Skip to main content

Immunogenicity Prediction and Dose Optimization using Clinically-Validated In Silico Modeling and Simulation

Biologic drug development is a rapidly evolving sector in the biopharmaceutical industry. Immunogenicity is an inherent challenge with this complex class of drugs. A quantitative systems pharmacology approach can be used to predict and better manage immunogenicity, and as a tool to guide clinical and regulatory decision-making in biologics drug development.

Using Model-based Meta-analysis to Improve Decision-making in Drug Development

Making the right choices in drug development often means the difference between getting a new medication to patients and it ending up in the scrap heap of failed programs. There is a surfeit of publicly available information on approved drugs as well as those currently in development. How can sponsors turn clinical trial data into understanding that helps chart the course for investigational drugs?

Model-based Meta-analysis: An Innovative Methodology Comes of Age

MBMA integrates internal and external drug development data to inform proprietary commercial and R&D decisions. The insights gained via MBMA support designing less costly and more precise trials with an eye toward achieving commercial success for both the drug and portfolio.

Three Questions, Piet van der Graaf

Staff writer Ron Rosenberg interviewed Piet van der Graaf, PharmD, PhD, vice president of quantitative systems pharmacology at Certara and former director of XenologiQ, a QSP consultancy.

PBPK Modeling in Regulatory Review, Product Labeling and Safety Monitoring

Physiologically-based pharmacokinetic (PBPK) modeling can address various questions raised in drug development and regulatory review, and is used most extensively to predict and quantify the extent of drug-drug interactions (DDIs) from both in vitro and clinical data. This assists with dose selection and the design of clinical studies as well as informing decisions relating to … Continued

1 of 4