Predicted Pharmacokinetics Parameters Research Articles

Evaluation of Drug–Drug Interactions Between Clarithromycin and Direct Oral Anticoagulants Using Physiologically Based Pharmacokinetic Models

Objective: This study assessed the pharmacokinetic (PK) interactions between clarithromycin (a P-glycoprotein [P-gp] inhibitor) and four direct oral anticoagulants (DOACs) (P-gp substrates) using physiologically based PK (PBPK) models to elucidate the influence of P-gp in the interaction between them. Methods: PBPK models for clarithromycin, DABE–dabigatran (DAB), rivaroxaban, apixaban, and edoxaban were constructed using GastroPlus™ (version 9.9), based on physicochemical data and PK parameters from the literature. The models were optimized and validated in healthy subjects. We evaluated the predictive performance of the established model and further assessed the impact of P-gp on the PK of the four DOACs. Successfully validated models were then used to evaluate potential drug–drug interactions (DDIs) between clarithromycin and the DOACs. Results: The established PBPK models accurately described the PK of clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban. The predicted PK parameters (Cmax, Tmax, AUC0-t) were within 0.5–2 times the observed values. A sensitivity analysis of P-gp parameters indicated that an increase in P-gp expression was reduced by in vivo exposure to DOACs. The models demonstrated good predictive ability for DDIs between clarithromycin and the anticoagulants, and the ratio of the predicted values to the observed values of Cmax and the area under the curve (AUC) in the DDI state was within the range of 0.5–2. Conclusions: Comprehensive PBPK models for clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban were developed, which can effectively predict DDIs mediated by P-gp’s function. These models provide theoretical support for clinical dose adjustments and serve as a foundation for future PBPK model development for DOACs under specific pathological conditions.

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human Vd (R2 = 0.72, RMSE = 0.31), human CL (R2 = 0.52, RMSE = 0.32), and others. In addition to predictive accuracy, we also considered the interpretability of the results and the definition of the model's application domain. Based on the findings, our model has great potential for practical applications in drug discovery.

Discriminative Power of the Flow through Cell Dissolution Tester in Predicting the In Vivo Performance of Pentoxifylline SR Product under Fed and Fasting Conditions

This study explored, for the first time the role of different designs of the Flow-Through-Cell (FTC, USP IV) dissolution Tester in predicting the in-vivo performance of Pentoxifylline (PTX) sustained-release (SR) market product, under fed & fasting conditions. Release studies of Trental® SR 400 mg (Sanofi, Egypt), were carried-out in the FTC under different conditions, including: different volumes / compositions of release media, variable FTC flow patterns as well as applying open / closed loop configuration setups. Pharmacokinetic (PK) data, obtained from literature, were converted to in-vivo fraction-absorbed [FA] using Wagner-Nelson (WN) method. A 1:1 IVIVC was investigated by comparing PTX fraction-dissolved [FD] under different FTC release designs versus calculated [FA]. Predicted PK parameters were evaluated, and compared with actual data, with estimation of prediction-error (PE%). The suggested FTC design; a closed-loop setup, with turbulent-flow pattern of the dissolution medium; provided the most acceptable PTX release according to USP labeled limits (USP 27). Also, results showed that PTX release was pronouncedly increased in a finite-volume of gradient-buffer system rather than water, which guarantee complete resemblance to GIT environment. This release design presented the most predictive IVIVC model with PTX in-vivo performance under fasting / fed states, with acceptable PE% values in terms of Cmax and AUCs. A suggested FTC design is proposed as an alternative dissolution model in the official USP-monograph for PTX SR products.Graphical

Prediction of Human Pharmacokinetics From Chemical Structure: Combining Mechanistic Modeling with Machine Learning

Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established Physiologically Based Pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.

