A Comprehensive Analysis of Biopharmaceutical Products Listed in the FDA's Purple Book.

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.

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks.

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.

PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy.

OBJECTIVE: This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants. METHODS: A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model. RESULTS: The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction. CONCLUSION: Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.

Dynamic immuno-nanomedicines in oncology.

Anti-cancer therapeutics have achieved significant advances due to the emergence of immunotherapies that rely on the identification of tumors by the patients' immune system and subsequent tumor eradication. However, tumor cells often escape immunity, leading to poor responsiveness and easy tolerance to immunotherapy. Thus, the potentiated anti-tumor immunity in patients resistant to immunotherapies remains a challenge. Reactive oxygen species-based dynamic nanotherapeutics are not new in the anti-tumor field, but their potential as immunomodulators has only been demonstrated in recent years. Dynamic nanotherapeutics can distinctly enhance anti-tumor immune response, which derives the concept of the dynamic immuno-nanomedicines (DINMs). This review describes the pivotal role of DINMs in cancer immunotherapy and provides an overview of the clinical realities of DINMs. The preclinical development of emerging DINMs is also outlined. Moreover, strategies to synergize the antitumor immunity by DINMs in combination with other immunologic agents are summarized. Last but not least, the challenges and opportunities related to DINMs-mediated immune responses are also discussed.

Erythrocyte-Leveraged Oncolytic Virotherapy (ELeOVt): Oncolytic Virus Assembly on Erythrocyte Surface to Combat Pulmonary Metastasis and Alleviate Side Effects.

Despite being a new promising tool for cancer therapy, intravenous delivery of oncolytic viruses (OVs) is greatly limited by poor tumor targeting, rapid clearance in the blood, severe organ toxicity, and cytokine release syndrome. Herein, a simple and efficient strategy of erythrocyte-leveraged oncolytic virotherapy (ELeOVt) is reported, which for the first time assembled OVs on the surface of erythrocytes with up to near 100% efficiency and allowed targeted delivery of OVs to the lung after intravenous injection to achieve excellent treatment of pulmonary metastases while greatly improving the biocompatibility of OVs as a drug. Polyethyleneimine (PEI) as a bridge to assemble OVs on erythrocytes also played an important role in promoting the transfection of OVs. It is found that ELeOVt approach significantly prolonged the circulation time of OVs and increased the OVs distribution in the lung by more than tenfold, thereby significantly improving the treatment of lung metastases while reducing organ and systemic toxicity. Taken together, these findings suggest that the ELeOVt provides a biocompatible, efficient, and widely available approach to empower OVs to combat lung metastasis.

Progress in oncolytic viruses modified with nanomaterials for intravenous application.

In oncolytic virus (OV) therapy, a critical component of tumor immunotherapy, viruses selectively infect, replicate within, and eventually destroy tumor cells. Simultaneously, this therapy activates immune responses and mobilizes immune cells, thereby eliminating residual or distant cancer cells. However, because of OVs' high immunogenicity and immune clearance during circulation, their clinical applications are currently limited to intratumoral injections, and their use is severely restricted. In recent years, numerous studies have used nanomaterials to modify OVs to decrease virulence and increase safety for intravenous injection. The most commonly used nanomaterials for modifying OVs are liposomes, polymers, and albumin, because of their biosafety, practicability, and effectiveness. The aim of this review is to summarize progress in the use of these nanomaterials in preclinical experiments to modify OVs and to discuss the challenges encountered from basic research to clinical application.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence.

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

Combining MSC Exosomes and Cerium Oxide Nanocrystals for Enhanced Dry Eye Syndrome Therapy.

Dry eye syndrome (DES) is a prevalent ocular disorder involving diminishe·d tear production and increased tear evaporation, leading to ocular discomfort and potential surface damage. Inflammation and reactive oxygen species (ROS) have been implicated in the pathophysiology of DES. Inflammation is one core cause of the DES vicious cycle. Moreover, there are ROS that regulate inflammation in the cycle from the upstream, which leads to treatment failure in current therapies that merely target inflammation. In this study, we developed a novel therapeutic nanoparticle approach by growing cerium oxide (Ce) nanocrystals in situ on mesenchymal stem cell-derived exosomes (MSCExos), creating MSCExo-Ce. The combined properties of MSCExos and cerium oxide nanocrystals aim to target the "inflammation-ROS-injury" pathological mechanism in DES. We hypothesized that this approach would provide a new treatment option for patients with DES. Our analysis confirmed the successful in situ crystallization of cerium onto MSCExos, and MSCExo-Ce displayed excellent biocompatibility. In vitro and in vivo experiments have demonstrated that MSCExo-Ce promotes corneal cell growth, scavenges ROS, and more effectively suppresses inflammation compared with MSCExos alone. MSCExo-Ce also demonstrated the ability to alleviate DES symptoms and reverse pathological alterations at both the cellular and tissue levels. In conclusion, our findings highlight the potential of MSCExo-Ce as a promising therapeutic candidate for the treatment of DES.

