Search Results (82)

Background: Drug development for leishmaniases treatment follows a very selective process in order to propose drug candidates that possess all the qualities that meet the strict specifications of the pharmaceutical industry. Drug resistance is a limiting factor that can impact the lifespan of a marketed drug. It is now essential that the risk of drug resistance be evaluated at the early stage of in vitro studies to discard a lead compound that could quickly generate drug resistance once available on the market. Objectives: This article aims to estimate the risk of drug resistance emergence for a promising drug candidate at the in vitro early stage of drug development. Methods: A sequential method is proposed to study some of the phenotypic characteristics and parameters of resistant parasites such as time to achieve maximal resistance during stepwise drug pressure, resistance amplitude, stability, fitness, and infectivity both in vitro and in vivo. Results: Some examples with drugs in clinical use and former drug candidates are given. Conclusions: This method providing an evaluation of the risk of drug resistance from an in vitro model of Leishmania donovani be extrapolated to other Leishmania species. Full article

Background/Objectives: Hepatic clearance is important in determining clinical drug administration strategies. Achieving accurate hepatic clearance predictions through in vitro-to-in vivo extrapolation (IVIVE) relies on appropriate model selection, which is a critical step. Although numerous models have been developed to estimate drug dosage, some may fail to predict liver drug clearance owing to inappropriate hepatic clearance models during IVIVE. To address this limitation, an in silico-based model selection approach for optimizing hepatic clearance predictions was introduced in a previous study. The current study extends this strategy by verifying the accuracy of the selected models using ex situ experimental data, particularly for drugs whose model choices are influenced by protein binding. Methods: Commonly prescribed drugs were classified according to their hepatic extraction ratios and protein-binding properties. Building on previous studies that employed multinomial logistic regression analysis for model selection, a three-phase classification method was implemented to identify five representative drugs: diazepam, diclofenac, rosuvastatin, fluoxetine, and tolbutamide. Subsequently, an isolated perfused rat liver (IPRL) system was used to evaluate the accuracy of the in silico method. Results: As the unbound fraction increased for diazepam and diclofenac, the most suitable predictive model shifted from the initially preferred well-stirred model (WSM) to the modified well-stirred model (MWSM). For rosuvastatin, the MWSM provided a more accurate prediction. These three capacity-limited, binding-sensitive drugs conformed to the outcomes predicted by the multinomial logistic regression analysis. Fluoxetine was best described by the WSM, which is consistent with its flow-limited classification. For tolbutamide, a representative capacity-limited, binding-insensitive drug, no significant differences were observed among the various models. Conclusions: These findings demonstrate the accuracy of an in silico-based model selection approach for predicting liver metabolism and highlight its potential for guiding dosage adjustments. Furthermore, the IPRL system serves as a practical tool for validating the accuracy of the results derived from this approach. Full article

Background: Glucuronide recycling in the gut and liver profoundly affects the systemic and/or local exposure of drugs and their glucuronide metabolites, impacting both clinical efficacy and toxicity. This recycling also alters drug exposure in the colon, making it critical to establish local concentration for drugs targeting colon (e.g., drugs for colon cancer and inflammatory bowel disease). Methods: In this study, a parent–metabolite middle-out physiologically based pharmacokinetic (PBPK) model was built for genistein and its glucuronide metabolite to estimate the systemic and local exposure of the glucuronide and its corresponding aglycone in rats by incorporating UDP-glucuronosyltransferase (UGT)-mediated metabolism and transporter-dependent glucuronide disposition in the liver and intestine, as well as gut microbial-mediated deglucuronidation that enables the recycling of the parent compound. Results: This parent–metabolite middle-out rat PBPK model utilized in vitro-to-in vivo extrapolated (IVIVE) metabolic and transporter clearance values based on in vitro kinetic parameters from surrogate species, the rat tissue abundance of relevant proteins, and saturable Michaelis–Menten mechanisms. Inter-system extrapolation factors (ISEFs) were used to account for transporter protein abundance differences between in vitro systems and tissues and between rats and surrogate species. Model performance was evaluated at multiple dose levels for genistein and its glucuronide. Model sensitivity analyses demonstrated the impact of key parameters on the plasma concentrations and local exposure of genistein and its glucuronide. Our model was applied to simulate the quantitative impact of glucuronide recycling on the pharmacokinetic profiles in both plasma and colonocytes. Conclusions: Our study underlines the importance of glucuronide recycling in determining local drug concentrations in the intestine and provides a preliminary modeling tool to assess the influence of transporter-mediated drug–drug interactions on glucuronide recycling and local drug exposure, which are often misrepresented by systemic plasma concentrations. Full article

