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CONTEXT: Nine steaming and nine drying is a traditional Chinese medicine (TCM) processing method and it is widely used for processing tonifying herbs. Modern research reveals that the repeated steaming and drying process varies the composition and clinical efficacy of TCM. OBJECTIVE: This paper analyzes and explores the historical evolution, research progress, development strategies, and problems encountered in the nine steaming and nine drying process so as to provide a reasonable explanation for this method. METHODS: English and Chinese literature from 1986 to 2023 was collected from databases including Web of Science, PubMed, Elsevier, Chinese Pharmacopoeia 2020 (CP), and CNKI (Chinese). Nine steaming and nine drying, processing, TCM and pharmacological activity were used as the key words. RESULTS: Nine steaming and nine drying has undergone thousands of years of clinical practice. Under specific processing conditions of nine steaming and nine drying, the ingredients of the TCM have significant changes, which in turn altered clinical applications. CONCLUSIONS: This review provides sufficient evidence to prove the rationality and scientific value of nine steaming and nine drying and puts forward a development direction for future research.

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Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

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The liver is the most important organ for biological transformation in the body and is crucial for maintaining the body's vital activities. Liver injury is a serious pathological condition that is commonly found in many liver diseases. It has a high incidence rate, is difficult to cure, and is prone to recurrence. Liver injury can cause serious harm to the body, ranging from mild to severe fatty liver disease. If the condition continues to worsen, it can lead to liver fibrosis and cirrhosis, ultimately resulting in liver failure or liver cancer, which can seriously endanger human life and health. Therefore, establishing an rodent model that mimics the pathogenesis and severity of clinical liver injury is of great significance for better understanding the pathogenesis of liver injury patients and developing more effective clinical treatment methods. The author of this article summarizes common chemical liver injury models, immune liver injury models, alcoholic liver injury models, drug-induced liver injury models, and systematically elaborates on the modeling methods, mechanisms of action, pathways of action, and advantages or disadvantages of each type of model. The aim of this study is to establish reliable rodent models for researchers to use in exploring anti-liver injury and hepatoprotective drugs. By creating more accurate theoretical frameworks, we hope to provide new insights into the treatment of clinical liver injury diseases.

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Drug discovery and development have been sped up because of the advances in computational science. In both industry and academics, artificial intelligence (AI) has been widely used. Machine learning (ML), an important component of AI, has been used in a variety of domains, including data production and analytics. One area that stands to gain significantly from this achievement of machine learning is drug discovery. The process of bringing a new drug to market is complicated and time-consuming. Traditional drug research takes a long time, costs a lot of money, and has a high failure rate. Scientists test millions of compounds, but only a small number make it to preclinical or clinical testing. It is crucial to embrace innovation, especially automated technologies, to lessen the complexity involved in drug research and avoid the high cost and lengthy process of bringing a medicine to the market. A rapidly developing field, a branch of artificial intelligence called machine learning (ML), is being used by numerous pharmaceutical businesses. Automating repetitive data processing and analysis processes can be achieved by incorporating ML methods into the drug development process. ML techniques can be used at numerous stages of the drug discovery process. In this study, we will discuss the steps of drug discovery and methods of machine learning that can be applied in these steps, as well as give an overview of each of the research works in this field.

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Early assessment of dosing requirements should be an integral part of developability assessments for a discovery program. If a very high dose is required to achieve the desired pharmacological effect, it may not be clinically feasible or commercially desirable to develop the biotherapeutic for the selected target unless extra measures are taken to develop a high concentration formulation or maximize yield during manufacturing. A quantitative understanding of the impact of target selection, biotherapeutic format, and optimal drug properties on potential dosing requirements to achieve efficacy can affect many early decisions. Early prediction of dosing requirements for biotherapeutics, as opposed to small molecules, is possible due to a strong influence of target biology on pharmacokinetics and dosing. Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) models leverage knowledge and competitor data available at an early stage of drug development, including biophysics of the target(s) and disease physiology, to rationally inform drug design criteria. Here we review how mathematical mechanistic PK/PD modeling can and has been applied to guide early drug development decisions.

