Field Of Pharmaceutical Research Research Articles

A computational method for the component screening of carrier-free nanodrugs: exemplified by berberine

The emergence of carrier-free nanodrugs as a promising drug delivery system is a significant development in the field of pharmaceutical research. The research on molecules that can form carrier-free nanodrugs can facilitate the expansion of their applications. The capacity of molecules to self-assemble with other molecules is the foundation upon which their formation of carrier-free nanodrugs is based. In this paper, we present a computational method for evaluating the assembly ability of molecules in aqueous environments. The method begins with screening the optimal conformation of a single molecule in water, then simulates the assembly behavior of two molecules in water, and finally calculates the optimal conformation of dimer in water. In comparison to the original computational method, this method describes the effect of water on molecular assembly behavior with the greatest possible realism by utilizing high-precision water environments in each step. The accuracy of the method has been validated by classical experiments, while its rationality has been demonstrated by electronic structure calculations of single molecules and assemblies. Furthermore, we employ the method to investigate the assembly capabilities of 54 molecules in the presence of berberine. The results demonstrate that anthraquinone and flavonoid molecules readily form robustly bound assemblies with berberine. This method offers a relatively efficient approach to compositional screening of carrier-free nanodrugs, thereby contributing to the advancement of the field.

The French Medicinal Chemistry Society-SCT, an Historical Member of EFMC.

The French Society of Medicinal Chemistry or " Société de Chimie Thérapeutique " (SCT) was founded in 1966. Since its inception, its mission has been to promote knowledge in the main fields of pharmaceutical research and development, in particular the research and validation of biological targets of therapeutic interest, the screening, design and optimization of drug candidates, chemical biology, medicinal chemistry, pharmacokinetics, metabolism and toxicity. Since 1964, the Society has organized an annual international congress (RICT), and later thematic days for young researchers and workshops on specific topics. The SCT is also a member of the European Federation for Medicinal Chemistry (EFMC) and organized the International Symposium on Medicinal Chemistry (ISMC) in Nice in 2022. Several new trends can be identified in the activities of the SCT, such as the organization of regular webinars, but also the recent creation of the Young MedChem Forum, as well as the distribution of a newsletter reporting scientific achievements in the French community and abroad, and an improved presence on social networks. These trends are in line with the current changes in the field in terms of scientific progress, means of communication in the community and with the public and inclusiveness.

Identification of Matrine as a Kirsten rats Arcomaviral oncogene homolog inhibitor alleviating chemotherapy-induced neuropathic pain

BackgroundChemotherapy-induced peripheral neuropathy (CIPN) represents a prevailing and severe clinical concern, characterized by limited availability of clinically effective treatment strategies. Current evidence endorses matrine's potential as a neuroprotective and analgesic agent for CIPN. Nevertheless, the precise targets and mechanisms of action of matrine remain insufficiently explored, impeding comprehensive pharmacological investigation and clinical application. ObjectiveThis study endeavors to elucidate the analgesic and neuroprotective effects of matrine in mice with vincristine-induced neuropathic pain. A focal point is the identification of matrine's specific target and the underlying molecular mechanisms governing its analgesic and neuroprotective actions. MethodsTo discern matrine's analgesic effects in CIPN mice, we conducted behavioral experiments encompassing the Von Frey filament test and Hargreaves Test. Furthermore, we conducted electrophysiological and histopathological assessments involving HE staining, Nissl staining, and Fluoro-Jade B staining to evaluate matrine's effects on neuroprotection within dorsal root ganglia and the spinal cord of CIPN mice. Sequentially, thermal shift assay, GTP hydrolysis assay, and nucleotide exchange assay were executed to validate matrine's inhibitory effects on KRAS. Molecular docking and site-directed mutagenesis experiments were implemented to identify the precise binding pocket of matrine on KRAS. Lastly, matrine's inhibitory effects on downstream signaling pathways of KRAS were confirmed through experiments conducted at animal model. ResultsMatrine exhibited a notable increase in mechanical withdrawal threshold and thermal withdrawal latency in vincristine-treated mice. This compound substantially ameliorated the neurofunctional blockade associated with sensory and motor functions induced by vincristine. Moreover, matrine mitigated pathological damage within DRG and the L4-L5 spinal cord regions. The study's MST experiments indicated matrine's substantial elevation of KRAS's melting temperature. The GTP hydrolysis and nucleotide exchange assays revealed concentration-dependent inhibition of KRAS activity by matrine. Molecular docking provided insight into the binding mode of matrine with KRAS, while site-directed mutagenesis verified the specific binding site of matrine on KRAS. Lastly, matrine's inhibition of downstream Raf/Erk1/2 and PI3K/Akt/mTOR signaling pathways of KRAS was confirmed in VCR mice. ConclusionCompared to previous studies, our research has identified matrine as a natural inhibitor of the elusive protein KRAS, often considered "undruggable." Furthermore, this study has revealed that matrine exerts its therapeutic effects on chemotherapy-induced peripheral neuropathy (CIPN) by inhibiting KRAS activation, subsequently suppressing downstream signaling pathways such as Raf/Erk1/2 and PI3K/Akt/mTOR. This investigation signifies the discovery of a novel target for matrine, thus expanding the potential scope of its involvement in KRAS-related biological functions and diseases. These findings hold the promise of providing a crucial experimental foundation for forthcoming drug development initiatives centered around matrine, thereby advancing the field of pharmaceutical research.

Wave-Assisted Techniques, a Greener and Quicker Alternative to Synthesis of Cyclodextrin-Based Nanosponges: A Review.

The ever-increasing applications of cyclodextrin and cyclodextrin-based nanosponges in formulation development has gained much attention from researchers towards needed research in this arena. Nanosponges are three-dimensional nanoporous versatile carriers in the pharmaceutical research field because of their capability to encapsulate lipophilic and hydrophilic drugs both in their crystalline structure by inclusion and non-inclusion phenomenon. This review sheds light on the advancements made in this field and the associated patents with regard to their synthesis while zooming in on the utilization of two novel energies (Microwave and ultrasonic) in accomplishing this goal and its future thereof. Microwave and ultrasound-assisted manufacturing of cyclodextrin-based nanosponges (CDNS) has been found superior to conventional heat-dependent methods due to rapid/homogenous heating and fast kinetics, which ultimately provide the final product with high yield and crystallinity relatively rapidly. The review article also defines several facets of microwave and ultrasound-assisted nanosponge synthesis including the synergism of microwave and ultrasonic energy and the theories behind them. This hitherto unexplored microwave-ultrasonic coupling technology could be a future technology to synthesize CD-NS with a better outcome. In the recent past, these novel energy processes have been used successfully in material synthesis at an industrial scale due to their swift and streamlined synthesis attributes. Likewise, these wave-assisted methods have the full potential to materialize the concept of CD-NS from lab scale to industrial scale as a competent and versatile drug carrier, having all the prerequisite characteristics, for commercialization.

A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling

This article explores the structural properties of eleven distinct chemical graphs that represent sulfonamide drugs using topological indices by developing python algorithm. To find significant relationships between the topological characteristics of these networks and the characteristics of the associated sulfonamide drugs. We use quantitative structure-property relationship (QSPR) approaches. In order to model and forecast these correlations and provide insights into the structure-activity relationships that are essential for drug design and optimization, linear regression is a vital tool. A thorough framework for comprehending the molecular characteristics and behavior of sulfonamide drugs is provided by the combination of topological indices, graph theory and statistical models which advances the field of pharmaceutical research and development.

Unlocking the Potential of Oleanolic Acid: Integrating Pharmacological Insights and Advancements in Delivery Systems.

The growing interest in oleanolic acid (OA) as a triterpenoid with remarkable health benefits prompts an emphasis on its efficient use in pharmaceutical research. OA exhibits a range of pharmacological effects, including antidiabetic, anti-inflammatory, immune-enhancing, gastroprotective, hepatoprotective, antitumor, and antiviral properties. While OA demonstrates diverse pharmacological effects, optimizing its therapeutic potential requires overcoming significant challenges. In the field of pharmaceutical research, the exploration of efficient drug delivery systems is essential to maximizing the therapeutic potential of bioactive compounds. Efficiently delivering OA faces challenges, such as poor aqueous solubility and restricted bioavailability, and to unlock its full therapeutic efficacy, novel formulation strategies are imperative. This discussion thoroughly investigates different approaches and advancements in OA drug delivery systems with the aim of enhancing the biopharmaceutical features and overall efficacy in diverse therapeutic contexts.

