Search Results (4)

The purpose of this paper is to elucidate the roles that microbes may be playing in the rootzone of the medicinal plant Daturainoxia. We hypothesized that the microbes associated with the Datura rootzone would be significantly different than the similar surrounding fields in composition and function. We also hypothesized that rhizospheric and endophytic microbes would be associated with similar metabolic functions to the plant rootzone they inhabited. The methods employed were microbial barcoding, tests of essential oils against antibiotic resistant bacteria and other soil bacterial isolates, 16S Next Generation Sequencing (NGS) metabarcoding, and Whole Genome Shotgun (WGS) taxonomic and functional analyses. A few of the main bacterial genera of interest that were differentially abundant in the Datura root microbiome were Flavobacterium (p = 0.007), Chitinophaga (p = 0.0007), Pedobacter (p = 6 × 10⁻⁵), Bradyhizobium (p = 1 × 10⁻⁸), and Paenibacillus (p = 1.46 × 10⁻⁶). There was significant evidence that the microbes associated with the Datura rootzone had elevated function related to bacterial chalcone synthase (p = 1.49 × 10⁻³) and permease genes (p < 0.003). There was some evidence that microbial functions in the Datura rootzone provided precursors to important plant bioactive molecules or were beneficial to plant growth. This is important because these compounds are phyto-protective antioxidants and are precursors to many aromatic bioactive compounds that are relevant to human health. In the context of known interactions, and current results, plants and microbes influence the flavonoid biosynthetic pathways of one other, in terms of the regulation of the phenylpropanoid pathway. This is the first study to focus on the microbial ecology of the Datura rootzone. There are possible biopharmaceutical and agricultural applications of the natural interplay that was discovered during this study of the Datura inoxia rhizosphere. Full article

The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug–bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings. Full article

Acacetin, an important ingredient of acacia honey and a component of several medicinal plants, exhibits therapeutic effects such as antioxidative, anticancer, anti-inflammatory, and anti-plasmodial activities. However, to date, studies reporting a systematic investigation of the in vivo fate of orally administered acacetin are limited. Moreover, the in vitro physicochemical and biopharmaceutical properties of acacetin in the gastrointestinal (GI) tract and their pharmacokinetic impacts remain unclear. Therefore, in this study, we aimed to systematically investigate the oral absorption and disposition of acacetin using relevant rat models. Acacetin exhibited poor solubility (≤119 ng/mL) and relatively low stability (27.5–62.0% remaining after 24 h) in pH 7 phosphate buffer and simulated GI fluids. A major portion (97.1%) of the initially injected acacetin dose remained unabsorbed in the jejunal segments, and the oral bioavailability of acacetin was very low at 2.34%. The systemic metabolism of acacetin occurred ubiquitously in various tissues (particularly in the liver, where it occurred most extensively), resulting in very high total plasma clearance of 199 ± 36 mL/min/kg. Collectively, the poor oral bioavailability of acacetin could be attributed mainly to its poor solubility and low GI luminal stability. Full article

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine. Full article