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches.

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future.

Physiologically Based Pharmacokinetic Modeling for Multiple Oral Administration Labetalol in Pregnant Women.

BACKGROUND: Labetalol has an irreplaceable role in treating Hypertensive disorders of pregnancy (HDP), a common disease during pregnancy with a prevalence of 5.2-8.2%. However, there were big differences in dosage regimens between various guidelines. PURPOSE: A physiologically-based pharmacokinetics (PBPK) model was established and validated to evaluate the existing oral dosage regimens, and to compare the difference in plasma concentration between pregnant and non-pregnant women. METHODS: First, non-pregnant woman models with specific plasma clearance or enzymatic metabolism (UGT1A1, UGT2B7, CYP2C19) were established and validated. For CYP2C19, slow, intermediate, and rapid metabolic phenotypes were considered. Then, a pregnant model with proper structure and parameters adjustment was established and validated against the multiple oral administration data. RESULTS: The predicted labetalol exposure captured the experimental data well. The following simulations with criteria lowering 15 mmHg blood pressure (corresponding to around 108 ng/ml plasma labetalol) found that the maximum daily dosage in the Chinese guideline may be insufficient for some severe HDP patients. Moreover, similar predicted steady-state trough plasma concentration was found between the maximum daily dosage in the American College of Obstetricians and Gynecologists (ACOG) guideline, 800 mg Q8h and a regimen of 200 mg Q6h. Simulations comparing non-pregnant and pregnant women found that the difference in labetalol exposure highly depended on the CYP2C19 metabolic phenotype. CONCLUSIONS: In summary, this work initially established a PBPK model for multiple oral administration of labetalol for pregnant women. This PBPK model may lead to personalized labetalol medication in the future.

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies.

Low aqueous solubility is a common and serious challenge for most drug substances not only in development but also in the market, and it may cause low absorption and bioavailability as a result. Amorphization is an intermolecular modification strategy to address the issue by breaking the crystal lattice and enhancing the energy state. However, due to the physicochemical properties of the amorphous state, drugs are thermodynamically unstable and tend to recrystallize over time. Glass-forming ability (GFA) is an experimental method to evaluate the forming and stability of glass formed by crystallization tendency. Machine learning (ML) is an emerging technique widely applied in pharmaceutical sciences. In this study, we successfully developed multiple ML models (i.e., random forest (RF), XGBoost, and support vector machine (SVM)) to predict GFA from 171 drug molecules. Two different molecular representation methods (i.e., 2D descriptor and Extended-connectivity fingerprints (ECFP)) were implemented to process the drug molecules. Among all ML algorithms, 2D-RF performed best with the highest accuracy, AUC, and F1 of 0.857, 0.850, and 0.828, respectively, in the testing set. In addition, we conducted a feature importance analysis, and the results mostly agreed with the literature, which demonstrated the interpretability of the model. Most importantly, our study showed great potential for developing amorphous drugs by in silico screening of stable glass formers.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion.

Amorphous solid dispersion (ASD) is one of the most important strategies to improve the solubility and dissolution rate of poorly water-soluble drugs. As a widely used technique to prepare ASDs, hot-melt extrusion (HME) provides various benefits, including a solvent-free process, continuous manufacturing, and efficient mixing compared to solvent-based methods, such as spray drying. Energy input, consisting of thermal and specific mechanical energy, should be carefully controlled during the HME process to prevent chemical degradation and residual crystallinity. However, a conventional ASD development process uses a trial-and-error approach, which is laborious and time-consuming. In this study, we have successfully built multiple machine learning (ML) models to predict the amorphization of crystalline drug formulations and the chemical stability of subsequent ASDs prepared by the HME process. We utilized 760 formulations containing 49 active pharmaceutical ingredients (APIs) and multiple types of excipients. By evaluating the built ML models, we found that ECFP-LightGBM was the best model to predict amorphization with an accuracy of 92.8%. Furthermore, ECFP-XGBoost was the best in estimating chemical stability with an accuracy of 96.0%. In addition, the feature importance analyses based on SHapley Additive exPlanations (SHAP) and information gain (IG) revealed that several processing parameters and material attributes (i.e., drug loading, polymer ratio, drug's Extended-connectivity fingerprints (ECFP) fingerprints, and polymer's properties) are critical for achieving accurate predictions for the selected models. Moreover, important API's substructures related to amorphization and chemical stability were determined, and the results are largely consistent with the literature. In conclusion, we established the ML models to predict formation of chemically stable ASDs and identify the critical attributes during HME processing. Importantly, the developed ML methodology has the potential to facilitate the product development of ASDs manufactured by HME with a much reduced human workload.

Investigation of ß-caryophyllene as terpene penetration enhancer: Role of stratum corneum retention.

