibrosis in chronic hepatitis B (CHB) patients is currently insufficiently controlled worldwide. The Yi Guan Jian decoction (YGJD) has been widely used in the treatment of liver fibrosis in CHB cases. Although animal studies have reported the antifibrotic effects of the decoction, the active ingredients of the YGJD remain unknown. This study aimed at identifying the potential active ingredients and exploring the mechanisms of action (MOA) of the decoction when treating CHB patients with fibrosis. Methods. Using data mining techniques and a structural clustering analysis, the potential active ingredients were determined. A network analysis of the differentially expressed genes was conducted to identify the potential targets. Selected compounds were docked to the potential targets for the compound-target interaction simulation. In vitro validation, including a cell proliferation assay and Western blot analysis, was conducted to evaluate the prediction results. Results. In the microarray data, 224 differentially expressed genes related to liver fibrosis were considered to be potential targets. Thirty active ingredients of the YGJD and 15 main targets and relevant pathways were identified. Among them, two active ingredients, methylophiopogonone A and 8-geranyloxypsoralen, were validated as exhibiting antifibrotic effects on hepatic stellate cells. Conclusions. We identified the potential active ingredients of the YGJD and proposed the possible explanation for the MOA in the treatment of CHB patients with liver fibrosis. Moreover, this study provides a methodological reference for the systematic investigation of the bioactive compounds and related MOA of a traditional Chinese medicine formula in a clinical context.

Asprellosides A-K, nine new ursane-type triterpenoid glycosides (1-9), and two new oleanane-type triterpenoid glycosides (10 and 11), including six rare sulfated triterpenoid glycosides, were isolated from the roots of Ilex asprella. Their structures were determined on the basis of comprehensive spectroscopic analysis and chemical methods. Among these compounds, asprelloside B (2) and asprelloside C (3) are the first examples of triterpenoid glycosides bearing a rare 3,4-O-disulfo-xylopyranosyl residue. All the saponins isolated showed no significant effects against respiratory syncytial virus (RSV) and lipopolysaccharide-induced nitric oxide production in Raw264.7 macrophages.