Open Models for Clinical Pharmacology

Kirouac, Daniel C.

doi:10.1002/cpt.1738

Cited by 3 publications

(7 citation statements)

References 9 publications

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“…However, it is recognized that significant investment is required to develop a large-scale QSP platform and that the time and resources this may take poses a practical barrier for individual research and development organisations. 67 The organizations co-authoring this publication have, therefore, decided to tackle this problem through a consortium model, where currently seven companies share resources, expertise, information, and data in a precompetitive manner to create and validate an industry standard, clinical IO QSP platform model. Members of our consortium have full access to the model code and details, ensuring transparency and scientific and technical rigor.…”

Section: Reviewmentioning

confidence: 99%

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Chelliah¹,

Lazarou²,

Bhatnagar

et al. 2020

Clin Pharma and Therapeutics

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Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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Section: Reviewmentioning

confidence: 99%

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Chelliah¹,

Lazarou²,

Bhatnagar

et al. 2020

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

show abstract

“…In drug discovery and development, as with any activity incentivized and rewarded through intellectual property, some data will have to remain confidential for a period of time, but there is much else that can be shared immediately, and we strongly encourage such activities. 30,31 As methods to investigate and learn from Big Data advance, this sharing becomes essential to allow us to tackle big problems. Examples in this issue include improving pediatric prescribing through collaborating to understand development ontogeny 32 and the identification of novel treatments for devastating, untreatable, or almost untreatable diseases.…”

Section: Editorialmentioning

confidence: 99%

“…33 Sharing models, or at least making them transparent, is equally important as a means to stimulate understanding, to further advances, and to encourage wider uptake of the models themselves. 30…”

Section: Editorialmentioning

confidence: 99%

“…One way to make data “bigger” is open sharing such that all of us have easier access to more data than one can collect in their own work. In drug discovery and development, as with any activity incentivized and rewarded through intellectual property, some data will have to remain confidential for a period of time, but there is much else that can be shared immediately, and we strongly encourage such activities . As methods to investigate and learn from Big Data advance, this sharing becomes essential to allow us to tackle big problems.…”

mentioning

confidence: 99%

“…Examples in this issue include improving pediatric prescribing through collaborating to understand development ontogeny and the identification of novel treatments for devastating, untreatable, or almost untreatable diseases . Sharing models, or at least making them transparent, is equally important as a means to stimulate understanding, to further advances, and to encourage wider uptake of the models themselves …”

mentioning

confidence: 99%

See 2 more Smart Citations

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

Peck¹,

Shah

Vamvakas

et al. 2020

Clin Pharma and Therapeutics

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Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

Kirouac,

Zmurchok,

Morris

2024

npj Syst Biol Appl

Self Cite

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Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these “living drugs” is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems. Using publicly available clinical data from Chimeric Antigen Receptor (CAR) T cells, we demonstrate how mathematical models can be used to quantify the relationships between product characteristics, patient physiology, pharmacokinetics and clinical outcomes. As scientists work to develop next-generation cell therapy products, mathematical models will be integral for contextualizing data and facilitating the translation of product designs to clinical strategy.

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Open Models for Clinical Pharmacology

Cited by 3 publications

References 9 publications

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Quantitative Systems Pharmacology Approaches for Immuno‐Oncology: Adding Virtual Patients to the Development Paradigm

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

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