A Review of Recent Developments in Biopolymer Nano-Based Drug Delivery Systems with Antioxidative Properties: Insights into the Last Five Years.

In recent years, biopolymer-based nano-drug delivery systems with antioxidative properties have gained significant attention in the field of pharmaceutical research. These systems offer promising strategies for targeted and controlled drug delivery while also providing antioxidant effects that can mitigate oxidative stress-related diseases. Generally, the healthcare landscape is constantly evolving, necessitating the continual development of innovative therapeutic approaches and drug delivery systems (DDSs). DDSs play a pivotal role in enhancing treatment efficacy, minimizing adverse effects, and optimizing patient compliance. Among these, nanotechnology-driven delivery approaches have garnered significant attention due to their unique properties, such as improved solubility, controlled release, and targeted delivery. Nanomaterials, including nanoparticles, nanocapsules, nanotubes, etc., offer versatile platforms for drug delivery and tissue engineering applications. Additionally, biopolymer-based DDSs hold immense promise, leveraging natural or synthetic biopolymers to encapsulate drugs and enable targeted and controlled release. These systems offer numerous advantages, including biocompatibility, biodegradability, and low immunogenicity. The utilization of polysaccharides, polynucleotides, proteins, and polyesters as biopolymer matrices further enhances the versatility and applicability of DDSs. Moreover, substances with antioxidative properties have emerged as key players in combating oxidative stress-related diseases, offering protection against cellular damage and chronic illnesses. The development of biopolymer-based nanoformulations with antioxidative properties represents a burgeoning research area, with a substantial increase in publications in recent years. This review provides a comprehensive overview of the recent developments within this area over the past five years. It discusses various biopolymer materials, fabrication techniques, stabilizers, factors influencing degradation, and drug release. Additionally, it highlights emerging trends, challenges, and prospects in this rapidly evolving field.

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development

Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiring advanced analytical techniques for deeper understanding of data itself. Machine learning is becoming an important tool in the rapidly changing field of pharmaceutical research, particularly in drug preformulation studies. This work aims to explore into the application of machine learning methods for the analysis of experimental data obtained by ODF characterization in order to obtain an insight into the factors governing ODF performance and use it as guidance in pharmaceutical development. Using a dataset derived from extensive experimental studies, various machine learning algorithms were employed to cluster and predict critical properties of ODFs. Our results demonstrate that machine learning models, including Support vector machine, Random forest and Deep learning, exhibit high accuracy in predicting the mechanical properties of ODFs, such as flexibility and rigidity. The predictive models offered insights into the complex interaction of formulation variables. This research is a pilot study that highlights the potential of machine learning as a transformative approach in the pharmaceutical field, paving the way for more efficient and informed drug development processes.

In silico elucidation for the identification of potential phytochemical against ACE-II inhibitors.

The present study aims to investigate the therapeutic potential of phytocompounds derived from Annona reticulata leaves for the treatment of hypertension, utilizing computational methodologies. Gaining a comprehensive understanding of the molecular interactions between neophytadiene and γ-sitosterol holds significant importance in the advancement of innovative therapeutic approaches. This study aims to examine the inhibitory effects of neophytadiene and γ-sitosterol using molecular docking and dynamics simulations. Additionally, we will evaluate their stability and predict their drug-like properties as well as their ADME/toxicity profiles. Neophytadiene and γ-sitosterol have a substantial binding affinity with 1O8A, as shown by the docking study. The stability of the complexes was confirmed through molecular dynamics simulations, while distinct clusters were identified using PCA. These findings suggest the presence of potential stabilizers. The drug-likeness and ADME/toxicity predictions revealed positive characteristics, such as efficient absorption rates, limited distribution volume and non-hazardous profiles. The neophytadiene and γ-sitosterol exhibit potential as hypertension medication options. Computational investigations reveal that these compounds exhibit high affinity for binding, stability and favourable pharmacokinetic properties. The results of this study lay the groundwork for additional experimental verification and highlight the promising prospects of utilizing natural compounds in the field of pharmaceutical research. Target proteins (1O8A) were used to perform molecular docking with representative molecules. Stability, conformational changes and binding energies were assessed through molecular dynamics simulations lasting 100ns. Principal component analysis (PCA) was utilized to analyze molecular dynamics (MD) simulation data, to identify potential compounds that could stabilize the main protease. The safety and pharmacokinetic profiles of the compounds were evaluated through drug-likeness and ADME/toxicity predictions.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search.

