Drug Development Research Articles

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Which experimental factors govern successful animal-to-human translation in multiple sclerosis drug development? A systematic review and meta-analysis

Despite successes in multiple sclerosis (MS) drug development, the effectiveness of animal studies in predicting successful bench-to-bedside translation is uncertain. Our goal was to identify predictors of successful animal-to-human translation for MS by systematically comparing animal studies of approved disease-modifying therapies (DMTs) with those that failed in clinical trials due to efficacy or safety concerns. Systematic review of animal studies testing MS DMTs, identified from searches in PubMed and EMBASE. A random effect meta-analysis was fitted to the data to compare outcome effect sizes for approved versus failed DMTs. Effect sizes and testing under diverse experimental conditions were assessed as potential predictors for successful translation. We included 497 animal studies, covering 15 approved and 11 failed DMTs, tested in approximately 30'000 animals. DMTs were tested in a small repertoire of experimental parameters: about 86% of studies used experimental autoimmune encephalomyelitis (EAE), 80% used mice, and 76% used female animals. There was no association between animal study outcomes or testing DMTs under varied conditions (e.g., different laboratories or models) and successful approval. Surprisingly, 91% of animal studies were published after first-in-MS trial and 91% after official regulatory approval. Our findings emphasize the complexity in carrying drugs from animals to clinical practice. Specific challenges include limited experimental methods in animal research and a disconnect between preclinical and clinical research. We advocate for efforts to streamline drug development for MS to improve animal research's relevance for patients. NIH, Swiss National Science Foundation, Universities Federation for Animal Welfare.

In Silico Prediction of Quercetin Analogs for Targeting Death-Associated Protein Kinase 1 (DAPK1) Against Alzheimer's Disease.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that greatly affects the health and life quality of the elderly population. Existing drugs mainly alleviate symptoms but fail to halt disease progression, underscoring the urgent need for the development of novel drugs. Based on the neuroprotective effects of flavonoid quercetin in AD, this study was designed to identify potential AD-related targets for quercetin and perform in silico prediction of promising analogs for the treatment of AD. Database mining suggested death-associated protein kinase 1 (DAPK1) as the most promising AD-related target for quercetin among seven protein candidates. To achieve better biological effects for the treatment of AD, we devised a series of quercetin analogs as ligands for DAPK1, and molecular docking analyses, absorption, distribution, metabolism, and excretion (ADME) predictions, as well as molecular dynamics (MD) simulations, were performed. The energy for drug-protein interaction was predicted and ranked. As a result, quercetin-A1a and quercetin-A1a1 out of 19 quercetin analogs exhibited the lowest interaction energy for binding to DAPK1 than quercetin, and they had similar dynamics performance with quercetin. In addition, quercetin-A1a and quercetin-A1a1 were predicted to have better water solubility. Thus, quercetin-A1a and quercetin-A1a1 could be promising agents for the treatment of AD. Our findings paved the way for further experimental studies and the development of novel drugs.

Research Progress on the Anticancer Activity of Plant Polysaccharides.

Tumor is a serious threat to human health, with extremely high morbidity and mortality rates. However, tumor treatment is challenging, and the development of antitumor drugs has always been a significant research focus. Plant polysaccharides are known to possess various biological activities. They have many pharmacological properties such as immunomodulation, antitumor, antiviral, antioxidative, antithrombotic, and antiradiation effects, reduction of blood pressure and blood sugar levels, and protection from liver injury. Among these effects, the antitumor effect of plant polysaccharides has been widely studied. Plant polysaccharides can inhibit tumor proliferation and growth by inhibiting tumor cell invasion and metastasis, inducing cell apoptosis, affecting the cell cycle, and regulating the tumor microenvironment. They also have the characteristics of safety, high efficiency, and low toxicity, which can alleviate, to a certain extent, the adverse reactions caused by traditional tumor treatment methods such as surgery, radiotherapy, and chemotherapy. Therefore, this paper systematically summarizes the direct antitumor effects of plant polysaccharides, their regulatory effects on the tumor microenvironment, and intervening many common high-incidence tumors in other ways. It also provides data support for the administration of plant polysaccharides in modern tumor drug therapy, enabling the identification of new targets and development of new drugs for tumor therapy.

