Special Issue "The Prediction of Pharmacokinetics/Pharmacodynamics Using In-Silico Modeling"

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 17 December 2021.

Special Issue Editors

Dr. Jung-woo Chae
E-Mail Website
Guest Editor
College of Pharmacy, Chungnam National University 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
Interests: pharmacokinetics and pharmacodynamics; pharmacometrics; drug–drug interaction; in vitro–in vivo extrapolation
Dr. In-hwan Baek
E-Mail
Guest Editor
College of Pharmacy, Kyungsung University, 309 Suyeong-ro, Nam-gu, Busan 48434, Korea
Interests: pharmacokinetics; pharmacodynamics; PK/PD modeling

Special Issue Information

Dear Colleagues,

This Special Issue aims to cover, in a broad spectrum, pharmacokinetics (PK), pharmacodynamics (PD), in vitro–in vivo extrapolation (IVIVE), drug–drug interaction (DDI), pharmacometrics, and population PK related to chemical drugs, biological products, and natural products. To better understand the clinical effects, PK or PD should be explored to confirm its ADME (absorption, distribution, metabolism, and elimination) or biomarkers in vitro/vivo. Based on those results, in silico simulation in humans could precisely predict the real pattern of PK or PD. We sincerely welcome all the abovementioned topics as well as related works to our Special Issue.

Dr. Jung-woo Chae
Dr. In-hwan Baek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Pharmacokinetics (PK)
  • Pharmacodynamics (PD)
  • In vitro–in vivo extrapolation (IVIVE)
  • Drug–drug interaction (DDI)
  • Pharmacometrics
  • Population pharmacokinetics

Published Papers (7 papers)