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Exploring a Marine Zoanthid - Zoanthus sansibaricus – A Potential candidate for Drug Discovery

The oceans and all the marine life forms that they harbor remain a major source of treasure to mankind, covering 71% of the earth's surface and representing over 95% of the biosphere. Zoanthids, a subclass of the benthic Anthozoans, are found in almost all marine environments. Despite this fact, the order Zoantharia is still one of the most taxonomically neglected and least examined orders of the phylum Cnidaria. However, they are gaining importance in the pharmaceutical research field due to the significance of bioactive compounds secreted from their bodies. Zoanthus sansibaricus species collected from the Indian coast have been explored in the present study. The study aimed to isolate chemical constituents from the methanol and methanol: chloroform (1:1) extract of Zoanthus sp. by subjecting it to a series of chromatographic analysis. In total, eight compounds were purified from petroleum ether, chloroform, n-butanol, and methanol-soluble parts of aqueous fractions. The compounds were characterized using spectroscopic techniques such as Ultraviolet-Visible (UV-Vis), Fourier Transform Infrared (FTIR), 1H and 13C Nuclear Magnetic Resonance (NMR), and Mass spectrometry. Total phenolic content and total flavonoid content were estimated using the Folin-Ciocalteu method and Aluminum chloride method, respectively. The phenolic compound contents of the petroleum ether and methanol-soluble part of the aqueous fraction were 18.92mg/g GAE and 7.02mg/g GAE, respectively. Total flavonoid content in the petroleum ether fraction was 8.05mg/g QUE and 1.38mg/g QUE in the methanol-soluble part of the aqueous fraction. This study emphasizes the need for a more extensive compilation of such data to extend our pool of knowledge about marine natural products as potential candidates for drug discovery.

Advancements in application of chitosan and cyclodextrins in biomedicine and pharmaceutics: recent progress and future trends.

The global community is faced with numerous health concerns such as cancer, cardiovascular and neurological diseases, diabetes, joint pain, osteoporosis, among others. With the advancement of research in the fields of materials chemistry and medicine, pharmaceutical technology and biomedical analysis have entered a new stage of development. The utilization of natural oligosaccharides and polysaccharides in pharmaceutical/biomedical studies has gained significant attention. Over the past decade, several studies have shown that chitosan and cyclodextrin have promising biomedical implications in background analysis, ongoing development, and critical applications in biomedical and pharmaceutical research fields. This review introduces different types of saccharides/natural biopolymers such as chitosan and cyclodextrin and discusses their wide-ranging applications in the biomedical/pharmaceutical research area. Recent research advances in pharmaceutics and drug delivery based on cyclodextrin, and their response to smart stimuli, as well as the biological functions of cyclodextrin and chitosan, such as the immunomodulatory effects, antioxidant, and antibacterial properties, have also been discussed, along with their applications in tissue engineering, wound dressing, and drug delivery systems. Finally, the innovative applications of chitosan and cyclodextrin in the pharmaceutical/biomedicine were reviewed, and current challenges, research/technological gaps, and future development opportunities were surveyed.

