Establishing Best Practices, Demonstrating QSP Value

Certara has been actively advancing the field of QSP in drug discovery and development. The team, led by Dr. Piet van der Graaf has been actively publishing on the topic, presenting its research and case studies to industry, academia and regulators at FDA, EMA and PMDA. Below are some of those most influential papers:

Download the QSP Factsheet

Here are some recent publications that will interest scientists like you:

View Paper

White Paper

Advancing QSP Technology to Address the Most Complex Areas of Drug Development

White paper that highlights Certara’s papers on the use of QSP modeling for Immunogenicity, Immuno-oncology, and Neurodegenerative Disease that were rated as top 10% of All Downloaded Papers in ASCPT publications in 2019.

View Paper
View Paper

White Paper

Managing Immunogenicity Using Quantitative Systems Pharmacology

Immunogenicity (IG) can be a showstopper in biologics, with 89% incidence rate and 49% impacting drug efficacy. A complex, multifactorial problem, IG incidence is highly variable across different biologicals and disease states with the impact of covariates not well established. This white paper introduces the work undertaken by Certara’s QSP IG Consortium in developing a unique Simulator platform to understand and manage IG in biologics development.

View Paper
View Paper

White Paper

Optimize Immuno-oncology Drug Discovery and Development Using QSP

Cancer IO drug therapy has had a huge impact on patient health and is expected to reach $39B by 2024. While monotherapy has delivered, it is combination therapy that will bring IO to its next level. This white paper outlines the forming of Certara’s IO QSP Consortium, focused on developing an IO simulator to test combination cancer therapies, different dose regimens and biomarkers in computer-generated, virtual patients.

View Paper
View Paper

Peer review Published Paper

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists

While it is well recognized that “omics” data is invaluable for discovery and biomarker steps, the use of this data source can also have huge impact for informing clinical pharmacology decision-making in the areas of dose and dosing regimen. This paper discusses the integration of omics data to inform mechanistic models in IO drug development. A minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma is used with a QSP platform that has been informed by omics data to study impact on IO development.

View Paper

View Paper

Peer review Published Paper

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

Developing safe and effective treatments for neurodegenerative diseases (ND) is the most vexing drug development challenge today. ND are complex and usually involve dysregulation in multiple biochemical pathways. This paper, written by a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. QSP is shown as a path forward toward better understanding of ND to create effective therapies.

View Paper
View Paper

Peer review Published Paper

A Quantitative Systems Pharmacology Consortium Approach to Managing Immunogenicity of Therapeutic Proteins

Authored by Certara and scientists from the seven members of the IG Consortium, this paper discusses how a QSP model integrating biologics PBPK (Simcyp Simulator) and mechanistic models of immune response can be used to inform management of IG in a similar way as PBPK of small molecules informs drug-drug interaction (DDI) management and regulatory interaction. Different target populations and patient cohorts can be considered to guide treatment optimization. “Virtual-twin” subjects can be created using HLA genotype, ex vivo assays, and peripheral blood flow cytometry data for actual individual patients, thus enabling a personalized-medicine approach to IG.

View Paper
View Paper

Peer review Published Paper

Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network

This paper acknowledges the enormous opportunity for QSP in drug development, but recognizes that the technology needs to adopt practices and processes to achieve widespread regulatory acceptance. The authors recommend adherence to well-understood tenets of software development, such as reproducibility, standardization, reusability, quality assurance, and model verification to advance the field of QSP.

View Paper
View Paper

Peer review Published Paper

Quantitative Systems Pharmacology Approaches for Immuno-oncology: Adding Virtual Patients to the Development Paradigm

The six pharma company members of the Certara IO Consortium collaborated to publish bthis article on the opportunity to use QSP to advance IO combination therapy, introducing their work on a new IO QSP Simulator. IO therapy can involve a limitless number of possible combination targets and dosing regimens, a dearth of testable subjects, and inefficiency in clinical trials. The paper outlines how QSP can address this challenge via the simulation of virtual combination trials and virtual patients.

View Paper

View Paper

Peer review Published Paper

Learning from Amyloid Trials in Alzheimer’s disease. A Virtual Patient Analysis using a Quantitative Systems Pharmacology Approach

Many trials of amyloid-modulating agents fail to improve cognitive outcome
in Alzheimer’s disease despite substantial reduction of amyloid 𝛽 levels. While there are several possible explanations, this paper considers the differential impact of A𝛽 baseline and rate of accumulation on cognitive outcomes, and the pharmacodynamic effect of comedications and genotypes on the dose-response of amyloid-modulating agents. QSP virtual patient simulations of clinical trials with aducanumab and different A𝛽 peptides on action potential firing of neuronal circuits are created and evaluated.

View Paper
View Paper

Peer review Published Paper

Mathematical Biology Models of Parkinson’s Disease

The second most-common progressive neurodegenerative disease Parkinson’s affects around seven million people worldwide. In this multifactorial disease, aging, environmental, and genetic factors contribute to neurodegeneration and dopamine deficiency in the brain. This paper presents a QSP modeling case study, specifically focused on α-synuclein (Asyn) aggregation, feedbacks among Asyn, DA, and mitochondria and proteolytic systems, as well as pathology propagation through the brain. Asyn, known to cause increased mitochondrial damage, which, in turn, increases oxidative stress leading to increased production of reactive oxygen species and reactive nitrogen species (ROS/RNS). Increased ROS/RNS leads to further Asyn misfolding. As a multi-factorial disease, there are several other factors beyond Asyn associated with Parkinson’s disease.

View Paper
View Paper

Peer review Published Paper

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development
of Bispecific Antibodies in Oncology

The use of bispecific antibodies (bsAbs) have emerged as an alternative to combination therapy for tumor targeting and as dual immune modulators, with almost 60 of these engineered proteins in clinical trials. This paper demonstrates how QSP can be used to aid in clinical translation, trial design and prediction of regimens and strategies to reduce bsAbs toxicity in dose selection. This tutorial provides several case study examples for this complex new modality.

View Paper
View Paper

Peer review Published Paper

Salvaging CNS Clinical Trials Halted Due to COVID-19

Among other issues, COVID-19 has caused both slowdowns and halts to clinical trials, some of which are multi-year programs for disorders including Alzheimer’s disease. This paper proposes the use of modeling and simulation to ‘connect’ trial data, to combine useful information from those that completed the trials with those under existing protocols that may have been halted, with those that will begin later in the trial. The authors propose the concept of mechanistic modeling-based virtual twin patients as a possible solution to harmonize the readouts from these complex and fragmented clinical datasets in a biologically relevant way.

View Paper

To Learn More

Back to top