The development of alternative methods that link cellular and predictive toxicity to high-level toxicity is a key focus of current research within the framework of the 3Rs in animal experimentation. In this context, this study aimed to evaluate the previously developed in vitro approach using the zebrafish liver cell line (ZFL) for assessing bioaccumulation and biotransformation of the compound BDE-47, which is more hydrophobic than phenanthrene, and is the compound used in the previous study. For this purpose, experimentally, the internal concentrations in the cells (C_cell) and the exposure medium of both BDE-47 and its main metabolites were quantified at different exposure times by GC-MS. Additionally, the free bioavailable concentration (C_free) was determined with a solid-phase microextraction (SPME) experiment. With the aim of refine models, C_cell and C_free were also estimated using a predictive chemical distribution model (MBM). Bioconcentration factors (BCFs) were determined by relating all these values, as well as by toxicokinetic fitting and by in vitro–in vivo extrapolation modelling (IVIVE). The results showed a high concordance with the values obtained in vivo. Moreover, the study highlighted the importance of experimentally determining C_free and C_cell, as the predicted values can vary depending on the chemical, thereby influencing the BCF outcome. This variation occurs because models do not account for the absorption and biotransformation kinetics of the compounds. The data presented may contribute to refining predictive models. Full article

The characterization of a drug’s ADME (absorption, distribution, metabolism, and excretion) profile is crucial for accurately determining its safety and efficacy. The rising prevalence of polypharmacy has significantly increased the risk of drug-drug interactions (DDIs). These interactions can lead to altered drug exposure, potentially compromising efficacy or increasing the risk of adverse drug reactions (ADRs), thereby posing significant clinical and regulatory concerns. Traditional methods for assessing potential DDIs rely heavily on in vitro models, including enzymatic assays and transporter studies. While indispensable, these approaches have inherent limitations in scalability, cost, and ability to predict complex interactions. Recent advancements in analytical technologies, particularly the development of more sophisticated cellular models and computational modeling, have paved the way for more accurate and efficient DDI assessments. Emerging methodologies, such as organoids, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI), demonstrate significant potential in this field. A powerful and increasingly adopted approach is the integration of in vitro data with in silico modeling, which can lead to better in vitro-in vivo extrapolation (IVIVE). This review provides a comprehensive overview of both conventional and novel strategies for DDI predictions, highlighting their strengths and limitations. Equipping researchers with a structured framework for selecting optimal methodologies improves safety and efficacy evaluation and regulatory decision-making and deepens the understanding of DDIs. Full article

Quantitative in vitro to in vivo extrapolation (QIVIVE) utilizes in vitro data to predict in vivo toxicity. However, there may be differences between reported nominal concentrations and the biologically effective free concentrations in media or cells. This study evaluated the performance of four in vitro mass balance models for predicting free media or cellular concentrations. Comparing model predictions to experimentally measured values for a wide range of chemicals and test systems, we found that predictions of media concentrations were more accurate than those for cells, and that the Armitage model had slightly better performance overall. Through sensitivity analyses, we found that chemical property-related parameters were most influential for media predictions, while cell-related parameters were also important for cellular predictions. Assessing the impact of these models on QIVIVE accuracy for a small dataset of 15 chemicals with both in vitro and regulatory in vivo points-of-departure, we found that incorporating in vitro and in vivo bioavailability resulted in at best modest improvements to in vitro–in vivo concordance. Based on these results, we conclude that a reasonable first-line approach for incorporating in vitro bioavailability into QIVIVE would be to use the Armitage model to predict media concentrations, while prioritizing accurate chemical property data as input parameters. Full article