Cost-effective Digital Prescription using Pharmaceutical Knowledge Graph

In the current era of the advancing medical domain and the ever-evolving use of technology in fields of pharmaceutical research, remote monitoring, and decision support systems in healthcare, prescription management has transformed from handwritten prescriptions to digital ones. This transition however does not imply that these prescriptions are comprehensive and provide optimized treatment outcomes. These digital prescriptions still reflect the formerly used handwritten prescriptions. Thus, recipients face the daunting challenge of making cost-effective, holistic, informed, and personalized decisions without compromising the legitimacy and authenticity of the original prescription, all due to a lack of readily available alternative medicine options. This problem can be addressed by utilizing the knowledge graph that we built, which contains carefully curated medical information collected from reliable and diverse sources, ensuring the authenticity and relevance of the information. Delving into the intricate interconnections among diverse medical entities and their properties, the medical knowledge graph presents an invaluable solution, empowering the generation of smart digital prescriptions in a fast and precise manner. Specifically, this study focuses on the transformative potential of digital prescriptions, elucidating their role in streamlining healthcare processes and enhancing communication between healthcare providers and patients. By leveraging the insights derived from our medical knowledge graph, we aim to contribute to the advancement of digital prescription systems, fostering more effective, personalized, and technology-driven healthcare solutions.

Doxorubicin/poly(caprolactone)/polyethylene glycol nanoparticles prepared by electrospray

AbstractThe development of sustainable drug delivery systems is very important in the field of pharmaceutical research and has many advantages over the usual methods of drug administration, including improving effectiveness, reducing side effects, and increasing patient compliance. Among various techniques, electrospraying as a voltage‐driven process has emerged as a promising technology for the fabrication of drug‐loaded nanoparticles. In recent years, the application of electrospray approach for drug release has been widely investigated. However, optimization of electrospray process parameters and development of mathematical models to predict drug release kinetics from microparticles are still challenging. To address these issues, a combination of Central Composite Design (CCD) and Response Surface Methodology (RSM) has been used. This paper focuses on the application of CCD‐RSM for modeling and optimization of doxorubicin (DOX)‐poly(caprolactone) (PCL)‐polyethylene glycol (PEG) microparticles prepared using electrospray method. Doxorubicin, a widely used anticancer drug, exhibits potent cytotoxicity against a wide range of cancers. PCL‐PEG microparticles are promising as drug carriers due to their biocompatibility, controlled release properties, and ability to protect the encapsulated drug from degradation. The CCD‐RSM approach allows systematic investigation and optimization of several formulation and process variables, including polymer concentration, PCL‐PEG weight ratio, and applied voltage on drug release behavior. In addition, the effect of rheology and electrical conductivity of polymer fluid on the size of particles obtained from electrospraying was investigated. The effect of adding folic acid and doxorubicin in the optimized sample of nanoparticles and the effect of different concentrations of doxorubicin were investigated, and SEM, DLS, and FTIR techniques were used to characterize the microparticles, and the MMT test was performed to evaluate their cytotoxicity. The results showed that the optimized formulation is effective for sustained drug release over a long period for drug delivery applications.

Data driven analysis of aromatase inhibitors through machine learning, database mining and library generation

Designing of novel drugs using data-driven and virtual screening approaches is a popular research topic in the pharmaceutical industry. Machine learning (ML) and data mining have recently emerged as useful tools for finding potent compounds and predicting their biological activities. In this study, data was collected from academic research articles to train ML models. Molecular descriptors were utilized for training over forty ML models. The best two models (Decision Tree regressor and Extra Tree regressor) were selected based on statistical parameters, and their hyperparameters were optimized to identify the best compounds with high pIC50 values for aromatase inhibitors. A database of more than 5000 compounds was extracted from PubChem, and the best ML model was used to predict their aromatase inhibition values. The top three reference compounds from the database were elected, and new compounds were designed using the library enumeration methodology. The two best ML models (Decision Tree regressor and Extra Tree regressor) were able to accurately predict the aromatase inhibition values of the compounds in our database. In conclusion, our study shows that data-driven and virtual screening approaches using machine learning and data mining can be used to design novel molecules as drugs, specifically in the case of aromatase inhibitors. The results of our study have the potential to contribute significantly to the field of pharmaceutical research and development.