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Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

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Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

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INTRODUCTION: We see a development in the field of long-acting products to serve patients with chronic diseases by providing benefits in adherence, efficacy, and safety of the treatment. This review investigates features of long-acting products on the market/pipeline to understand which drug substance (DS) and drug product (DP) characteristics likely enable a successful patient-centric, low-dosing frequency product. AREAS COVERED: This review evaluates marketed/pipeline long-acting products with greater than 1 week release of small molecules and peptides by oral and injectable route of administration (RoA), with particular focus on patient centricity, adherence impact, health outcomes, market trends, and the match of DS/DP technologies which lead to market success. EXPERT OPINION: Emerging trends are expected to change the field of long-acting products in the upcoming years by increasing capability in engineered molecules (low solubility, long half-life, high potency, etc.), directly developing DP as long-acting oral/injectable, increasing the proportion of products for local drug delivery, and a direction toward more subcutaneous, self-administered products. Among long-acting injectable products, nanosuspensions show a superiority in dose per administration and dosing interval, overwhelming the field of infectious diseases with the recently marketed products.

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AIMS: To study new chemical compounds with the potential cure proper, and to develop modifications of the preferred structure of dihydroquercetin. BACKGROUND: Producing the new drugs needs the study of the cure properties of the chemical composes first of all, and computer modeling can make this process more informative and easy. OBJECTIVE: Computer projection of the chemical compounds' potential cure properties. METHODS: The reactivity of the studied models was evaluated by comparing the energies of the boundary molecular orbitals (HOMO and LUMO), as well as the difference in their values. The reaction center of the model molecules was determined via the analysis of the charge characteristics of atoms in each of them. RESULTS: A theoretical model of new chemical compounds with the potential properties of drugs was substantiated and modifications of the preferred structure of dihydroquercetin have been developed. The concept of new compounds has been expanded and opportunities for the modification of compounds with high pharmacological activity have been discussed. Using the AM1, PM3, and RM1 methods spatial characteristics were calculated. The results of quantum-chemical studies of model derivatives of dihydroquercetin via the RM1 method were carried out. CONCLUSIONS: Calculation of the enthalpies of formation of model molecules allowed evaluating their thermodynamic stability. An analysis of the electric dipole moments made a possibility to determine the preferred (polar) nature of the solvents for the studied model molecular systems.

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Background and Aim: More than half of the small-molecule kinase inhibitors (KIs) induced liver injury clinically. Meanwhile, studies have shown a close relationship between mitochondrial damage and drug-induced liver injury (DILI). We aimed to study KIs and the binding between drugs and mitochondrial proteins to find factors related to DILI occurrence. Methods: A total of 1,223 oral FDA-approved drugs were collected and analyzed, including 44 KIs. Fisher's exact test was used to analyze DILI potential and risk of different factors. A total of 187 human mitochondrial proteins were further collected, and high-throughput molecular docking was performed between human mitochondrial proteins and drugs in the data set. The molecular dynamics simulation was used to optimize and evaluate the dynamic binding behavior of the selected mitochondrial protein/KI complexes. Results: The possibility of KIs to produce DILI is much higher than that of other types (OR = 46.89, p = 9.28E-13). A few DILI risk factors were identified, including molecular weight (MW) between 400 and 600, the defined daily dose (DDD) ≥ 100 mg/day, the octanol-water partition coefficient (LogP) ≥ 3, and the degree of liver metabolism (LM) more than 50%. Drugs that met this combination of rules were found to have a higher DILI risk than controls (OR = 8.28, p = 4.82E-05) and were more likely to cause severe DILI (OR = 8.26, p = 5.06E-04). The docking results showed that KIs had a significant higher affinity with human mitochondrial proteins (p = 4.19E-11) than other drug types. Furthermore, the five proteins with the lowest docking score were selected for molecular dynamics simulation, and the smallest fluctuation of the backbone RMSD curve was found in the protein 5FS8/KI complexes, which indicated the best stability of the protein 5FS8 bound to KIs. Conclusions: KIs were found to have the highest odds ratio of causing DILI. MW was significantly related to the production of DILI, and the average docking scores of KI drugs were found to be significantly different from other classes. Further analysis identified the top binding mitochondrial proteins for KIs, and specific binding sites were analyzed. The optimization of molecular docking results by molecular dynamics simulation may contribute to further studying the mechanism of DILI.

