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Botulinum neurotoxins (BoNTs) are commonly used in therapeutic and cosmetic applications. One such neurotoxin, BoNT type A (BoNT/A), has been studied widely for its effects on muscle function and contraction. Despite the importance of BoNT/A products, determining the blood concentrations of these toxins can be challenging. To address this, researchers have focused on pharmacodynamic (PD) markers, including compound muscle action potential (CMAP) and digit abduction scoring (DAS). In this study, we aimed to develop a probabilistic kinetic-pharmacodynamic (K-PD) model to interpret CMAP and DAS data obtained from mice and rats during the development of BoNT/A products. The researchers also wanted to gain a better understanding of how the estimated parameters from the model relate to the bridging of animal models to human responses. We used female Institute of Cancer Research mice and Sprague-Dawley (SD) rats to measure CMAP and DAS levels over 32 weeks after administering BoNT/A. We developed a muscle-contraction inhibition model using a virtual pharmacokinetic (PK) compartment combined with an indirect response model and performed model diagnostics using goodness-of-fit analysis, visual predictive checks (VPC), and bootstrap analysis. The CMAP and DAS profiles were dose-dependent, with recovery times varying depending on the administered dose. The final K-PD model effectively characterized the data and provided insights into species-specific differences in the PK and PD parameters. Overall, this study demonstrated the utility of PK-PD modeling in understanding the effects of BoNT/A and provides a foundation for future research on other BoNT/A products.
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Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID-19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long-acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose-exposure relationship, a hamster model was selected, given its utility in previous COVID-19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power-law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness-of-fit and visual predictive checks, were employed to assess the models. The power-law-based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power-law-based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.
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Antivirales , Tratamiento Farmacológico de COVID-19 , Pulmón , Modelos Biológicos , Niclosamida , Animales , Niclosamida/farmacocinética , Niclosamida/administración & dosificación , Antivirales/farmacocinética , Antivirales/administración & dosificación , Pulmón/metabolismo , Inyecciones Intramusculares , SARS-CoV-2 , Cricetinae , Relación Dosis-Respuesta a Droga , Masculino , COVID-19RESUMEN
Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.
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Modelos Biológicos , Humanos , Biología Computacional/métodos , Simulación por Computador , Preparaciones Farmacéuticas/metabolismo , Farmacocinética , Reproducibilidad de los ResultadosRESUMEN
Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.
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Camostat mesylate is expected to be promising as a treatment option for COVID-19, in addition to other indications for which it is currently used. Furthermore, in vitro experiments have confirmed the potential of camostat and its metabolites to be effective against COVID-19. Therefore, clinical trials were conducted to evaluate the safety and pharmacokinetic characteristics of camostat after single-dose administration. Additionally, we aim to predict the pharmacokinetics of repeated dosing through modeling and simulation based on clinical trials. Clinical trials were conducted on healthy Korean adults, and an analysis was carried out of the metabolites of camostat, GBPA, and GBA. Pharmacokinetic modeling and simulation were performed using Monolix. There were no safety issues (AEs, physical examinations, clinical laboratory tests, vital sign measurements, and ECG) during the clinical trial. The pharmacokinetic characteristics at various doses were identified. It was confirmed that AUC last and Cmax increased in proportion to dose in both GBPA and GBA, and linearity was also confirmed in log-transformed power model regression. Additionally, the accumulation index was predicted (1.12 and 1.08 for GBPA and GBA). The pharmacokinetics of camostat for various dose administrations and indications can be predicted prior to clinical trials using the developed camostat model. Furthermore, it can be used for various indications by connecting it with pharmacodynamic information.