Physiologically based pharmacokinetic modeling to assess the drug-drug interactions of anaprazole with clarithromycin and amoxicillin in patients undergoing eradication therapy of H. pylori infection

ObjectiveThis study aimed to assess the pharmacokinetic (PK) interactions of anaprazole, clarithromycin, and amoxicillin using physiologically based pharmacokinetic (PBPK) models. MethodsThe PBPK models for anaprazole, clarithromycin, and amoxicillin were constructed using the GastroPlus™ software (Version 9.7) based on the physicochemical data and PK parameters obtained from literature, then were optimized and validated in healthy subjects to predict the plasma concentration-time profiles of these three drugs and assess the predictive performance of each model. According to the analysis of the properties of each drug, the developed and validated models were applied to evaluate potential drug-drug interactions (DDIs) of anaprazole, clarithromycin, and amoxicillin. ResultsThe developed PBPK models properly described the pharmacokinetics of anaprazole, clarithromycin, and amoxicillin well, and all predicted PK parameters (Cmax,ss, AUC0-τ,ss) ratios were within 2.0-fold of the observed values. Furthermore, the application of these models to predict the anaprazole-clarithromycin and anaprazole-amoxicillin DDIs demonstrates their good performance, with the predicted DDI Cmax,ss ratios and DDI AUC0-τ,ss ratios within 1.25-fold of the observed values, and all predicted DDI Cmax,ss, and AUC0-τ,ss ratios within 2.0-fold. The simulated results show no need to adjust the dosage when co-administered with anaprazole in patients undergoing eradication therapy of H. pylori infection since the dose remained in the therapeutic range. ConclusionThe whole-body PBPK models of anaprazole, clarithromycin, and amoxicillin were built and qualified, which can predict DDIs that are mediated by gastric pH change and inhibition of metabolic enzymes, providing a mechanistic understanding of the DDIs observed in the clinic of clarithromycin, amoxicillin with anaprazole.

A physiologically based pharmacokinetic model of cefepime to predict its pharmacokinetics in healthy, pediatric and disease populations

The physiologically based pharmacokinetic modeling (PBPK) approach can predict drug pharmacokinetics (PK) by combining changes in blood flow and pathophysiological alterations for developing drug-disease models. Cefepime hydrochloride is a parenteral cephalosporin that is used to treat pneumonia, sepsis, and febrile neutropenia, among other things. The current study sought to identify the factors that impact cefepime pharmacokinetics (PK) following dosing in healthy, diseased (CKD and obese), and pediatric populations. For model construction and simulation, the modeling tool PK-SIM was utilized. Estimating cefepime PK following intravenous (IV) application in healthy subjects served as the primary step in the model-building procedure. The prediction of cefepime PK in chronic kidney disease (CKD) and obese populations were performed after the integration of the relevant pathophysiological changes. Visual predictive checks and a comparison of the observed and predicted values of the PK parameters were used to verify the developed model. The results of the PK parameters were consistent with the reported clinical data in healthy subjects. The developed PBPK model successfully predicted cefepime PK as observed from the ratio of the observed and predicted PK parameters as they were within a two-fold error range.

Bioactive Compounds from Mimosa pudica Leaves Extract with Their α- glucosidase and Protein Tyrosine Phosphatase 1B Inhibitory Activities in vitro and in silico Approaches

<p>Background: Mimosa pudica Linn has been used in traditional medicine to support the treatment of type 2 diabetes. In the present study, we aimed to isolate and evaluate α-glucosidase and Protein Tyrosine Phosphatase 1B (PTP1B) inhibitory activities of bioactive compounds from Mimosa pudica’s leaf extract. <p> Methods: Mimosa pudica leaves were extracted with 80% of ethanol. Bioactive compounds were isolated using a column chromatographic technique and elucidated the structure based on the nuclear magnetic resonance and electrospray ionization mass spectrometry spectral data. The α- glucosidase and PTP1B inhibitory activities of the isolated compounds were evaluated using pnitrophenyl phosphate and p-nitrophenyl-α-D-glucopyranoside as a substrate, respectively. Molecular docking and molecular dynamics are used to study the interaction between isolated compounds and proteins. Lipinski’s rule of five was used to evaluate the drug-like properties of isolated compounds. Predict pharmacokinetic parameters were evaluated using the pkCSM tool. <p> Results: Protocatechuic acid and syringic acid were isolated and identified using spectroscopic methods. Protocatechuic acid and syringic acid considerably inhibited α-glucosidase enzyme at IC<sub>50</sub> values of 416.17 ± 9.41 μM and 490.78 ± 9.28 μM, respectively. Furthermore, protocatechuic acid and syringic acid expressed strong PTP1B inhibitory activity at IC<sub>50</sub> values of 248.83 ± 7.66 μM and 450.31 ± 7.77 μM, respectively. Molecular docking and molecular dynamics results showed the interactions of protocatechuic acid and syringic acid with amino acids of PTP1B and α-glucosidase enzyme. Lipinski’s rule of five and absorption, distribution, metabolism, excretion, and toxicity studies predicted that protocatechuic acid and syringic acid have drug-likeness properties. In molecular docking simulation, protocatechuic acid and syringic acid gave relatively negative free binding energies and interacted with many amino acids in the active sites of PTP1B and α-glucosidase. The molecular dynamics simulation results of the complexes were also relatively stable. <p> Conclusion: Our results showed that protocatechuic and syringic acids could be promising compounds for type 2 diabetes treatment.</p>

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes.

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Another Step Toward Qualification of Pediatric Physiologically Based Pharmacokinetic Models to Facilitate Inclusivity and Diversity in Pediatric Clinical Studies.

Robust prediction of pharmacokinetics (PKs) in pediatric subjects of diverse ages, ethnicities, and morbidities is critical. Qualification of pediatric physiologically-based pharmacokinetic (P-PBPK) models is an essential step toward enabling precision dosing of these vulnerable groups. Twenty-two manuscripts involving P-PBPK predictions and corresponding observed PK data (e.g., area under the curve and clearance) for 22 small-molecule compounds metabolized by CYP (3A4, 1A2, and 2C9), UGT (1A9 and 2B7), FMO3, renal, non-renal, and complex routes were identified; ratios of mean predicted/observed (P/O) PK parameters were calculated. Seventy-eight of 115 mean predicted PK parameters were within 0.8 to 1.25-fold of observed data, 98 within 0.67 to 1.5-fold, 109 within 2-fold, and only 6 P/O ratios were outside of these bounds. A set of 12 CYP3A4-metabolized compounds and a set of 6 metabolized by other enzymes, CYP1A2 (1 compound), CYP2C9 (2 compounds), UGT1A9 (1 compound) and UGT2B7 (2 compounds) had 56 of 59 and 22 of 25 mean P/O ratios, respectively, that fell within the > 0.5 and < 2.0-fold boundaries. For compounds covering renal, non-renal, complex, and FM03 routes of elimination, 29 of 31 mean P/O ratios fell within the 0.67 to 1.5-fold bounds, including 4 of 5 P/O ratios from newborns. P-PBPK modeling and simulation is a strategic component of the complement of precision dosing methods and has a vital role to play in dose adjustment in vulnerable pediatric populations, such as those with disease or in different ethnic groups. Qualification of such models is an essential step toward acceptance of this methodology by regulators.

Study of Anti-inflammatory Activity of New Derivative of Indomethacin: A Computational Study

This article presents the synthesis of new derivative from thiodaizole, based on the reaction of Thiosemicarbazide with Indomethacin in the presence of Phosphorus (V) oxychloride as catalyst. COX enzyme is divided to two isoforms COX-1 and COX-2 with similar structure and high sequence identity. The novel Inhibitor used a thiodaizole ring in combination with Indomethacin to reduce the risk of analogs interfering with COX-1’s small hydrophobic tunnel. Furthermore, the absence of a carboxylic group in the new Inhibitor reduces the Inhibitor’s ability to form a salt bridge with Arg120 (Figure 2), preventing the inhibition of the COX-1 enzyme. The aim of this study is to investigate the Anti-inflammatory activity of Indomethacin against the human second isoform of prostaglandin synthase (cyclooxygenase, COX-2). COX-2 enzyme 3D structures (PDB ID: 1CVU) were extracted from the protein databank. The PDB format of Indomethacin and its derivative were prepared by use Discovery Studio 2016 Client. Molecular docking study appeared that the new derivative exhibited better binding energy (-10.9 kcal/mol) compared with Indomethacin that have binding energy (-10.09 kcal/mol) and it’s have high selectivity towered COX-2 enzyme in the other hand In the present study, the predicted Pharmacokinetic (PK) values of Inhibitor (1) and Indomethacin (NSAID) deduce that Inhibitor (1) satisfies all PK parameters and has qualified as best lead candidate as an Anti-inflammatory agent compared to Indomethacin

Development and application of a pediatric mechanistic kidney model.

Pediatric physiologically‐based pharmacokinetic (P‐PBPK) models have been used to predict age related changes in the pharmacokinetics (PKs) of renally cleared drugs mainly in relation to changes in glomerular filtration rate. With emerging data on ontogeny of renal transporters, mechanistic models of renal clearance accounting for the role of active and passive secretion should be developed and evaluated. Data on age‐related physiological changes and ontogeny of renal transporters were applied into a mechanistic kidney within a P‐PBPK model. Plasma concentration–time profile and PK parameters of cimetidine, ciprofloxacin, metformin, tenofovir, and zidovudine were predicted in subjects aged 1 day to 18 years. The predicted and observed plasma concentration–time profiles and PK parameters were compared. The predicted concentration–time profile means and 5th and 95th percent intervals generally captured the observed data and variability in various studies. Overall, based on drugs and age bands, predicted to observed clearance were all within two‐fold and in 11 of 16 cases within 1.5‐fold. Predicted to observed area under the curve (AUC) and maximum plasma concentration (Cmax) were within two‐fold in 12 of 14 and 12 of 15 cases, respectively. Predictions in neonates and early infants (up to 14 weeks postnatal age) were reasonable with 15–20 predicted PK parameters within two‐fold of the observed. ciprofloxacin but not zidovudine PK predictions were sensitive to basal kidney uptake transporter ontogeny. The results indicate that a mechanistic kidney model accounting for physiology and ontogeny of renal processes and transporters can predict the PK of renally excreted drugs in children. Further data especially in neonates are required to verify the model and ontogeny profiles.

Physiologically Based Biopharmaceutics Model of Vildagliptin Modified Release (MR) Tablets to Predict In Vivo Performance and Establish Clinically Relevant Dissolution Specifications.

The objective of the study was to predict pharmacokinetic (PK) and pharmacodynamic (PD) parameters of matrix-based modified release (MR) drug product of vildagliptin. Physiologically based biopharmaceutics modeling (PBBM) was developed using GastroPlus™ based on the available data including immediate-release (IR) drug product of vildagliptin. In vitro-in vivo correlation (IVIVC) was developed using mechanistic deconvolution to predict plasma concentration-time profile and PK parameters for a MR drug product planned for clinical use. Both methods, i.e., PBBM and IVIVC, were compared for the predicted PK parameters. Integration of DDDPlus™ and GastroPlus™ modeling was performed to explore clinically relevant dissolution specifications for vildagliptin MR tablets. The bioequivalence (BE) between batches with different dissolution specifications was evaluated using virtual clinical trials. The PD effect of dipeptidyl peptidase-IV (DPP-IV) inhibition was simulated utilizing PDPlus™ model in GastroPlus™. The results indicated that IVIVC best correlated the simulated PK parameters with those observed with the clinical study. The outcomes highlight the importance of integration of in vitro and in silico tools towards predictability of PK and PD parameters for a MR drug product. However, the post absorptive phase was found to be more dependent on the demographics of the healthy subjects.

Predicted Pharmacokinetic Parameters in Camels Obtained by Allometric Scaling from Other Species is Accurate

The objective of the present study was to evaluate how accurate the predicted pharmacokinetic (PK) parameters of some drugs are in camels, obtained by allometric scaling from animal species when compared to observed values established by us previously. The PK parameters tested were plasma clearance (Cl) and volume of distribution at steady state (Vss). The drugs evaluated were amikacin, gentamicin, kanamycin, tobramycin, antipyrine, flunixin, ketoprofen (R), ketoprofen (S), meloxicam, phenylbutazone, theophylline, and tramadol. The PK parameters scaled well for all drugs tested except meloxicam, where the predicted Cl was 12.5 fold greater than the observed value.

Conoidecyclics A-C from marine macroalga Turbinaria conoides: Newly described natural macrolides with prospective bioactive properties

Intertidal marine brown alga Turbinaria conoides (J.Agardh) Kützing (family Sargassaceae) is considered as one of the largely abundant species, available in the coastal zones of the Indian subcontinent. Bioactivity-guided chromatographic fractionation of the organic extract of T. conoides resulted in three previously undescribed macrocyclic lactone homologues, named as conoidecyclics A-C. Conoidecyclic A displayed greater attenuation potential against cyclooxygenase-2 (IC50 1.75 mM) and 5-lipoxygenase (IC50 4.24 mM) in comparison with other analogues. Conoidecyclic A exhibited higher attenuation potential against 5-lipoxygenase than that displayed by an anti-inflammatory agent, ibuprofen (IC50 4.51 mM). The higher selectivity index of conoidecyclic A (1.79) recognized its selective attenuation potential against the inducible cyclooxygenase-2 enzyme. Inhibition potential of conoidecyclic A against angiotensin converting enzyme-I (IC50 1.23 mM) and protein tyrosine phosphatase-1B (IC50 1.39 mM) were non-competitive, as deduced by kinetic studies. In-silico molecular modeling study of conoidecyclic A with the allosteric sites of the targeted enzymes exhibited least binding energy of −14.51 to −11.27 kcal mol−1 compared to those exhibited by other studied macrolide homologues. Reaction kinetic studies of conoidecyclic A coupled with lesser apparent Vmax inferred that it could efficiently bind with the allosteric site of targeted enzymes in a non-competitive manner to diminish the reaction velocity resulting in enzyme inhibition. Drug-likeness and predictive pharmacokinetic parameters of conoidecyclic A exhibited an acceptable oral bioavailability. These reports inferred that conoidecyclic A encompassing pentacosa macrocyclic moiety could be a promising therapeutic lead to inhibit the enzymes related to the development and progression of pathological conditions leading to inflammation, hypertension and type-2 diabetes.

Biocompatible solvent selection based on thermodynamic and computational solubility models, in-silico GastroPlus prediction, and cellular studies of ketoconazole for subcutaneous delivery

We aimed to identify a suitable solvent [dimethyl acetamide (DMA), ethyl acetate, (EA), N-methyl pyrrolidone (NMP), cremophor-EL (CEL), transcutol-HP (THP), polyethylene glycol 400 (PEG-400), limonene and ethanol] based on experimental solubility, thermodynamic/computational parameters for subcutaneous (sub-Q) delivery. The “vanʼt Hoff” model validated solubility values in several solvents at “T = 298.2 K–318.2 K” and “p = 0.1 MPa”. In silico prediction study was carried out for sub-Q delivery using GastroPlus. The selected KETO-DMA construct was evaluated for cellular interaction, cellular uptake (L929 cells), cytotoxicity (L929 and J774A.1) and antifungal activities (C. albicans, C. glabrata, C. Tropicalis, and A. niger). The maximum mole fractional solubility of KETO were found in three solvents such as DMA (4.8 × 10−2) ˃ EA (2.8 × 10−2) ˃ NMP (11.7 × 10−3) at “T = 318.2 K”. Analysis confirmed “endothermic and entropy” driven solubilization process. GastroPlus predicted pharmacokinetics parameters were influenced with nonsaturable metabolic clearance in subcutaneous tissue of human. Interaction study of KETO-DMA solution with Candida cells showed maximized cellular internalization. KETO-DMA solution exhibited poor cellular uptake by L929 cell lines (murine fibroblast cells) compared to KETO aqueous solution which was further supported with cytotoxicity study in L929 and J774A.1 cell lines. Reduced MIC values of KETO-DMA solution as compared to KETO aqueous solution against four strains corroborated improved efficacy of KETO probably due to augmented solubility in DMA and lack of surfactant. Thus, KETO-DMA solution can be a suitable construct for sub-Q delivery to control fungal infections.

Application of a Physiologically Based Pharmacokinetic Model to Develop a Veterinary Amorphous Enrofloxacin Solid Dispersion.

