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Pharmaceutics
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Model-Informed Drug Discovery and Development, 2nd Edition
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Model-Informed Drug Discovery and Development, 2nd Edition

A special issue of Pharmaceutics (ISSN 1999-4923). This special issue belongs to the section "Pharmacokinetics and Pharmacodynamics".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 59589

Special Issue Editor

Prof. Dr. Yu Chul Kim

E-Mail Website
Guest Editor
Department of Pharmaceutical Engineering, Inje University, Gimhae 50834, Republic of Korea
Interests: pharmacokinetics; drug metabolism; drug–drug interaction; PK/PD; modeling and simulation; PBPK; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The usefulness of modeling and simulation (M&S) technology has been verified in a variety of academic and industrial fields. M&S has also expanded its application into the pharmaceutical industry since started being used during drug development and regulatory approval processes as a method to overcome the low productivity of new drug development. Currently, the M&S approach is utilized in almost all stages of drug discovery and development and as a regulatory process evolving from the concept of “model-based” to “model-informed”. Overall, model-informed decision making helps to increase success rates in drug development.

This Special Issue volume II aims to highlight the latest research activities using M&S from all the stages of drug discovery and development, which include PK/PD modeling, PBPK, pharmacometrics, systems pharmacology, etc. We invite researchers to submit original research articles and reviews in this field.

Prof. Dr. Yu Chul Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • modeling and simulation
  • PK/PD
  • PBPK
  • pharmacometrics
  • systems pharmacology
  • drug discovery and development

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Published Papers (7 papers)

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Research

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14 pages, 1836 KiB  
Article
A Physiologically-Based Pharmacokinetic Simulation to Evaluate Approaches to Mitigate Efavirenz-Induced Decrease in Levonorgestrel Exposure with a Contraceptive Implant
by Lilian W. Adeojo, Rena C. Patel and Nancy C. Sambol
Pharmaceutics 2024, 16(8), 1050; https://doi.org/10.3390/pharmaceutics16081050 - 7 Aug 2024
Viewed by 955
Abstract
Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based [...] Read more.
Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based pharmacokinetic (PBPK) model for levonorgestrel that we developed within the Simcyp® program, we evaluated a higher dose of levonorgestrel implant, a lower dose of efavirenz, and the combination of both, as possible methods to mitigate the interaction. In addition, we investigated the impact on levonorgestrel total and unbound concentrations of other events likely to be associated with efavirenz coadministration: changes in plasma protein binding of levonorgestrel (as with displacement) and high variability of efavirenz exposure (as with genetic polymorphism of its metabolism). The range of fraction unbound tested was 0.6% to 2.6%, and the range of efavirenz exposure ranged from the equivalent of 200 mg to 4800 mg doses. Results: Levonorgestrel plasma concentrations at any given time with a standard 150 mg implant dose are predicted to be approximately 68% of those of control when given with efavirenz 600 mg and 72% of control with efavirenz 400 mg. With double-dose levonorgestrel, the predictions are 136% and 145% of control, respectively. A decrease in levonorgestrel plasma protein binding is predicted to primarily decrease total levonorgestrel plasma concentrations, whereas higher efavirenz exposure is predicted to decrease total and unbound concentrations. Conclusions: Simulations suggest that doubling the dose of levonorgestrel, particularly in combination with 400 mg daily efavirenz, may mitigate the drug interaction. Changes in levonorgestrel plasma protein binding and efavirenz genetic polymorphism may help explain differences between model predictions and clinical data but need to be studied further. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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15 pages, 2571 KiB  
Article
Pharmacokinetic Modeling of Bepotastine for Determination of Optimal Dosage Regimen in Pediatric Patients with Allergic Rhinitis or Urticaria
by Sukyong Yoon, Byung Hak Jin, Choon Ok Kim, Kyungsoo Park, Min Soo Park and Dongwoo Chae
Pharmaceutics 2024, 16(3), 334; https://doi.org/10.3390/pharmaceutics16030334 - 27 Feb 2024
Cited by 1 | Viewed by 1453
Abstract
Bepotastine, a second-generation antihistamine for allergic rhinitis and urticaria, is widely used in all age groups but lacks appropriate dosing guidelines for pediatric patients, leading to off-label prescriptions. We conducted this study to propose an optimal dosing regimen for pediatric patients based on [...] Read more.
Bepotastine, a second-generation antihistamine for allergic rhinitis and urticaria, is widely used in all age groups but lacks appropriate dosing guidelines for pediatric patients, leading to off-label prescriptions. We conducted this study to propose an optimal dosing regimen for pediatric patients based on population pharmacokinetic (popPK) and physiologically based pharmacokinetic (PBPK) models using data from two previous trials. A popPK model was built using NONMEM software. A one-compartment model with first-order absorption and absorption lag time described our data well, with body weight incorporated as the only covariate. A PBPK model was developed using PK-Sim software version 10, and the model well predicted the drug concentrations obtained from pediatric patients. Furthermore, the final PBPK model showed good concordance with the known properties of bepotastine. Appropriate pediatric doses for different weight and age groups were proposed based on the simulations. Discrepancies in recommended doses from the two models were likely due to the incorporation of age-dependent physiological factors in the PBPK model. In conclusion, our study is the first to suggest an optimal oral dosing regimen of bepotastine in pediatric patients using both approaches. This is expected to foster safer and more productive use of the drug. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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18 pages, 4391 KiB  
Article
Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation
by Jeong-Min Lee, Jin-Ha Yoon, Han-Joo Maeng and Yu Chul Kim
Pharmaceutics 2024, 16(2), 280; https://doi.org/10.3390/pharmaceutics16020280 - 16 Feb 2024
Cited by 1 | Viewed by 1688
Abstract
The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug–drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct [...] Read more.
The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug–drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague–Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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16 pages, 3178 KiB  
Article
Development of a Physiologically Based Pharmacokinetic Model for Nitrofurantoin in Rabbits, Rats, and Humans
by Raju Prasad Sharma, Elsje J. Burgers and Joost B. Beltman
Pharmaceutics 2023, 15(9), 2199; https://doi.org/10.3390/pharmaceutics15092199 - 25 Aug 2023
Cited by 3 | Viewed by 1691
Abstract
Nitrofurantoin (NFT) is a commonly used antibiotic for the treatment of urinary tract infections that can cause liver toxicity. Despite reports of hepatic adverse events associated with NFT exposure, there is still limited understanding of the interplay between NFT exposure, its disposition, and [...] Read more.
Nitrofurantoin (NFT) is a commonly used antibiotic for the treatment of urinary tract infections that can cause liver toxicity. Despite reports of hepatic adverse events associated with NFT exposure, there is still limited understanding of the interplay between NFT exposure, its disposition, and the risk of developing liver toxicity. In this study, we aim to develop a physiologically based pharmacokinetic (PBPK) model for NFT in three different species (rabbits, rats, and humans) that can be used as a standard tool for predicting drug-induced liver injury (DILI). We created several versions of the PBPK model using previously published kinetics data from rabbits, and integrated enterohepatic recirculation (EHR) using rat data. Our model showed that active tubular secretion and reabsorption in the kidney are critical in explaining the non-linear renal clearance and urine kinetics of NFT. We subsequently extrapolated the PBPK model to humans. Adapting the physiology to humans led to predictions consistent with human kinetics data, considering a low amount of NFT to be excreted into bile. Model simulations predicted that the liver of individuals with a moderate-to-severe glomerular filtration rate (GFR) is exposed to two-to-three-fold higher concentrations of NFT than individuals with a normal GFR, which coincided with a substantial reduction in the NFT urinary concentration. In conclusion, people with renal insufficiency may be at a higher risk of developing DILI due to NFT exposure, while at the same time having a suboptimal therapeutic effect with a high risk of drug resistance. Our PBPK model can in the future be used to predict NFT kinetics in individual patients on the basis of characteristics like age and GFR. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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17 pages, 1352 KiB  
Article
Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug–Drug Interactions between Elexacaftor/Tezacaftor/Ivacaftor and Tacrolimus in Lung Transplant Recipients
by Eunjin Hong, Eugeniu Carmanov, Alan Shi, Peter S. Chung, Adupa P. Rao, Kevin Forrester and Paul M. Beringer
Pharmaceutics 2023, 15(5), 1438; https://doi.org/10.3390/pharmaceutics15051438 - 8 May 2023
Cited by 9 | Viewed by 2394
Abstract
Elexacaftor/tezacaftor/ivacaftor (ETI) treatment has potential benefits in lung transplant recipients, including improvements in extrapulmonary manifestations, such as gastrointestinal and sinus disease; however, ivacaftor is an inhibitor of cytochrome P450 3A (CYP3A) and may, therefore, pose a risk for elevated systemic exposure to tacrolimus. [...] Read more.
Elexacaftor/tezacaftor/ivacaftor (ETI) treatment has potential benefits in lung transplant recipients, including improvements in extrapulmonary manifestations, such as gastrointestinal and sinus disease; however, ivacaftor is an inhibitor of cytochrome P450 3A (CYP3A) and may, therefore, pose a risk for elevated systemic exposure to tacrolimus. The aim of this investigation is to determine the impact of ETI on tacrolimus exposure and devise an appropriate dosing regimen to manage the risk of this drug–drug interaction (DDI). The CYP3A-mediated DDI of ivacaftor–tacrolimus was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach, incorporating CYP3A4 inhibition parameters of ivacaftor and in vitro enzyme kinetic parameters of tacrolimus. To further support the findings in PBPK modeling, we present a case series of lung transplant patients who received both ETI and tacrolimus. We predicted a 2.36-fold increase in tacrolimus exposure when co-administered with ivacaftor, which would require a 50% dose reduction of tacrolimus upon initiation of ETI treatment to avoid the risk of elevated systemic exposure. Clinical cases (N = 13) indicate a median 32% (IQR: −14.30, 63.80) increase in the dose-normalized tacrolimus trough level (trough concentration/weight-normalized daily dose) after starting ETI. These results indicate that the concomitant administration of tacrolimus and ETI may lead to a clinically significant DDI, requiring the dose adjustment of tacrolimus. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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Review