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Methylphenidate (MPH), a first-line treatment for attention-deficit hyperactivity disorder (ADHD) management, has been the focus of debate for decades regarding its effect on growth. The aim of this PRISMA meta-analysis was to determine the effect of MPH on height in children/adolescents with ADHD and its predictive factors based on literature reports. Available full-text articles were systematically reviewed to identify clinical studies of pediatric ADHD patients with height Z-score (HZS) data for monotherapy MPH-treated and non-treated groups. We estimated standardized mean differences (SMDs) of HZS or its changes from baseline (ΔHZS) between groups, then identified associated factors through subgroup analyses and meta-regression. For before-after treatment studies, the paired standard errors of ΔHZS were re-estimated to demonstrate in the forest plot. Risk of bias was analyzed using the Newcastle-Ottawa Scale. Among the 29 eligible studies, 26 reported ΔHZS with self-control groups, and ΔHZS or absolute HZS were compared to other external controls in 11 studies. A significant reduction was observed between post-MHP and pre-MPH use, with high heterogeneity (SMD = - 0.40; 95% confidence interval = [ - 0.54, - 0.27]; I2 = 91%). The study region, ADHD subtype, and stimulant-naïve status of patients at baseline may modify the effect on HZS. Because of the high clinical heterogeneity in observational studies, clinicians should consider the negative effect of MPH on height in ADHD patients by determining whether patients fulfill appropriate high-risk criteria. Further well-designed longitudinal studies are required to better quantify this effect, especially with prolonged treatment.
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Atezolizumab (a PD-L1 inhibitor) has shown remarkable efficacy and tolerability in various cancer types. Despite its efficacy and safety, atezolizumab monotherapy has limitations, such as acquired resistance and adverse events. Bojungikki-tang (BJIKT) is an herbal decoction widely prescribed in Asian countries and used to treat cancer-related symptoms including fatigue, appetite loss, gastrointestinal disorders, and other side effects from cancer therapy. Due to its immunomodulatory effects, Bojungikki-tang has been investigated as a combined treatment with anticancer agents. We evaluated the potential drug-drug interaction (DDI) between Bojungikki-tang and the anti-PD-L1 antibody based on the Food and Drug Administration (FDA) guidelines. In the study, we conducted an in vivo drug-drug interaction study using a syngeneic mouse model of CMT-167 in C57BL/6. We then determined the antibody concentrations to evaluate the pharmacokinetic (PK) drug-drug interaction and measured variable biomarkers related to therapeutic efficacy and immune response. The pharmacodynamic (PD) drug-drug interaction study investigated changes in response between anti-PD-L1 antibody monotherapy and combination therapy. Using the pharmacokinetic and pharmacodynamic data, we conducted a statistical analysis to assess drug-drug interaction potential. In the presence of Bojungikki-tang, the pharmacokinetic characteristics of the anti-PD-L1 antibody were not changed. This study suggested that combination treatment with Bojungikki-tang and atezolizumab is a safe treatment option for non-small cell lung cancer. Clinical studies are warranted to confirm this finding.
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Rivaroxaban (RIV) is one of the direct oral anticoagulants used to prevent and treat venous and arterial thromboembolic events. Considering the therapeutic indications, RIV is likely to be concomitantly administered with various other drugs. Among these is carbamazepine (CBZ), one of the recommended first-line options to control seizures and epilepsy. RIV is a strong substrate of cytochrome P450 (CYP) enzymes and Pgp/BCRP efflux transporters. Meanwhile, CBZ is well known as a strong inducer of these enzymes and transporters. Therefore, drug-drug interaction (DDI) between CBZ and RIV is expected. This study aimed to predict the DDI profile of CBZ and RIV in humans by using a population pharmacokinetics (PK) model-based approach. We previously investigated the population PK parameters of RIV administered alone or with CBZ in rats. In this study, those parameters were extrapolated from rats to humans by using simple allometry and liver blood flow scaling, and then applied to back-simulate the PK profiles of RIV in humans (20 mg RIV per day) used alone or with CBZ (900 mg CBZ per day). Results showed that CBZ significantly reduced RIV exposure. The AUCinf and Cmax of RIV decreased by 52.3% and 41.0%, respectively, following the first RIV dose, and by 68.5% and 49.8% at the steady state. Therefore, the co-administration of CBZ and RIV warrants caution. Further studies investigating the extent of DDIs between these drugs should be conducted in humans to fully understand their safety and effects.