Neglected tropical diseases (NTDs), including Chagas disease, human African trypanosomiasis (HAT), leishmaniasis, and malaria, remain a major global health challenge, disproportionately affecting low-income populations. Current therapies for these diseases suffer from significant limitations, such as reduced efficacy, high toxicity, and emerging parasite resistance, highlighting the urgent need for new therapeutic strategies. In response, substantial efforts have been directed toward the synthesis of new molecules with improved potency, selectivity, and pharmacokinetic profiles. However, despite many of these compounds exhibiting favorable ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles and strong in vitro activity, their translation into in vivo models remains limited. Key challenges include the lack of investment, the absence of fully representative experimental models, and difficulties in extrapolating cell-based assay results to more complex biological systems. In this review, we analyzed the latest advancements (2019–2024) in the development of these compound classes, correlating predictive parameters with their observed biological activity. Among these parameters, we highlighted the partition coefficient (LogP), which measures a compound’s lipophilicity and influences its ability to cross biological membranes, and Caco-2 cell permeability, an in vitro model widely used to predict intestinal drug absorption. Additionally, we prioritized the most promising molecules and structural classes for pharmaceutical development, discussing structure–activity relationships (SARs) and the remaining challenges that must be overcome to enable the clinical application of these compounds in the treatment of NTDs. Full article

Background: Extrapolation of intrinsic clearance from in vitro systems such as liver microsomes or hepatocytes is an established approach to predict clearance in preclinical species and in humans. A common discussion in the literature is whether the predictive accuracy of such extrapolations is influenced by the chemotype and whether these methods are also applicable to compounds studied in early drug discovery programs. Compounds in such programs are frequently lipophilic and show low solubility and low free fraction in plasma, which may pose challenges to the extrapolation of clearance different from those of the final clinical candidates. A similar discussion has been raised about compounds residing beyond the traditional small-molecule property space, such as PROTACs© and other molecules incompatible with Lipinski’s rule-of-five. Methods: To further enlighten the field on these matters, we present a study comparing the predictive accuracy between mouse hepatocytes and microsomes for a set of molecules (N = 211) from the Merck Healthcare drug discovery pipeline. This set was dominated by compounds belonging to class 2 and 4 of the extended clearance classification systems (ECCS). It contained a similar proportion of molecules compliant with the Lipinski rule-of-five (N = 127) and molecules lacking such compliance (N = 84). Results: This study showed no or little differences in predictive accuracy nor bias between the two groups, with an average fold error close to 1, an absolute average fold error of just over 2, and around 50% being within 2-fold and >90% being within 5-fold of the predicted unbound clearance in both in vitro systems. Furthermore, no significant differences in accuracy were observed for compounds with an extremely low free fraction (down to 0.05%) in plasma. Conclusions: The accuracy of in vitro–in vivo extrapolation of female CD-1 mouse clearance was not affected by the physicochemical properties. Full article

Objectives: There is growing concern over tyrosine kinase inhibitor (TKI)-induced cardiotoxicity, particularly regarding left ventricular dysfunction and heart failure in clinical treatment. These adverse effects often lead to treatment discontinuation, severely impacting patient outcomes. Therefore, there is an urgent need for more precise risk assessment methods. This study aimed to assess the cardiotoxicity of TKIs, refine in vitro to in vivo extrapolation (IVIVE) methodologies to improve predictive accuracy, and identify critical in vitro parameters for assessment. Methods: By leveraging high-throughput cardiotoxicity screening with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), a mechanism-based toxicodynamic (TD) model for TKIs was constructed. A QSP-PK-TD model was developed by integrating pharmacokinetic (PK) and quantitative systems pharmacology (QSP) models. This model incorporates critical drug exposure factors, such as plasma protein binding, tissue–plasma partitioning, and drug distribution heterogeneity to enhance extrapolation accuracy. Results: The QSP-PK-TD model validated the reliability of IVIVE and identified the area under the curve of drug effects on mitochondrial membrane potential (AEMMP) and cardiomyocyte contractility (AEAAC) as key in vitro parameters for assessing TKI-induced cardiotoxicity. Incorporating critical drug exposure factors obviously improved qualitative and quantitative extrapolation accuracy. Conclusions: This study established a framework for predicting in vivo cardiotoxicity from in vitro parameters, enabling efficient translation of preclinical data into clinical risk assessment. These findings provide valuable insights for drug development and regulatory decision-making, offering a powerful tool for evaluating TKI-induced cardiotoxicity. Full article

