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Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Brain Levels of Drug in Rat.

ABSTRACT: One of the main obstacles in neurological disease treatment is the presence of the blood-brain barrier. New predictive high-throughput screening tools are essential to avoid costly failures in the advanced phases of development and to contribute to the 3 Rs policy. The objective of this work was to jointly develop a new in vitro system coupled with a physiological-based pharmacokinetic (PBPK) model able to predict brain concentration levels of different drugs in rats. Data from in vitro tests with three different cells lines (MDCK, MDCK-MDR1 and hCMEC/D3) were used together with PK parameters and three scaling factors for adjusting the model predictions to the brain and plasma profiles of six model drugs. Later, preliminary quantitative structure-property relationships (QSPRs) were constructed between the scaling factors and the lipophilicity of drugs. The predictability of the model was evaluated by internal validation. It was concluded that the PBPK model, incorporating the barrier resistance to transport, the disposition within the brain and the drug-brain binding combined with MDCK data, provided the best predictions for passive diffusion and carrier-mediated transported drugs, while in the other cell lines, active transport influence can bias predictions.

SUBMITTER: Sanchez-Dengra B

PROVIDER: S-EPMC8471455 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective.

Project description:The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.

| S-EPMC7520419 | biostudies-literature

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Project description: Not available

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| S-EPMC6738369 | biostudies-literature

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| S-EPMC4014769 | biostudies-literature

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery.

Project description:Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a "bottom-up" approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

| S-EPMC9257750 | biostudies-literature

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Project description:Colorectal cancer is the third leading cause of cancer-related deaths in the United States. Treatment of colorectal cancer remains a challenge to clinicians as well as drug developers. Irinotecan, a Camptothecin derivative, is successfully used for the treatment of this rapidly progressing malignancy and finds its place in the first line of therapeutic agents. Irinotecan is also effective in treating SCLC, malignant glioma and pancreatic adenocarcinoma. However, its adverse effects limit its clinical application. Mainly metabolized by hepatic route, and excreted through biliary tract, this dug has been found to possess high variation in patients in its pharmacokinetic (PK) profile. Physiologically based pharmacokinetic (PBPK) models using compartmental approach have attained their position to foresee the possible PK behavior of different drugs before their administration to patients and such models have been proposed for several anticancer agents. In this work, we used WB-PBPK technology to develop a model in a population of tumor patients who used IV irinotecan therapy. This model depicted the concentration of drug and its pharmacologically active metabolite in human body over a specific period of time. Knowledge about pharmacokinetic parameters is extracted from this profile and the model is evaluated by the observed results of clinical study presented in literature. The predicted behavior of the drug by this approach is in good agreement with the observed results and can aid in further exploration of PK of irinotecan in cancer patients, especially in those concomitantly suffer from other morbidity.

| S-EPMC5564636 | biostudies-other

Application of Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of an FDA Public Workshop on PBPK.

Project description:The US Food and Drug Administration (FDA) public workshop, entitled "Application of Physiologically-based Pharmacokinetic (PBPK) Modeling to Support Dose Selection focused on the role of PBPK in drug development and regulation. Representatives from industry, academia, and regulatory agencies discussed the issues within plenary and panel discussions. This report summarizes the discussions and provides current perspectives on the application of PBPK in different areas, including its utility, predictive performance, and reporting for regulatory submissions.

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Physiologically-Based Pharmacokinetic Modeling for Predicting Drug Interactions of a Combination of Olanzapine and Samidorphan.

Project description:A combination of the antipsychotic olanzapine and the opioid receptor antagonist samidorphan (OLZ/SAM) is intended to provide the antipsychotic efficacy of olanzapine while mitigating olanzapine-associated weight gain. As cytochrome P450 (CYP) 1A2 and CYP3A4 are the major enzymes involved in metabolism of olanzapine and samidorphan, respectively, physiologically-based pharmacokinetic (PBPK) modeling was applied to predict any drug-drug interaction (DDI) potential between olanzapine and samidorphan or between OLZ/SAM and CYP3A4/CYP1A2 inhibitors/inducers. A PBPK model for OLZ/SAM was developed and validated by comparing model-simulated data with observed clinical study data. Based on model-based simulations, no DDI between olanzapine and samidorphan is expected when administered as OLZ/SAM. CYP3A4 inhibition is predicted to have a weak effect on samidorphan exposure and negligible effect on olanzapine exposure. CYP3A4 induction is predicted to reduce both samidorphan and olanzapine exposure. CYP1A2 inhibition or induction is predicted to increase or decrease, respectively, olanzapine exposure only.

| S-EPMC7020312 | biostudies-literature

Predicting Drug-Drug Interactions Between Rifampicin and Long-Acting Cabotegravir and Rilpivirine Using Physiologically Based Pharmacokinetic Modeling.

Project description:BackgroundCabotegravir and rilpivirine are 2 long-acting (LA) antiretrovirals that can be administered intramuscularly; their interaction with rifampicin, a first-line antituberculosis agent, has not been investigated. The aim of this study was to simulate and predict drug-drug interactions (DDIs) between these LA antiretroviral agents and rifampicin using physiologically based pharmacokinetic (PBPK) modeling.MethodsThe designed PBPK models were qualified (according to European Medicines Agency guidelines) against observed data for oral formulations of cabotegravir, rilpivirine, and rifampicin. Induction potential of rifampicin was also qualified by comparing the DDI between oral cabotegravir and oral rilpivirine with rifampicin. Qualified PBPK models were utilized for pharmacokinetic prediction of DDIs.ResultsPBPK models predicted a reduction in both area under the curve (AUC0-28 days) and trough concentration (Ctrough, 28th day) of LA cabotegravir of 41%-46% for the first maintenance dose coadministered with 600 mg once-daily oral rifampicin. Rilpivirine concentrations were predicted to decrease by 82% for both AUC0-28 days and Ctrough, 28th day following the first maintenance dose when coadministered with rifampicin.ConclusionsThe developed PBPK models predicted the theoretical effect of rifampicin on cabotegravir and rilpivirine LA intramuscular formulations. According to these simulations, it is likely that coadministration of rifampicin with these LA formulations will result in subtherapeutic concentrations of both drugs.

| S-EPMC6500558 | biostudies-literature

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