Research

17 pages, 7736 KiB

Open AccessArticle

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

by Jinwei Zhu, Yicui Zhang, Yixin Zhao, Jingwei Zhang, Kun Hao and Hua He

Pharmaceutics 2023, 15(9), 2274; https://doi.org/10.3390/pharmaceutics15092274 - 03 Sep 2023

Cited by 1 | Viewed by 1225

Abstract

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer [...] Read more.

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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15 pages, 3184 KiB

Open AccessArticle

Prediction of CYP-Mediated Drug Interaction Using Physiologically Based Pharmacokinetic Modeling: A Case Study of Salbutamol and Fluvoxamine

by Lara Marques and Nuno Vale

Pharmaceutics 2023, 15(6), 1586; https://doi.org/10.3390/pharmaceutics15061586 - 24 May 2023

Cited by 3 | Viewed by 1819

Abstract

Drug–drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome [...] Read more.

Drug–drug interactions (DDIs) represent a significant concern in healthcare, particularly for patients undergoing polytherapy. DDIs can lead to a range of outcomes, from decreased therapeutic effectiveness to adverse effects. Salbutamol, a bronchodilator recommended for the treatment of respiratory diseases, is metabolized by cytochrome P450 (CYP) enzymes, which can be inhibited or induced by co-administered drugs. Studying DDIs involving salbutamol is crucial for optimizing drug therapy and preventing adverse outcomes. Here, we aimed to investigate CYP-mediated DDIs between salbutamol and fluvoxamine through in silico approaches. The physiologically based pharmacokinetic (PBPK) model of salbutamol was developed and validated using available clinical PK data, whereas the PBPK model of fluvoxamine was previously verified by GastroPlus. Salbutamol–fluvoxamine interaction was simulated according to different regimens and patient’s characteristics (age and physiological status). The results demonstrated that co-administering salbutamol with fluvoxamine enhanced salbutamol exposure in certain situations, especially when fluvoxamine dosage increased. To sum up, this study demonstrated the utility of PBPK modeling in predicting CYP-mediated DDIs, making it a pioneer in PK DDI research. Furthermore, this study provided insights into the relevance of regular monitoring of patients taking multiple medications, regardless of their characteristics, to prevent adverse outcomes and for the optimization of the therapeutic regimen, in cases where the therapeutic benefit is no longer experienced. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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18 pages, 8129 KiB

Open AccessArticle

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease

by Smiljana Tomasevic, Miljan Milosevic, Bogdan Milicevic, Vladimir Simic, Momcilo Prodanovic, Srboljub M. Mijailovich and Nenad Filipovic

Pharmaceutics 2023, 15(3), 793; https://doi.org/10.3390/pharmaceutics15030793 - 28 Feb 2023

Cited by 3 | Viewed by 1514

Abstract

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming [...] Read more.

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid–structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs’ influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca²⁺ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure–volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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21 pages, 10578 KiB

Open AccessArticle

CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network

by Žan Hafner Petrovski, Barbara Hribar-Lee and Zoran Bosnić

Pharmaceutics 2023, 15(1), 119; https://doi.org/10.3390/pharmaceutics15010119 - 29 Dec 2022

Cited by 2 | Viewed by 1940

Abstract

Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its [...] Read more.

Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model’s performance shows that its achieved sensitivity is

0.829

, specificity is

0.98

, and

F_{1}

score is

0.517

, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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18 pages, 4328 KiB

Open AccessArticle

Numerical Modeling of Particle Dynamics Inside a Dry Powder Inhaler

by Tijana Šušteršič, Aleksandar Bodić, Jelisaveta Ignjatović, Sandra Cvijić, Svetlana Ibrić and Nenad Filipović

Pharmaceutics 2022, 14(12), 2591; https://doi.org/10.3390/pharmaceutics14122591 - 24 Nov 2022

Viewed by 1193

Abstract

The development of novel dry powders for dry powder inhalers (DPIs) requires the in vitro assessment of DPI aerodynamic performance. As a potential complementary method, in silico numerical simulations can provide additional information about the mechanisms that guide the particles and their behavior [...] Read more.

The development of novel dry powders for dry powder inhalers (DPIs) requires the in vitro assessment of DPI aerodynamic performance. As a potential complementary method, in silico numerical simulations can provide additional information about the mechanisms that guide the particles and their behavior inside DPIs. The aim of this study was to apply computational fluid dynamics (CFDs) coupled with a discrete phase model (DPM) to describe the forces and particle trajectories inside the RS01^® as a model DPI device. The methodology included standard fluid flow equations but also additional equations for the particle sticking mechanism, as well as particle behavior after contacting the DPI wall surface, including the particle detachment process. The results show that the coefficient of restitution between the particle and the impact surface does not have a high impact on the results, meaning that all tested combinations gave similar output efficiencies and particle behaviors. No sliding or rolling mechanisms were observed for the particle detachment process, meaning that simple bouncing off or deposition particle behavior is present inside DPIs. The developed methodology can serve as a basis for the additional understanding of the particles’ behavior inside DPIs, which is not possible using only in vitro experiments; this implies the possibility of increasing the efficiency of DPIs. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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Review

Jump to: Research, Other

21 pages, 2370 KiB

Open AccessReview

Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives

by Thi Tuyet Van Tran, Hilal Tayara and Kil To Chong

Pharmaceutics 2023, 15(4), 1260; https://doi.org/10.3390/pharmaceutics15041260 - 17 Apr 2023

Cited by 14 | Viewed by 6458

Abstract

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting [...] Read more.

Drug metabolism and excretion play crucial roles in determining the efficacy and safety of drug candidates, and predicting these processes is an essential part of drug discovery and development. In recent years, artificial intelligence (AI) has emerged as a powerful tool for predicting drug metabolism and excretion, offering the potential to speed up drug development and improve clinical success rates. This review highlights recent advances in AI-based drug metabolism and excretion prediction, including deep learning and machine learning algorithms. We provide a list of public data sources and free prediction tools for the research community. We also discuss the challenges associated with the development of AI models for drug metabolism and excretion prediction and explore future perspectives in the field. We hope this will be a helpful resource for anyone who is researching in silico drug metabolism, excretion, and pharmacokinetic properties. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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25 pages, 8371 KiB

Open AccessReview

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

by Aleksandra Krstevska, Jelena Đuriš, Svetlana Ibrić and Sandra Cvijić

Pharmaceutics 2023, 15(1), 107; https://doi.org/10.3390/pharmaceutics15010107 - 28 Dec 2022

Cited by 3 | Viewed by 3282

Abstract

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various [...] Read more.

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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Other

Jump to: Research, Review

6 pages, 662 KiB

Open AccessOpinion

Artificial Intelligence and Anticancer Drug Development—Keep a Cool Head

by Caroline Bailleux, Jocelyn Gal, Emmanuel Chamorey, Baharia Mograbi and Gérard Milano

Pharmaceutics 2024, 16(2), 211; https://doi.org/10.3390/pharmaceutics16020211 - 31 Jan 2024

Viewed by 704

Abstract

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are [...] Read more.

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology. Full article

(This article belongs to the Special Issue In Silico Drug Testing and Optimization, Coupling Physical-Based Modeling and Machine Learning)

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