Sensitive quantitation of ultra-trace toxic aconitines in complex matrices by perfusion nano-electrospray ionization mass spectrometry combined with gas-liquid microextraction

The accurate analysis of ultra-trace (e.g. <10−4 ng/mL) substances in complex matrices is a burdensome but vital problem in pharmaceutical analysis, with important implications for precise quality control of drugs, discovery of innovative medicines and elucidation of pharmacological mechanisms. Herein, an innovative constant-flow perfusion nano-electrospray ionization (PnESI) technique was developed firstly features significant quantitative advantages in high-sensitivity ambient MS analysis of complex matrix sample. More importantly, double-labeled addition enrichment quantitation strategies of gas-liquid microextraction (GLME) were proposed for the first time, allowing highly selective extraction and enrichment of specific target analytes in a green and ultra-efficient (>1000-fold) manner. Using complex processed Aconitum herbs as example, PnESI-MS directly enabled the qualitative and absolute quantitative analysis of the processed Aconitum extracts and characterized the target toxic diester alkaloids with high sensitivity, high stability, wide linearity range, and strong resistance to matrix interference. Further, GLME device was applied to obtain the highly specific enrichment of the target diester alkaloids more than 1000-fold, and accurate absolute quantitation of trace aconitine, mesaconitine, and hypaconitine in the extracts of Heishunpian, Zhichuanwu and Zhicaowu was accomplished (e.g., 0.098 pg/mL and 0.143 pg/mL), with the quantitation results well below the LODs of aconitines from any analytical instruments available. This study built a systematic strategy for accurate quantitation of ultra-trace substances in complex matrix sample and expected to provide a technological revolution in many fields of pharmaceutical research.

Electrochemical Simulation of Phase I Hepatic Metabolism of Voriconazole Using a Screen-Printed Iron(II) Phthalocyanine Electrode.

Understanding the metabolism of pharmaceutical compounds is a fundamental prerequisite for ensuring their safety and efficacy in clinical use. However, conventional methods for monitoring drug metabolism often come with the drawbacks of being time-consuming and costly. In an ongoing quest for innovative approaches, the application of electrochemistry in metabolism studies has gained prominence as a promising approach for the synthesis and analysis of drug transformation products. In this study, we investigated the hepatic metabolism of voriconazole, an antifungal medication, by utilizing human liver microsomes (HLM) assay coupled with LC-MS. Based on the obtained results, the electrochemical parameters were optimized to simulate the biotransformation reactions. Among the various electrodes tested, the chemometric analysis revealed that the iron(II) phthalocyanine electrode was the most effective in catalyzing the formation of all hepatic voriconazole metabolites. These findings exemplify the potential of phthalocyanine electrodes as an efficient and cost-effective tool for simulating the intricate metabolic processes involved in drug biotransformation, offering new possibilities in the field of pharmaceutical research. Additionally, in silico analysis showed that two detected metabolites may exhibit significantly higher acute toxicity and mutagenic potential than the parent compound.

Revolutionizing Pharmaceutical Research: Harnessing Machine Learning for a Paradigm Shift in Drug Discovery

The fusion of machine learning (ML) and artificial intelligence (AI) is experiencing a dramatic transition in the field of pharmaceutical research and development. This study examines the several effects of machine learning (ML) on different phases of medication discovery, development, and patient care. The capability of ML to quickly process huge chemical libraries and forecast interactions with target proteins is studied, starting with compound screening and selection. The potential for fewer false positives and negatives, improved hit prediction accuracy, and ensemble technique use are underlined. The part that machine learning plays in enhancing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile is then explained. ML models anticipate compound actions inside the human body by analyzing molecular structures and characteristics, improving assessments of drug safety and efficacy. The article goes into further detail about predictive modeling, highlighting how machine learning may be used to find prospective therapeutic targets and confirm their applicability. The combination of multi-omics data, deep learning, and the possibility to identify similar molecular pathways across diseases highlight the game-changing potential of machine learning in this field. The article also covers the use of ML in clinical trials, highlighting its benefits for trial planning, patient recruitment, real-time monitoring, and individualized therapy predictions. By utilizing computational analysis and quantum physics, the power of machine learning-driven de novo drug creation is examined, revealing the potential to develop new therapeutic candidates. In this article, the ethical issues surrounding AI-driven drug discovery are discussed, with a focus on the necessity of transparent data utilization, human oversight, and responsible data consumption. The report ends by predicting ML's potential for pharmaceutical R&amp;D in the future. Accelerated drug discovery pipelines, the rise of customized medicine powered by predictive models, optimized clinical trials, and a change in medication repurposing tactics are all envisaged in this. The report emphasizes the revolutionary potential of ML in altering pharmaceutical research and development while noting obstacles in data quality, model interpretability, ethics, and interdisciplinary collaboration. It is suggested that the ethical integration of AI technologies, interdisciplinary cooperation, and regulatory modifications are essential steps to unlock the full potential of ML and AI and, ultimately, provide patients throughout the world with safer, more efficient, and individualized treatments.

Effect of 4-mercaptophenylboronic acid functionalized MoS2 quantum dots on amyloid aggregation of bovine serum albumin

In recent years, seeking and screening of compounds that can inhibit and/or depolymerize protein aggregation has been a hot issue in the field of pharmaceutical research. As a new material, quantum dots have been widely concerned by medical researchers. In present study, a novel 4-mercaptophenylboronic acid functionalized MoS2 quantum dots (4-MPBA-MoS2 QDs) was successfully synthesized through a one-pot hydrothermal approach by using molybdate dehydrate and 4-mercaptophenylboronic acid as Mo and S source, respectively. Transmission electron microscopy observation showed that the morphology and microstructure of the 4-MPBA-MoS2 QDs displayed uniform spherical shape with a diameter of approximately 1.5 ∼ 3.0 nm. The UV and fluorescence spectra experiments indicated that the prepared QDs had good water solubility and two weak absorption peaks were appeared at 280 nm and 310 nm. When the excitation wavelength was set to 310 nm, the 4-MPBA-MoS2 QDs had the strongest fluorescence intensity, and the maximum emission wavelength appeared at 405 nm. In vitro experiments showed that the 4-MPBA-MoS2 QDs could significantly reduce the aggregation of bovine serum albumin (BSA). Especially when the mass ratio of BSA to 4-MPBA-MoS2 QDs was 1:5, the inhibition rate could reach 76.4%. Cell experiment showed that the presence of 4-MPBA-MoS2 QDs could obviously decrease the cytotoxicity induced by BSA amyloid fibrils. Moreover, the depolymerization of BSA amyloid fibrils by 4-MPBA-MoS2 QDs and its excellent cell permeability were also observed. Molecular docking studies have shown that 4-MPBA-MoS2 QDs may stabilize the BSA structure through van der Waals forces, hydrophobic force, electrostatic interactions and hydrogen bonds formed between the outer layer of 4-MPBA and BSA to prevent fibrosis aggregation. The results of this study suggested that 4-MPBA-MoS2 QDs showed low cytotoxicity, good biocompatibility and solubility, and had a great potential in the design of new drugs for the treatment of amyloid-related diseases.

Combinatorial 3D printed dosage forms for a two-step and controlled drug release

Fused deposition modeling (FDM) and selective laser sintering (SLS) are two of the most employed additive manufacturing (AM) techniques within the pharmaceutical research field. Despite the numerous advantages of different AM methods, their respective drawbacks have yet to be fully addressed, and therefore combinatorial systems are starting to emerge. In the present study, hybrid systems comprising SLS inserts and a two-compartment FDM shell are developed to achieve controlled release of the model drug theophylline. Via the use of SLS a partial amorphization of the drug is demonstrated, which can be advantageous in the case of poorly soluble drugs, and it is shown that sintering parameters can regulate the dosage and release kinetics of the drug from the inserts. Furthermore, via different combinations of inserts within the FDM-printed shell, various drug release patterns, such as a two-step or prolonged release, can be achieved. The study serves as a proof of concept, highlighting the advantages of combining two AM techniques, both to overcome their respective shortcomings and to develop modular and highly tunable drug delivery devices.