Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions.

Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative.

Effect of choline alfoscerate in older adult patients with dementia: an observational study from the claims data of national health insurance.

Background: Choline alfoscerate, a cholinergic precursor with limited evidence of efficacy in dementia management, has been used for various cognitive impairments in Korea. Partly due to its insurance coverage, this agent appears to incur significant expense for the insurance system. Thus, we aimed to describe choline alfoscerate prescription patterns and analyze their long-term effects in an older adult cohort with dementia.

Pharmacokinetic and Pharmacodynamic Interaction of Finerenone with Diltiazem, Fluconazole, and Ritonavir in Rats.

Background And Objectives: Finerenone, a novel selective non-steroidal mineralocorticoid receptor antagonist, has been indicated in chronic kidney disease associated with type 2 diabetes mellitus. Considering the potential complications of diabetes, finerenone can be co-administered with various drugs, including fluconazole, diltiazem, and ritonavir. Given that finerenone is a substrate of cytochrome P450 (CYP) 3A4, the concurrent administration of finerenone with CYP3A4 inhibitors (diltiazem or fluconazole or ritonavir) could potentially lead to drug interactions, which may cause adverse events such as hyperkalemia.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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 ( ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( ) is also assumed to be one because is usually unknown.

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.

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.

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.

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.

Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.

Background: Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited.

Objective: We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery.

Methods: EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography).

Molecular design of anticancer drugs from marine fungi derivatives.

Heat shock protein 90 (Hsp90) is one of the most potential targets in cancer therapy. We have demonstrated using a combination of molecular docking and fast pulling of ligand (FPL) simulations that marine fungi derivatives can be possible inhibitors, preventing the biological activity of Hsp90. The computational approaches were validated and compared with previous experiments.

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.

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.

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.

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.

Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data.

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone.