Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power-law based pharmacokinetic modeling.

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.

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.

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.

Differential effects of two phosphodiesterase 4 inhibitors against lipopolysaccharide-induced neuroinflammation in mice.

BACKGROUND: Several phosphodiesterase 4 (PDE4) inhibitors have emerged as potential therapeutics for central nervous system (CNS) diseases. This study investigated the pharmacological effects of two selective PDE4 inhibitors, roflumilast and zatolmilast, against lipopolysaccharide-induced neuroinflammation. RESULTS: In BV-2 cells, the PDE4 inhibitor roflumilast reduced the production of nitric oxide and tumor necrosis factor-α (TNF-α) by inhibiting NF-κB phosphorylation. Moreover, mice administered roflumilast had significantly reduced TNF-α, interleukin-1ß (IL-1ß), and IL-6 levels in plasma and brain tissues. By contrast, zatolmilast, a PDE4D inhibitor, showed no anti-neuroinflammatory effects in vitro or in vivo. Next, in vitro and in vivo pharmacokinetic studies of these compounds in the brain were performed. The apparent permeability coefficients of 3 µM roflumilast and zatolmilast were high (> 23 × 10-6 cm/s) and moderate (3.72-7.18 × 10-6 cm/s), respectively, and increased in a concentration-dependent manner in the MDR1-MDCK monolayer. The efflux ratios were < 1.92, suggesting that these compounds are not P-glycoprotein substrates. Following oral administration, both roflumilast and zatolmilast were slowly absorbed and eliminated, with time-to-peak drug concentrations of 2-2.3 h and terminal half-lives of 7-20 h. Assessment of their brain dispositions revealed the unbound brain-to-plasma partition coefficients of roflumilast and zatolmilast to be 0.17 and 0.18, respectively. CONCLUSIONS: These findings suggest that roflumilast, but not zatolmilast, has the potential for use as a therapeutic agent against neuroinflammatory diseases.

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.

An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms.

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

In vitro metabolic characterization of the SARS-CoV-2 papain-like protease inhibitors GRL0617 and HY-17542.

The SARS-CoV-2 pandemic requires a new therapeutic target for viral infection, and papain-like protease (Plpro) has been suggested as a druggable target. This in-vitro study was conducted to examine the drug metabolism of the GRL0617 and HY-17542, Plpro inhibitors. Metabolism of these inhibitors was studied to predict the pharmacokinetics in human liver microsomes. The hepatic cytochrome P450 (CYP) isoforms responsible for their metabolism were identified using recombinant enzymes. The drug-drug interaction potential mediated by cytochrome P450 inhibition was estimated. In human liver microsomes, the Plpro inhibitors had phase I and phase I + II metabolism with half-lives of 26.35 and 29.53 min, respectively. Hydroxylation (M1) and desaturation (-H2, M3) of the para-amino toluene side chain were the predominant reactions mediated with CYP3A4 and CYP3A5. CYP2D6 is responsible for the hydroxylation of the naphthalene side ring. GRL0617 inhibits major drug-metabolizing enzymes, including CYP2C9 and CYP3A4. HY-17542 is structural analog of GRL0617 and it is metabolized to GRL0617 through non-cytochrome P450 reactions in human liver microsomes without NADPH. Like GRL0617 and HY-17542 undergoes additional hepatic metabolism. The in-vitro hepatic metabolism of the Plpro inhibitors featured short half-lives; preclinical metabolism studies are needed to determine therapeutic doses for these inhibitors.

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.

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.

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.

Clinical Benefits of Oral Anticoagulant Use in Cancer Patients at Increased Risk for Venous Thromboembolism per Khorana Index.

BACKGROUND: Cancer patients are at increased risk for venous thromboembolism (VTE) due to cancer-induced hypercoagulability. However, current guidelines do not routinely recommend prophylactic use of oral anticoagulants to prevent VTE in cancer patients. OBJECTIVE: To evaluate the efficacy and safety of novel oral anticoagulants (NOACs) versus no anticoagulant use (no-use) and, additionally, differential effects between NOACs and warfarin, in VTE and adverse bleeding prevention among cancer patients, in consideration of risk stratification by gender, high-risk chemotherapy exposure, and Khorana index. METHODS: This national health insurance data-based study with a 180-day follow-up enrolled cancer patients with or without oral anticoagulant use in 2017. The primary outcome was VTE risk in oral anticoagulant users vs non-users. Four propensity score-matched comparison pairs were designed: use vs no-use, NOAC vs no-use, warfarin vs no-use, and NOAC vs warfarin. A logistic regression model was used to investigate between-group differences in VTE and bleeding risk. RESULTS: When compared to no-use, NOACs showed substantial effects in preventing VTE complications (OR=0.40, p<0.001), primarily deep vein thrombosis (DVT) events (OR=0.38, p<0.001), in both male and female cancer patients as well as those with a Khorana score ≥1. Adverse bleeding risk was comparable or lower in NOAC-receiving female patients (p=0.13) and male patients (p=0.04), respectively. In contrast, no protective effects were found with warfarin compared to no-use in controlling thrombosis and adverse bleeding risk. In a head-to-head comparison of NOACs versus warfarin, DVT risk in those patients exposed to high-risk chemotherapy was significantly decreased with NOAC use (OR=0.19, p=0.03). CONCLUSION: NOACs can be a promising thromboprophylactic option in both male and female cancer patients with VTE risk.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates.

AIM: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS: A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.