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27 pages, 3759 KiB  
Review
Lipid-Based Nanotechnology: Liposome
by Yanhao Jiang, Wenpan Li, Zhiren Wang and Jianqin Lu
Pharmaceutics 2024, 16(1), 34; https://doi.org/10.3390/pharmaceutics16010034 - 26 Dec 2023
Cited by 15 | Viewed by 5187
Abstract
Over the past several decades, liposomes have been extensively developed and used for various clinical applications such as in pharmaceutical, cosmetic, and dietetic fields, due to its versatility, biocompatibility, and biodegradability, as well as the ability to enhance the therapeutic index of free [...] Read more.
Over the past several decades, liposomes have been extensively developed and used for various clinical applications such as in pharmaceutical, cosmetic, and dietetic fields, due to its versatility, biocompatibility, and biodegradability, as well as the ability to enhance the therapeutic index of free drugs. However, some challenges remain unsolved, including liposome premature leakage, manufacturing irreproducibility, and limited translation success. This article reviews various aspects of liposomes, including its advantages, major compositions, and common preparation techniques, and discusses present U.S. FDA-approved, clinical, and preclinical liposomal nanotherapeutics for treating and preventing a variety of human diseases. In addition, we summarize the significance of and challenges in liposome-enabled nanotherapeutic development and hope it provides the fundamental knowledge and concepts about liposomes and their applications and contributions in contemporary pharmaceutical advancement. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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46 pages, 39479 KiB  
Review
Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
by Lalitkumar K. Vora, Amol D. Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K. Solanki and Vivek P. Chavda
Pharmaceutics 2023, 15(7), 1916; https://doi.org/10.3390/pharmaceutics15071916 - 10 Jul 2023
Cited by 170 | Viewed by 44973
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. [...] Read more.
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Full article
(This article belongs to the Special Issue Model-Informed Drug Discovery and Development, 2nd Edition)
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