Terpenes are usually used as penetration enhancers (PE) for transdermal drug delivery (TDD) of various molecules. However, TDD of hydrophilic macromolecules is becoming an urgent challenge due to their potent activities. The aim of this study was to investigate the potential application of ß-caryophyllene (ß-CP), a sequiterpene, as PE for TDD of hydrophilic macromolecules for the first time. Commonly used PEs, namely azone and 1,8-cineole (1,8-CN), were applied as controls. Transepidermal water loss (TEWL) analysis revealed that the reduction of skin barrier function caused by ß-CP was reversible. Transdermal experiments showed that when skin was treated with ß-CP or azone, there was a significant permeation-enhancing effect on fluorescein isothiocyanate (FITC) and FITC-dextran with different molecular weight (MW) of 4k or 10k. CLSM analysis confirmed that ß-CP and azone can facilitate the penetration of FD-4k through epidermis and dermis. However, the cytotoxicity of azone against epidermal keratinocytes was significantly higher than ß-CP and 1,8-CN. Additionally, application of ß-CP and 1,8-CN didn't increase erythema index (EI) but the EI values of azone group increased significantly and irreversibly, indicating the high biocompatibility of the natural terpenes. ß-CP had better permeation-enhancing effect and higher stratum corneum (SC) retention than 1,8-CN due to its increased carbon chain length and lipophilicity, as further demonstrated by molecular dynamics (MD) simulation studies. Skin electrical resistance (SER) and attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) studies revealed a significant interfering effect of ß-CP on SC lipids. Taken together, ß-CP exhibited significant penetration enhancement of hydrophilic macromolecules due to its SC retention and SC lipid fluidization ability.

Quantitative Analysis for Chinese and US-listed Pharmaceutical Companies by the LightGBM Algorithm.

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.

Machine learning in accelerating microsphere formulation development.

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.

Synthesis of Carrier-Free Paclitaxel-Curcumin Nanoparticles: The Role of Curcuminoids.

The systemic administration of paclitaxel (PTX)-based combinatorial therapies is significantly restricted due to the multidrug resistance. Curcumin (CUR) not only inhibits cancer-cell proliferation but also reverses the PTX resistance. However, achieving codelivery of these two drugs is a challenge due to their poor water solubility. Herein, we synthesized carrier-free PTX NPs by a facile nanoprecipitation method with the help of CUR and other curcuminoids present in turmeric extract. The prepared NPs demonstrated spherical morphologies with high conformational stability. Experimental studies showed that the presence of both bisdemethoxycurcumin and demethoxycurcumin is essential for the successful formation of spherical and monodisperse NPs. Computational studies revealed that the presence of the more sterically available curcuminoids BMC and DMC makes the self-assembly procedure more adaptable with a higher number of potential conformations that could give rise to more monodisperse PTX-CUR NPs. Compared with PTX alone, PTX-CUR NPs have shown comparable therapeutic efficiency in vitro and demonstrated a higher cellular internalization, highlighting their potential for in vivo applications. The successful formation of PTX-CUR NPs and the understanding of how multiple drugs behave at the molecular level also provide guidance for developing formulations for the synthesis of high-quality and effective carrier-free nanosystems for biomedical applications.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms.

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

Molecular Dynamics Simulation of Drug Solubilization Behavior in Surfactant and Cosolvent Injections.

Surfactants and cosolvents are often combined to solubilize insoluble drugs in commercially available intravenous formulations to achieve better solubilization. In this study, six marketed parenteral formulations with surfactants and cosolvents were investigated on the aggregation processes of micelles, the structural characterization of micelles, and the properties of solvent using molecular dynamics simulations. The addition of cosolvents resulted in better hydration of the core and palisade regions of micelles and an increase in both radius of gyration (Rg) and the solvent accessible surface area (SASA), causing a rise in critical micelle concentration (CMC), which hindered the phase separation of micelles. At the same time, the presence of cosolvents disrupted the hydrogen bonding structure of water in solution, increasing the solubility of insoluble medicines. Therefore, the solubilization mechanism of the cosolvent and surfactant mixtures was successfully analyzed by molecular dynamics simulation, which will benefit future formulation development for drug delivery.

Traditional Chinese medicine injections: where we are after 80-year development.

Traditional Chinese medicine (TCM) injection is the combination of modern pharmaceutical technology and traditional Chinese prescription, which was born in 1941 and played a great role in the backward medical conditions at that time. However, the debate over TCM injections has never stopped due to adverse drug reactions (ADRs). The regulation on TCM injections has been further strengthened since 2017, which has prompted many TCM injections to carry out re-evaluations on quality, safety, efficiency as well as pharmacoeconomics, which made significant changes and progress. This review presented an up-to-date analysis of the types, amounts, and ADRs of TCM injections based on the published data and literature. This review also summarized the potential reasons for the ADRs and re-evaluation strategies. This review will provide some useful clues for TCM injections and their clinical use.