PBBM / PBPK modeling & simulation software package which simulates all major dosing routes and drug-drug interactions (DDIs) in virtual human & animal population groups.
Easy, fast, and powerful tool for parameter estimation in non-linear mixed effect models, model diagnosis and assessment, and advanced graphical representation.
Flagship machine learning platform for ADMET modeling with extended capabilities for data analysis, metabolism prediction, high-throughput pharmacokinetic simulation (HTPK), and AI-driven drug design.
Quantitative Systems Toxicology (QST) software designed to be used during drug development to provide deeper insight into the mechanisms responsible for observed drug-induced liver injury (DILI) responses.
A mechanistic simulation engine for the in vitro dissolution experiment that predicts in vitro disintegration and dissolution of pharmaceutical dosage forms.
Quantitative Systems Pharmacology (QSP) software for mechanistic mathematical modeling of nonalcoholic fatty liver disease (NAFLD.)
A detailed modeling & simulation program for in vitro metabolism & transport studies.
Graphical and interactive software for non-compartmental and compartmental analysis.
A powerful and flexible simulator for clinical trial pharmacometrics.
Quantitative Systems Pharmacology (QSP) software providing the ability to predict the efficacy of drugs being developed to treat idiopathic pulmonary fibrosis (IPF.)
Quantitative Systems Pharmacology (QSP) software for mechanistic mathematical modeling of interstitial lung disease (ILD) associated with system sclerosis (SSc.)
Quantitative Systems Toxicology (QST) software for predicting and understanding drug-induced kidney injury.
FREE chemical sketching software tool which combines innovative molecule drawing features.
Custom QSP software for predicting efficacy & guiding design.
Cardiovascular toxicity is a significant cause of candidate failure in drug development. Pharmacokinetic/pharmacodynamic (PK/PD) modeling may reduce attrition by improving the understanding of the relationship between drug exposure and changes in cardiovascular endpoints. Diverse examples are discussed that elucidate how modeling can facilitate the interpretation of cardiovascularsafety data in animals and enable quantitative translation of preclinical findings to man.
