Quantitative Systems Pharmacology: A Case for Disease Models

Musante, CJ; Ramanujan, Saroja; Bj, Schmidt; Ghobrial, OG; Lu, James; Heatherington, AC

doi:10.1002/cpt.528

Cited by 54 publications

(55 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scope of a QSP model is an important decision made early in model development: whether to begin initial development by focusing the model on a few key pathways or to develop a broader disease platform model. 101 In immuno-oncology, alternative models of the cancerimmunity cycle describing interactions between tumor, immune cells, cytokines, and molecular players including the process of antigen presentation, priming and trafficking of T cells, infiltration of immune cells, killing of tumor cells, and release of cancer immunotherapy cycle cancer antigen have been described, with the primary aim being to impact translational and clinical development. [102][103][104][105] Models to date have generally differed in their inclusion of biomarkers, have employed varied degrees of initial mechanistic comprehensiveness, and have adapted alternate equation forms.…”

Section: Pharmacometric Strategies: Application Of Systems Modelingmentioning

confidence: 99%

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Sheng

Srivastava

Sanghavi

et al. 2017

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Immuno-oncology works through activation of the patient's immune system against cancer, with several advantages over other treatment approaches, including cytotoxic agents and molecular-targeted therapies. The most notable feature of immuno-oncology treatments is the nature of the patient responses achieved, which can be more durable and sustained than with other modalities. Increased understanding of immune system complexity has provided a number of opportunities to advance several strategies for the development of immuno-oncology therapies. This review outlines the clinical pharmacology characteristics and development challenges for the 6 approved immunomodulatory monoclonal antibodies that target 2 immune checkpoint pathways: ipilimumab (an anti-cytotoxic T-lymphocyte antigen-4 antibody) and, more recently, nivolumab and pembrolizumab (both anti-programmed death-1 antibodies) and atezolizumab, avelumab, and durvalumab (all anti-programmed death ligand-1 antibodies). These agents have revealed much about the clinical pharmacology features of immune checkpoint inhibitors as a class, as well as the pharmacometric approaches used to support their clinical development and regulatory approval. The development experiences with these pioneering immuno-oncology agents are likely to serve as useful guides in the discovery, progression, and approval of future drugs or combination of drugs in this class. This review includes summaries of the pharmacokinetics and exposure-response of the immune checkpoint inhibitors approved to date, as well as an overview of some quantitative systems pharmacology approaches. The ability of immuno-oncology to meet its full potential will depend on overcoming development challenges, including the need for clear strategies to determine optimal dose and scheduling for monotherapy as well as combination approaches. Keywords immunopharmacology, oncology, clinical pharmacology, clinical trials, pharmacology, immunotherapy, checkpoint inhibitorsIt could be said that if there were a book chronicling the history and progression of cancer treatment, we are now moving on from the chapter describing the time of sole reliance on chemotherapy, the chapter outlining the advent of targeted therapy is partially completed, and a new chapter on immuno-oncology is just beginning. The past few years have seen the regulatory approval of several immuno-oncology agents (Figure 1), including 6 immune checkpoint inhibitors: ipilimumab (Yervoy; Bristol-Myers Squibb), pembrolizumab (Keytruda; Merck), nivolumab (Opdivo; Bristol-Myers Squibb), atezolizumab (Tecentriq; Genentech), avelumab (Bavencio; EMD Serono), and durvalumab (Imfinzi; AstraZeneca). 1,2Immunotherapy in oncology is a switch from the cell-killing modality using relatively nonspecific cytotoxic methods (chemotherapy) or therapies that target cancer-specific pathways to methods that employ the patients' immune system to attack cancer. The immuno-oncology approach calls on the understanding of complex signaling processes of effector and regulatory...

show abstract

Section: Pharmacometric Strategies: Application Of Systems Modelingmentioning

confidence: 99%

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Sheng

Srivastava

Sanghavi

et al. 2017

The Journal of Clinical Pharma

View full text Add to dashboard Cite

show abstract

“…Furthermore, to be maximally impactful within preclinical drug discovery, QSP models should be fit for purpose to address specific questions, be actionable, and built within a time frame that accommodates the rapid pace of decision making. Although a detailed discussion of the technical aspects of QSP modeling is beyond the scope of this work, several reviews and technical papers on QSP modeling are available …”

Section: Basic Principlesmentioning

confidence: 99%

Applications of Quantitative Systems Pharmacology in Model‐Informed Drug Discovery: Perspective on Impact and Opportunities

Bradshaw

Spilker

Zang³

et al. 2019

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

show abstract

“…In brief, this model describes the hemodynamics of blood flow through the kidney, filtration and reabsorption of substances along the nephron, the resulting whole‐body balance of fluid and electrolytes, the distribution of body fluid and sodium between the blood and interstitium, systemic blood pressure, and the neurohormonal systems regulating these processes—including the renin‐angiotensin‐aldosterone system (RAAS) . The process of QSP drug‐disease model development has been described elsewhere . Here we focus on the application of the presented workflow for using an existing QSP drug‐disease model for a new application.…”

Section: Case Study 1: Cardio‐renal Drug‐disease Qsp Modeling Enabledmentioning

confidence: 99%

“…QSP modeling has found multiple domains of use and impact in the industry. QSP models are often used to generate hypotheses and support a quantitative understanding of novel compound mechanism(s) of action, in a specific tissue, disease, or nonclinical experimental or clinical patient population context …”

mentioning

confidence: 99%

“…Mechanistically oriented QSP models also prove useful in placing biomarkers of efficacy, safety, or disease pathophysiology and phenotype in the appropriate quantitative and dynamic context for a therapeutic treatment of choice . In the course of QSP model development and testing, QSP modeling may help reconcile (or not) what, at a first glance, may appear as discrepancies in data, e.g., as obtained from different animal models or trials or discrepancies between in vitro and in vivo (nonhuman) findings or in vivo and clinical findings .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Helmlinger

Соколов²,

Peskov

et al. 2019

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

show abstract

Quantitative Systems Pharmacology: A Case for Disease Models

Cited by 54 publications

References 8 publications

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Clinical Pharmacology Considerations for the Development of Immune Checkpoint Inhibitors

Applications of Quantitative Systems Pharmacology in Model‐Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative Systems Pharmacology: An Exemplar Model‐Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Contact Info

Product

Resources

About