Modeling Analysis Services
At Materials Metric, we leverage cutting-edge computational approaches to analyze biological, pharmacological, and material interactions. Our Modeling Analysis Services provide advanced simulations, predictive analytics, and data-driven solutions to optimize research outcomes and accelerate innovation across various scientific domains.
Non-Compartmental Analysis (NCA)
Non-Compartmental Analysis (NCA) is a widely used pharmacokinetic (PK) modeling method that provides rapid and reliable insights into drug absorption, distribution, metabolism, and elimination (ADME).
Capabilities:
PK parameter estimation (AUC, Cmax, Tmax, half-life, clearance)
Bioavailability and bioequivalence studies
Dose-exposure relationship analysis
First-in-human (FIH) and preclinical PK modeling
Pharmacometric Analysis
Pharmacometric analysis integrates pharmacokinetics (PK), pharmacodynamics (PD), and biomarker data to optimize drug development, dosage regimens, and therapeutic strategies.
Capabilities:
PK/PD modeling & simulation
Population pharmacokinetics (PopPK) for interpatient variability assessment
Exposure-response modeling for efficacy & safety prediction
Drug-drug interaction (DDI) modeling
Quantitative Systems Pharmacology (QSP)
QSP bridges molecular, cellular, and physiological data with drug action mechanisms, helping to predict clinical outcomes and optimize therapeutic interventions.
Capabilities:
Mechanistic disease modeling for drug discovery
Predictive simulations of drug effects across biological scales
Virtual clinical trials & dose-response simulations
Multi-scale modeling of drug-target interactions
AI & Machine Learning Algorithms
Artificial Intelligence (AI) and machine learning accelerate biomedical research, drug discovery, and materials design through predictive analytics and automated data processing.
Capabilities:
AI-driven drug repurposing & target identification
Deep learning models for structure-activity relationship (SAR) predictions
Predictive toxicity & ADMET profiling
Machine learning-assisted material discovery & property prediction
Computational Genomic Modeling & Drug Docking
Our computational genomics and molecular docking approaches enable genotype-phenotype correlations, drug-target affinity predictions, and personalized treatment strategies.
Capabilities:
Molecular docking & molecular dynamics simulations
Genome-wide association studies (GWAS) & epigenetic modeling
CRISPR & gene editing computational analysis
Predictive modeling of drug resistance mechanisms