Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

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.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

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.

Safety Evaluation and Population Pharmacokinetics of Camostat Mesylate and Its Major Metabolites Using a Phase I Study.

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.

Effect of methylphenidate on height in pediatric attention-deficit hyperactivity disorder patients: a systematic review and meta-analysis.

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.

Evaluation of the potential herb-drug interaction between Bojungikki-tang and PD-L1 immunotherapy in a syngeneic mouse model.

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.

Application of the Population Pharmacokinetics Model-Based Approach to the Prediction of Drug-Drug Interaction between Rivaroxaban and Carbamazepine in Humans.

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.

Development of a Physiologically Based Pharmacokinetic Model for Tegoprazan: Application for the Prediction of Drug-Drug Interactions with CYP3A4 Perpetrators.

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.

Fractal Kinetic Implementation in Population Pharmacokinetic Modeling.

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.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions Through Cytochrome P450 3A4 Induction.

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.

Application of physiologically-based pharmacokinetic model approach to predict pharmacokinetics and drug-drug interaction of rivaroxaban: A case study of rivaroxaban and carbamazepine.

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.

Anti-neuroinflammatory Effects and Brain Pharmacokinetic Properties of Selonsertib, an Apoptosis signal-regulating Kinase 1 Inhibitor, in mice.

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.

Prothionamide Dose Optimization Using Population Pharmacokinetics for Multidrug-Resistant Tuberculosis Patients.

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.

Fine-tuning of BERT Model to Accurately Predict Drug-Target Interactions.

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.

A compatibility evaluation between the physiologically based pharmacokinetic (PBPK) model and the compartmental PK model using the lumping method with real cases.

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.

A simple time-to-event model with NONMEM featuring right-censoring.

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.

The Effect of CYP2D6 Phenotypes on the Pharmacokinetics of Propafenone: A Systematic Review and Meta-Analysis.

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.

Model-Based Equivalent Dose Optimization to Develop New Donepezil Patch Formulation.

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.

Model-Based Efficacy and Toxicity Comparisons of Moxifloxacin for Multidrug-Resistant Tuberculosis.

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.

Bioanalysis of lanreotide in dog plasma using liquid chromatography-tandem mass spectrometry and its application in a pharmacokinetic study of beagle dogs after single subcutaneous injection.

Lanreotide is similar to a naturally occurring hormone, somatostatin; thus, it may be used to treat acromegaly or metastatic gastroenteropancreatic neuroendocrine tumours. Here, a bioanalytical method coupling ultra-performance liquid chromatography with tandem mass spectrometry to quantify lanreotide and an internal standard (IS) was developed and validated in dog plasma. The plasma samples were extracted using typical protein precipitation processes. The analyte and internal standard were separated on Phenomenex Kinetex® C18 with 0.1% formic acid and acetonitrile in the mobile phase at a flow rate of 0.4 mL/min. The fragmentation of precursor ions to product ions was optimized at m/z 548.8 â†’ 170.0 for lanreotide [M + 2H]2+ and 472.2 â†’ 436.2 for IS [M + H]+. The peak retention times of lanreotide and IS were 1.09 min and 1.22 min, respectively. The calibration curve samples in dog plasma ranged from 0.3 to 1000 ng/mL and showed good linearity, with a correlation coefficient of r2=0.9996. The lower limit of quantitation was 0.3 ng/mL. The intra- and inter-day precision (relative standard deviation) values for each quality control level were < 9.7 % and < 9.3 %, respectively; intra- and inter-day accuracy were < 109.3% and < 110.4%, respectively. Lanreotide in dog plasma was stable in various conditions. The maximum plasma concentration of lanreotide in male beagle dogs after subcutaneous injection of Somatuline® (lanreotide) Autogel 120 mg was 88.1 ng/mL. The half-life (T1/2) of lanreotide in beagle dogs was long, approximately 198.6 h; the area under the plasma-concentration curve from 0 to 840 h (day 35) was 6,995 ng⋅h/mL. This novel quantification method using UPLC-MS/MS was successfully applied to the pharmacokinetic analysis of lanreotide in dog plasma. The results will assist future studies of drug formulation and repurposing.

Evaluation for Potential Drug-Drug Interaction of MT921 Using In Vitro Studies and Physiologically-Based Pharmacokinetic Models.

MT921 is a new injectable drug developed by Medytox Inc. to reduce submental fat. Cholic acid is the active pharmaceutical ingredient, a primary bile acid biosynthesized from cholesterol, endogenously produced by liver in humans and other mammals. Although individuals treated with MT921 could be administered with multiple medications, such as those for hypertension, diabetes, and hyperlipidemia, the pharmacokinetic drug-drug interaction (DDI) has not been investigated yet. Therefore, we studied in vitro against drug-metabolizing enzymes and transporters. Moreover, we predicted the potential DDI between MT921 and drugs for chronic diseases using physiologically-based pharmacokinetic (PBPK) modeling and simulation. The magnitude of DDI was found to be negligible in in vitro inhibition and induction of cytochrome P450s and UDP-glucuronosyltransferases. Organic anion transporting polypeptide (OATP)1B3, organic anion transporter (OAT)3, Na+-taurocholate cotransporting polypeptide (NTCP), and apical sodium-dependent bile acid transporter (ASBT) are mainly involved in MT921 transport. Based on the result of in vitro experiments, the PBPK model of MT921 was developed and evaluated by clinical data. Furthermore, the PBPK model of amlodipine was developed and evaluated. PBPK DDI simulation results indicated that the pharmacokinetics of MT921 was not affected by the perpetrator drugs. In conclusion, MT921 could be administered without a DDI risk based on in vitro study and related in silico simulation. Further clinical studies are needed to validate this finding.