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INTRODUCTION: When pediatric data are not available for a drug, allometric and other methods are applied to scale drug clearance across the pediatric age-range from adult values. This is applied when designing first-in-child studies, but also for off-label drug prescription. AREAS COVERED: This review provides an overview of the systematic accuracy of allometric and other pediatric clearance scaling methods compared to gold-standard PBPK predictions. The findings are summarized in decision tables to provide a priori guidance on the selection of appropriate pediatric clearance scaling methods for both novel drugs for which no pediatric data are available and existing drugs in clinical practice. EXPERT OPINION: While allometric scaling principles are commonly used to scale pediatric clearance, there is no universal allometric exponent (i.e. 1, 0.75, or 0.67) that can accurately scale clearance for all drugs from adults to children of all ages. Therefore, pediatric scaling decision tables based on age, drug elimination route, binding plasma protein, fraction unbound, extraction ratio, and/or isoenzyme maturation are proposed to a priori select the appropriate (allometric) clearance scaling method, thereby reducing the need for full PBPK-based clearance predictions. Guidance on allometric scaling when estimating pediatric clearance values is provided as well.

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BACKGROUND: Vision impairment remains a major health problem worldwide. Elevated intraocular pressure is a prime risk factor for blindness in the elderly. Netarsudil is a Rho-associated protein kinase (ROCK) inhibitor, which also inhibits norepinephrine transport. This narrative review summarizes the properties and clinical significance of netarsudil, a promising drug in topical glaucoma therapy. METHODS: We searched PubMed, Medline and Scopus databases using relevant keywords to retrieve information on the physicochemical properties, formulation, mechanism of action, clinical pharmacokinetics, dose and toxicity of netarsudil. RESULTS: Netarsudil showed promising effects in lowering the elevated intraocular pressure by two mechanisms. The US FDA approved netarsudil for clinical use in 2017 under the trademark of Rhopressa® while European Medicines Agency approved Rhokiinsa® in 2019. This drug is available as a 0.02% ophthalmic solution for once-daily topical application. CONCLUSION: The discovery of netarsudil is a breakthrough in the therapy of glaucoma with proven efficacy in a wide range of eye pressures and is well tolerated in cases with ocular hypertension and chronic glaucoma.

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The development of high-throughput methods for the estimation of physicochemical and biological properties of drug candidates is highly desired in the pharmaceutical landscape. Affinity to plasma protein is one of the most important biological properties, which should be taken under concern during the design and assessment of future potential medicines. The main goal of this study was to develop a quantitative retention-activity relationship model, with rationalized in vivo and in silico approach to predict the affinity to human serum albumin (HSA), which is one of the most important plasma proteins. To achieve this goal, a set of 27 chemically diverse drugs with known affinity to HSA were analyzed by micellar electrokinetic chromatography (MEKC). The proposed model for HSA affinity assessment was based on retention in hexadecyltrimethylmonium bromide (CTAB) pseudostationary phase and chemically advanced template search (CATS) pharmacophore descriptors. The comparison of various regression methods, namely multiple linear regression (MLR), partial least squares regression (PLS), orthogonal partial least squares (OPLS), and support vector machine (SVM) were performed to develop a model with highest predictability. The obtained models are suitable for the prediction of drug affinity to human serum albumin using retention factor determined by MEKC and CATS descriptors, and only slightly differ in terms of coefficients of determination, Q2 value calculated using leave-one-out cross-validation technique and root-mean-squared error of cross-validation (RMSECV) as well as root-mean-square error in prediction (RMSEP) obtained by external validation.

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Chemical composition of Chinese materia medica (CMM) is easy to occur self-recognition and self-assembly as it has unique structure, many modification sites and abundant sources. The phenomenon has a broad application in the study of CMM. It can be used to reveal the property, processing, compatibility, extraction, separation and enrichment of the components of CMM, and study the preparation of CMM. This paper reviews research results of molecular recognition and self-assembly of chemical components of CMM in recent years, including their characteristics and applications, in order to expend and provide some ideas for the modernization of CMM.