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Research

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Article
Evaluation for Potential Drug–Drug Interaction of MT921 Using In Vitro Studies and Physiologically–Based Pharmacokinetic Models
Pharmaceuticals 2021, 14(7), 654; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14070654 - 07 Jul 2021
Cited by 1 | Viewed by 1038
Abstract
MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could [...] Read more.
MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could be administered with multiple medications, such as those for hypertension, diabetes, and hyperlipidemia, the pharmacokinetic drug–drug interaction (DDI) has not been investigated yet. Therefore, we studied in vitro against drug-metabolizing enzymes and transporters. Moreover, we predicted the potential DDI between MT921 and drugs for chronic diseases using physiologically-based pharmacokinetic (PBPK) modeling and simulation. The magnitude of DDI was found to be negligible in in vitro inhibition and induction of cytochrome P450s and UDP-glucuronosyltransferases. Organic anion transporting polypeptide (OATP)1B3, organic anion transporter (OAT)3, Na+-taurocholate cotransporting polypeptide (NTCP), and apical sodium-dependent bile acid transporter (ASBT) are mainly involved in MT921 transport. Based on the result of in vitro experiments, the PBPK model of MT921 was developed and evaluated by clinical data. Furthermore, the PBPK model of amlodipine was developed and evaluated. PBPK DDI simulation results indicated that the pharmacokinetics of MT921 was not affected by the perpetrator drugs. In conclusion, MT921 could be administered without a DDI risk based on in vitro study and related in silico simulation. Further clinical studies are needed to validate this finding. Full article
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Article
Meta-Assessment of Metformin Absorption and Disposition Pharmacokinetics in Nine Species
Pharmaceuticals 2021, 14(6), 545; https://doi.org/10.3390/ph14060545 - 07 Jun 2021
Viewed by 1085
Abstract
The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a [...] Read more.
The objective of this study was to systematically assess literature datasets and quantitatively analyze metformin PK in plasma and some tissues of nine species. The pharmacokinetic (PK) parameters and profiles of metformin in nine species were collected from the literature. Based on a simple allometric scaling, the systemic clearances (CL) of metformin in these species highly correlate with body weight (BW) (R2 = 0.85) and are comparable to renal plasma flow in most species except for rabbit and cat. Reported volumes of distribution (VSS) varied appreciably (0.32 to 10.1 L/kg) among species. Using the physiological and anatomical variables for each species, a minimal physiologically based pharmacokinetic (mPBPK) model consisting of blood and two tissue compartments (Tissues 1 and 2) was used for modeling metformin PK in the nine species. Permeability-limited distribution (low fd1 and fd2) and a single tissue-to-plasma partition coefficient (Kp) value for Tissues 1 and 2 were applied in the joint mPBPK fitting. Nonlinear regression analysis for common tissue distribution parameters along with species-specific CL values reasonably captured the plasma PK profiles of metformin across most species, except for rat and horse with later time deviations. In separate fittings of the mPBPK model to each species, Tissue 2 was considered as slowly-equilibrating compartment consisting of muscle and skin based on in silico calculations of the mean transit times through tissues. The well-fitted mPBPK model parameters for absorption and disposition PK of metformin for each species were compared with in vitro/in vivo results found in the literature with regard to the physiological details and physicochemical properties of metformin. Bioavailability and absorption rates decreased with the increased BW among the species. Tissues such as muscle dominate metformin distribution with low permeability and partitioning while actual tissue concentrations found in rats and mice show likely transporter-mediated uptake in liver, kidney, and gastrointestinal tissues. Metformin has diverse pharmacologic actions, and this assessment revealed allometric relationships in its absorption and renal clearance but considerable variability in actual and modeled tissue distribution probably caused by transporter differences. Full article
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Article
Development and In Vitro Evaluation of Controlled Release Viagra® Containing Poloxamer-188 Using Gastroplus PBPK Modeling Software for In Vivo Predictions and Pharmacokinetic Assessments
Pharmaceuticals 2021, 14(5), 479; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050479 - 18 May 2021
Cited by 1 | Viewed by 1093
Abstract
Sildenafil is the active substance in Viagra® tablets, which is approved by the FDA to treat sexual dysfunction in men. Poor solubility and short half-life, however, can limit the span of its effectiveness. Therefore, this study focused on an oral controlled release [...] Read more.
Sildenafil is the active substance in Viagra® tablets, which is approved by the FDA to treat sexual dysfunction in men. Poor solubility and short half-life, however, can limit the span of its effectiveness. Therefore, this study focused on an oral controlled release matrix system with the aim to improve solubility, control the drug release, and sustain the duration of drug activity. The controlled release matrices were prepared with poloxamer-188, hydroxypropyl methylcellulose, and magnesium stearate. Various formulations of different ratios were developed, evaluated in vitro, and assessed in silico. Poloxamer-188 appeared to have a remarkable influence on the release profile of sildenafil citrate. In general, the rate of drug release decreased as the amount of polymer was gradually increased in the matrix system, achieving a maximum release period over 12 h. The in silico assessment by using the GastroPlus™ PBPK modeling software predicted a significant variation in Cmax, tmax, t1/2, and AUC0-t among the formulations. In conclusion, the combination of polymers in matrix systems can have substantial impact on controlling and modifying the drug release pattern. Full article
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Article
Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects
Pharmaceuticals 2021, 14(5), 429; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050429 - 02 May 2021
Viewed by 1020
Abstract
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a [...] Read more.
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development. Full article
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Article
Investigation of the Impact of CYP3A5 Polymorphism on Drug–Drug Interaction between Tacrolimus and Schisantherin A/Schisandrin A Based on Physiologically-Based Pharmacokinetic Modeling
Pharmaceuticals 2021, 14(3), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14030198 - 27 Feb 2021
Viewed by 885
Abstract
Wuzhi capsule (WZC) is commonly prescribed with tacrolimus in China to ease drug-induced hepatotoxicity. Two abundant active ingredients, schisantherin A (STA) and schisandrin A (SIA) are known to inhibit CYP3A enzymes and increase tacrolimus’s exposure. Our previous study has quantitatively demonstrated the contribution [...] Read more.
Wuzhi capsule (WZC) is commonly prescribed with tacrolimus in China to ease drug-induced hepatotoxicity. Two abundant active ingredients, schisantherin A (STA) and schisandrin A (SIA) are known to inhibit CYP3A enzymes and increase tacrolimus’s exposure. Our previous study has quantitatively demonstrated the contribution of STA and SIA to tacrolimus pharmacokinetics based on physiologically-based pharmacokinetic (PBPK) modeling. In the current work, we performed reversible inhibition (RI) and time-dependent inhibition (TDI) assays with CYP3A5 genotyped human liver microsomes (HLMs), and further integrated the acquired parameters into the PBPK model to predict the drug–drug interaction (DDI) in patients with different CYP3A5 alleles. The results indicated STA was a time-dependent and reversible inhibitor of CYP3A4 while only a reversible inhibitor of CYP3A5; SIA inhibited CYP3A4 and 3A5 in a time-dependent manner but also reversibly inhibited CYP3A5. The predicted fold-increases of tacrolimus exposure were 2.70 and 2.41, respectively, after the multidose simulations of STA. SIA also increased tacrolimus’s exposure but to a smaller extent compared to STA. An optimized physiologically-based pharmacokinetic (PBPK) model integrated with CYP3A5 polymorphism was successfully established, providing more insights regarding the long-term DDI between tacrolimus and Wuzhi capsules in patients with different CYP3A5 genotypes. Full article
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Article
Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data
Pharmaceuticals 2021, 14(2), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14020114 - 03 Feb 2021
Viewed by 779
Abstract
Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed [...] Read more.
Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials. Full article
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Review

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Review
Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs
Pharmaceuticals 2021, 14(5), 472; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050472 - 17 May 2021
Cited by 4 | Viewed by 1099
Abstract
Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of [...] Read more.
Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review. Full article
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