Xenobiotica最新文献

The analysis of pharmacokinetic data has been in a constant state of evolution since the introduction of the term pharmacokinetics. Early work focused on mechanistic understanding of the absorption, distribution, metabolism and excretion of drug products.The introduction of non-linear mixed effects models to perform population pharmacokinetic analysis initiated a paradigm shift. The application of these models represented a major shift in evaluating variability in pharmacokinetic parameters across a population of subjects.While technological advancements in computing power have fueled the growth of population pharmacokinetics in drug development efforts, there remain many challenges in reducing the time required to incorporate these learnings into a model-informed development process. These challenges exist because of expanding datasets, increased number of diagnostics, and more complex mathematical models.New machine learning tools may be potential solutions for these challenges. These new methodologies include genetic algorithms for model selection, machine learning algorithms for covariate selection, and deep learning models for pharmacokinetic and pharmacodynamic data. These new methods promise the potential for less bias, faster analysis times, and the ability to integrate more data.While questions remain regarding the ability of these models to extrapolate accurately, continued research in this area is expected to address these questions.

The intranasal (IN) route of administration is important for topical drugs and drugs intended to act systemically. More recently, direct nose-to-brain input was considered to bypass the blood-brain barrier.Processes related to IN absorption and nose-to-brain distribution are complex and depend, sometimes in contrasting ways, on chemico-physical and structural parameters of the compounds, and on formulation options.Due to the intricacies of these processes and despite the large number of articles published on many different IN compounds, it appears that absorption after IN dosing is not yet fully understood. In particular, at variance of the understanding and modelling approaches that are available for predicting the pharmacokinetics (PK) following oral administration of xenobiotics, it appears that there is not a similar understanding of the chemico-physical and structural determinants influencing drug absorption and disposition of compounds after IN administration, which represents a missed opportunity for this research field. This is even more true regarding the understanding of the direct nose-to-brain input. Due to this, IN administrations may represent an interesting and open research field for scientists aiming to develop PK property predictions tools, mechanistic PK models describing rate and extent of IN absorption, and translational tools to anticipate the clinical PK following IN dosing based on in vitro and in vivo non clinical experiments.This review intends to provide: i) some basic knowledge related to the physiology of PK after IN dosing, ii) a non-exhaustive list of preclinical and clinical examples related to compounds explored for the potential nose-to-blood and nose-to-brain passage, and iii) the identification of some areas requiring improvements, the understanding of which may facilitate the development of IN drug candidates.

A drug's pharmacokinetic (PK) profile will determine its dose and the frequency of administration as well as the likelihood of observing any adverse drug reactions.It is important to understand these PK properties as early as possible in the drug discovery process, ideally, to accurately predict these prior to synthesising the molecule leading to significant improvements in efficiency.In this paper, we describe the approaches used within AstraZeneca to improve our ability of predicting the preclinical and human pharmacokinetic profiles of novel molecules using machine learning and artificial intelligence.We will show how combining chemical structure-based approaches with experimentally derived properties enables improved predictions of in vivo pharmacokinetics and can be extended to molecules that go beyond the classical Lipinski's rule-of-five space.We will also discuss how combining these in vitro and in vivo predictive models could ultimately improve our ability to predict the human outcome at the point of chemical design.

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

In vitro studies using rat, mouse, and human microsomes and hepatocytes on the bacterial β-glucuronidase inhibitor 1-((6,8-dimethyl-2-oxo-1,2-dihydroquinolin-3-yl)methyl)-3-(4-ethoxyphenyl)-1-(2-hydroxyethyl)thiourea) (Inh 1) revealed extensive metabolism in all species.The intrinsic clearances of Inh 1 in human, mouse, and rat hepatic microsomes were 30.9, 67.8, and 201 µL/min/mg, respectively. For intact hepatocytes intrinsic clearances of 21.6, 96.0, and 129 µL/min/10⁶ cells were seen for human, mouse and rat, respectively.The metabolism of Inh 1 involved an uncommon desulphurisation reaction in addition to oxidation, deethylation, and conjugation reactions at multiple sites. Six metabolites were detected in microsomal incubations in human and rat, and seven for the mouse. With hepatocytes, 18 metabolites were characterised, 9 for human, and 11 for mouse and rat.Following IV administration to mice (3 mg/kg), plasma concentrations of Inh 1 exhibited a monophasic decline with a terminal elimination half-life of 0.91 h and low systemic clearance (11.8% of liver blood flow). After PO dosing to mice (3 mg/kg), peak observed Inh 1 concentrations of 495 ng/mL were measured 0.5 h post dose, declining to under 10 ng/mL at 8 h post dose. The absolute oral bioavailability of Inh 1 in the mouse was ca. 26%.

P-glycoprotein (P-gp), a multidrug efflux pump encoded by the ABCB1 (formerly MDR1) gene, plays a crucial role in limiting drug absorption and eliminating toxic compounds in both humans and dogs. However, species-specific differences in P-gp substrates necessitate the development of canine-specific evaluation systems. Canine intestinal organoids derived monolayers offer a promising platform for studying drug transport, yet P-gp-mediated transport in these models remains unexplored.We generated canine colonoid-derived 2D monolayers to investigate ABCB1 gene expression and P-gp function. We employed widely recognised P-gp substrates, Rhodamine 123 and Doxorubicin, in conjunction with the P-gp inhibitor PSC833 at Days 5 and 10 of culture.A significant increase in gene expression of P-gp encoded by the ABCB1 was noted on Day 10 compared to Day 5 of culture. Despite this disparity in gene expression, the transport activity of P-gp, as assessed by the efflux of Rhodamine 123 and Doxorubicin with PSC833 inhibition, did not exhibit significant differences between these two time points. However, the inhibition of P-gp function by PSC833 confirms the presence of functional P-gp in our model.Canine intestinal organoid-derived monolayers provide a valuable tool for investigating P-gp-mediated drug transport. These findings highlight the potential for predicting drug bioavailability and adverse reactions in veterinary medicine, aligning with principles of ethical and sustainable research.

The use of topical photoprotection is necessary to reduce adverse effects caused by excessive exposure to ultraviolet radiation. Despite the high standards set for UV filters, many of them may contribute to the occurrence of adverse effects. The newly synthesised compound K-116, the (E)-cinnamoyl xanthone derivative, could be an alternative. We conducted extended in vitro safety evaluation of compound K-116. The research included assessment of irritation potential on skin tissue, evaluation of penetration through the epidermis, and assessment of phototoxicity, and mutagenicity. Additionally, the eco-safety of compound K-116 was evaluated, including an examination of its degradation pathway in the Cunninghamella echinulata model, as well as in silico simulation of the toxicity of both the parent compound and its degradation products. The research showed that compound K-116 tested in future application conditions is deprived of skin irritant potential additionally it does not penetrate through the epidermis. Results showed that K-116 concentrate is not phototoxic and not mutagenic. The eco-safety studies showed that it undergoes biodegradation in 27% in Cunninghamella echinulata model. The parent compound and formed metabolite are less toxic than reference UV filters (octinoxate and octocrylene).

We aimed to elucidate the toxic effects and biological activities of 3-phenoxybenzoic acid (3PBA) and its metabolite products.

Numerous in silico methods were used to identify the toxic effects and biological activities of 3PBA, including PASS online, molecular docking, ADMETlab 2.0, ADMESWISS, MetaTox, and molecular dynamic simulation.

Ten metabolite products were identified via Phase II reactions (O-glucuronidation, O-sulfation, and methylation).

All of the investigated compounds were followed by Lipinski's rule, indicating that they were stimulants or inducers of hazardous processes.

Because of their high gastrointestinal absorption and ability to reach the blood-brain barrier, the studied compounds' physicochemical and pharmacokinetic properties matched existing evidence of harmful effects, including haematemesis, reproductive dysfunction, allergic dermatitis, toxic respiration, and neurotoxicity.

The studied compounds have been linked to the apoptotic pathway, the reproductivity system, neuroendocrine disruptors, phospholipid-translocating ATPase inhibitors, and JAK2 expression.

An O-glucuronidation metabolite product demonstrated higher binding affinity and interaction with CYP2C9, CYP3A4, caspase 3, and caspase 8 than 3PBA and other metabolite products, whereas metabolite products from methylation were predominant and more toxic.

Our in silico findings partly meet the 3Rs principle by minimizing animal testing before more study is needed to identify the detrimental effects of 3PBA on other organs (liver, kidneys).

Future research directions may involve experimental validation of in silico predictions, elucidation of molecular mechanisms, and exploration of therapeutic interventions.

These findings contribute to our understanding of the toxicological profile of 3PBA and its metabolites, which has implications for risk assessment and regulatory decisions.

To study the pharmacokinetics of deoxypodophyllotoxin and its metabolites in non-small cell lung cancer (NSCLC) bearing mice.Using the established LC-MS/MS method for simultaneous determination of deoxypodophyllotoxin and its three main metabolites (M1, M2 and M7) in biological samples, the concentrations of deoxypodophyllotoxin and its metabolites in plasma, tumour and major tissues of tumour-bearing mice were investigated after 6.25 and 25 mg/kg intravenous administration of deoxypodophyllotoxin.The exposure results of drug concentration showed that after intravenous injection of 6.25 and 25 mg/kg of DPT into tumour-bearing mice, the AUC ratio of DPT in tumour tissue to DPT in plasma was 4.23 and 3.80, respectively. While, the AUC ratio of metabolite M2 in tumour tissue to M2 in plasma was 0.82 and 0.76, respectively.Deoxypodophyllotoxin had higher affinity with tumour tissues than plasma, while its metabolite M2 had less affinity with tumour tissues than deoxypodophyllotoxin, but the exposure level of M2 in plasma was higher than that of deoxypodophyllotoxin. Deoxypodophyllotoxin was widely distributed in tumour-bearing mice. After intravenous injection of 25 mg/kg deoxypodophyllotoxin, the concentration of deoxypodophyllotoxin in other tissues except liver and muscle was relatively high, especially in lung, fat and reproductive organs.