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Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug-drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration-time curve (AUC) and Cmax of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and Cmax of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.
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Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.
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The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a ) is also assumed to be one because F a is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m and E T , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m and E T becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.
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Citocromo P-450 CYP3A , Sistema Enzimático del Citocromo P-450 , Humanos , Interacciones Farmacológicas , Preparaciones Farmacéuticas , Sistema Enzimático del Citocromo P-450/metabolismo , Rifampin/farmacología , MidazolamRESUMEN
Selonsertib is a first-in-class apoptosis signal-regulating kinase 1 (ASK1) inhibitor in clinical trials for treating NASH and diabetic kidney disease due to its anti-inflammatory and anti-fibrotic activities. In the present study, we investigated the anti-neuroinflammatory effects and brain pharmacokinetic properties of selonsertib. It inhibited inflammatory cytokines and NO production by suppressing phosphorylated ASK1 in the LPS-stimulated microglial cell line, BV2 cells. Consistent with the in vitro results, selonsertib attenuated plasma and brain TNF-α levels in the LPS-induced murine neuroinflammation model. In vitro and in vivo pharmacokinetic studies of selonsertib were conducted in support of central nervous system (CNS) drug discovery. In both Caco-2 and MDR-MDCK cells, selonsertib exhibited a high efflux ratio, showing that it is a P-gp substrate. Selonsertib was rapidly and effectively absorbed into the systemic circulation after oral treatment, with a Tmax of 0.5 h and oral bioavailability of 74%. In comparison with high systemic exposure with Cmax of 16.2 µg/ml and AUC of 64 µg·h/mL following oral dosing of 10 mg/kg, the brain disposition of selonsertib was limited, with Cmax of 0.08 µg/g and Kp value of 0.004. This study demonstrates that selonsertib can be a therapeutic agent for neuroinflammatory diseases.
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Lipopolisacáridos , MAP Quinasa Quinasa Quinasa 5 , Animales , Ratones , Encéfalo/metabolismo , Células CACO-2 , Lipopolisacáridos/farmacología , MAP Quinasa Quinasa Quinasa 5/metabolismo , MAP Quinasa Quinasa Quinasa 5/farmacología , Microglía/metabolismoRESUMEN
Rivaroxaban (RIV; Xarelto; Janssen Pharmaceuticals, Beerse, Belgium) is one of the direct oral anticoagulants. The drug is a strong substrate of cytochrome P450 (CYP) enzymes and efflux transporters. This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model for RIV. It contained three hepatic metabolizing enzyme reactions (CYP3A4, CYP2J2, and CYP-independent) and two active transporter-mediated transfers (P-gp and BCRP transporters). To illustrate the performance of the developed RIV PBPK model on the prediction of drug-drug interactions (DDIs), carbamazepine (CBZ) was selected as a case study due to the high DDI potential. Our study results showed that CBZ significantly reduces the exposure of RIV. The area under the concentration-time curve from zero to infinity (AUCinf ) of RIV was reduced by 35.2% (from 2221.3 to 1438.7 ng*h/ml) and by 25.5% (from 2467.3 to 1838.4 ng*h/ml) after the first dose and at the steady-state, respectively, whereas the maximum plasma concentration (Cmax ) of RIV was reduced by 37.7% (from 266.3 to 166.1 ng/ml) and 36.4% (from 282.3 to 179.5 ng/ml), respectively. The developed PBPK model of RIV could be paired with PBPK models of other interested perpetrators to predict DDI profiles. Further studies investigating the extent of DDI between CBZ and RIV should be conducted in humans to gain a full understanding of their safety and effects.
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Modelos Biológicos , Rivaroxabán , Humanos , Rivaroxabán/farmacocinética , Transportador de Casetes de Unión a ATP, Subfamilia G, Miembro 2 , Proteínas de Neoplasias , Interacciones Farmacológicas , Citocromo P-450 CYP3A/metabolismo , Sistema Enzimático del Citocromo P-450/metabolismo , CarbamazepinaRESUMEN
Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration-time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.
