ADMET Predictor® Tutorial Series: Working with Data and Graphs

ADMET Predictor® Tutorial Series: Working with Data and Graphs

Authors: Lawless M
Software: ADMET Predictor®
Division: Cheminformatics

In this video, Simulations Plus explains how to examine data and utilize advanced graphing tools within the platform. Key features include managing spreadsheet columns, creating custom tabs, and visualizing molecular properties through various chart types.

ADMET Predictor® Tutorial Series: Calculating Properties

ADMET Predictor® Tutorial Series: Calculating Properties

Authors: Jamois E
Software: ADMET Predictor®
Division: Cheminformatics

In this video, we explore the core functionality of calculating chemical and biological properties. It covers selecting specific models, adjusting pH settings, and configuring multi-threading to maximize processing speed.

ADMET Predictor® Tutorial Series: MedChem Designer

ADMET Predictor® Tutorial Series: MedChem Designer

Authors: Jamois E
Software: ADMET Predictor®
Division: Cheminformatics

In this video, we provide an interactive tutorial on using MedChem Designer to edit, create, and save chemical structures. You will also learn how to calculate ADMET properties directly within the designer and import structures from online resources like DrugBank.

Prediction of the Lurasidone–Posaconazole Drug–Drug Interaction Using Physiologically Based Pharmacokinetic Modeling

Prediction of the Lurasidone–Posaconazole Drug–Drug Interaction Using Physiologically Based Pharmacokinetic Modeling

Authors: Shan Y
Publication: ProQuest Dissertations
Software: GastroPlus®

Lurasidone is an atypical antipsychotic drug that metabolized by cytochrome P4503A4 (CYP3A4). Posaconazole is a triazole antifungal agent known to inhibit CYP3A4activity.

Metabolic Profiling and Detoxification of Eupalinolide A and B in Human Liver Microsomal Systems

Metabolic Profiling and Detoxification of Eupalinolide A and B in Human Liver Microsomal Systems

Publication: Toxics
Software: ADMET Predictor®

Eupalinolide A (EA, Z-configuration) and Eupalinolide B (EB, E-configuration) are cis-trans isomeric sesquiterpenoid monomers isolated from Eupatorium lindleyanum DC. (Asteraceae).

Development of a Quantitative Systems Toxicology Model to Predict Drug-Induced Liver Injury in Pediatrics

Development of a Quantitative Systems Toxicology Model to Predict Drug-Induced Liver Injury in Pediatrics

Conference: ACoP
Software: DILIsym®, GastroPlus®

Drug-induced liver injury (DILI) is an underrecognized cause of pediatric liver disease which accounts for almost 20% of pediatric acute liver failure cases, and is a major reason for liver transplantation in the USA [1].

A Pediatric Pbpk Model of Atropine Gel To Predict Atropine Levels in Children With Neurological Disorders After Administration to Oral Cavity

A Pediatric Pbpk Model of Atropine Gel To Predict Atropine Levels in Children With Neurological Disorders After Administration to Oral Cavity

Conference: ACoP
Software: GastroPlus®

Sialorrhea, or excessive salivation, is a chronic and serious problem in children with cerebral palsy (CP) and neurodevelopmental disorders.[1–5] Sialorrhea occurs in up to 60% of children with CP...

Automated Concentration-QT data preparation, model selection and reporting in R

Automated Concentration-QT data preparation, model selection and reporting in R

Conference: International Society of Pharmacometrics
Software: Monolix®

Since the publication of the ICH E14 guidance in 2015, QT interval prolongation as-sessment can be carried out with a concentration-QTc modeling approach as part of single- or mul-tiple- dose escalation studies, instead of conducting a thorough QT/QTc study.

mlxDesignEval: A novel R package for design evaluation based on MonolixSuite, and its comparison to popED and PFIM

mlxDesignEval: A novel R package for design evaluation based on MonolixSuite, and its comparison to popED and PFIM

Conference: American Conference of Pharmacometrics
Software: Monolix®, Simulx®

Designing clinical trials to support population PK/PD modeling requires careful choices of sampling times, number of subjects, dose groups and other trial features to
ensure precise parameter estimation - with low relative standard errors [1].