scholarly journals Evaluation of Pharmacokinetic Drug–Drug Interactions: A Review of the Mechanisms, In Vitro and In Silico Approaches

Metabolites ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 75
Author(s):  
Yaru Peng ◽  
Zeneng Cheng ◽  
Feifan Xie
Keyword(s):  
Drug Interactions ◽  
In Silico ◽  
Drug Efficacy ◽  
Primary Concern ◽  
Factors Affecting ◽  
Pbpk Models ◽  
Drug Concentrations ◽  
Pharmacokinetic Drug

Pharmacokinetic drug–drug interactions (DDIs) occur when a drug alters the absorption, transport, distribution, metabolism or excretion of a co-administered agent. The occurrence of pharmacokinetic DDIs may result in the increase or the decrease of drug concentrations, which can significantly affect the drug efficacy and safety in patients. Enzyme-mediated DDIs are of primary concern, while the transporter-mediated DDIs are less understood but also important. In this review, we presented an overview of the different mechanisms leading to DDIs, the in vitro experimental tools for capturing the factors affecting DDIs, and in silico methods for quantitative predictions of DDIs. We also emphasized the power and strategy of physiologically based pharmacokinetic (PBPK) models for the assessment of DDIs, which can integrate relevant in vitro data to simulate potential drug interaction in vivo. Lastly, we pointed out the future directions and challenges for the evaluation of pharmacokinetic DDIs.

scholarly journals Multi-scale modeling of drug binding kinetics to predict drug efficacy

2019 ◽  
Vol 77 (3) ◽  
pp. 381-394 ◽  
Author(s):  
Fabrizio Clarelli ◽  
Jingyi Liang ◽  
Antal Martinecz ◽  
Ines Heiland ◽  
Pia Abel zur Wiesch
Keyword(s):  
Mathematical Models ◽  
Drug Efficacy ◽  
Drug Binding ◽  
Binding Kinetics ◽  
Multi Scale ◽  
Drug Concentrations ◽  
Body Compartments ◽  
Target Binding

AbstractOptimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

scholarly journals Understanding Carrier Performance in Low-Dose Dry Powder Inhalation: An In Vitro–In Silico Approach

Pharmaceutics ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 297
Author(s):  
Joana T. Pinto ◽  
Inês Cachola ◽  
João F. Pinto ◽  
Amrit Paudel
Keyword(s):  
Particle Size ◽  
In Silico ◽  
Low Dose ◽  
Drug Product ◽  
Dry Powder Inhaler ◽  
Dry Powder ◽  
Bulk Properties ◽  
Pbpk Models

The use of physiologically based pharmacokinetic (PBPK) models to support drug product development has become increasingly popular. The in vitro characterization of the materials of the formulation provides valuable descriptors for the in silico prediction of the drug’s pharmacokinetic profile. Thus, the application of an in vitro–in silico framework can be decisive towards the prediction of the in vivo performance of a new medicine. By applying such an approach, this work aimed to derive mechanistic based insights into the potential impact of carrier particles and powder bulk properties on the in vivo performance of a lactose-based dry powder inhaler (DPI). For this, a PBPK model was developed using salbutamol sulphate (SS) as a model drug and the in vitro performance of its low-dose blends (2% w/w) with different types of lactose particles was investigated using different DPI types (capsule versus reservoir) at distinct airflows. Likewise, the influence of various carrier’s particle and bulk properties, device type and airflow were investigated in silico. Results showed that for the capsule-based device, low-dose blends of SS had a better performance, when smaller carrier particles (Dv0.5 ≈ 50 μm) with about 10% of fines were used. This resulted in a better predicted bioavailability of the drug for all the tested airflows. For the reservoir type DPI, the mean particle size (Dv0.5) was identified as the critical parameter impacting performance. Shear cell and air permeability or compressibility measurements, particle size distribution by pressure titration and the tensile strength of the selected lactose carrier powders were found useful to generate descriptors that could anticipate the potential in vivo performance of the tested DPI blends.