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The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug-target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for chemical compounds and target proteins. This study sought to improve DTI predictive models using a Bidirectional Encoder Representations from the Transformers (BERT)-pretrained model, ChemBERTa, for chemical compounds. Pretraining features the use of a simplified molecular-input line-entry system (SMILES). We also employ the pretrained ProBERT for target proteins (pretraining employed the amino acid sequences). The BIOSNAP, DAVIS, and BindingDB databases (DBs) were used (alone or together) for learning. The final model, taught by both ChemBERTa and ProtBert and the integrated DBs, afforded the best DTI predictive performance to date based on the receiver operating characteristic area under the curve (AUC) and precision-recall-AUC values compared with previous models. The performance of the final model was verified using a specific case study on 13 pairs of subtrates and the metabolic enzyme cytochrome P450 (CYP). The final model afforded excellent DTI prediction. As the real-world interactions between drugs and target proteins are expected to exhibit specific patterns, pretraining with ChemBERTa and ProtBert could teach such patterns. Learning the patterns of such interactions would enhance DTI accuracy if learning employs large, well-balanced datasets that cover all relationships between drugs and target proteins.
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Prothionamide, a second-line drug for multidrug-resistant tuberculosis (MDR-TB), has been in use for a few decades. However, its pharmacokinetic (PK) profile remains unclear. This study aimed to develop a population PK model for prothionamide and then apply the model to determine the optimal dosing regimen for MDR-TB patients. Multiple plasma samples were collected from 27 MDR-TB patients who had been treated with prothionamide at 2 different study hospitals. Prothionamide was administered according to the weight-band dose regimen (500 mg/day for weight <50 kg and 750 mg/day for weight >50 kg) recommended by the World Health Organization. The population PK model was developed using nonlinear mixed-effects modeling. The probability of target attainment, based on systemic exposure and MIC, was used as a response target. Fixed-dose regimens (500 or 750 mg/day) were simulated to compare the efficacies of various dosing regimens. PK profiles adequately described the two-compartment model with first-order elimination and the transit absorption compartment model with allometric scaling on clearance. All dosing regimens had effectiveness >90% for MIC values <0.4 µg/mL in 1.0-log kill target. However, a fixed dose of 750 mg/day was the only regimen that achieved the target resistance suppression of ≥90% for MIC values of <0.2 µg/mL. In conclusion, fixed-dose prothionamide (750 mg/day), regardless of weight-band, was appropriate for adult MDR-TB patients with weights of 40 to 67 kg.
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Protionamida , Tuberculosis Resistente a Múltiples Medicamentos , Adulto , Antituberculosos/efectos adversos , Humanos , Protionamida/uso terapéutico , Tuberculosis Resistente a Múltiples Medicamentos/tratamiento farmacológicoRESUMEN
Propafenone (PPF) is a class 1C antiarrhythmic agent mainly metabolized by cytochrome (CYP) 2D6, CYP1A2, and CYP3A4. Previous studies have shown that CYP2D6 polymorphism influences the pharmacokinetics (PK) of PPF. However, the small sample sizes of PK studies can lead to less precise estimates of the PK parameters. Thus, this meta-analysis was performed to merge all current PK studies of PPF to determine the effects of the CYP2D6 phenotype more accurately on the PPF PK profile. We searched electronic databases for published studies to investigate the association between the PPF PK and CYP2D6 phenotype. Four PK-related outcomes were included: area under the time-concentration curve (AUC), maximum concentration (Cmax), apparent clearance (CL/F), and half-life (t1/2). A total of five studies were included in this meta-analysis (n = 56). Analyses were performed to compare PK parameters between poor metabolizers (PMs) versus extensive metabolizers (EMs). PPF has a non-linear pharmacokinetics; therefore, analyses were performed according to dose (300 mg and 400 mg). At 300 mg, the AUC mean (95% CI), Cmax, and t1/2 of PPF in PMs were 15.9 (12.5-19.2) µg·h/mL, 1.10 (0.796-1.40) µg/mL, and 12.8 (11.3-14.3) h, respectively; these values were 2.4-, 11.2-, and 4.7-fold higher than those in the EM group, respectively. At 400 mg, a comparison was performed between S- and R-enantiomers. The CL/F was approximately 1.4-fold higher for the R-form compared with the S-form, which was a significant difference. This study demonstrated that CYP2D6 metabolizer status could significantly affect the PPF PK profile. Adjusting the dose of PPF according to CYP2D6 phenotype would help to avoid adverse effects and ensure treatment efficacy.
