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ASCPT Ranks Four Certara QSP Papers in the Top 10 Percent

By: Ellen Leinfuss

In the past, the “Top 10” described the most popular records in the music charts that week. A similar concept has been adopted in scientific circles and the American Society for Clinical Pharmacology & Therapeutics (ASCPT) now celebrates the Top 10% of scientific papers downloaded from its journals’ websites each year. We are proud to report that four papers co-authored by Certara quantitative systems pharmacology (QSP) were included in ASCPT’s latest list.

QSP combines computational modeling and experimental data to explore a drug’s relationships with biological systems and the disease process. By combining quantitative drug data with knowledge of its mechanism of action, QSP can predict how a drug, or a combination of drugs, modifies cellular networks and interacts with human pathophysiology.

Highlights from the four prized papers are described below.

  • Immunogenicity in Clinical Practice and Drug Development: When is it Significant?

Biologics drugs are highly effective; they provide targeted delivery with few side effects, except for immunogenicity (IG). The US Food and Drug Administration (FDA) defines IG as the propensity of the therapeutic protein to generate immune responses to itself and to related proteins. Development of antidrug antibodies (ADA) against these therapies represents a significant challenge for drug development and clinical practice, especially with long-term treatment of pediatric patients.

This paper describes IG considerations in clinical practice, IG testing and current limitations, IG risk assessment and mitigation, and a QSP model to address these challenges.

Recognizing QSP’s potential to improve understanding and mitigate the risk of ADA development against biologics, Certara partnered with six pharma companies in 2017 to create a pre-competitive QSP IG Consortium. The Consortium’s multidisciplinary team of 50 scientists then set to work to develop a QSP platform and simulator to assess IG risk.

The resulting IG Simulator combines literature-based, mechanistic models of immune response and ADA synthesis with the Simcyp® Simulator’s physiologically based pharmacokinetic (PBPK) biologics module. As a result, the IG Simulator can predict ADA impact on PK in different patient populations. It can also conduct virtual trials to evaluate the effects of different dosing regimens, patient characteristics, and co-therapies.

As new clinical data become available, they can further inform the QSP models, allowing them to assist in decision making for later-stage trials and support post-marketing assessment of combination therapies.

  • Best Practices to Maximize the Use and Reuse of QSP Models

QSP’s use in drug development is growing rapidly. But many published models do not follow accepted standards, so they cannot be adapted by other scientists to meet new objectives.

This paper features UK QSP Network recommendations to help scientists create reproducible, reusable, and verifiable QSP models that provide maximum project and stakeholder value.

QSP models are powerful tools. They enable drug treatment results to be extrapolated to different diseases with the same underlying mechanism of action and permit identifying optimal drug combinations. These models can address target validation, modality selection, and dose assessment challenges, especially for pediatric patients and other vulnerable populations. QSP models can also be used to explore potential reasons for variability in patient response.

This paper provides computational and mathematical recommendations with literature references to address the six most common issues that prevent models from being reused. Those include lack of a stated purpose, scope or underlying assumptions, missing data or other quantitative information, and no justification for model impact. The paper provides a set of standard requirements that should resolve those six issues and describes how to document QSP models for publication.

Creating industry standards is expected to accelerate acceptance and adoption of QSP modeling by pharmaceutical companies and regulatory agencies. It proved a successful strategy for pharmacokinetic/pharmacodynamic (PK/PD) and PBPK modeling and simulation.

  • QSP for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

Developing safe and effective treatments for neurodegenerative diseases (NDD) is challenging because these diseases usually involve dysregulation in multiple biochemical pathways. Progress is also hindered by the lack of quantitative, validated biomarkers, the subjectivity of many clinical endpoints, and complex PK/PD relationships involved.

While there are drugs available to treat some NDD symptoms effectively, there are few disease-modifying therapies.

This paper postulates that treating NDD requires a novel approach, such as QSP, which factors in all the complex interactions between different brain circuits across multiple biophysical scales.3

As QSP combines pharmacology, target engagement, and neurophysiology, it is ideally suited to simulate PD interactions. Merging systems biology and PK/PD approaches in a QSP framework could greatly facilitate drug discovery and development for complex NDDs that are not defined by a single molecular target.

  • Mathematical Biology Models of Parkinson’s Disease

Parkinson’s disease (PD) affects about seven million people worldwide. With PD, aging, environmental, and genetic factors contribute to neurodegeneration and dopamine deficiency in the brain. Drugs that restore dopamine provide symptomatic relief, but PD remains incurable.

This paper describes how QSP models of PD can improve our understanding of this complex NDD. Fifteen genes linked to PD and its pathogenesis are associated with the α-synuclein (Asyn) protein. Therefore, we focused our mechanistic models on Asyn protein aggregation, feedback between Asyn, dopamine, and the mitochondria and proteolytic systems, and pathology propagation through the brain. We have highlighted 32 PD models based on PD pathology alone.

We are now using the Asyn model to research specific drug development targets for client companies.


The beauty of QSP is that it allows quantitative, mechanistic models to be built of diseases and drug activity, allowing an entire system to be studied instead of individual processes. We anticipate that QSP models will soon join PK/PD and PBPK models as expected components in new drug and biologics applications to global regulatory agencies.

Ellen Leinfuss is a senior vice president at Certara.


  1. Shakhnovich, V. et al. Immunogenicity in clinical practice and drug development: When is it significant? Clin Transl Sci (2020) 13, 219–223.
  2. Cucurull-Sanchez, L. et al. Best practices to maximize the use and reuse of QSP models: recommendations from the United Kingdom Quantitative and Systems Pharmacology Network. CPT Pharmacometrics Syst. Pharmacol. (2019) 8, 259–272.
  3. Geerts, H. et al. QSP for neuroscience drug discovery and development: current status, opportunities, and challenges. CPT Pharmacometrics Syst. Pharmacol. (2019), 1–16.
  4. Bakshi, Suruchi, et al. Mathematical biology models of Parkinson’s disease. CPT Pharmacometrics Syst. Pharmacol (2019) 8, 77-86.

About the author

Ellen Leinfuss
By: Ellen Leinfuss
Ellen Leinfuss serves as SVP and Chief Commercial Officer for Certara’s Simcyp Division. Simcyp is the global leader in mechanistic modeling, which includes the renowned Simcyp Simulator for physiologically based pharmacokinetics (PBPK) and its Quantitative Systems Pharmacology (QSP) immunogenicity, immune-oncology, vaccine and gene therapy modeling & simulation platforms. Ellen has been at Certara for the past seven years, serving for many years as Chief Marketing Officer for the corporation. With a degree in chemistry and MBA in marketing, Ellen has provided management, business development and marketing leadership to organizations in the biopharmaceutical, environmental and energy fields. She is a published author and speaks regularly on topics related to model-informed drug development.

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