Zoonotic intestinal pathogens threaten human health and cause huge economic losses in farming. Enrofloxacin (ENR) shows high antibacterial activity against common intestinal bacteria. However, its poor palatability and low aqueous solubility limit the clinical application of ENR. To obtain an ENR oral preparation with good palatability and high solubility, a granule containing an amorphous ENR solid dispersion (ENR-SD) was prepared. Meanwhile, a PBPK model of ENR in pigs was built based on the physiological parameters of pigs and the chemical-specific parameters of ENR to simulate the pharmacokinetics (PK) of ENR-SD granules in the intestinal contents. According to the results of parameter sensitivity analysis (PSA) and the predicted PK parameters at different doses of the model, formulation strategies and potential dose regimens against common intestinal infections were provided. The DSC and XRD results showed that no specific interactions existed between the excipients and ENR during the compatibility tests, and ENR presented as an amorphous form in ENR-SD. Based on the similar PK performance of ENR-SD granules and the commercial ENR soluble powder suggesting continued enhancement of the solubility of ENR, a higher drug concentration in intestinal contents could not be obtained. Therefore, a 1:5 ratio of ENR and stearic acid possessing a saturated aqueous solubility of 1190 ± 7.71 µg/mL was selected. The predictive AUC24h/MIC90 ratios against Campylobacter jejuni, Salmonella, and Escherichia coli were 133, 266 and 8520 (>100), respectively, suggesting that satisfactory efficacy against common intestinal infections would be achieved at a dose of 10 mg/kg b.w. once daily. The PSA results indicated that the intestinal absorption rate constant (Ka) was negatively correlated with the Cmax of ENR in the intestine, suggesting that we could obtain higher intestinal Cmax using P-gp inducers to reduce Ka, thus obtaining a higher Cmax. Our studies suggested that the PBPK model is an excellent tool for formulation and dose design.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

Assessment of the Utility of Physiologically-based Pharmacokinetic Model for prediction of Pharmacokinetics in Chinese and Japanese Populations.

The objective for the present analyses was to evaluate the utility of physiologically-based pharmacokinetic (PBPK) modeling for prediction of the pharmacokinetics (PK) in Chinese and Japanese populations with a panel of Pfizer internal compounds. Twelve compounds from Pfizer internal development pipeline with available Westerner PK data and available PK data in at least one of the subpopulations of Japanese and Chinese populations were identified and included in the current analysis. These selected compounds represent various elimination pathways across different therapeutic areas. The Simcyp® PBPK simulator was used to develop and verify the PBPK models of individual compounds. The developed models for these compounds were verified by using the clinical PK data in Westerners. The verified PBPK models were further used to predict the PK of these compounds in Chinese and Japanese populations and the predicted PK parameters were compared with the observed PK parameters. Ten of the 12 compounds had PK data in Chinese, and all the 12 compounds had PK data in Japanese. In general, the PBPK models performed well in predicting PK in Chinese and Japanese, with 8 of 10 drugs in Chinese and 7 of 12 drugs in Japanese has AAFE values less than 1.25-fold. PBPK-guided predictions of the relative PK difference were successful for 75% and 50%, respectively, between Chinese and Western and between Japanese and Western of the tested drugs using 0.8-1.25 as criteria. In conclusion, well verified PBPK models developed using data from Westerners can be used to predict the PK in Chinese and Japanese populations.

Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight.

Rosuvastatin is a frequently used probe to study transporter-mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient's individual time-concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293-transfected cells, and developed a semimechanistic model with a bottom-up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two-fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (Cmax ; 76%; AFE: 1.05), and terminal half-life (t1/2 ; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.

Preliminary physiologically based pharmacokinetic modeling of renally cleared drugs in Chinese pregnant women.

The aim of this study was to build and verify a preliminary physiologically based pharmacokinetic (PBPK) model of Chinese pregnant women. The model was used to predict maternal pharmacokinetics (PK) of 6 predominantly renally cleared drugs. Based on SimCYP Caucasian pregnancy population dataset, the preliminary Chinese pregnant population was built by updating several key parameters and equations according to physiological parameters of Chinese (or Japanese) pregnant women. Drug-specific parameters of 6 renally cleared drugs were validated through PBPK modeling of Caucasian non-pregnant, Caucasian pregnant and Chinese non-pregnant population. The preliminary PBPK model of Chinese pregnant population was then developed by integrating the preliminary Chinese pregnant population and the drug-specific parameters. This model was verified by comparing the predicted maternal PK of these 6 drugs with the observed in vivo data from the literature. The preliminary Chinese pregnant population PBPK model successfully predicted the PK of 6 target drugs for different pregnancy stages. The predicted plasma concentrations time profiles fitted the observed data well, and most predicted PK parameters were within 2-fold of observed data. The preliminary Chinese pregnant population PBPK model provided a useful tool to predict the maternal PK of 6 predominantly renally cleared drugs in Chinese pregnant women.