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In healthcare situations, time-to-event (TTE) data are common outcomes. A parametric approach is often employed to handle TTE data because it is possible to easily visualize different scenarios via simulation. Not all pharmacometricians are familiar with the use of non-linear mixed effects models (NONMEMs) to deal with TTE data. Therefore, this tutorial simply explains how to analyze TTE data using NONMEM. We show how to write the code and evaluate the model. We also provide an example of a hands-on model for training.
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Donepezil patch was developed to replace the original oral formulation. To accurately describe the pharmacokinetics of donepezil and investigate compatible doses between two formulations, a population pharmacokinetic model for oral and transdermal patches was built based on a clinical study. Plasma donepezil levels were analyzed via liquid chromatography/tandem mass spectrometry. Non-compartmental analyses were performed to derive the initial parameters for compartmental analyses. Compartmental analysis (CA) was performed with NLME software NONMEM assisted by Perl-speaks-NONMEM, and R. Model evaluation was proceeded via visual predictive checks (VPC), goodness-of-fit (GOF) plotting, and bootstrap method. The bioequivalence test was based on a 2 × 2 crossover design, and parameters of AUC and Cmax were considered. We found that a two-compartment model featuring two transit compartments accurately describes the pharmacokinetics of nine subjects administered in oral, as well as of the patch-dosed subjects. Through evaluation, the model was proven to be sufficiently accurate and suitable for further bioequivalence tests. Based on the bioequivalence test, 114 mg/101.3 cm2-146 mg/129.8 cm2 of donepezil patch per week was equivalent to 10 mg PO donepezil per day. In conclusion, the pharmacokinetic model was successfully developed, and acceptable parameters were estimated. However, the size calculated by an equivalent dose of donepezil patch could be rather large. Further optimization in formulation needs to be performed to find appropriate usability in clinical situations.
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BACKGROUND: Moxifloxacin (MOX) is used as a first-choice drug to treat multidrug-resistant tuberculosis (MDR-TB); however, evidence-based dosing optimization should be strengthened by integrative analysis. The primary goal of this study was to evaluate MOX efficacy and toxicity using integrative model-based approaches in MDR-TB patients. METHODS: In total, 113 MDR-TB patients from 5 different clinical trials were analyzed for the development of a population pharmacokinetics (PK) model. A final population PK model was merged with a previously developed lung-lesion distribution and QT prolongation model. Monte Carlo simulation was used to calculate the probability target attainment value based on concentration. An area under the concentration-time curve (AUC)-based target was identified as the minimum inhibitory concentration (MIC) of MOX isolated from MDR-TB patients. RESULTS: The presence of human immunodeficiency virus (HIV) increased clearance by 32.7% and decreased the AUC by 27.4%, compared with HIV-negative MDR-TB patients. A daily dose of 800 mg or a 400-mg, twice-daily dose of MOX is expected to be effective in MDR-TB patients with an MIC of ≤0.25 µg/mL, regardless of PK differences resulting from the presence of HIV. The effect of MOX in HIV-positive MDR-TB patients tended to be decreased dramatically from 0.5 µg/mL, in contrast to the findings in HIV-negative patients. A regimen of twice-daily doses of 400 mg should be considered safer than an 800-mg once-daily dosing regimen, because of the narrow fluctuation of concentrations. CONCLUSIONS: Our results suggest that a 400-mg, twice-daily dose of MOX is an optimal dosing regimen for MDR-TB patients because it provides superior efficacy and safety.