scholarly journals Carboxymefloquine, the Major Metabolite of the Antimalarial Drug Mefloquine, Induces Drug-Metabolizing Enzyme and Transporter Expression by Activation of Pregnane X Receptor

2014 ◽  
Vol 59 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Rita Piedade ◽  
Stefanie Traub ◽  
Andreas Bitter ◽  
Andreas K. Nüssler ◽  
José P. Gil ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Antimalarial Drug ◽  
Pregnane X Receptor ◽  
Antimalarial Drugs ◽  
Drug Metabolites ◽  
Protein Levels ◽  
Two Hybrid ◽  
Pharmacokinetic Drug

ABSTRACTMalaria patients are frequently coinfected with HIV and mycobacteria causing tuberculosis, which increases the use of coadministered drugs and thereby enhances the risk of pharmacokinetic drug-drug interactions. Activation of the pregnane X receptor (PXR) by xenobiotics, which include many drugs, induces drug metabolism and transport, thereby resulting in possible attenuation or loss of the therapeutic responses to the drugs being coadministered. While several artemisinin-type antimalarial drugs have been shown to activate PXR, data on nonartemisinin-type antimalarials are still missing. Therefore, this study aimed to elucidate the potential of nonartemisinin antimalarial drugs and drug metabolites to activate PXR. We screened 16 clinically used antimalarial drugs and six major drug metabolites for binding to PXR using the two-hybrid PXR ligand binding domain assembly assay; this identified carboxymefloquine, the major and pharmacologically inactive metabolite of the antimalarial drug mefloquine, as a potential PXR ligand. Two-hybrid PXR-coactivator and -corepressor interaction assays and PXR-dependent promoter reporter gene assays confirmed carboxymefloquine to be a novel PXR agonist which specifically activated the human receptor. In the PXR-expressing intestinal LS174T cells and in primary human hepatocytes, carboxymefloquine induced the expression of drug-metabolizing enzymes and transporters on the mRNA and protein levels. The crucial role of PXR for the carboxymefloquine-dependent induction of gene expression was confirmed by small interfering RNA (siRNA)-mediated knockdown of the receptor. Thus, the clinical use of mefloquine may result in pharmacokinetic drug-drug interactions by means of its metabolite carboxymefloquine. Whether thesein vitrofindings are ofin vivorelevance has to be addressed in future clinical drug-drug interaction studies.

scholarly journals Growth of mixed cancer cell population – in silico the size matters

2020 ◽  
Author(s):  
Adam Kłóś ◽  
Przemysław M. Płonka
Keyword(s):  
Tumor Growth ◽  
Cell Size ◽  
In Silico ◽  
Small Cells ◽  
Factors Affecting ◽  
Cancer Heterogeneity ◽  
Number Of Cells ◽  
Heterogeneous Tumor

Cancer heterogeneity is still underexplored and difficult to investigate. The whole network of factors engaged in tumor growth makes clinical cases, as well as the in vivo and in vitro experiments of limited use in terms of understanding cancer heterogeneity. Our idea was to start from scratch and focus on the simplest distinctive feature in a heterogeneous tumor, namely the cell size. To exclude any other factors, we created a rudimentary cellular automata model of mixed cancer culture with two lines of different cell sizes. We tested the model with various sets of parameters to explore how the cell size affects cancer co-culture growth. It turned out that the cell size plays a crucial role in in silico heterogeneous tumor growth. The dominance of bigger cells decreases the number of cells in the overall mixed cancer population. In contrast, the small cells increase the total number of cells, even without a parallel enlargement of the macroscopic tumor size. The predominance of the smaller cells is particularly visible in overcrowded conditions. Although our model was primarily designed for verification of experimental hypothesis and as a mean for better understanding of the cancer heterogeneity itself, it is as well of some practical value. Our findings can affect today’s practice of estimating tumor growth based on its macroscopic size and may propose a new approach to interpreting histological data. After modifications, the model may serve to test other factors affecting the growth of mixed populations of cancer cells differing in size.

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