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Overcoming poor solubility is a significant issue in drug discovery. The most common solution is to replace carbon atoms with polar heteroatoms such as N and O or by attaching a solubilizing appendage. This approach can lead to other issues such as poor activity and PK or the increased risk for toxicity. However, there are more subtle structural changes which can be employed that lead to an increase in solubility. These include, excising hydrophobic groups which do not efficiently contribute to binding, modifying stereo- and regiochemistry, increasing or decreasing the degree of unsaturation or adding small hydrophobic groups such as fluorine or methyl.

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The results of previous studies showed potential correlations between the penetration enhancement effect of essential oils and the drug properties of traditional Chinese medicine based on the data mining method. As chemical composition is the material basis of drug properties of traditional Chinese medicine, this article further analyzed the correlation between the chemical composition of essential oils and the drug properties. Firstly, essential oils were extracted by steam distillation, and then physicochemical parameters of essential oils, such as relative density and refractive index, were measured. The chemical components of 20 essential oils were analyzed by GC-MS, and divided into 12 categories according to skeleton features and functional groups. Finally, Logistic regression analysis was applied to reveal the correlations. The results proved that five flavors, four tastes and channel tropisms showed the correlation with chemical composition of essential oils (P<0.05). In conclusion, there were obvious correlations and regularity between the drug properties of traditional Chinese medicine and the chemical composition of essential oils.

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The results of previous studies showed potential correlations between the penetration enhancement effect of essential oils and the drug properties of traditional Chinese medicine based on the data mining method. As chemical composition is the material basis of drug properties of traditional Chinese medicine, this article further analyzed the correlation between the chemical composition of essential oils and the drug properties. Firstly, essential oils were extracted by steam distillation, and then physicochemical parameters of essential oils, such as relative density and refractive index, were measured. The chemical components of 20 essential oils were analyzed by GC-MS, and divided into 12 categories according to skeleton features and functional groups. Finally, Logistic regression analysis was applied to reveal the correlations. The results proved that five flavors, four tastes and channel tropisms showed the correlation with chemical composition of essential oils (P<0.05). In conclusion, there were obvious correlations and regularity between the drug properties of traditional Chinese medicine and the chemical composition of essential oils.

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Authors presented the connection of concept, effect, source, and drug-properties of sour-taste in the five flavors of Chinese material medica (CMM) in this review. Bionic technology of electronic tongue and chemical analysis tools are used to study material basis of sour-taste. The study method of characterization pathway and separation was presented. And authors also discussed the clinical applications and compatibility of CMM with sour-taste, expanded the scope of clinical applications, and laid a foundation for the expression of sour-taste properties of CMM.

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INTRODUCTION: Physiochemical drug properties, such as aqueous solubility are considered to be a major factor in determining the ultimate success or failure of experimental agents. Solubility is important because it determines the maximum dose which can be taken up. As the size and hydrophobicity of drug candidates has increased over the years, poor solubility has become a more prevalent issue. Recent examples from the literature show that an improved understanding of the relationship between molecular structure and solubility allows this issue to be approached using rational design. AREAS COVERED: This review provides selected examples from the recent drug discovery literature that demonstrate various tactics, which have been applied successfully towards improving drug solubility. The examples that were selected demonstrate the underlying principles behind aqueous solubility, such as hydrophobicity and crystalline stability. EXPERT OPINION: From a strategic point of view, improving the solubility of a compound should be straightforward because it can be accomplished by simply reducing hydrophobicity or crystalline stability. However, the structural elements and physical properties which control solubility also influence potency, pharmacokinetics and toxicity. Furthermore, there are practical aspects such as the quantity and quality of solubility-related data, which hamper the development of structure-solubility relationships. Given that poor aqueous solubility remains a primary issue in drug discovery, there is a continuous need for novel methods to overcome it.

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In the title compound, C20H17NO3, the methyl-idene-cyclo-hexa-2,4-dienone moiety is approximately planar [maximum deviation = 0.0615 (10) Å] and is oriented at diherdral angles of 69.60 (7) and 1.69 (9)° to the phenyl and hy-droxy-benzene rings, respectively. The amino group links with the carbonyl O atom via an intra-molecular N-H⋯O hydrogen bond, forming an S(6) ring motif. In the crystal, the mol-ecules are linked by O-H⋯O hydrogen bonds and weak C-H⋯O and C-H⋯π inter-actions, forming a three-dimensional supra-molecular architecture.