The present study aimed to conduct a preliminary phytochemical analysis of aqueous, ethanol, and hexane leaf extracts from Anisomeles malabarica (L.) R.Br. ex Sims, Leucas aspera (Willd.) Link., Ocimum tenuiflorum L., and Plectranthus amboinicus (Lour.) Spreng. The analysis revealed the presence of terpenoids, saponins, glycosides, phenolics, fats, oils, tannins, quinines, and phlobatannins. Quantitative measurements showed soluble sugars at 1.92, 3.76, 2.45, and 2.07 mg/g, amino acids at 0.53, 4.74, 2.3, and 3.25 mg/g, and proteins at 5.43, 2.8, 7.32, and 4.75 mg/g, respectively. Flavonoid contents were 2.43, 6.85, 4.8, and 3.20 μg/g. Phenolic content was highest in Anisomeles malabarica (1.2 mg/g). Chlorophyll levels ranged from 0.4 to 2.15 mg/g, while carotenoids were highest in Plectranthus amboinicus (5.45 μg/g). All leaf extracts exhibited hydroxyl radical and superoxide anion scavenging activities, which increased with extract concentration. FT-IR analysis confirmed the presence of various functional groups. These findings suggest the potential of these Lamiaceae leaves in developing antibiotics and insecticides.
Developing a new drug and bringing it to the market is a complex and time-consuming process that involves multiple phases of drug discovery and development. However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. In the current age, discovering and developing drugs is a collaborative effort that involves scientific breakthroughs, technological advancements, and regulatory oversight. The pharmaceutical industry is constantly innovating new techniques, fostering interdisciplinary collaboration, and prioritizing patient-centered approaches. In this review, we explore the latest and most updated information about using advanced technologies in drug discovery. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning.
Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experimental methods are often slow and expensive, driving the need for advanced AI-based approaches in PK modeling. This study compares cutting-edge machine learning models, including Graph Neural Networks (GNNs), Transformers, and Stacking Ensembles, against traditional models like Random Forest and XGBoost, using a dataset of over 10,000 bioactive compounds from the ChEMBL database. The Stacking Ensemble model achieved the highest accuracy (R2 of 0.92, MAE of 0.062), outperforming GNNs (R2 of 0.90) and Transformers (R2 of 0.89). These AI models excelled in capturing complex molecular interactions and long-range dependencies, significantly improving PK predictions. The high accuracy achieved (R2 = 0.92) by the Stacking Ensemble method indicates that AI models can streamline the drug discovery process by reducing costly in vivo experiments, enabling faster go/no-go decisions during preclinical evaluations, and ultimately accelerating the development of new thera-peutics. This reduction in time and cost could facilitate broader industry adoption of AI-driven PK modeling. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters, further enhancing the performance and robustness of these predictive models.
Aim: To develop a highly sensitive and interference-resistant ultra performance liquid chromatography (UPLC) coupled with high-resolution selected ion monitoring (HR-SIM) mass spectrometry method for the quantification of semaglutide in biological samples, and to apply it for pharmacokinetic analysis.
Method: The UPLC-HR-SIM method was utilized to quantify semaglutide in beagle plasma, requiring minimal sample pretreatment. The method demonstrated a lower limit of quantification (LLOQ) of 5.0 ng/mL for semaglutide in beagle plasma.
Results: Following intravenous (iv) administration at a dose of 0.030 mg/kg, the plasma concentration of semaglutide in beagles exhibited a multi-exponential decay pattern, with an average elimination half-life (t1/2) of 44.90 ± 11.45 h.
Conclusions: The UPLC-HR-SIM method has proven to be a highly sensitive and robust approach for the quantification of peptide-based drugs. This method will enhance the understanding of the pharmacokinetics and pharmacodynamics of semaglutide and facilitate further research into the pharmacokinetics of other peptide therapeutics.
Aims: To study the dose effect relationship of sodium-glucose cotransporter-2 inhibitor (SGLT-2i) in reducing blood glucose and blood pressure in type 2 diabetes mellitus (T2DM).
Materials and methods: We searched PubMed, Embase, Web of Science, Cochrane Library, and clinicaltrials.gov for related literature, with the search period spanning from the establishment of each platform to May 1, 2024. The main analysis method used is model-based network meta-analysis.
Results: A total of 192 RCTs involving 67,677 patients with T2DM were included in this study. The results showed that SGLT-2i reduced glycated hemoglobin A1c (HbA1c) in T2DM by 0.50 % (95 % CI: 0.49 % ~ 0.50 %) compared with placebo. The hypoglycemic effects of Luseogliflozin and Henagliflozin on HbA1c ranked first and second, with values of 0.92 % (95 % CI: 0.61 % ~ 1.28 %) and 0.91 % (95 % CI: 0.61 % ~ 1.36 %), respectively. Compared with placebo, the results showed that SGLT-2i lowered systolic blood pressure (SBP) by 3.23 mmHg (95 % CI: 3.19 mmHg ~ 3.26 mmHg) and diastolic blood pressure (DBP) by 4.16 mmHg (95 % CI: 4.13 mmHg ~ 4.18 mmHg) in patients with T2DM, respectively. Canagliflozin showed the greatest reduction in SBP and Luseogliflozin showed the greatest reduction in DBP, respectively.
Conclusions: The effect of SGLT-2i in reducing HbA1c in patients with T2DM increased with increasing daily dose, with Luseogliflozin and Henagliflozin being the most effective. SGLT-2i significantly reduced both SBP and DBP in T2DM, but there was no significant dose-response relationship. Among the SGLT-2i, Canagliflozin and Luseo-gliflozin exhibited better antihypertensive effects.