Hematological malignancies originating from blood, bone marrow, and lymph nodes include leukemia, lymphoma, and myeloma, which necessitate the use of a distinct chemotherapeutic approach. Drug resistance frequently complicates their treatment, highlighting the need for predictive tools to guide therapeutic decisions. Conventional 2D/3D cell cultures do not fully encompass in vivo criteria, and translating disease models from mice to humans proves challenging. Organ-on-a-chip technology presents an avenue to surmount genetic disparities between species, offering precise design, concurrent manipulation of various cell types, and extrapolation of data to human physiology. The development of bone-on-a-chip (BoC) systems is crucial for accurately representing the in vivo bone microenvironment, predicting drug responses for hematological cancers, mitigating drug resistance, and facilitating personalized therapeutic interventions. BoC systems for modeling hematological cancers and drug research can encompass intricate designs and integrated platforms for analyzing drug response data to simulate disease scenarios. This review provides a comprehensive examination of BoC systems applicable to modeling hematological cancers and visualizing drug responses within the intricate context of bone. It thoroughly discusses the materials pertinent to BoC systems, suitable in vitro techniques, the predictive capabilities of BoC systems in clinical settings, and their potential for commercialization. Full article

(1) Background: Advances in food processing practices and health care are some of the most significant advances in modern daily life. The goal of this study is to evaluate the safety of potassium and sodium nitrates and nitrites when they are used as fertilizers in agriculture and food additives, as well as the known conversion of nitrate to nitrite in humans. (2) Methods: Various bioassays were conducted to investigate the effects of nitrates and nitrites in the Drosophila melanogaster genetic tester system. These assays focused on the modulation of degenerative processes at the molecular, cellular, individual, and population levels. Additionally, we assessed the chemopreventive potential and the ability to induce DNA strand breaks in HL-60 tumour cells. (3) Results: All nitrate and nitrite concentrations tested were shown to not be toxic or genotoxic in Drosophila since none of the compounds reached the LD₅₀ and significant genetic mutation. A positive or null protective capacity against a toxic agent was found for nitrates, not for nitrites, showing that sodium nitrite has a synergistic effect when combined with the oxidant toxin hydrogen peroxide; and a nutraceutical potential in the lifespan only for sodium nitrate to improve the quality of life in 5 days at ADI concentration. The in vitro results in human leukemia cells showed a chemopreventive potential only for potassium nitrate and sodium nitrite due to reducing the viability of HL-60 cells growth to 18% and 29%, respectively, compared to the controls at ADI (acceptable daily intake) concentrations. However, neither of these showed DNA damage or methylation modifications. (4) Conclusions: The tested compounds were shown to be safe to use during in vivo and in vitro tests when used at the extrapolated ADI concentrations. Full article

Background/Objectives: SPT-07A, a D-borneol, is currently being developed in China for the treatment of ischemic stroke. We aimed to create a whole-body physiologically-based pharmacokinetic (PBPK) model to predict the pharmacokinetics of SPT-07A in rats, dogs, and humans. Methods: The in vitro metabolism of SPT-07A was studied using hepatic, renal, and intestinal microsomes. The pharmacokinetics of SPT-07A in rats were simulated using the developed PBPK model and in vitro data. Following validation using pharmacokinetic data in rats, the developed PBPK model was scaled up to dogs and humans. Results: Data from hepatic microsomes revealed that SPT-07A was primarily metabolized by UDP-glucuronosyltransferase (UGTs). Glucuronidation of SPT-07A also occurred in the kidney and intestine. The in vitro to in vivo extrapolation analysis showed that hepatic clearance of SPT-07A in rats, dogs, and humans accounted for 62.2%, 87.3%, and 76.5% of the total clearance, respectively. The renal clearance of SPT-07A in rats, dogs, and humans accounted for 32.6%, 12.7%, and 23.1% of the total clearance, respectively. Almost all of the observed concentrations of SPT-07A following single or multi-dose to rats, dogs, and humans were within the 5th–95th percentiles of simulations from 100 virtual subjects. Sensitivity analysis showed that hepatic metabolic velocity, renal metabolic velocity, and hepatic blood flow remarkably affected the exposure to SPT-07A in humans. Dedrick plots were also used to predict the pharmacokinetics of SPT-07A in humans. Prediction accuracy using the PBPK model is superior to that of Dedrick plots. Conclusions: We elucidate UGT-mediated SPT-07A metabolism in the liver, kidney, and intestine of rats, dogs, and humans. The pharmacokinetics of SPT-07A were successfully simulated using the developed PBPK model. Full article

