Trial Simulator for NONMEM Modelers

Examples Guide Version 1 Applies to: Trial Simulator 2.3, NONMEM 7.3 As a courtesy to Certara’s Trial Simulator users, we have created this guide with 23 hypothetical examples where NONMEM codes and the corresponding TS codes are provided so that users may use these examples as templates to enter their models into Trial Simulator for … Continued

Payer-Pharma Perspectives on Outcomes-based Contracting

The adoption of outcomes based agreements (OBA) is growing given the urge among payers to reduce their exposure to risks of uncertain clinical value and budgetary impact, and the demand for drug manufacturers to demonstrate real world value to justify new, high-priced therapies and guarantee access to existing products exposed to increasing rebate levels. Prior to discussing practical considerations for the OBA implementation in this white paper, we’ve included the voices and rationales of two seasoned OBA pioneers and let you be party to their personal reflections.

The Shifting Landscape for Outcomes-Based Contracting

As health systems are driven to accept increasing accountability, payers and providers are looking for the biopharmaceutical industry to share the risks around performance of their products. Outcomes-based agreements (OBA) can be seen as the next chapter of the pay-for-value trend wherein the reimbursement for the pharmaceutical product is tied to the measurable ‘real world’ value it provides in terms of predefined outcomes.

The Six Stages to a Successful Value-Based Risk-Sharing Agreement

Outcome based agreements (OBAs) are a type of value-based risk sharing agreement between payers and drug manufacturers. OBAs are a useful tool for managing uncertainty regarding a drug’s real world clinical benefit, the economic impact to a payer’s budget, and market penetration. Read this white paper to learn about a six-stage process that will put you on the right path for attaining a successful OBA!

Optimize Immuno-oncology Drug Discovery and Development Using QSP

A Quantitative Systems Pharmacology (QSP) approach for developing combination immune-oncology therapies can be used to better predict effective drug combinations, especially to more accurately correlate the physiological differences between preclinical models and human patients.

Managing Immunogenicity Using Quantitative Systems Pharmacology

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.

The Modernization of Orphan Drug Development

Orphan drugs affect 350,000 people worldwide, including 10% of the US population and 1 in 25 Europeans. Model-informed drug development (MIDD) approaches, such as PBPK and PopPK have been embraced by sponsors and regulators, and play a key role in modernizing and accelerating orphan drug development.