Insights into the anticancer effects of galangal and galangin: A comprehensive review

BackgroundsCancer continues to be the leading cause of death worldwide, significantly impacting both health and the economy. Natural products have emerged as promising sources for the development of new anticancer drugs, with galangal and their active ingredient, galangin, garnering substantial interest. PurposeThis study summarizes recent findings on the anticancer properties of galangal and galangin, highlighting their potential to target various cancer types. MethodsWe systematically searched the literature across PubMed, Web of Science, and Google Scholar, using keywords such as “Alpinia officinarum,” “Alpinia galanga”, “galangal,” and “galangin.” This thorough approach allowed us to gather and compile a comprehensive collection of existing research on the topic. ResultsThis article provided a thorough analysis of the distribution of galangal, the methods used to extract the active compounds of galangal, and the anticancer properties of both galangin and galangal. It is important to note that galangal and galangin primarily function by regulating the signaling pathways of PI3K/Akt, MAPK, AMPK, p53, NF-κB, and Ras/RAF/MEK/ERK, which in turn triggers apoptosis, autophagy, and ROS while preventing the migration and invasion of cancer cells. We also discussed their toxicity, bioavailability, and clinical uses. ConclusionIn conclusion, galangal extract and galangin have a lot of promise for treating cancer. It is anticipated that this review will further advance the use of galangal extract and galangin as potential cancer treatment medications. Moreover, the discovery and development of drugs based on galangal has enormous potential for the therapy of cancer.

Experiences and Initiatives on Pharmacokinetics Modeling and Simulation Data analyses: Perspectives from the Brazilian Health Regulatory Agency (ANVISA).

The landscape of drug product development and regulatory sciences is evolving, driven by the increasing application of systems thinking and modeling and simulation (M&S) techniques, especially from a biopharmaceutics perspective. Patient-centric quality standards can be achieved within this context through the application of quality by design (QbD) principles and M&S, specifically by defining clinically relevant dissolution specifications (CRDS). To this end, it is essential to bridge in vitro results to drug product in vivo performance, emphasizing the need to explore the translational capacity of biopharmaceutics tools. Physiologically based M&S analyses offer a unique avenue for integrating the drug, drug product, and biological properties of a target organism to study their interactions on the pharmacokinetic response. Accordingly, Physiologically Based Biopharmaceutics Modeling (PBBM) has seen increasing use to support drug development and regulatory applications globally. In Brazil, a Model-Informed Drug Development (MIDD) policy and strategic project are not yet established, limiting applicability of M&S techniques. Drawing from the experience of the ANVISA-Academia PBBM Working Group (WG), this article assesses the opportunities and challenges for pharmacometrics (PMx) in Brazil and proposes strategies to advance the adoption of M&S analyses into regulatory decision-making.

Identify Diabetes-related Targets based on ForgeNet_GPC.

Research on potential therapeutic targets and new mechanisms of action can greatly improve the efficiency of new drug development. Polygenic genetic diseases, such as diabetes, are caused by the interaction of multiple gene loci and environmental factors. In this study, a disease target identification algorithm based on protein recognition is proposed. In this method, the related and unrelated targets are collected from literature databases for treating diabetes. The transcribed proteins corresponding to each target are queried in order to construct a protein dataset. Six protein feature extraction algorithms (AAC, CKSAAGP, DDE, DPC, GAAP, and TPC) are utilized to obtain the feature vectors of each protein, which are merged into the full feature vectors. A novel classifier (forgeNet_GPC) based on forgeNet and Gaussian process classifier (GPC) is proposed to classify the proteins. In forgeNet_GPC, forgeNet is utilized to select the important features, and GPC is utilized to solve the classification problem. The experimental results reveal that forgeNet_GPC performs better than 22 classifiers in terms of ROC-AUC, PR-AUC, MCC, Youden Index, and Kappa.

Cuphea Genus: A Systematic Review on the Traditional uses, Phytochemistry, Pharmacology, and Toxicology

Introduction: Species from the Cuphea genus (Lythraceae) have wide usage in traditional medicine for the treatment of various diseases. Cuphea comprises about 260 species distributed worldwide among five continents. Methods: This systematic review aimed to synthesize the information available about the traditional uses, phytochemistry, pharmacology, and toxicology of Cuphea spp. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, with a search in the scientific databases, namely Scopus, Web of Science, PubMed, and ScienceDirect, using keywords, such as “Cuphea”, “sete-sangrias”, “Cuphea” and “polyphenols”, “Cuphea” and “flavonoid”, “Cuphea” and “pharmacology”, “Cuphea” and “traditional uses”, “Cuphea” and “toxicology”, and all references found were analyzed. Among 1438 publications identified in the initial screening, 86 studies were included based on eligibility criteria. The chemical composition of Cuphea spp. is mainly polyphenols related to therapeutic use, especially as an antioxidant, antimicrobial, antitumoral, anti-inflammation, and antihypertensive. Results: In summary, this systematic review provides an overview of the active compounds responsible for the pharmacological actions of the genus, often linked to traditional use. Conclusion: However, its safety and potential toxicology need to be further studied, along with the bioactive mechanisms and discussion of the structure-activity relationship, besides future clinical studies for novel drug development.

Trends on Novel Targets and Nanotechnology-Based Drug Delivery System in the Treatment of Parkinson's disease: Recent Advancement in Drug Development.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts a significant portion of the population. Despite extensive research, an effective cure for PD remains elusive, and conventional pharmacological treatments often face limitations in efficacy and management of symptoms. There has been a lot of discussion about using nanotechnology to increase the bioavailability of small- molecule drugs to target cells in recent years. It is possible that PD treatment might become far more effective and have fewer side effects if medication delivery mechanisms were to be improved. Potential alternatives to pharmacological therapy for molecular imaging and treatment of PD may lie in abnormal proteins such as parkin, α-synuclein, leucine-rich repeat serine and threonine protein kinase 2. Published research has demonstrated encouraging outcomes when nanomedicine-based approaches are used to address the challenges of PD therapy. So, to address the present difficulties of antiparkinsonian treatment, this review outlines the key issues and limitations of antiparkinsonian medications, new therapeutic strategies, and the breadth of delivery based on nanomedicine. This review covers a wide range of subjects, including drug distribution in the brain, the efficacy of drug-loaded nano-carriers in crossing the blood-brain barrier, and their release profiles. In PD, the nano-carriers are also used. Novel techniques of pharmaceutical delivery are currently made possible by vesicular carriers, which eliminate the requirement to cross the blood-brain barrier (BBB).

Mechanism of Action and Related Natural Regulators of Nrf2 in Nonalcoholic Fatty Liver Disease.

With the acceleration of people's pace of life, non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world, which greatly threatens people's health and safety. Therefore, there is still an urgent need for higher-quality research and treatment in this area. Nuclear factor Red-2-related factor 2 (Nrf2), as a key transcription factor in the regulation of oxidative stress, plays an important role in inducing the body's antioxidant response. Although there are no approved drugs targeting Nrf2 to treat NAFLD so far, it is still of great significance to target Nrf2 to alleviate NAFLD. In recent years, studies have reported that many natural products treat NAFLD by acting on Nrf2 or Nrf2 pathways. This article reviews the role of Nrf2 in the pathogenesis of NAFLD and summarizes the currently reported natural products targeting Nrf2 or Nrf2 pathway for the treatment of NAFLD, which provides new ideas for the development of new NAFLD-related drugs.

Impact of Fab1/Vac14 inhibition on β-1,3-glucanase localization at the tip in Saccharomyces cerevisiae

Deep mycosis is a severe fungal disease that could result in fatal outcomes. However, there is still a demand for highly effective and safe antifungal drugs, given the side effects of the existing treatments and the increase in the resistance to them. In this study, we evaluated the involvement of the lipid kinase Fab1 and its activator Vac14 (Fab1/Vac14) in tip growth in Saccharomyces cerevisiae INVSc1, along with their impact on cell proliferation, using a genetic approach to inhibit them. The results revealed that Fab1/Vac14 inhibition suppressed growth and caused an increase in the rate of β-1,3-glucanase (BGL2) fused with emerald green fluorescent protein (EmGFP) (BGL2-EmGFP) localization at the tip. The inhibition of the endocytic pathway using a lysosome inhibitor also resulted in an increased localization of BGL2-EmGFP at the tip. The overexpression of wild-type BGL2-EmGFP, but not that of the inactive mutant BGL2, led to a complete loss of the cell proliferation ability. These findings suggested that the Fab1/Vac14 complex could be a novel target for the development of antifungal drugs based on tip growth regulation, possibly via excessive cell wall degradation.

Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state. This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state. In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD. Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state. The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Liver Metabolism Insights into the Anti-Hyperuricemia Effects of Polygonum cuspidatum in Mice

At present, hyperuricemia (China) has shown a trend of high incidence and younger age, which has attracted widespread attention. Polygonum cuspidatum extracts are thought to have a role in regulating HUA (Hyperuricemia). Here, we use a combination of GC-TOF/MS-based and UPLC-MSMS-based metabolomic and lipidomic techniques, it was revealed that a Polygonum cuspidatum extract (1-(4-hydroxy-2-methoxy phenyl) -2-(4-hydroxy-3,5-dimethylphenyl) butane-1,2,3-triol) regulates the effect of HUA by altering the liver metabolism of HUA mice. The research results indicate that the anti-HUA activity of P. cuspidatum is closely related to pathways such as sugar metabolism, amino acid metabolism, and purine and pyrimidine metabolism. Participating in metabolic activities exerts antioxidant effects and alleviates UA (Uric acid) accumulation. Additionally, liver lipidomics studies have shown that the overall content of four major phospholipids (Phosphatidyl glycerol, Lysophosphatidyl glycerol, Phosphatidyl cholines, and phosphatidylcholine) and sphingomyelin lipids in sphingolipids are significantly upregulated, while the overall level of CER (Ceramide) lipids is reduced. This indicates that the anti-HUA effect of the components of P. cuspidatum is mainly related to improving glycerophospholipid metabolism and regulating the SM(sphingomyelin)-CER pathway, thereby reducing inflammation and abnormal liver metabolism, and further maintaining uric acid homeostasis. This study provides an in-depth mechanism analysis for the potential application of P. cuspidatum in the treatment of HUA, laying a foundation for the development and clinical application of related drugs.

Strategic and Innovative Roles of lncRNAs Regulated by Naturally-derived Small Molecules in Cancer Therapy.

In the field of precision and personalized medicine, the next generation sequencing method has begun to take an active place as genome-wide screening applications in the diagnosis and treatment of diseases. Studies based on the determination of the therapeutic efficacy of personalized drug use in cancer treatment in the size of the transcriptome and its extension, lncRNA, have been increasing rapidly in recent years. Targeting and/or regulating noncoding RNAs (ncRNAs) consisting of long noncoding RNAs (lncRNAs) are promising strategies for cancer treatment. Within the scope of rapidly increasing studies in recent years, it has been shown that many natural agents obtained from biological organisms can potentially alter the expression of many lncRNAs associated with oncogenic functions. Natural agents include effective small molecules that provide anti-cancer effects and have been used as chemotherapy drugs or in combination with standard anti-cancer drugs used in routine treatment. In this review, it was aimed to provide detailed information about the potential of natural agents to regulate and/or target non-coding RNAs and their mechanisms of action to provide an approach for cancer therapy. The discovery of novel anti-cancer targets and subsequent development of effective drugs or combination strategies that are still needed for most cancers will be promising for cancer treatment.

Synthesis and Evaluation of amyloid beta peptide/ Ruthenium III -based complex drugs as drug delivery and anticancer activity

The development and characterization of anticancer complex drugs (ACD), specifically Amyloid Beta Peptide (ABP) - Ruthenium III (Ru III) - nivolumab (NB), were explored through analytical techniques. Fourier-transform infrared (FTIR) spectroscopy demonstrated the structural transformation of peptides from α-helical to β-sheet formations, aligning with amyloid fibril aggregation. Ruthenium (III) complex synthesis was confirmed through distinct absorption peaks in FTIR analysis. High-resolution scanning electron microscopy (HRSEM) revealed the fibrous and smooth morphology of ACD, while thermogravimetric analysis (TGA) confirmed the decomposition stages and stability of the ruthenium complexes. The encapsulation efficiency and in vitro release profile of nivolumab (NB) within ABP-RuIII-NB were investigated, showing a two-phase release over 40h. Cytotoxicity studies using acridine orange and ethidium bromide staining techniques indicated significant apoptosis in human oral squamous cell carcinoma (OSCC) -treated cells. These findings highlight the potential of ABP-RuIII-NB as an effective cancer treatment with controlled drug release and high cytotoxicity against cancer cells.

N,N-Disubstituted 4-Sulfamoylbenzoic Acid Derivatives as Inhibitors of Cytosolic Phospholipase A2α: Synthesis, Aqueous Solubility, and Activity in a Vesicle and a Whole Blood Assay.

