scholarly journals Utilizing in vitro transporter data in IVIVE-PBPK: an overview

ADMET & DMPK ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 201-211 ◽  
Author(s):  
Pankajini Mallick
Keyword(s):  
Drug Discovery ◽  
Drug Transporters ◽  
Drug Discovery And Development ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Human Pharmacokinetic ◽  
Development Processes ◽  
Key Aspects

In vitro-in vivo extrapolation (IVIVE) integrated in physiologically-based pharmacokinetic (PBPK) models have been increasingly used during drug discovery and development processes to predict human pharmacokinetic (PK) parameters. Drug transporters can influence drug pharmacokinetics and are key aspects contributing to the development of a successful drug. This review provides a snapshot of challenges or shortcomings of in vitro and in vivo techniques for understanding the contribution of drug transporters to a drug’s pharmacokinetics. The paper also describes the potential of IVIVE-PBPK models as prospective approaches to predict the role of drug transporters in drug discovery and development.

scholarly journals Development of Physiologically Based Pharmacokinetic Model for Orally Administered Fexuprazan in Humans

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 813
Author(s):  
Yoo-Seong Jeong ◽  
Min-Soo Kim ◽  
Nora Lee ◽  
Areum Lee ◽  
Yoon-Jee Chae ◽  
...  
Keyword(s):  
Gastric Acid ◽  
Pbpk Model ◽  
Pbpk Modeling ◽  
Drug Candidate ◽  
Metabolite Formation ◽  
Pbpk Models ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based

Fexuprazan is a new drug candidate in the potassium-competitive acid blocker (P-CAB) family. As proton pump inhibitors (PPIs), P-CABs inhibit gastric acid secretion and can be used to treat gastric acid-related disorders such as gastroesophageal reflux disease (GERD). Physiologically based pharmacokinetic (PBPK) models predict drug interactions as pharmacokinetic profiles in biological matrices can be mechanistically simulated. Here, we propose an optimized and validated PBPK model for fexuprazan by integrating in vitro, in vivo, and in silico data. The extent of fexuprazan tissue distribution in humans was predicted using tissue-to-plasma partition coefficients in rats and the allometric relationships of fexuprazan distribution volumes (VSS) among preclinical species. Urinary fexuprazan excretion was minimal (0.29–2.02%), and this drug was eliminated primarily by the liver and metabolite formation. The fraction absorbed (Fa) of 0.761, estimated from the PBPK modeling, was consistent with the physicochemical properties of fexuprazan, including its in vitro solubility and permeability. The predicted oral bioavailability of fexuprazan (38.4–38.6%) was within the range of the preclinical datasets. The Cmax, AUClast, and time-concentration profiles predicted by the PBPK model established by the learning set were accurately predicted for the validation sets.

scholarly journals Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson
Keyword(s):  
Drug Discovery ◽  
Drug Interactions ◽  
Development Strategy ◽  
Drug Exposure ◽  
Rapid Development ◽  
Drug Combinations ◽  
Pharmacodynamic Modeling ◽  
Drug Discovery And Development

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

scholarly journals Comprehensive physicochemical, pharmacokinetic and activity profiling of anti-TB agents

2014 ◽  
Vol 70 (3) ◽  
pp. 857-867 ◽  
Author(s):  
Suresh B. Lakshminarayana ◽  
Tan Bee Huat ◽  
Paul C. Ho ◽  
Ujjini H. Manjunatha ◽  
Véronique Dartois ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Volume Of Distribution ◽  
Metabolic Stability ◽  
New Drugs ◽  
Pharmacokinetic Parameters ◽  
Lipinski’S Rule ◽  
Drug Classes ◽  
Human Pharmacokinetic

Abstract Objectives The discovery and development of TB drugs has met limited success, with two new drugs approved over the last 40 years. Part of the difficulty resides in the lack of well-established in vitro or in vivo targets of potency and physicochemical and pharmacokinetic parameters. In an attempt to benchmark and compare such properties for anti-TB agents, we have experimentally determined and compiled these parameters for 36 anti-TB compounds, using standardized and centralized assays, thus ensuring direct comparability across drugs and drug classes. Methods Potency parameters included growth inhibition, cidal activity against growing and non-growing bacteria and activity against intracellular mycobacteria. Pharmacokinetic parameters included basic physicochemical properties, solubility, permeability and metabolic stability. We then attempted to establish correlations between physicochemical, in vitro and in vivo pharmacokinetic and pharmacodynamic indices to tentatively inform future drug discovery efforts. Results Two-thirds of the compounds tested showed bactericidal and intramacrophage activity. Most compounds exhibited favourable solubility, permeability and metabolic stability in standard in vitro pharmacokinetic assays. An analysis of human pharmacokinetic parameters revealed associations between lipophilicity and volume of distribution, clearance, plasma protein binding and oral bioavailability. Not surprisingly, most compounds with favourable pharmacokinetic properties complied with Lipinski's rule of five. Conclusions However, most attempts to detect in vitro–in vivo correlations were unsuccessful, emphasizing the challenges of anti-TB drug discovery. The objective of this work is to provide a reference dataset for the TB drug discovery community with a focus on comparative in vitro potency and pharmacokinetics.

scholarly journals Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Jane C. Caldwell ◽  
Marina V. Evans ◽  
Kannan Krishnan
Keyword(s):  
Risk Assessment ◽  
Model Development ◽  
Pbpk Modeling ◽  
Human Populations ◽  
Pbpk Models ◽  
Modeling Process ◽  
Physiologically Based Pharmacokinetic ◽  
State Of The Science

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation betweenin vitroexposures,in vivophysiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.

ADMET Profiling in Drug Discovery and Development: Perspectives of in silico, in vitro and integrated approaches

2021 ◽  
Vol 22 ◽  
Author(s):  
Nour El-Huda Daoud ◽  
Pobitra Borah ◽  
Pran Kishore Deb ◽  
Katharigatta N. Venugopala ◽  
Wafa Hourani ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Predictive Power ◽  
Early Stage ◽  
In Vitro Models ◽  
Prediction Ability ◽  
Drug Discovery And Development ◽  
In Silico Admet

: In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The early-stage ADMET profiling has introduced a new dimension to leading development. Although several high-throughput in vitro models are available for ADMET profiling, however, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate and, therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.

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