One of the key factors of the interaction ‘osteoplastic material—organism’ is the state of the implant surface. Taking into account the fact that the equilibrium in regeneration conditions is reached only after the reparative histogenesis process is completed, the implant surface is constantly modified. This work is devoted to the numerical description of the dynamic bilateral material–medium interaction under close to physiological conditions, as well as to the assessment of the comparability of the model with in vitro and in vivo experimental results. The semi-empirical model obtained on the basis of chemical kinetics allows us to describe numerically the processes occurring in the in vitro systems and extrapolates well to assess the behavior of dicalcium phosphate dihydrate (DCPD) material under conditions of ectopic (subcutaneous) implantation in Wistar rats. It is shown that an experiment conducted using a perfusion–diffusion bioreactor in a cell culture medium with the addition of fetal bovine serum (FBS) allows for achieving morphologically and chemically identical changes in the surface of the material in comparison with the real organism. This fact opens up wide possibilities for the creation of an analog of a ‘laboratory-on-a-chip’ and the transition from classical in vivo models to more controlled and mathematically based in vitro systems. Full article

In the present study, a customized device (Epi-ExPer) was designed and fabricated to facilitate an epithelial organ culture, allowing for controlled exposure to exogenous chemical stimuli and accommodating the evaluation of permeation of the tissue after treatment. The Epi-ExPer system was fabricated using a stereolithography (SLA)-based additive manufacturing (AM) method. Human and porcine oral epithelial mucosa tissues were inserted into the device and exposed to resinous monomers commonly released by dental restorative materials. The effect of these xenobiotics on the morphology, viability, permeability, and expression of relevant markers of the oral epithelium was evaluated. Tissue culture could be performed with the desired orientation of air-liquid interface (ALI) conditions, and exposure to xenobiotics was undertaken in a spatially guarded and reproducible manner. Among the selected monomers, HEMA and TEGDMA reduced tissue viability at high concentrations, while tissue permeability was increased by the latter. Xenobiotics affected the histological image by introducing the vacuolar degeneration of epithelial cells and increasing the expression of panCytokeratin (pCK). Epi-ExPer device offers a simple, precise, and reproducible study system to evaluate interactions of oral mucosa with external stimuli, providing a biocompatibility and permeability assessment tool aiming to an enhanced in vitro/ex vivo-to-in vivo extrapolation (IVIVE) that complies with European Union (EU) and Food and Durg Administration (FDI) policies. Full article

Toxicokinetic (TK) assays and in vitro–in vivo extrapolation (IVIVE) models are New Approach Methods (NAMs) used to translate in vitro points of departure to exposure estimates required to reach equivalent blood concentrations. Per- and polyfluoroalkyl substances (PFAS) are a large chemical class with wide-ranging industrial applications for which only limited toxicity data are available for human health evaluation. To address the lack of TK data, a pooled primary human hepatocyte suspension model was used with targeted liquid chromatography–mass spectrometry to investigate substrate depletion for 54 PFAS. A median value of 4.52 μL/(min x million cells) was observed across those that showed significant clearance, with 35 displaying no substrate depletion. Bayesian modeling propagated uncertainty around clearance values for use in IVIVE models. Structural evaluations showed the fluorotelomer carboxylic acids were the only PFAS carboxylates showing appreciable clearance, and per- and polyfluorosulfonamides were more readily metabolized than other PFAS sulfonates. Biotransformation product prediction, using the chemical transformation simulator, suggested hydrolysis of PFAS sulfonamides to more stable sulfonic acids, which is an important consideration for exposure modeling. This effort greatly expands the PFAS in vitro toxicokinetic dataset, enabling refined TK modeling, in silico tool development, and NAM-based human health evaluations across this important set of emerging contaminants. Full article