MS07 - OTHE-08

Quantitative Systems Pharmacology: Linking mathematical biology to model informed drug development (MIDD) - Pharmacometrics Subgroup (Part 1)

Thursday, July 17 at 3:50pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Marissa Renardy (Quantitative Systems Pharmacology, GSK), Kathryn G. Link, Quantitative Systems Pharmacology, Pfizer Inc.

Description:

Quantitative systems pharmacology (QSP) combines mathematical and computational modeling tools with mechanistic understanding of biology and pharmacology to guide drug discovery and development. QSP is used in the pharmaceutical industry to accelerate and de-risk drug discovery and development across multiple stages, from target discovery/validation to clinical trial design to lifecycle management. In recent years, QSP has been increasingly used in regulatory submissions for clinical trials across many therapeutic areas (PMID:34734497). In this session, speakers will present recent advances and perspectives in the field of QSP. This minisymposia will have two sessions. The first session will consist of four technical talks. The second session will be comprised of three technical talks and an industry panel discussion with prepared and audience-driven questions.

Christian T. Michael

University of Michigan - Michigan Medicine

"Regimen-ranking methodology influences outcomes in a multi-scale systems pharmacology model of tuberculosis treatment."

Pulmonary tuberculosis, caused by lung infection with Mycobacterium tuberculosis (Mtb), is a potentially-fatal disease affecting one quarter of the world's population. Treatment of pulmonary TB requires antibiotic regimens that are expensive, intensive, and extensive, requiring 6 months of consistent treatment with multiple antibiotics. To explore optimal treatments, we created a multi-scale quantitative systems pharmacology model that we calibrated using multimodal pharmacokinetics and pharmacodynamics datasets from humans, rabbits, non-human primates, and in vitro studies. We have integrated this model with our previously-published whole-host model of pulmonary Mtb infection, HostSim. Using this platform, we studied the efficacy of dozens of front-line multi-drug antibiotic regimens in the form of virtual pre-clinical trials. By computing virtual analogues of efficacy measurements from clinical trials and experiments, we validated our model by recapitulating the relative efficacy several well-studied and frequently-prescribe antibiotic regimens. However, we found several cases in which regimen efficacy rankings differed substantially if calculated using seemingly-similar measurements. This highlights the problems that arise with in silico or in vivo studies when we use one single heuristic for drug efficacy as an intuitive proxy for another, which may cause us to infer seemingly- contradictory conclusions. Conversely, examining differences between seemingly-similar ranking schemes may provide insights into subtle behaviors of the underlying biological system.

Olivia Walch

Arcascope

"Better drug discovery through circadian science: theoretical considerations for chronomedicine."

Time-specifically biological, circadian time—is an underexploited dimension in drug development. This presentation will discuss the dose-dependent nature of optimal timing: how the best time to administer a drug varies with the dose, the drug’s half-life, and the temporal dynamics of the biological target. Shorter-acting drugs may require precise timing, while longer-acting ones may shift the window of efficacy. Moreover, variations in circadian amplitude can alter optimal strategies for both dosing and trial design. Finally, a case study will illustrate how neglecting time-of-day effects in a clinical trial could lead to the erroneous conclusion that a truly effective drug lacks efficacy.

Kathryn G. Link

Pfizer Inc.

"Virtual Clinical Trial Simulations Using a Quantitative Systems Pharmacology (QSP) Model of Antibody Drug Conjugate (ADC) Therapy in Patients with HER2- positive and HER2-low metastatic breast cancer."

Antibody-drug conjugates (ADCs) are typically composed of a monoclonal antibody (mAbs) backbone covalently attached to a cytotoxic drug, known as payload, via chemical linker. They combine both the advantages of highly specific targeting ability of the antibody with the highly potent killing mechanism of the payload to eliminate cancer cells. Given the increased success of approved vedotin ADCs (ADCENTRIS, PADCEV, AIDIX, and TIDCEV) and continued interest in vedotin-based therapeutics, a quantitative systems pharmacology (QSP) model of vedotin-based ADC disposition and efficacy could streamline the development of innovative medicines by assessing dose regimens and combination therapy strategies. In this talk, we discuss the development of a mechanistic ADC model capturing ADC disposition, target-specific binding, tumor growth inhibition, and efficacy. In vitro potency and in vivo TGI data inform initial model calibrations and validation. Additionally, the model was calibrated with published clinical PK and target expression data. Next, we implemented an integrated quantitative systems pharmacology virtual population approach to incorporate oncology efficacy endpoints. A HER2-positive and HER2-low virtual population were matched to the progression free survival (PFS) and best percentage change in sum of diameters from baseline to published RC48 C001 C003 CANCER studies. Here we present a virtual population pipeline utilizing a mechanistic QSP model of tumor growth, target expression, ADC disposition, preclinical potency and TGI data, as well as clinical PK/efficacy data. Our ADC QSP model captures key PK, PD, and target expression dynamics observed from clinical studies of vedotin-based therapeutic interventions. The model can further support future drug development by informing questions such as the selection of optimal dosing regimens for pivotal clinical trials.

Morgan Craig

Universite de Montreal

"Targeting tumour-associated macrophages and microglia in glioblastoma"

Glioblastoma is a deadly brain cancer for which standard-of-care (SOC) provides only moderate survival benefits, with 100% of patients experiencing recurrence. Despite high expression of PD-L1 in glioblastoma, with or without SOC, all immune checkpoint inhibitor (ICI) clinical trials have failed to efficacy in mixed patient populations. Using mathematical and computational models combined with spatial data, we have shown that the peculiarities of the tumour microenvironment and the immune response in the brain limit ICI success in glioblastoma. To study potential immunotherapeutic treatment options for glioblastoma, we developed a comprehensive, mechanistic mathematical model of SOC and nivolumab that describes tumour-immune interactions within the tumour microenvironment. Our results suggest that tumour-associated macrophages/microglia (TAMs) are compelling targets to improve treatment outcomes and lay the framework for continued experimental work developing TAM-targeting therapies for glioblastoma.

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Annual Meeting for the Society for Mathematical Biology, 2025.