Cytosolic phospholipase A2α (cPLA2α) is the key enzyme that initiates the arachidonic acid cascade through which pro-inflammatory lipid mediators can be formed. Therefore, cPLA2α is considered an interesting target for the development of anti-inflammatory drugs. Although several effective inhibitors of the enzyme have been developed, none of them has yet reached clinical application. Recently, we have prepared new 4-sulfamoylbenzoic acid derivatives based on a cPLA2α inhibitor found in a ligand-based virtual screening. The most effective of these compounds were now subjected to further variations in which the substitution pattern on the sulfamoyl nitrogen atom was changed.. The new compounds were tested in vitro in a vesicle assay for cPLA2α inhibition as well as for their water solubility, metabolic stability, and selectivity towards related enzymes. In addition, they were evaluated ex vivo in a whole blood assay in which metabolites of the arachidonic acid cascade formed after activation of cPLA2α were quantified using a combined online dilution/ online solid phase extraction HPLC-MS method. Inhibitors with submicromolar inhibitory in vitro potency were found with favourable water solubility and selectivity. However, their efficacy did not match that of the highly effective, known, structurally related cPLA2α inhibitor giripladib, which was also tested as a reference. One advantage of some of the new compounds compared to giripladib was their significantly improved water solubility. When analyzing the substances in the ex vivo whole blood assay, it was found that the obtained inhibition data correlated better with the in vivo results when the phorbol ester 12-Otetradecanoylphorbol- 13-acetate was used for activation of the enzyme in the blood cells instead of the calcium ionophore A23187. New compounds with good activity towards cPLA2α and reasonable physicochemical properties were identified. Overall, the results obtained could be helpful in the development of clinically applicable inhibitors of this enzyme.

Purification, characterization and molecular docking studies of analogous alpha amylase inhibitor compounds

BackgroundA large number of protein inhibitors are found in higher plants, cereals and legumes. These inhibitors are helpful in the prevention as well as medical treatment of metabolic syndromes such as type 2 diabetes. Basically, the diabetes mellitus is a chronic disorder that occurs either due to inadequate insulin secretions or when body fails to utilize the produced insulin. The alpha amylase inhibitors are termed as starch blockers. They catalyze the hydrolysis of α-(1,4)-D-glycosidic linkages of starch and other glucose polymer. They play a significant role in inhibition of the activity of salivary and pancreatic amylase in vitro and in vivo. MethodThe present study was based on the purification, characterization and molecular docking studies of the alpha amylase inhibitor isolated from the kidney bean sample. The seed sample was collected from G.B. Pant Nagar University. The crude extract was prepared, the in-vitro studies and heat stability were determined. Following it, purification was carried out using ammonium sulphate precipitation and size exclusion chromatography was done. Later, characterization and molecular docking studies of the purified sample was done after obtaining GC–MS results. ResultThe in-vitro analysis was done and noted that the inhibitory activity was 96.5 ± 0.84 % post size exclusion chromatography. The molecular weight was about 54 kDa. The molecular docking studies revealed that there were interactions between the ligand molecules (the constituents that were selected from the GC–MS chromatogram peaks based on the height and area) and the human pancreatic alpha amylase (1HNY). The hydrogen bonding as affinity binding capacity was also been observed in each of the ligand–protein interaction. ConclusionFrom the tests, it has been elucidated that the alpha amylase inhibitor isolated from the sample has a great potential to serve as an anti-diabetic drug. However, in order to check the potential effects and to explore the optimization and development of the anti-diabetic drug the in-vivo studies can also be done further.

Design, synthesis and optimization of pyrazolo[3,4-b] pyridine derivatives as Hsp110-STAT3 interaction disruptors for the treatment of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive and fatal cardiovascular disorder that is characterized by pulmonary vascular remodeling. Our previous results demonstrated that heat shock protein (Hsp110) was significantly activated to induce vascular remodeling by enhancing the Hsp110-STAT3 interaction. The development of inhibitors that disrupt this association represents a novel strategy for the treatment of PAH. This study is committed to finding new inhibitors targeting the Hsp110-STAT3 interaction based on the structure of the lead compound 2h. A fusion design principle was employed in conjunction with structural optimization in the identification of the compound 10b. In vitro data indicates that 10b exhibited greater potency in the inhibition of pulmonary vascular cells malignant phenotypes via impeding the chaperone function of Hsp110 and the Hsp110-STAT3 interaction. In hypoxia-induced PAH rats, administration of 10b significantly attenuated vascular remodeling and right ventricular hypertrophy by inhibiting the Hsp110-STAT3 association. In short, this work identified a novel and promising lead compound for the development of anti-PAH drugs targeting the Hsp110-STAT3 interaction.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research.

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.